DESC:RESERVATION OF RIGHTS
A portion of the disclosure of this patent document contains material, which is subject to intellectual property rights such as, but are not limited to, copyright, design, trademark, IC layout design, and/or trade dress protection, belonging to Jio Platforms Limited (JPL) or its affiliates (hereinafter referred as owner). The owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all rights whatsoever. All rights to such intellectual property are fully reserved by the owner. Further, the patent document also proposes techniques that may contribute to the 3GPP Technical Specification (TS) current and future generation network technologies (i.e., 5G/6G networks). Certain aspects of the disclosure may contribute to 3GPP Technical specification for SON, for example, 3GPP TS 32.500.

TECHNICAL FIELD
[0001] The present disclosure relates to a distributed quantum computing system, and in particular to optimization of network resources in a distributed network using quantum computing resources.

BACKGROUND
[0002] As research in the 6G domain begins to advance, the cellular industry is entering an inflection point to pursue interesting challenges to work on newer network architecture paradigms and to explore newer technologies. The increased heterogeneity of technologies for accessing networks such as those based on 4G/5G/6G or WiFi6 or 5G-NR-Unlicensed technologies, variability in large and small cells, options for centralized versus disaggregated RAN solutions, and programmable infrastructure, ensuring different levels of quality of service for different network slices, distributed and partitioned private and public 5G and edge networks provide interesting challenges in terms of system optimization. In addition, smart utilization of local and distributed intelligence in networks, and enabling new applications and services also pose challenges for system optimization in such networks. Such emerging systems will process information in an increasingly distributed manner.
[0003] Quantum-based systems support the notion of a probabilistic state vector across different state dimensions in a system. For emerging distributed processing systems in networks, quantum-state vectors can be used to represent the overall distributed state information in the system. The state of the system can then evolve probabilistically based on unitary transforms associated with quantum state variables. The amplitudes associated with quantum states can be progressively refined such that a collapse of the state can result in an optimal solution to a system optimization problem. Such an approach can be utilized for different types of system optimization problems such as SON/PCI optimization in heterogeneous and disaggregated networks, distributed energy management in networks, load balancing in heterogeneous networks, or network-access coordination across access nodes and devices. If the number of nodes that interact with each other are large, particularly in dense urban areas, then optimization techniques can be expensive as well. In this regard, quantum-inspired algorithms could help with approaching solutions to such problems differently, which could help with faster and more efficient processing in such networks.
[0004] Traditionally, PCI optimization is formulated as an NP-complete graph coloring-based optimization problem with each cell as a node in the graph, with edges in the graph between neighboring cells. To be relevant to emerging heterogeneous networks operating in virtualized infrastructure, one can create a virtualPCI (vPCI) number that is managed in such infrastructure and associated with the cells such that an extended range beyond is possible in the network, and the cell IDs associated with non-4G or non-5G-NR cells could take on values beyond the available range if desired. In the past, most SON functions have been executed in a centralized manner at a remote node (such as the OAM – Operations, Administration and Management node). This remote node aggregates information in the RAN across different geographical regions, executes the optimization algorithm, and distributes the results (such as a PCI allocation for different cells) to the different eNodeBs/gNodeBs. In some scenarios, distributed SON is utilized so that the SON algorithm executes at the eNodeB/gNodeB. The existing Hybrid SONs are not flexible and do not allow the distributed optimization components to focus on local optimization, and the central components do not optimize across the distributed components. Further, the existing methods and systems do not satisfactorily solve the problem of load balancing and energy savings in the emerging network scenarios and hence are sub-optimal in these aspects.
[0005] Hence, there is a need to provide for a method and a system for optimizing self-optimizing networks (SON) to make it flexible and robust to address the above-mentioned shortcomings.

OBJECTS OF THE INVENTION
[0006] Some of the objects of the present disclosure, which at least one embodiment herein satisfy are as listed herein below.
[0007] In a general aspect, the present disclosure provides a system and a method for optimizing SON using a quasi-quantum-based approach for resource management in wireless networks.
[0008] In another aspect, the present disclosure provides a solution for the PCI SON optimization problem for mobility management in emerging cellular or Wi-Fi or hybrid (cellular + Wi-Fi) networks.
[0009] In yet another aspect, the present disclosure enables software emulation of PCI SON optimization problem on a classical computer or executed on a quantum computer as well.
[0010] In an aspect, the present disclosure enables efficient energy management in current and future communication networks.
[0011] In a still further aspect, the present disclosure provides for efficient load balancing in current and future communication networks.
[0012] In another aspect, the present disclosure provides for efficient selection of UE to communicate over current and future communication networks.

