DESC:PRIORITY DETAILS
This application is based on and derives the benefit of Indian Provisional Applications 3974/CHE/2015, the contents of which are incorporated herein by reference.
TECHNICAL FIELD 5
[001] The embodiments herein generally relate to the field of wireless communication systems and more particularly to field of Long Term Evolution (LTE) small cell deployments.
BACKGROUND 10
[002] Third Generation Partnership Project (3GPP) specifications for Long Term Evolution (LTE) provide mechanisms to meet the data rate demand of the UE, either through Carrier Aggregation (CA) or Dual Connectivity (DC) by adding radio resources to the UE. CA or DC is carried out with support of small cells, also referred as secondary evolved 15 NodeB’s (SeNBs) in licensed spectrum. However, currently LTE networks can use unlicensed bands (LTE-Unlicensed (LTE-U)). LTE network entities such as User Equipments (UEs), eNBs and the like, supporting usage of unlicensed band are said to support the LTE Licensed Assisted Access (LAA) technology. Due to coexistence of communications in the 20 unlicensed band, it is desirable to determine spectrum usage in the unlicensed band to better provide LAA communications in the unlicensed band.
[003] The LAA however has to ensure fairness for existing Wi-Fi networks in the unlicensed band so as not to impact Wi-Fi services more 25 than that caused by another Wi-Fi network. Further, the design of LAA should ensure fairness among different operators within the LTE system as well as other systems operating in the unlicensed band by satisfying the regulations such as Listen Before Talk (LBT), Dynamic Frequency
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Selection (DFS) and the like. As part of LBT rules, a transmitting entity (for example, a small cell or Secondary eNB) has to perform Clear Channel Assessment (CCA) prior to transmission in the unlicensed spectrum. For CCA to succeed, the SeNB can transmit in the unlicensed spectrum only if the energy level in the channel bandwidth is below a threshold referred to 5 as energy detect (ED) threshold for specified time duration. Thus, throughput results of LAA for a LTE system with additional unlicensed bandwidth need to be higher.
[004] An existing method achieves an improvement in throughput by determining the optimal transmission power in the licensed and 10 unlicensed band and then using this optimal power to determine the fraction of time that the small cell or SeNB transmitter can transmit in the unlicensed band. However, the existing method is based on assumption that only a single Wi-Fi transmitter exists that interferes with the small cell trying to transmit in the unlicensed spectrum. This assumption is not 15 applicable for practical scenarios where interference from other operators and Wi-Fi access points (AP) is present. Existing methods to achieve higher performance or throughput in licensed spectrum are based on opportunistic centralized scheduling of component carriers (CCs). Network entities that may perform as centralized schedulers can be Cloud Radio 20 Access Network (C-RAN). However, applying the centralized scheduling in the environment where unlicensed spectrum is utilized along with the licensed spectrum may not be effective. One of the reasons is that with presence of non cellular entities that operate in the same environment such as uncoordinated Wi-Fi Access Points (AP’s) and SeNB’s from other 25 operators an unlicensed band may be occupied in 20µs while the centralized scheduling decision may be communicated to the SeNB only at the order of 1ms which may no longer be valid.
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OBJECT OF INVENTION
[005] The principal object of the embodiments herein is to provide methods and wireless communication systems for hybrid scheduling of component carriers (CCs) in unlicensed spectrum for a plurality of Secondary eNodeBs (SeNBs) in a Long Term Evolution (LTE) License 5 Assisted Access (LAA) environment.
[006] Another object of the embodiments herein to provide methods and wireless communication systems for the hybrid scheduling of CCs by performing a first computation at a centralized entity to determine a probability distribution for a plurality of permissible power levels for the 10 plurality of SeNBs and performing a second computation at a SeNB to determine a local probability distribution to allocate a transmit power level to a CC for serving a User Equipment (UE) lying within coverage area of the SeNB.
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SUMMARY
[007] In view of the foregoing, an embodiment herein provides a method for hybrid scheduling of component carriers (CCs) for a Long Term Evolution (LTE) License Assisted Access (LAA). The method comprises receiving a probability distribution from a centralized entity, 5 wherein the probability distribution provides a plurality of permissible power levels for a CC to be scheduled by the SeNB for serving a User Equipment (UE). Further, the method comprises determining a local probability distribution based on a learning mechanism that utilizes the received plurality of permissible power levels. Furthermore, the method 10 comprises allocating a transmit power level to the CC based on the determined local probability distribution to serve the UE on the CC.
