FIELD OF INVENTION
[0001] The present subject matter relates to communication networks and, particularly,
but not exclusively, to centralized signal processing of base stations in the communication
network.
BACKGROUND
[0002] Communication devices, such as cellular phones, smart phones, and personal
digital assistants (PDAs), provide users with a variety of mobile communications services and
networking capabilities. Such communication devices have seemingly become a ubiquitous part
of today’s lifestyle. The communication devices allow data exchange between communication
network and multiple users through network services provided by various service providers. The
service providers are faced with a challenge to meet user demands of high speed data
connectivity at all places and all the time. For this, the service providers generally provide data
connectivity services to users through various means, such as wired broadband connections,
wireless internet access, connectivity through radio access networks (RAN), and other wireless
access points.
[0003] Communication network service providers currently operate not only on the
prevalent RAN systems using the GSM and CDMA standards for mobile communications, but
also on networks, such as IP Multimedia Service (IMS) and telecommunication networks using
the new and evolved 3rd generation (3G) Universal Mobile Telecommunications Service
(UMTS) and cdma2000 standards. However, in all radio access networks (RAN) working on
different and developing standards, to provide communication and data connectivity to user
communication devices, base stations are utilized. Such base stations support communication
needs of users along with high rates of data exchange, to provide enhanced user experience. With
development of communications systems and protocols, enhanced connectivity allows the users
to utilize data intensive multimedia services like push to talk, video calling, conference calling,
high data rate internet connectivity, live media streaming, audio and video
downloading/streaming, voice communications, video communications, conference
communications, online gaming, and real time social networking. However, to provide
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uninterrupted services to users with varied load at different time instances, base stations require
homogenous onsite processing capabilities.
SUMMARY
[0004] This summary is provided to introduce concepts related to generation of virtual
distribution lists. This summary is not intended to identify essential features of the claimed
subject matter nor is it intended for use in determining or limiting the scope of the claimed
subject matter.
[0005] In one implementation, a method for centralized processing of baseband signals in
Radio Access Network (RAN) is described. The method includes identifying at least one base
station for centralized processing of baseband signals based on identification parameters and,
assessing at least one possible processing configuration for a centralized computing resource
based on real time processing constraints associated with one or more computing resources of the
centralized computing resource. The method further includes partitioning the at least one
identified base station to form one or more super base stations based on the assessed possible
processing configuration based on variable size bin packing algorithm; and processing the one or
more super base stations in the at least one assessed processing configuration.
[0006] In another implementation, a system for centralized processing of baseband
signals in Radio Access Network (RAN) is described. The system includes a processor, and
modules coupled to the processor. The system includes a scheduling module coupled to the
processor, configured to assess at least one possible processing configuration for a centralized
computing resource based on real time processing constraints associated with one or more
computing resources of the centralized computing resource. Further, each of the at least one
possible processing configuration is associated with processing parameters defining processing
requirements. The system also includes a partitioning module coupled to the processor,
configured to identify at least one base station for centralized processing of baseband signals
based on identification parameters; and partition the at least one identified base station to form
one or more super base stations based on the assessed possible processing configuration, where
each of the one or more super base stations comprises at least one identified base station. The
system may also include a super base processing module configured to process the one or more
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super base stations in the at least one assessed processing configuration for the centralized
computing resource.
[0007] In another implementation, a computer-readable medium having embodied
thereon a computer readable program code for executing a method is described. The method
includes identifying at least one base station for centralized processing of baseband signals based
on identification parameters and, assessing at least one possible processing configuration for a
centralized computing resource based on real time processing constraints associated with one or
more computing resources of the centralized computing resource. The method further includes
partitioning the at least one identified base station to form one or more super base stations based
on the assessed possible processing configuration based on variable size bin packing algorithm;
and processing the one or more super base stations in the at least one assessed processing
configuration.
BRIEF DESCRIPTION OF THE FIGURES
[0008] The detailed description is described with reference to the accompanying figures.
In the figures, the left-most digit(s) of a reference number identifies the figure in which the
reference number first appears. The same numbers are used throughout the figures to reference
like features and components. Some embodiments of system and/or methods in accordance with
embodiments of the present subject matter are now described, by way of example only, and with
reference to the accompanying figures, in which:
[0009] Fig. 1 illustrates an exemplary network environment, implementing an allocation
system for baseband signal processing, according to an embodiment of the present subject
matter;
[0010] Fig. 2 schematically illustrates components of the allocation system, in
accordance with an embodiment of the present subject matter;
[0011] Fig. 3 illustrates a method to allow centralized processing of base stations, in
accordance with an embodiment of the present subject matter
[0012] In the present document, the word "exemplary" is used herein to mean "serving as
an example, instance, or illustration." Any embodiment or implementation of the present subject
5
matter described herein as "exemplary" is not necessarily to be construed as preferred or
advantageous over other embodiments.
[0013] It should be appreciated by those skilled in the art that any block diagrams herein
represent conceptual views of illustrative systems embodying the principles of the present
subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state
transition diagrams, pseudo code, and the like represent various processes which may be
substantially represented in computer readable medium and so executed by a computer or
processor, whether or not such computer or processor is explicitly shown.
DESCRIPTION OF EMBODIMENTS
[0014] Systems and methods for realizing centralized architecture to enable efficient
processing of multiple different base stations in a radio access network (RAN) are described. The
methods can be implemented in various computing devices communicating through various
networks. Although the description herein is with reference to a wireless network, the methods
and systems may be implemented in other networks providing communication services to users,
albeit with a few variations, as will be understood by a person skilled in the art. Further, the
description has been provided for base band signal processing of base stations; however,
different forms of signal with different processing requirements can be processed at the
centralized architecture, as will be apparent to those skilled in the art.
[0015] The techniques described herein may be used for various wireless communication
systems such as Code Division Multiple Access (CDMA), Time Division Multiple Access
(TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency-Division
Multiple Access (OFDMA), Single Carrier Frequency Division Multiple Access (SC-FDMA)
and other systems. A CDMA system may implement a radio technology such as Universal
Terrestrial Radio Access (UTRA), cdma2000, etc. UTRA includes Wideband CDMA
(WCDMA) and other variants of CDMA. cdma2000 covers IS-2000, IS-95 and IS-856
standards. A TDMA system may implement a radio technology such as Global System for
Mobile Communications (GSM). An OFDMA system may implement a radio technology such
as Evolved UTRA (E-UTRA), Ultra Mobile Broadband (UMB), IEEE 802.20, IEEE 802.16
(WiMAX), 802.11 (WiFiTM), Flash-OFDM®, etc. UTRA and E-UTRA are part of Universal
Mobile Telecommunication System (UMTS). 3GPP Long Term Evolution (LTE) is an upcoming
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release of UMTS that uses E-UTRA. UTRA, E-UTRA, UMTS, LTE and GSM are described in
documents from an organization "3rd Generation Partnership Project" (3GPP). cdma2000 and
UMB are described in documents from an organization named "3rd Generation Partnership
Project 2" (3GPP2).
[0016] The systems and methods can be implemented in a variety of entities, such as
communication devices, and computing systems. The entities that can implement the described
method(s) include, but are not limited to, desktop computers, hand-held devices, laptops or other
portable computers, tablet computers, mobile phones, PDAs, Smartphones, and the like. Further,
the method may also be implemented by devices capable of baseband processing. Such devices
may include, signal processing servers, blade servers, digital signal processing (DSP) servers,
and the like. Although the description herein is explained with reference to a communicating
device such as a Smartphone, the described method(s) may also be implemented in any other
devices, as will be understood by those skilled in the art.
[0017] In current distributed cellular architecture, computing resources to provide
processing capabilities for baseband processing are located at each base station at individual cell
site of the communication network. Providing individual base stations with huge onsite
processing capabilities has been necessitated by the increasing demand of communication
services. However, providing each base station of RAN with such huge processing capabilities is
extremely expensive and resource intensive that results in increased data plan charges by the
service providers to provide communication services to the users.
[0018] Often, in a distributed architecture, computing resources are underutilized as
different base stations experience different load at different time instances. For example, among
a group of 40 base stations in a locality, each base station may experience a maximum load of
Lmax whilst providing communication services to users. However, such maximum load may be
experienced by these base stations at different time instance. Hence, in such situations, at any
given time instance, whilst some of the base stations may be processing to their near maximum
capacity load of Lmax, other base stations may be under loaded. Therefore, providing individual
computing resources according to a distributed architecture does not allow maximum or
optimum utilization of computing resources that often results in low return on investment.
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[0019] Therefore, to efficiently utilize computing resources, centralized architectures for
processing baseband signals have been considered by service providers. In a centralized
architecture, the computing resources are located at a central location. Such a centralized
architecture is generally realized by transporting the cellular signals or In phase-Quadrature (IQ)
phase signals, received by the base station through receiving antennas at the cell site, over
dedicated high speed fiber to the central location. Centralized architecture promises of improved
computational capabilities that can potentially enable signal processing techniques that improve
network performance which in turn also envisions reduction in cost of delivering services to the
users. However considerations are required whilst extending a centralized architecture for
processing needs of multiple base stations.
[0020] In a centralized architecture, for processing baseband signals from multiple base
stations, multiple computing resources may be utilized based on processing capability of each
computing resource. Since the centralized solution aims at efficient utilization of computing
resources, combinations of different base stations to be processed by a computing resources is to
be determined. Further, whilst multiple base stations are processed by a single computing
resource at a central location, scheduling the processing of such base stations is to be determined
to meet real time processing constraints and deadlines.
[0021] According to an embodiment of the present subject matter, system and methods
for realizing centralized architecture to enable efficient processing of multiple different base
stations in a RAN are described. The system and methods as described in the present subject
matter, on one hand, efficiently partitions the available base stations into different sets to be
processed by one or more computing resources and, on the other, provide technique to schedule
different sets of base stations on the one or more computing resources. The one or more
computing resources, for example, servers and computing systems that can implement the
described method(s) include, but are not limited to, mail server, central directory servers,
database server, file server, print server, web server, application server, notebooks, tablets,
network access adaptors, and the like.
[0022] Although centralized architecture for processing baseband signals of multiple
base stations can be implemented in different RANs, the methods and systems described herein
are network independent, and support multiple network types including Global System for
8
Mobile (GSM), Wideband Code Division Multiple Access (W-CDMA), Code Division Multiple
Access (CDMA), and the like.
[0023] According to an implementation of the present subject matter, base stations to be
processed simultaneously on a centralized location are indentified based on identification
parameters. Such identification parameters may include relative distance between the base
stations and a centralized location or based on the maximum number of base stations that can be
processed by the centralized location. In either situation, for a centralized location, multiple base
stations are identified for which baseband processing is carried out by the computing resources at
the centralized location.
[0024] It would be appreciated by those skilled in the art that, to simultaneously process
more than one base station on a computing resource, the computing resource is to be configured
accordingly, as one computing resource is capable of processing base stations in only one
configuration. For example, a computing resource capable of processing a max load of L’max may
be configured to process 5 base stations with an individual maximum load of L#
max, where
5*L#
max is less than or equal to the maximum load L’max that the computing resource can process.
Similarly, the same computing resource may instead be configured to process 15 bases stations
with an individual maximum load of L†
max where 15*L†
max is les than or equal to the maximum
load L’max that the computing resource can process. Hence, one computing resource may be
configured into different configurations to simultaneously process one or more base stations.
However these configurations that are supported by a computing resource are identified based on
the consideration that the computing resource has to process the baseband signals of each
configured base station within real time processing deadlines, such real time deadlines would be
apparent to the ones skilled in the art.
[0025] Hence, in one implementation of the present subject matter, the configurations
that can be processed by a computing resource considering the real time constraints are
identified. In said implementation, scheduling algorithms are applied to realize the real time
constraints and deadlines while processing multiple base stations on one computing resource,
based on which configurations that can be processed on the computing resource are identified. In
other words, based on the computing resources available at the central location, one or more
configurations, such as x base stations with an individual maximum load of y are determined.
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[0026] Although it has been described that one computing resource can process base
stations based on one particular configuration at any given instance, it would be appreciated that
computing resources capable of processing two or more configurations may also be utilized.
However, for the sake of explanation of this description, one computing resource processing
multiple base stations in one defined configuration has been described.
[0027] In another implementation of the present subject matter, for efficient and optimal
utilization of computing resources, the identified base stations are partitioned into different sets
to minimize the number of utilized computing resources. In said implementation, the partitioning
may be done based on the maximum load of each base station. For example, there may be 6 base
stations with an individual maximum load of L?
max identified to be processed at a central
location. The computing resources of such a central location may support only a configuration of
2 base stations with an individual maximum load of L’max, where L?
max < L’max. In such a
situation, the 6 base stations may be partitioned into 3 different sets to form a group of 2 base
stations per set. Since the individual load of each base station is less than that of the maximum
load processing capability of the computing resource based on the configuration, the partitioned
set of the base stations can be processed by the computing resources. It would be appreciated that
since the base stations have been partitioned into three sets where each set supports one
configuration, the central location may implement three computing resources for processing of
the baseband signals of all the 6 base stations.