[008] Embodiments further disclose a wireless communication system for hybrid scheduling of component carriers (CCs) for a Long Term Evolution (LTE) License Assisted Access (LAA). The system 15 comprises a centralized entity configured to determine a probability distribution for a plurality of Secondary eNodeBs (SeNBs) based on measurement reports received from plurality of UEs configured for periodic measurement and send the probability distribution to the plurality of SeNBs through downlink. Further, the system comprises a SeNB, among the 20 plurality of SeNBs, configured to receive the probability distribution from the centralized entity, wherein the probability distribution provides the SeNB with a plurality of permissible power levels for a CC to be scheduled by the SeNB for serving a User Equipment (UE). Furthermore, the SeNB is configured to determine a local probability distribution 25 based on a learning mechanism that utilizes the received plurality of permissible power levels. Furthermore, the SeNB is configured to allocate a transmit power level to the CC based on the determined local probability distribution to serve the UE on the CC.
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[009] Embodiments further disclose a centralized entity for hybrid scheduling of component carriers (CCs) for a Long Term Evolution (LTE) License Assisted Access (LAA). The centralized entity comprises a central CC scheduling module configured to determine a probability distribution for a plurality of Secondary eNodeBs (SeNBs) based on measurement 5 reports received from plurality of UEs configured for periodic measurement. The probability distribution provides the plurality of SeNBs with a plurality of permissible power levels for a CC to be scheduled by each SeNB for serving a User Equipment (UE). Furthermore, the central CC scheduling module is configured to send the probability distribution to 10 the plurality of SeNBs through downlink.
[0010] Embodiments further disclose a Secondary evolved NodeB (SeNB) for hybrid scheduling of component carriers (CCs) for a Long Term Evolution (LTE) License Assisted Access (LAA). The SeNB comprises a local CC scheduling module configured to receive a 15 probability distribution from a centralized entity. The probability distribution provides the SeNB with a plurality of permissible power levels for a CC to be scheduled by the SeNB for serving a User Equipment (UE). Further, the local CC scheduling module configured to determine a local probability distribution based on a learning mechanism that utilizes the 20 received plurality of permissible power levels. Further, the local CC scheduling module configured to allocate a transmit power level to the CC based on the determined local probability distribution to serve the UE on the CC.
[0011] These and other aspects of the embodiments herein will be 25 better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating embodiments and numerous specific details thereof, are given by way of
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illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.
5
BRIEF DESCRIPTION OF FIGURES
[0012] The embodiments of this invention are illustrated in the accompanying drawings, throughout which like reference letters indicate corresponding parts in the various figures. The embodiments herein will be 10 better understood from the following description with reference to the drawings, in which:
[0013] FIG. 1 illustrates an example wireless communication system for Long Term Evolution (LTE) License Assisted Access (LAA) environment for hybrid scheduling of component carriers (CCs) in 15 unlicensed spectrum for a plurality of Secondary eNodeBs (SeNBs), according to embodiments as disclosed herein;
[0014] FIG. 2 illustrates plurality of components of a centralized entity for hybrid scheduling of the CCs, at the centralized entity end, in the unlicensed spectrum for the plurality of SeNBs, according to embodiments 20 as disclosed herein;
[0015] FIG. 3 is a flow diagram illustrating a method for hybrid scheduling of CCs, at the centralized entity end, in the unlicensed spectrum for the plurality of SeNBs, according to embodiments as disclosed herein;
[0016] FIG. 4 illustrates plurality of components of a SeNB for 25 hybrid scheduling of the CCs, at the SeNB end, in the unlicensed spectrum, according to embodiments as disclosed herein; and
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[0017] FIG. 5 is a flow diagram illustrating a method for hybrid scheduling of CCs, at the SeNB end, in the unlicensed spectrum for, according to embodiments as disclosed herein.
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DETAILED DESCRIPTION
[0018] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-5 known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples 10 should not be construed as limiting the scope of the embodiments herein.