[0028] However, situations might occur when the identified base stations may have
varied maximum load, and in particular, much less than the individual maximum load of a base
station defined by a configuration. For example, in the above mentioned situation, from amongst
the 6 identified base stations, the third and fifth base stations may have a maximum load of
(L?
max)/3 and, the fourth and sixth base station may have a maximum load of (L?
max)/2. In such a
situation, if upon partitioning, the fifth base station and the sixth base station are associated into
the same set, and the third and fourth base station are associated into the same set, computing
resource allocated for the processing of these sets would be underutilized and would only utilize
~42% of its total processing capability. Since there would be another such set of base stations
third and fourth, two computing resources of the central location would essentially be
underutilized at 42% efficiency.
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[0029] Hence, according to another implementation of the present subject matter, the
partitioning of base stations into different sets is based on the maximum load of each base station
to form super base stations for the purpose of baseband signal processing. In said
implementation, the processing of such super base stations would ensure optimum utilization of
computing resources. To this end, each super base station is defined to virtually include one or
more base stations to form a maximum load of the super base station. It would be understood that
the maximum load of the super base station would be the combination of the maximum load of
included bases stations. Further, a super base station may act as a virtual base station with its
own individual maximum load to be processed by a computing resource and hence, with
implementation of super base stations, multiple base stations are combined to achieve a higher
aggregate maximum load that can be processed by a single computing resource. The combination
of multiple base stations allows on one hand, flexibility to combine smaller loads of base stations
to form a larger load, and on the other, allows optimal usage of computing resources.
[0030] For example, in the above described situation of 6 base stations where the third
and fifth base stations have a maximum load of (L?
max)/3 and, the fourth and sixth base station
have a maximum load of (L?
max)/2, if the third and fifth base stations are associated virtually to
form one super base station, the maximum load of the super base station equals 2(L?
max)/3.
Similarly, if the fourth and sixth base stations are associated to form another super base station,
the maximum load of this super base station would equal L?
max. Based on the above described
scenario, in a central location where the configuration of 2 base stations with an individual
maximum load of L’max can be processed, the 6 base stations can now be processed by use of
only two computing resources instead of three where; two super base stations are processed by
one computing resource and the first and the second base stations are processed by the other.
Therefore, based on the association of one or more base stations to form a super base station,
efficient utilization of computing resources can be realized.
[0031] Hence, according to an implementation of the present subject matter, based on the
configuration constraints of computing resources, the different base stations from amongst the
group of identified base stations to be processed at a central location are combined to form super
base stations are determined. In said implementation, a variable size bin packing algorithm is
utilized to determine the combinations of base stations to form super base stations, such that
minimum super base stations are formed to be processed by computing resources in defined
11
processing configurations. It would be apparent to those skilled in the art that the variable size
bin packing algorithm aims at optimizing the number of bins while packing different entities into
each bin. In said implementation, the variable size bin packing algorithm treats each super base
station as a bin that can include multiple base stations based on individual maximum load of each
base station and the maximum load that can be aggregated to the super base station based on
configuration constraint.
[0032] Hence, based on the variable size bin packing algorithm, partitioning of the
identified base stations can be done to form multiple different super base stations that can then be
processed at the computing resources of the central location.
[0033] Further, as described before, at one central location multiple computing resources
may be utilized to process the baseband signals of different base stations in different processing
configurations. Since multiple computing resources are utilized, each computing resource may
have a different processing capability due to which different processing configurations are
possible. For example, one configuration may define that 3 base stations can be processed with a
maximum load of each base station less than L3
max. The other configuration may define that 5
base stations can be processed with a maximum load of each base station less than L5
max. In such
situations, super base stations formed to be processed based on former processing configuration
may not be processable based on latter processing configuration due to different maximum loads
allowable. And hence, it would be understood by those skilled in the art that a super base station
formed for processing in a particular configuration may not be processed in another processing
configuration due to difference in maximum load of each base station defined in the processing
configuration.
[0034] Therefore, according to an implementation of the present subject matter, to
minimize and optimally utilize the available computing resources based on the possible
processing configurations, the identified base stations are associated to form super base stations
to be processed in different configurations. The formation of such super base stations is based on
the variable size bin packing algorithm that combines different base stations to form super base
stations compatible with different processing configurations.
[0035] In another implementation of the present subject matter, upon partitioning the
identified base stations into different sets where each set forms a super base station, the super
12
base stations are allocated to different computing resources to determine the total number of
required computing resources.
[0036] For example, based on the variable size bin packing algorithm, to minimize the
number of super base stations while considering the different possible configurations and their
associated load constraints 10 base stations may be partitioned. In such a scenario the 10 base
stations may be partitioned into 5 different super base stations. 1st and 2nd super base station may
be formed to satisfy the configuration of 2 base stations with a maximum individual load of
L2
max. The 3rd, 4th, and 5th super base station may be formed to satisfy the configuration of 3 base
stations with a maximum individual load of L3
max. Upon such partitioning, the 1st and 2nd super
base station may be provided to one computing resource configured in a processing
configuration of 2 base stations with a maximum individual load of L2
max and; 3rd, 4th, and 5th
super base station may be provided to one computing resource configured in a processing
configuration of 3 base stations with a maximum individual load of L3
max. In the described
example, it would be understood that since there are in total 10 identified base stations to start
with that have been partitioned into 5 super base stations, one super base station may include
more than one base stations and another super base station may only include a single base
station. As explained before, it would be apparent to those skilled in the art that the partitioning
is based on variable size bin packing algorithm to minimize the number of super base stations
that can be accommodated in different processing configurations at different computing
resources.
[0037] Hence, based on the described techniques, the centralized architecture for
baseband signal processing of base stations can be configured with minimum computing
resources with optimum utilization of each resource. Further, the combination of multiple base
stations to form a super base station allows processing of these multiple base stations on a single
computing resource, in the given configuration, thereby enabling efficient utilization of
computing resources.
[0038] The described methodologies can be implemented in hardware, firmware,
software, or a combination thereof. For a hardware implementation, the processing units can be
implemented within one or more application specific integrated circuits (ASICs), digital signal
processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices
13
(PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers,
microprocessors, electronic devices, other electronic units designed to perform the functions
described herein, or a combination thereof. Herein, the term "system" encompasses logic
implemented by software, hardware, firmware, or a combination thereof.