[0019] The embodiments herein achieve methods and wireless communication systems for hybrid scheduling of component carriers (CCs) in unlicensed spectrum for a plurality of Secondary eNodeBs (SeNBs) for a Long Term Evolution (LTE) License Assisted Access (LAA) environment. 15 The hybrid scheduling of CCs is performed with computations carried out at two ends, first at a centralized entity end and then at a SeNB end, also referred as a small cell end. Further, the SeNB schedules the CC by allocating a transmit power level for serving a User Equipment (UE) within the SeNBs coverage area. Thus, the hybrid scheduling, proposed by the 20 method, comprises combination of a centralized and distributed processing providing reinforcement learning based power control of CCs of the SeNB with inputs from the centralized entity for updating the optimal CC power allocation at the SeNB.
[0020] Referring now to the drawings, and more particularly to 25 FIGS. 1 through 5, where similar reference characters denote corresponding features consistently throughout the figures, there are shown embodiments.
[0021] FIG. 1 illustrates an example wireless communication system 100 for Long Term Evolution (LTE) License Assisted Access
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(LAA) environment for hybrid scheduling of component carriers (CCs) in unlicensed spectrum for a plurality of Secondary eNodeBs (SeNBs), according to embodiments as disclosed herein. In an embodiment, the LTE LAA environment comprises a centralized entity 102, a plurality of SeNBs 104a, 104b and 104c respectively within coverage area of a master eNB 5 (MeNB) (not shown). The MeNB is not shown explicitly in the wireless communication system 100 as in an embodiment; the MeNB itself can be the centralized entity 102. The SeNBs 104a, 104b and 104c respectively assist the MeNB during implementation of LAA in the unlicensed spectrum. A UE 106d, not lying within the coverage area of any of the 10 SeNBs 104a, 104b and 104c may be served by the MeNB. However, UEs 106a, 106b and 106b respectively within the coverage of SeNBs 104a, 104b and 104c respectively can be additionally served on the unlicensed spectrum through LAA to enhance through put that can be almost close to throughput of a pure centralized scheme for power allocation of CCs. 15 However, the proposed hybrid scheduling additionally provides advantages of a distributed scheme for power allocation of the CCs. A first computation, at the centralized entity 102 end, determines a probability distribution for a plurality of permissible power levels for the plurality of SeNBs 104a, 104b and 104c respectively that need to schedule the CCs. 20 The computation of probability distribution at the centralized entity 102 is explained in conjunction with Fig. 2 and FIG. 3 to avoid repetition and maintain brevity.
[0022] Further, a second computation individually at each SeNB such as the SeNB 104a is performed to determine a local probability 25 distribution based on computation performed at the centralized entity. The computation of local probability distribution at the SeNB 104a is explained in conjunction with FIG. 4 and FIG. 5 to avoid repetition and maintain brevity. The probability distribution determined by the centralized entity
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102a is communicated to the SeNBs 104a, 104b and 104c respectively through an interface such as downlink. Further, the SeNB 104a can be configured to allocate a transmit power level to the CC based on the computed local probability distribution to serve the UE on the CC.
[0023] In an embodiment, the centralized entity can be a macro cell, 5 also referred as the MeNB, a Cloud radio Access Network (C-RAN), a Radio Network Controller (RNS) or any other network entity capable of determining the probability distribution and providing it to the SeNB.
[0024] In an embodiment, the SeNB can be any small cell within the LTE LAA environment with a transmitter having any transmit power, 10 but having a coverage of 50 – 200 meters. Thus SeNBs can include current 3GPP pico cells, micro cells as well as possible cells in millimeter wave (mm-wave) spectrum.
[0025] In an embodiment, the UE is a mobile phone, a tablet, a personal digital assistant, a laptop, a wearable device or any other 15 electronic device.