[0039] For a firmware and/or software implementation, the methodologies can be
implemented with modules (e.g., procedures, functions, and so on) that perform the functions
described herein. Any machine readable medium tangibly embodying instructions can be used in
implementing the methodologies described herein. For example, software codes and programs
can be stored in a memory and executed by a processing unit. Memory can be implemented
within the processing unit or may be external to the processing unit. As used herein the term
"memory" refers to any type of long term, short term, volatile, nonvolatile, or other storage
devices and is not to be limited to any particular type of memory or number of memories, or type
of media upon which memory is stored.
[0040] In another firmware and/or software implementation, the functions may be stored
as one or more instructions or code on a non transitory computer-readable medium. Examples
include computer-readable media encoded with a data structure and computer-readable media
encoded with a computer program. Computer-readable media may take the form of an article of
manufacturer. Computer-readable media includes physical computer storage media. A storage
medium may be any available medium that can be accessed by a computer. By way of example,
and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CDROM
or other optical disk storage, magnetic disk storage or other magnetic storage devices, or
any other medium that can be used to store desired program code in the form of instructions or
data structures and that can be accessed by a computer; disk and disc, as used herein, includes
compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and Blu-ray
disc where disks usually reproduce data magnetically, while discs reproduce data optically with
lasers. Combinations of the above should also be included within the scope of computer-readable
media.
[0041] In addition to storage on computer readable medium, instructions and/or data may
be provided as signals on transmission media included in a communication apparatus. For
example, a communication apparatus may include a transceiver having signals indicative of
14
instructions and data. The instructions and data are configured to cause one or more processors to
implement the functions outlined in the claims. That is, the communication apparatus includes
transmission media with signals indicative of information to perform disclosed functions. At a
first time, the transmission media included in the communication apparatus may include a first
portion of the information to perform the disclosed functions, while at a second time the
transmission media included in the communication apparatus may include a second portion of
the information to perform the disclosed functions.
[0042] It should be noted that the description merely illustrates the principles of the
present subject matter. It will thus be appreciated that those skilled in the art will be able to
devise various arrangements that, although not explicitly described herein, embody the principles
of the present subject matter and are included within its spirit and scope. Furthermore, all
examples recited herein are principally intended expressly to be only for pedagogical purposes to
aid the reader in understanding the principles of the invention and the concepts contributed by
the inventor(s) to furthering the art, and are to be construed as being without limitation to such
specifically recited examples and conditions. Moreover, all statements herein reciting principles,
aspects, and embodiments of the invention, as well as specific examples thereof, are intended to
encompass equivalents thereof.
[0043] The manner in which the systems and methods shall be implemented has been
explained in details with respect to the Fig. 1-3. While aspects of described systems and methods
for providing virtual distribution list can be implemented in any number of different computing
systems, transmission environments, and/or configurations, the embodiments are described in the
context of the following exemplary system(s).
[0044] It will also be appreciated by those skilled in the art that the words during, while,
and when as used herein are not exact terms that mean an action takes place instantly upon an
initiating action but that there may be some small but reasonable delay, such as a propagation
delay, between the initial action and the reaction that is initiated by the initial action.
Additionally, the word “connected” and “coupled” is used throughout for clarity of the
description and can include either a direct connection or an indirect connection.
[0045] Fig. 1 illustrates a network environment 100 implementing a central architecture
for baseband signal processing based on one or more centralized computing resources 102. The
15
computing resource 102 may be connected to one or more cell sites 104-1, 104-2, 104-3,
…….,104-N for processing of baseband signals, according to an embodiment of the present
subject matter. The one or more cell cites 104-1, 104-2, 104-3, ……., 104-N may hereinafter
individually and collectively, be referred to as base station(s) 104.
[0046] In one implementation, the centralized computing resource 102 may include
multiple processing units to centrally process baseband signals of the different base stations 104.
Since multiple real time processing constraints, such as, processing speed, maximum load,
maximum permissible latency are to be met, it would be understood that the centralized
computing resource 102 may process the baseband signals based on a pre-defined configuration
identified based on scheduling algorithms implemented to meet the real time constraints. To
process multiple base stations 104 in multiple configurations, the centralized computing resource
102 may include multiple processing units where each processing unit is configured to process
baseband signals in one particular configuration. The centralized computing resource 102 may
include, but are not limited to, signal processing servers, blade servers, digital signal processing
(DSP) servers, processing platforms, desktop computers, hand-held devices, laptops or other
portable computers, tablet computers, mobile phones, PDAs, Smartphones, and the like.
[0047] In another implementation, the base stations 104 may include fixed stations that
communicate with the users and may include one or more Node B or evolved Node B (eNB),
base stations, access points, etc. It would be understood that the base stations 104 provides
communication coverage for a particular geographic area. The coverage area of each base station
104 may be partitioned into multiple smaller areas. Each smaller area may be served by a
respective base station subsystem. Further, although it has been described that the cell sites
include base stations 104 and Node Bs, the cell sites may also include entities capable of
exchanging data to provide connectivity to different communicating devices and computing
systems. Such entities may be implemented at the base stations 104 for providing cellular
connectivity to the users. Such entities may include Radio Network Controller (RNC), Base
Transceiver Station (BTS), Mobile Switching Centre (MSC), Short Message Service Centre
(SMSC), Base Station Subsystem (BSS), Home Location Register (HLR), Visitor Location
Register (VLR), Authentication Center (AuC), mobile adapters, wireless (WiFiTM) adapters,
routers, and the like.
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[0048] The base stations 104 providing connectivity may connect to users through
computing devices, such as 106-1, 106-2, ……., 106-N. The computing devices 106-1, 106-2,
……., 106-N may hereinafter individually and collectively, be referred to as computing device(s)
106. The computing devices 106 may include, but are not limited to, desktop computers, handheld
devices, laptops or other portable computers, tablet computers, mobile phones, PDAs,
Smartphones, and the like. Further, the computing devices 106 may include devices capable of
exchanging data to connect to different base stations 104. Such computing devices 104 may
include, but are not limited to, data cards, mobile adapters, wireless (WiFiTM) adapters, routers, a
wireless modem, a wireless communication device, a cordless phone, a wireless local loop
(WLL) station, and the like. As computing devices 106 may be stationary or mobile, they may
also be referred to as a mobile station, a terminal, an access terminal, a subscriber unit, a station,
etc.
[0049] The base stations 104 may be connected to the centralized computing resource
102 through a network 108. The network 108 may be a wireless network, wired network, or a
combination thereof. The connection can in turn be implemented as one of the different types of
networks, such as intranet, telecom network, electrical network, optical fiber network, Internet
core network, local area network (LAN), wide area network (WAN), Virtual Private Network
(VPN), internetwork, Global Area Network (GAN), and such. The connection may either be a
dedicated connection or a shared connection, which represents an association of the different
types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol
(HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application
Protocol (WAP), etc., to communicate with each other. Further, the connection may include a
variety of network devices, including routers, bridges, servers, computing devices, storage
devices and the like.
[0050] In one implementation, the network environment 100 also includes an allocation
system 110 to enable centralized baseband signal processing of multiple base stations 104 in a
radio access network (RAN). For the sake of explanation, the allocation system 110 has been
referred to as a system 110 hereinafter. In one implementation, the system 110 includes, amongst
other things, a partitioning module 112. The partitioning module 112 can also be provided in an
external storage media, which may interface with the system 110. Although the system 110 has
been shown external to the centralized computing resource 102 and implemented at different
17
geographical location, in one implementation, the system 110 is implemented along with the
centralized computing resource 102 at a common geographical location. In one implementation,
the partitioning module 112 is configured to allocate the base stations 104 into different sets to
form super base stations. As explained earlier, each super base station is a combination of one or
more base stations.
[0051] In one implementation, the partitioning module 112 analyzes different parameters,
such as, maximum load capable if being processed of each base station, maximum permissible
load capable of being processed of a super base station, and the configuration in which the super
base station is to be processed, to determine the sets of base stations to be combined to form the
super base station. Further, since each super base station may include only a limited number of
base stations depending upon the maximum permissible load capable of being processed of the
super base station, the partitioning module 112 may form multiple super base stations to be
processed at the centralized computing resource 102. As described earlier, it would be
understood that the computing resource 102 may include multiple processing units to process
baseband signals in different configurations and accordingly, the partition module 112 may form
multiple super base stations with different number of base stations 104 and with different
maximum load. The parameters based on which super base stations are formed and processed
have been described in more details with respect to Fig. 2 in the description.
[0052] Fig. 2 illustrates various components of the system 110 to enable efficient
baseband signal processing in a centralized architecture, according to an embodiment of the
present subject matter. In one implementation, the system 110 includes processor(s) 202. The
processor 202 may be implemented as one or more microprocessors, microcomputers,
microcontrollers, digital signal processors, central processing units, state machines, logic
circuitries, and/or any devices that manipulate signals based on operational instructions. Among
other capabilities, the processor(s) is configured to fetch and execute computer-readable
instructions stored in the memory.
[0053] The functions of the various elements shown in the figure, including any
functional blocks labeled as “processor(s)”, may be provided through the use of dedicated
hardware as well as hardware capable of executing software in association with appropriate
software. When provided by a processor, the functions may be provided by a single dedicated
18
processor, by a single shared processor, or by a plurality of individual processors, some of which
may be shared. Moreover, explicit use of the term “processor” should not be construed to refer
exclusively to hardware capable of executing software, and may implicitly include, without
limitation, digital signal processor (DSP) hardware, network processor, application specific
integrated circuit (ASIC), field programmable gate array (FPGA), read only memory (ROM) for
storing software, random access memory (RAM), non-volatile storage. Other hardware,
conventional and/or custom, may also be included.
[0054] Also, the system 110 includes interface(s) 204. The interfaces 204 may include a
variety of software and hardware interfaces that allow the system 110 to interact with the entities
of the network 108, or with each other. The interfaces 204 may facilitate multiple
communications within a wide variety of networks and protocol types, including wire networks,
for example, LAN, cable, etc., and wireless networks, for example, WLAN, cellular, satellitebased
network, etc.
[0055] The system 110 may also include a memory 206. The memory 206 may be
coupled to the processor 202. The memory 206 can include any computer-readable medium
known in the art including, for example, volatile memory, such as static random access memory
(SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as
read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical
disks, and magnetic tapes.
[0056] Further, the system 110 may include module(s) 208 and data 210. The modules
208, amongst other things, include routines, programs, objects, components, data structures, etc.,
which perform particular tasks or implement particular abstract data types. The modules 208 may
also be implemented as, signal processor(s), state machine(s), logic circuitries, and/or any other
device or component that manipulate signals based on operational instructions.
[0057] Further, the modules 208 can be implemented in hardware, instructions executed
by a processing unit, or by a combination thereof. The processing unit can comprise a computer,
a processor, a state machine, a logic array or any other suitable devices capable of processing
instructions. The processing unit can be a general-purpose processor which executes instructions
to cause the general-purpose processor to perform the required tasks or, the processing unit can
be dedicated to perform the required functions.
19
[0058] In another aspect of the present subject matter, the modules 208 may be machinereadable
instructions (software) which, when executed by a processor/processing unit, perform
any of the described functionalities. The machine-readable instructions may be stored on an
electronic memory device, hard disk, optical disk or other machine-readable storage medium or
non-transitory medium. In one implementation, the machine-readable instructions can be also be
downloaded to the storage medium via a network connection.
[0059] In an implementation, the module(s) 208 includes the partitioning module 112, a
scheduling module 212, a super base processing module 214, and, and other module(s) 216. The
other module(s) 216 may include programs or coded instructions that supplement applications or
functions performed by the system 110. In said implementation, the data 210 includes a base
station data 220, a configuration data 222, a super base data, and other data 226. The other data
226, amongst other things, may serve as a repository for storing data that is processed, received,
or generated as a result of the execution of one or more modules in the module(s) 208. Although
the data 210 is shown internal to the system 110, it may be understood that the data 210 can
reside in an external repository (not shown in the figure), which may be coupled to the system
110. The system 110 may communicate with the external repository through the interface(s) 204
to obtain information from the data 210.
[0060] As mentioned before, in one implementation of the present subject matter, the
system 110 is configured to enable efficient processing of base band signals at the centralized
computing resource 102. To this end, the system 110 includes the scheduling module 212
configured to identify configurations that each computing resource, at the centralized computing
resource 102, can process. As already described, each computing resource is capable of
processing base stations 104 based on certain configuration, where related to each computing
resource, there are associated real time processing constraints. For example, for a computing
resource CR1, the scheduling module 212 may identify a processing configuration C1 suitable for
processing based on the real time constraints associated with the computing resource, such as the
processing capability of a maximum load of La
max, processing speed of a maximum of Sa
max, and
maximum permissible latency Lata
max.
[0061] Since, each processing configuration is identified based on the real time
constraints associated with the computing resource, each processing configuration to be
20
processed on a computing resource may define certain processing parameters. In one
implementation, the scheduling module 212 may identify such processing parameters to satisfy
the real time processing constraints. In said implementation, the processing parameters of a
processing configuration may include: the number of base stations that can be processed, the
maximum load of each base station to be processed, and the maximum combined load of all the
base stations that can be processed.