[0026] FIG. 2 illustrates plurality of components of a centralized entity for hybrid scheduling of the CCs, at the centralized entity end, in the unlicensed spectrum for the plurality of SeNBs, according to embodiments as disclosed herein. In an embodiment, the centralized entity 102 can 20 include a processor 202, an Input/Output (I/O) 204, a central CC scheduling module 206 and a memory module 208. 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 25 based on operational instructions. The I/O interface 204 may include a variety of software and hardware interfaces. The I/O interface 204 may allow the centralized entity 102 to communicate with other entities of the wireless communication system 100 such as the SeNBs through the
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downlink, UEs through wireless network protocols and so on. The I/O interface 204 may facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, Local Area network (LAN), cable, wireless networks, such as Wireless LAN, cellular networks such as LTE and the like. 5
[0027] In an embodiment, the central CC scheduling module 206 can be configured to configure the plurality of UEs such as UE 106a, 106b and 106c respectively for periodic measurement. Further, the central CC scheduling module 206 can be configured to receive measurement reports from plurality of UEs and compute power allocation for the plurality of 10 small cells such as SeNBs 104a, 104b and 104c respectively in the LTE LAA environment. Further, the central CC scheduling module 206 can be configured to observe the received measurement reports and determine the probability distribution for a plurality of permissible power levels for a CC to be scheduled by each of a plurality of SeNBs 104a, 104b and 104c 15 respectively for serving UE 106a, 106b and 106c respectively through the LAA. The mathematical analysis for computation of probability distribution at the centralized entity 102 is explained in conjunction with FIG. 3 to avoid repetition and maintain brevity. Further, the central CC scheduling module 206 can be configured to provide the computed 20 probability distribution to the plurality of SeNBs through the X- interface, wherein the SeNBs104a, 104b and 104c respectively determine the local probability distribution individually at their corresponding ends.
[0028] FIG. 3 is a flow diagram illustrating a method 300 for hybrid scheduling of CCs, at the centralized entity 102 end, in the unlicensed 25 spectrum for the plurality of SeNBs, according to embodiments as disclosed herein. At step 302, the method 300 includes allowing the central CC scheduling module 206 to configure the plurality of UEs 106a, 106b and 106c respectively for periodic measurement. At step 304, the method
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300 includes allowing the central CC scheduling module 206 to receive measurement reports from plurality of UEs and determine power allocation for the plurality of small cells (SeNB 104a, 104b and 104c respectively) in the LTE LAA environment provided by the wireless communication system 100. At step 306, the method 300 includes allowing the central CC 5 scheduling module 206 to observe the measurement reports and determine probability distribution for the plurality of permissible power levels for the CC to be scheduled by each of the plurality of SeNBs 104a, 104b and 104c for serving the UEs 106a, 106b and 106c respectively through the LAA. At step 308, the method 300 includes allowing the central CC scheduling 10 module 206 to provide the computed probability distribution to the plurality of SeNBs through X- interface.
[0029] In an embodiment, the determination of probability distribution is computed using a greedy centralized method that minimizes interference to increase system throughput and spectral efficiency. Plurality 15 of other methods known in the art may be used for determination of probability distribution at the centralized entity 102.
[0030] The mathematical analysis for the greedy centralized method is explained with assumption that the wireless communication system 100 comprises of ‘P’ small cells (for example, SeNB 106a, 106b, 106c) that 20 may be pico cells with ‘F’ number of ‘CC’s per small cell. Further, a power allocation vector for cluster of small cells is given as, S = [s1,1, s1,2,.. S1,F, …sP,1, sP,2,..sP,F]. The greedy centralized method enables the centralized entity 102 to jointly allocate power for each CC of each active pico cell using binary power control, quantized power levels and the like. The active 25 pico cell may refer a transmission point that has data to be transmitted to one or more UE’s to which it is assigned to serve data. The greedy centralized method allows the centralized entity 102 to find an optimal power scaling vector S for the pico cells in the cluster S. The greedy
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centralized method provides non-intuitive objective function (OBF) for evaluating throughput for a choice of S.
[0031] In an embodiment, the OBF is computed using a graph based approach which is applicable for scenarios targeting > 40% gains in data rates, at the cost of higher execution time of 10 ~ 1000 ms (depending on 5 the deployment and system configuration)
[0032] In an embodiment, the OBF is computed using the greedy approach applicable for scenarios targeting 30 ~ 40 % gains in data rates, with execution time of < 10 ms
[0033] Steps for computation of OBF using the greedy centralized 10 method are provided below :
[0034] With assumption that number of Pico cells =P, number of CC per Pico = F, number of power levels = L, power allocation vector S = [s1 s2…sPF] and power level vector = a; a tree is constructed where level I represents L discrete values for each of the fth frequency of pth pico cell, 15 where f?[1,F] and p?[1,P] for any one of Li nodes as provided in equation 1, 2 and 3 below:
For ith level f= F if (i%F) ? 0 (1)
For ith level f=( i%F) if (i%F) = 0 (2)
For ith level p= |(i-1)/F+1| (3) 20
[0035] The partitioning S vector includes Sd = [s1 s2…si] : S vector defined till ith level and Sud = [si+1 si+2…sPF]: undefined S vector after ith level.