[0062] For example, in the above described situation, for the computing resource CR1,
the scheduling module 212 may have identified a processing configuration C1 suitable for
processing of base stations 104 based on the real time constraints associated with the computing
resource. For this computing resource, the processing configuration of C1 may have the
following processing parameters: maximum of three base stations 104, each with a maximum
load of L3
max, and the maximum load of all the three base stations to be less than La
max. The
scheduling module 212 may ensure, based on the implemented scheduling algorithm, that upon
implementing the processing configuration of C1 to the computing resource CR1, the real time
processing constraints associated with the computing resource are met to provide a predetermined
Quality-Of-Service.
[0063] It would be understood that the centralized computing resource 102 may include
more than one computing resource where each computing resource may have different associated
real time processing constraints. Such as, different computing resources may have different load
processing capabilities and may also have different processing speeds.
[0064] Although it has been described that the centralized computing resource may
commonly process base band signals of the base stations 104 of a network, in certain situations,
different computing resources of the centralized computing resource 102 may process base band
signals for different networks working on different standards. In such situations different
computing resources processing base band signals of different networks may have different
associated real time processing constrains, such as, different maximum permissible latencies.
Therefore, it would be understood by those skilled in the art that for different computing
resources, the scheduling module 212 may identify different processing configurations. It would
also be understood that two computing resources with equal processing capabilities may be
21
identified to be implemented with separate processing configurations based on associated real
time constraints other that the processing capability, such as the maximum permissible latency.
[0065] Further, it has been described that for a particular computing resource, based on
the scheduling algorithms, the scheduling module 212 may identify an applicable processing
configuration. However, in another implementation of the present subject matter, the scheduling
module 212 may also identify more than one processing configurations that can be realized on
the computing resource based on the associated real time processing constraints. In other words,
there might multiple processing configurations available at the centralized computing resource
102 for processing of base stations, each with different processing parameters.
[0066] For example, consider that for a computing resource CR1, processing
configuration of C1 that allows processing of a maximum of three base stations 104, each with a
maximum load of L3
max is identified. Similarly, for a computing resource CR2 processing
configuration of C2 that allows processing of a maximum of 4 base stations 104, each with a
maximum load of L4
max may be identified. Further, for the computing resource, another possible
processing configuration of C2’ may be identified that allows processing of a maximum of 8 base
stations 104, each with a maximum load of L8
max. Therefore, for different computing resources,
different processing configurations C1, C2, …, Cn may be identified with different processing
parameters.
[0067] However, it would be appreciated that upon identification of multiple possible
configurations for a computing resource, one such possible processing configuration is
implemented for processing of base band signals. It would also be appreciated that two
computing resources associated with similar real time constraints may be identified to be
processing same possible processing configurations. In one implementation, all the possible
configurations and associated processing parameters are stored in the configuration data 222.
[0068] In another implementation of the present subject matter, upon identification of
different possible processing configurations for different computing resources, base stations 104
to be processed on the centralized computing resource 102 are identified. In one implementation,
the identification of the base stations 104 is based on identification parameters that include the
relative distance between each base station 104 and the centralized computing resource 102. In
such implementation, the partitioning module 112 is configured to identify base stations 104
22
within a pre-defined range of distance from the computing resource 102, to be processed at the
centralized computing resource 102. Similarly, in another implementation, the base stations 104
to be processed at the centralized computing resource 102 may be identified based on
identification parameters that include the maximum number of base stations 104 that can be
processed by the centralized computing resource 102. In said implementation, the partitioning
module 112 is configured to identify the number of base stations 104 that can be processed by
the centralized computing resource 102. It would be understood that the partitioning module 112
may identify the base stations 104 based on various different criteria’s as well, such as
identifying only those base stations 104 that have a maximum load greater than a predefined
threshold maximum load; only those base stations 104 that have a maximum load less than a
predefined threshold maximum load, and the like. Therefore, based on such different criteria, the
base stations 104 to be processed at the centralized computing resource 102 are identified and
stored in base station data 220.
[0069] In one implementation of the present subject matter, the identified base stations
104 are partitioned into different sets to form super base stations and stored in the super base
data 224. As described earlier, each super base station is a virtual base station having one or
more base stations 104 to be processed in a processing configuration, at one of the computing
resource of the centralized computing resource 102. In other words, for a processing
configuration, if the processing parameters define: m base stations to be processed with an
individual maximum load of n, the system 110 may utilize m super base stations with an
individual maximum load of n. Hence, multiple base stations 104 virtually combined to form one
super base station can be processed as a single entity in a processing configuration of the
computing resource 102.
[0070] For example, for the computing resource CR1, the scheduling module 212 based
on the real time constraints, may access a processing configuration of C1 that allows processing
of a maximum of three base stations 104, each with a maximum load of L3
max. In such a
situation, the partitioning module 112 is configured to form super base stations such that, each
super base station has one, or more than one, base stations 104 and the maximum load of each
such super base station is less than L3
max. Therefore, the formation of super base stations by the
partitioning module 112 may enable processing of more than three base stations 104 in the given
processing configuration C1. However, it would be understood that the processing configuration
23
C1 would still process not more than three super base stations. Hence, the implementation of
super base stations allows an efficient and optimal utilization of computing resources of the
centralized computing resource 102.
[0071] As described before, based on different real time constraints of a computing
resource, multiple possible processing configurations for the computing resource may be
identified. Further, for different computing resources, multiple different processing
configurations may be identified. In other words, multiple different processing configurations
might be available for baseband processing of the base stations at the centralized computing
resource 102. Since, the partitioning module 112 forms the super base stations, these super base
stations are processed in one of such processing configuration instead of the individual base
stations 104.
[0072] In one implementation of the present subject matter, for the formation of the super
base stations, the base stations 104 are partitioned into different sets such that, the total number
of computing resources required for the processing of the thus formed super base stations, is
optimum.
[0073] To this end, the partitioning module 112 is configured to partition the base
stations 104 into different sets, where each such set is a super base station and, includes one or
more base stations 104. In one implementation, the partitioning module 112 is configured to
partition the base stations 104 based on variable bin size packing algorithm. The variable bin size
packing algorithm aims at filling bins of different sizes with different entities such that minimum
number of bins is utilized after all the entities have been filled. In centralized processing of
baseband signals, the different configurations that are available are identified as bins of variable
sizes and the base stations 104 identified to be processed at the centralized computing resource
102 are ascertained as entities.
[0074] For example, if in the centralized computing resource 102, the possible processing
configurations identified are C1 and C2, where C1 allows three base stations with each of a
maximum load of L3
max to be processed and, C2 allows four base stations with each of a
maximum load of L4
max to be processed; the processing module 112 identifies, a total of 7 (3
from C1 processing configuration and 4 from C2 processing configuration) bins. In such a
scenario, the 3 bins identified from the C1 processing configuration have a maximum size of
24
L3
max units and the 4 bins identified from the C2 processing configuration have a maximum size
of L4
max units. Therefore, 7 bins with 2 different sizes may be identified. Further, the maximum
load of each base station 104 is treated as the size of the base station 104, being ascertained as an
entity filling the bins, such as a base station with a maximum load of La
max is considered to be
size La
max units.
[0075] Although it has been described by way of example that there might be two
different possible processing configurations, multiple processing configurations are possible that
in turn might allow different processing parameters. In other words, bins of multiple variable
sizes are possible based on the possible configurations.
[0076] Hence, in one implementation, the partitioning module 112, based on the variable
size bin packing algorithm, fills the identified bins with the available base stations 104. For
example, in the above described scenario, the partitioning module 112 may partition 20 available
base stations 104 into the available 7 bins. Each base station 104 and the bin may have a
different size. While minimizing the number of bins, the partitioning module 112 may arrange
the base stations 104 into sets of 3, 2, 5 for the three bins of C1 processing configuration and a set
of 2, 3, 4, 1 for the 4 bins of processing configuration C2. Based on the partitioned sets, the
partition module 112 may thereafter form 7 super base station where 3 super base stations can be
processed in the C1 processing configuration with 1st super base station with 3 base stations 104,
2nd super base station with 2 base stations 104, and the 3rd super base station with 5 base stations
104. Similarly, the other 4 super base stations may be formed with 4th super base station with 2
base stations 104, 5th super base station with 3 base stations 104, 6th super base station with 4
base stations 104, and 7th super base station with only 1 base station 104.
[0077] In one implementation of the present subject matter, the super bases, once created
by the partitioning module 112, are assigned to computing resources for processing. To this end,
the super base processing module 214 is configured to identify the super base stations
appropriate for processing in different configurations. For example, in the above described
scenario, the super base processing module 214 may identify the 1st, 2nd, and 3rd super base
stations to be processed in the processing configuration of C1 on the computing resource CR1.
Similarly, the super base processing module 214 may also identify the super base stations, 4th,
25
5th, 6th, and 7th to be processed in the processing configuration of C2 on the computing resource
CR2.
[0078] In general, it would be understood that the partition module 112 would form
multiple super base stations where each such super base station is capable of being processed in
one processing configuration. The super base processing module 214 is thus configured to assign
such formed super base stations to the respective computing resource for baseband processing.
[0079] In another implementation, the super base processing module 214 is further
configured to determine the total number of computing resources required for the processing of
the base stations 104 in each of the super base stations. That is, it may so happen that from
amongst the multiple possible processing configurations, to optimally utilize the computing
resources, the partitioning module 112 may only form super base stations corresponding to few
of such processing configurations and may choose to leave the other processing configurations.
Therefore, the super base processing module 214 determines the number of computing resources
to be utilized for the processing of the identified base stations 104. For example, in the above
described scenario of two available processing configurations C1 and C2, in case of more
available computing resources the scheduling module 212 might assess two other configurations
of C3 and C4 available for processing at the centralized computing resource 102. In such a
scenario, based on the variable size bin packing algorithm, the portioning module 112 may still
identify to form 7 super base stations with 3 of them to be processed in C1 processing
configuration and 4 to be processed in processing configuration of C2, thereby utilizing only 2
computing resources in spite of four available computing resources.
[0080] In one implementation, the computing resources to be utilized for the processing
of the formed super base stations may be determined based on the super base stations formed and
their associated parameters. For instance, in one scenario where 18 base stations 104 are to be
partitioned to form super based stations, the partitioning module 112, instead of identifying C1
and C2 processing configurations similar to the previous scenario, may now only identify C1 as
the best optimal processing configuration. Thereby, the partitioning module 112 based on the
individual maximum load of the 18 base stations 104, may form two sets of 3, 2, 4 and 3, 3, 2.
Therefore, based on the 6 super base stations formed by the partition module in this scenario, the
super base station processing module 104 may determine the use of 2 CR1 computing resources.
26
Hence, the computing resources to be utilized are based on the formed super base stations by the
partition module 112 for optimum utilization of the computing resource.
[0081] Fig. 3 illustrates method 300 for centralized processing of baseband signals,
according to an embodiment of the present subject matter. The order in which the method 300 is
described is not intended to be construed as a limitation, and any number of the described
method blocks can be combined in any order to implement the method 300, or any alternative
methods. Additionally, individual blocks may be deleted from the method without departing
from the spirit and scope of the subject matter described herein. Furthermore, the method can be
implemented in any suitable hardware, software, firmware, or combination thereof.
[0082] The method may be described in the general context of computer executable
instructions. Generally, computer executable instructions can include routines, programs, objects,
components, data structures, procedures, modules, functions, etc., that perform particular
functions or implement particular abstract data types. The method may also be practiced in a
distributed computing environment where functions are performed by remote processing devices
that are linked through a communications network. In a distributed computing environment,
computer executable instructions may be located in both local and remote computer storage
media, including memory storage devices.
[0083] A person skilled in the art will readily recognize that steps of the method can be
performed by programmed computers. Herein, some embodiments are also intended to cover
program storage devices, for example, digital data storage media, which are machine or
computer readable and encode machine-executable or computer-executable programs of
instructions, where said instructions perform some or all of the steps of the described method.
The program storage devices may be, for example, digital memories, magnetic storage media
such as a magnetic disks and magnetic tapes, hard drives, or optically readable digital data
storage media. The embodiments are also intended to cover both communication network and
communication devices configured to perform said steps of the exemplary methods.
[0084] Referring to Fig. 3, at block 302, one or more base stations 104 for centralized
processing of baseband signals are identified. In one implementation, the system 110 may be
utilized to identify the one or more base stations 104. In said implementation, the partitioning
module 112 may identify the base stations 104 based one or more criteria, such as, relative
27
distance between the base station 104 and the centralized computing resource 102 and maximum
number of base stations 102 that can be processed by the centralized computing resource 102. In
one implementation, the base stations 102 identified for centralized processing might be
associated with one network, such as GSM and CDMA. However, in another implementation,
base stations 104 of different networks may also be identified to be processed at the centralized
computing resource 102.