[0036] The definition of the objective function for throughput estimation is provided by equations 4, 5 and 6 below: 25
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???????????? ????=1 ????=1 log2 1+??????????????*????????0 + ??????????????* ??'????????=1,????? ??????=1????
(4)
Where,
Mpf : pfth element in the vector (Sd||Sud) where Sud is a unit vector;
M’pf : pfth element in the vector (Sd||Sud) where Sud is a zero vector; 5
???????????? ????=1 ????=1 log2 1+??????????????*????????0 + ??????????????* ??'????????=1,????? /??????????????=1????
(5)
Where, uavg = average data served by UE ‘u’
10 ???????????? ????=1 ????=1 log2 1+??????????????*????????0 + ??????????????* ??'????????=1,????? /??????????????=1????
(6)
Where, pavg = average data served by Pico ‘p’
[0037] The probability distribution computed is based on equation 7 provided below that is computed using equation 8 and equation 9 below:
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????,??,??*= ????,??,?? exp?(??????,??,?? ) ??????,??,?? exp?(??????,??,?? )????=1 (7)
5
Where,
ri,f,l= [ ui,f al*?n,fpi,f n ,vn,f=1 - ui,f ( ?n,f,vn,f) ??????=????????-???? 10
Where, ui,f ?n,f,vn,f = Ri,f ?n,f if ,vn,f=10 otherwise (8)
Ri,f p(t) = pk?Kilog2(1+?SINRki,f)??(ki,f,t)
(9)
(Where, SINR is the signal to interference plus noise ratio that is computed 15 as per known schemes.
[0038] The various actions in method 300 may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some actions listed in FIG. 3 may be omitted.
[0039] FIG. 4 illustrates plurality of components of a SeNB for 20 hybrid scheduling of the CCs, at the SeNB end, in the unlicensed spectrum, according to embodiments as disclosed herein. In an embodiment, the SeNB 104a can include a processor 402, an Input/Output (I/O) 404, a local CC scheduling module 406 and a memory module 408. The processor 402 may be implemented as one or more microprocessors, microcomputers, 25
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microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. The I/O interface 404 may include a variety of software and hardware interfaces. The I/O interface 404 may allow the SeNB 104a to communicate with other entities of the wireless 5 communication system 100 such as the centralized entity 102 through the downlink, the UEs through wireless network protocols for LTE LAA and the like. The I/O interface 404 may facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, Local Area network (LAN), cable, wireless 10 networks, such as Wireless LAN, cellular networks such as LTE and the like.
[0040] In an embodiment, the local CC scheduling module 406 can be configured to receive the probability distribution from the centralized entity 102. The probability distribution provides the plurality of permissible 15 power levels for a CC to be scheduled by the SeNB for serving any UE through the LTE LAA. Further, the local CC scheduling module 406 can be configured to determine the local probability distribution based on an advanced learning mechanism that utilizes the received plurality of permissible power levels derived from the probability distribution received 20 from the centralized entity 102. The mathematical analysis for computation of local probability distribution at the SeNB 104a 102 is explained in conjunction with FIG. 5 to avoid repetition and maintain brevity. Further, the local CC scheduling module 406 can be configured to allocate the transmit power level to the CC based on the computed local probability 25 distribution to serve the UE on the CC.
[0041] FIG. 5 is a flow diagram illustrating a method 500 for hybrid scheduling of CCs, at the SeNB 104a end, in the unlicensed spectrum, according to embodiments as disclosed herein. At step 502, the method 500
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includes allowing the local CC scheduling module 406 to receive the probability distribution from the centralized entity 102, wherein the received probability distribution provides the plurality of permissible power levels for the CC to be scheduled by the SeNB 104a for serving the UE such as UE 106a through a LTE LAA 5
[0042] At step 504, the method 500 includes allowing the local CC scheduling module 406 to determine the local probability distribution based on the advanced learning mechanism that utilizes the received plurality of permissible power levels by solving the probability distribution received from the centralized entity and is given in equation 10 below: 10
(10)
Where p is the probability distribution associated with each power level, rk is the regret associated with kth power level, q is the reference distribution as indicated by the macro cell (MeNB which can also be the centralized entity 102) to a particular small cell SeNB, D(p || q) is the distance between 15 distribution p and q. The distance between two probability distributions can be Kullback Leibler divergence, Pearson distance, Heilinger distance (Aryabhatta distance) or the like. The probability distribution is obtained for various distances as provided in the equations 11, 12 and 13 below:
(11) 20
(12)
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(13)
[0043] The distances may not be restricted to above mentioned distances only and other distances such as can be used. At step 506, the method 500 includes allowing the local CC scheduling module 406 to allocate the transmit power level to the CC based on the determined local 5 probability distribution to serve the UE on the CC.