[0085] At block 304, at least one possible configuration for a centralized computing
resource is assessed based on real time processing constraints associated with one or more
computing resources. In one implementation, each computing resources of the centralized
computing resource, to process the base stations, is evaluated based on scheduling algorithms to
realize the real time processing constraints. The real time processing constraints may include
processing speed, maximum load, maximum permissible latency, and the like.
[0086] In one implementation, based on the evaluation, a possible processing
configuration for the computing resource is identified. The processing configuration may define
the processing parameters that can be supported by the computing resource encompassing the
real time constraints. The processing parameters may include criteria, such as, the number of
base stations that can be processed, the maximum load of each base station to be processed, and
the maximum combined load of all the base stations that can be processed by the computing
resource.
[0087] At block 306, the one or more base stations are partitioned to form one or more
super base stations based at least on variable size bin packing algorithm and the assessed
possible processing configurations. In one implementation of the present subject matter, super
base stations combine one or more base stations such that each of the super base station is
processed by the computing resource as a virtual base station. In said implementation, base
stations may be partitioned to form super base stations based on variable size bin pacing
algorithm. The variable size bin packing algorithm may allow efficient partitioning of base
stations such that each super base station thus formed can be processed based on the processing
parameters of different processing configurations. Furthermore, each super base station may
include base stations such that minimum number of base stations are required, thereby
facilitating optimum utilization of the available computing resources.
28
[0088] At block 308, the formed super base stations are provided to the one or more
computing resources for processing of the baseband signals. In one implementation, the
computing resources to be utilized for the efficient processing of the base stations are also
determined based on the allocation of the super base stations for processing.
[0089] Although the subject matter has been described with reference to specific
embodiments, this description is not meant to be construed in a limiting sense. Various
modifications of the disclosed embodiments, as well as alternate embodiments of the subject
matter, will become apparent to persons skilled in the art upon reference to the description of the
subject matter. It is therefore contemplated that such modifications can be made without
departing from the spirit or scope of the present subject matter as defined.
29
I/we claim:
1. A method for centralized processing of baseband signals in Radio Access Network
(RAN), the method comprising:
identifying at least one base station for centralized processing of baseband signals
based on identification parameters;
assessing at least one possible processing configuration for a centralized
computing resource based on real time processing constraints associated with one or
more computing resources of the centralized computing resource, wherein each of the at
least one possible processing configuration is associated with processing parameters
defining processing requirements for centralized processing of baseband signals;
partitioning the at least one identified base station to form one or more super base
stations based on the assessed possible processing configuration, wherein each of the one
or more super base stations comprises at least one identified base stations; and
processing the baseband signals of the one or more super base stations in the at
least one assessed processing configuration for the centralized computing resource.
2. The method as claimed in claim 1, wherein the partitioning is further based on variable
size bin packing based mechanisms, such that each of the one or more super base stations form a
bin, having a size based on the at least one possible processing configuration in which the super
base station is processed, for including the at least one identified base stations.
3. The method as claimed in claim 1, wherein the processing parameters associated with
each of the at least one possible processing configuration comprise one or more of, the number
of base stations capable of being processed by the computing resource, the maximum load of
each base station that is processed by the computing resource, and the maximum combined load
of all the base stations that are processed by the computing resource.
4. The method as claimed in claim 1, wherein the real time processing constraints associated
with each of the one or more computing resource comprise one or more of, processing speed of
the computing resource, maximum load that is processed by the computing resource, and
maximum permissible latency allowed for the computing resource.
5. The method as claimed in claim 1, the method further comprises identifying a number of
computing resources essential for processing of the one or more super base stations.
6. The method as claimed in claim 1, wherein the maximum load of each of the one or more
super base station is less than the maximum load of a base station capable of being processed in
the at least one processing configuration in which the super base station is processed.
30
7. The method as claimed in claim 1, wherein the maximum load of each of the one or more
super base station is equal to the maximum load of a base station capable of being processed in
the at least one processing configuration in which the super base station is processed.
8. A system (110) for centralized processing of baseband signals in Radio Access Network
(RAN) comprising:
a processor (202);
a scheduling module (212) coupled to the processor (202), configured to assess at
least one possible processing configuration for a centralized computing resource (102)
based on real time processing constraints associated with one or more computing
resources of the centralized computing resource (102), wherein each of the at least one
possible processing configuration is associated with processing parameters defining
processing requirements for centralized processing of baseband signals;
a partitioning module (112) coupled to the processor (202), configured to:
identify at least one base station (104) for centralized processing of
baseband signals based on identification parameters; and
partition the at least one identified base station (104) to form one or more
super base stations based on the assessed possible processing configuration,
wherein each of the one or more super base stations comprises at least one
identified base station (104); and
a super base processing module (214) configured to process the one or more super
base stations in the at least one assessed processing configuration for the centralized
computing resource.
9. The system (110) as claimed in claim 8, wherein the partitioning module (112) is further
configured to partition the at least one base station based on variable size bin packing based
mechanisms, such that each of the one or more super base stations form a bin, having a size
based on the at least one possible processing configuration in which the super base station is
processed, for including the at least one identified base stations..
10. The system (110) as claimed in claim 8, wherein the scheduling module (212) is further
configured to determine processing parameters associated with each of the at least one possible
processing configuration, the processing parameters comprising one or more of, the number of
base stations capable of being processed by the computing resource, the maximum load of each
base station that is processed by the computing resource, and the maximum combined load of all
the base stations that are processed by the computing resource.
31
11. The system (110) as claimed in claim 8, wherein the scheduling module (212) is
configured to assess at least one possible processing configuration for a centralized computing
resource (102) based on real time processing constraints, wherein the real time processing
constraints comprise one or more of, processing speed of the computing resource, maximum load
that is processed by the computing resource, and maximum permissible latency allowed for the
computing resource.
12. A non-transitory computer-readable medium having embodied thereon a computer
readable program code for executing a method comprising:
identifying at least one base station for centralized processing of baseband signals
based on identification parameters;
assessing at least one possible processing configuration for a centralized
computing resource based on real time processing constraints associated with one or
more computing resources of the centralized computing resource, wherein each of the at
least one possible processing configuration is associated with processing parameters
defining processing requirements for centralized processing of baseband signals;
partitioning the at least one identified base station to form one or more super base
stations based on the assessed possible processing configuration, wherein each of the one
or more super base stations comprises at least one identified base stations; and
processing the baseband signals of the one or more super base stations in the at
least one assessed processing configuration for the centralized computing resource..