[0044] The various actions in method 500 may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some actions listed in FIG. 5 may be omitted.
[0045] The embodiments disclosed herein can be implemented 10 through at least one software program running on at least one hardware device and performing network management functions to control the network elements. The network elements shown in FIG. 1 through FIG. 5 include blocks which can be at least one of a hardware device, or a combination of hardware device and software module. 15
[0046] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are 20 intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that 25
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the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein. ,CLAIMS:STATEMENT OF CLAIMS
We claim:
1. A method for hybrid scheduling of component carriers (CCs) for a Long Term Evolution (LTE) License Assisted Access (LAA), the method comprising:
receiving, by a Secondary evolved Node B (SeNB), a probability distribution from a centralized entity, wherein the probability distribution provides a plurality of permissible power levels for a CC to be scheduled by the SeNB for serving a User Equipment (UE);
determining a local probability distribution based on a learning mechanism that utilizes the received plurality of permissible power levels; and
allocating a transmit power level to the CC based on the determined local probability distribution to serve the UE on the CC.
2. The method as claimed in claim 1, wherein the probability distribution received from the centralized entity is determined by the centralized entity for a plurality of SeNBs based on measurement reports received from plurality of UEs configured for periodic measurement.
3. The method as claimed in claim 1, wherein the probability distribution is received from the centralized entity through downlink.
4. The method as claimed in claim 1, wherein determining of the local probability distribution based on the learning mechanism comprises determining maximum probability distribution associated with each power level among the received plurality of permissible power levels.
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5. A wireless communication system for hybrid scheduling of component carriers (CCs) for a Long Term Evolution (LTE) License Assisted Access (LAA), wherein the wireless communication system comprises:
a centralized entity configured to:
determine a probability distribution for a plurality of Secondary eNodeBs (SeNBs) based on measurement reports received from plurality of UEs configured for periodic measurement;
send the probability distribution to the plurality of SeNBs through downlink;
a SeNB, among the plurality of SeNBs, configured to:
receive the probability distribution from the centralized entity, wherein the probability distribution provides the SeNB with a plurality of permissible power levels for a CC to be scheduled by the SeNB for serving a User Equipment (UE);
determine a local probability distribution based on a learning mechanism that utilizes the received plurality of permissible power levels; and
allocate a transmit power level to the CC based on the determined local probability distribution to serve the UE on the CC.
6. A centralized entity for hybrid scheduling of component carriers (CCs) for a Long Term Evolution (LTE) License Assisted Access (LAA), wherein the centralized entity comprises a central CC scheduling module configured to:
determine a probability distribution for a plurality of Secondary eNodeBs (SeNBs) based on measurement reports received from plurality of UEs configured for periodic measurement, wherein the probability distribution provides the
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plurality of SeNBs with a plurality of permissible power levels for a CC to be scheduled by each SeNB for serving a User Equipment (UE); and
send the probability distribution to the plurality of SeNBs through downlink.
7. A Secondary evolved NodeB (SeNB) for hybrid scheduling of component carriers (CCs) for a Long Term Evolution (LTE) License Assisted Access (LAA), wherein the SeNB comprises a local CC scheduling module configured to
receive a probability distribution from a centralized entity, wherein the probability distribution provides the SeNB with a plurality of permissible power levels for a CC to be scheduled by the SeNB for serving a User Equipment (UE);
determine a local probability distribution based on a learning mechanism that utilizes the received plurality of permissible power levels; and
allocate a transmit power level to the CC based on the determined local probability distribution to serve the UE on the CC.
8. The SeNB as claimed in claim 7, wherein local CC scheduling module configured to determine the local probability distribution based on the learning mechanism by determining maximum probability distribution associated with each power level among the received plurality of permissible power levels.