SUMMARY OF THE INVENTION
[0013] Embodiments of a system for quantum-based resource management in a communication network are disclosed. In an embodiment, the system includes a processor coupled to a memory, the memory comprising one or more instructions which when executed causes the processor to receive a set of data packets, each data packet corresponding to a microservice; execute a first set of instructions, the first set of instructions associated with an optimized allocation of a plurality of microservices to the plurality of nodes; and based on the executed first set of instructions, distribute the set of data packets to the plurality of nodes. In an embodiment, the microservice corresponds to one or more of automatic inventory, automatic software download, automatic neighbor relation (ANR), automatic physical cell ID (PCI) assignment, mobility robustness/handover (MRO), random access channel (RACH), load balancing, Inter-Cell Interference Coordination (ICIC), coverage and capacity, enhanced Inter-Cell Interference Coordination (eICIC), cell outage detection and compensation, minimization of drive testing, energy savings, handover, and self-healing coordination between various Self-Organizing Network (SON) functions. In an embodiment, the plurality of nodes corresponds to any or a combination of enodes and gnodes and access nodes.
[0014] In an embodiment, the system for quantum-based resource management in a communication network includes a processor coupled to a memory, the memory comprising one or more instructions which when executed causes the processor to: receive status data from a plurality of nodes, wherein the status data comprises information associated with one or more parameters associated with the plurality of nodes configured to communicate with one or more computing devices; and determine, based at least in part on the status data, one or more actionable insights for optimized allocation of network resources to the plurality of nodes, wherein the status data is indicative of a quantum-based representation of the node characteristics.
[0015] In an embodiment, the one or more parameters comprises a state of the communication network, a state of one or more of the plurality of nodes, a state of a microservice associated with one or more of the plurality of nodes, a state of network resources associated with the communication network, and the like. In an embodiment, the plurality of nodes corresponds one or more of a base station (BS), an access points (AP), or a distributed unit (DU). In an embodiment, the node characteristics comprises a current load, a current energy consumption, and a current set of device identifier allocation associated with each of the plurality of nodes. In an embodiment, the one or more actionable insights correspond to one or more of optimizing PCI, saving energy, and balancing load associated with the communication network. In an embodiment, the status data comprises a Quantum State Variable (QSV) associated with one or more physical qubits or one or more virtual qubits. In an embodiment, the memory comprises one or more instructions which when executed further causes the processor to determine probabilistic state information in the quantum state variables (QSVs) or entangled QSVs.
[0016] Embodiments of a method for quantum-based resource management in a communication network are disclosed. In an embodiment, the method includes receiving, by a processor, status data from a plurality of nodes, wherein the status data comprises information associated with one or more parameters associated with the plurality of nodes configured to communicate with one or more computing devices; and determining, by a processor, based at least in part on the status data, one or more actionable insights for optimized allocation of network resources to the plurality of nodes, wherein the status data is indicative of a quantum-based representation of the node characteristics.

BRIEF DESCRIPTION OF THE DRAWINGS
[0017] In the figures, similar components and/or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label with a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label.
[0018] The diagrams are for illustration only, which thus is not a limitation of the present disclosure, and wherein:
[0019] FIG. 1 illustrates exemplary architecture (100) in which or with which proposed system may be implemented, in accordance with an embodiment of the present disclosure.
[0020] FIG. 2 illustrates an exemplary representation (200) of a computing device (102) for signature-based verification of executable set of instructions, in accordance with an embodiment of the present disclosure.
[0021] FIG. 3 illustrates exemplary flow diagram of the proposed method, in accordance with an embodiment of the present disclosure.
[0022] FIG. 4 illustrates exemplary representation of a heterogeneous RAN Controller, in accordance with an embodiment of the present disclosure.
[0023] FIGs. 5A-5C illustrate exemplary representations collision conflicts with a predefined number of nodes, in accordance with an embodiment of the present disclosure.
[0024] FIG. 6 illustrates exemplary representation of a plurality of access nodes serving a mobile device, in accordance with an embodiment of the present disclosure.
[0025] FIG. 7 illustrates exemplary representations of Dynamic Quantum Load Optimization, in accordance with an embodiment of the present disclosure.
[0026] FIG. 8 illustrates an exemplary computer system in which or with which embodiments of the present invention can be utilized in accordance with embodiments of the present disclosure.

DETAILED DESCRIPTION
[0027] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.
[0028] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the invention as set forth.
[0029] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.
[0030] Also, it is noted that individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[0031] The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive—in a manner similar to the term “comprising” as an open transition word—without precluding any additional or other elements.
[0032] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[0033] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.
[0034] Disclosed embodiments implement quantum-inspired techniques and methods for resource management and optimization of emerging wireless networks. As wireless networks continue to evolve into 6th Generation (6G) and beyond, the deployment of networks will be increasingly heterogeneous comprising of base stations (BSs) or access points (APs) or distributed units (DUs) serving users in different spectra utilizing different technologies. Disclosed embodiments propose dynamic energy savings with a quadratic unconstrained binary optimization framework. In an embodiment, a quasi-quantum graph coloring algorithm can be considered using virtual cell identifiers to coordinate across access nodes and to enable self-organizing networks (SONs) to be optimized. In another embodiment, dynamic load balancing is proposed using a quantum state variable (QSV) that is entangled across nodes. In general, a distributed probabilistic QSV can be utilized to enable quantum-inspired resource optimization in emerging networks.
[0035] Further, the present disclosure relates to a distributed quantum computing system, and in particular to optimization of microservices to access distributed quantum computing resources. A Quantum State Variable (QSV) as referred to herein, can be associated with one or more physical “qubits”, or one or more virtual qubits. A qubit or quantum bit may be described as a basic unit of quantum information and may be considered as the quantum version of the classic binary bit physically realized with a two-state device. The qubit is a two-state (or two-level) quantum-mechanical system, one of the simplest quantum systems displaying the peculiarity of quantum mechanics. Examples of qubit include the spin of the electron in which the two levels can be taken as spin up and spin down; or the polarization of a single photon in which the two states can be taken to be the vertical polarization and the horizontal polarization. In a classical system, a bit would have to be in one state or the other. However, quantum mechanics allows the qubit to be in a coherent superposition of both states simultaneously, a property that is fundamental to quantum mechanics and quantum computing.
[0036] If the QSV is associated with a physical qubit, the state updates may be processed by a quantum computing unit that may be associated with a node in a blockchain network according to an embodiment. In the case of a physical qubit, the state of the qubit will not be fixed and will remain in a state of superposition, until it is measured. Prior to measurement, when a physical qubit is in a state of superposition, the amplitudes associated with the different possible states will not be known. If the state of a physical qubit is measured, then it will collapse to one of the possible states.
[0037] The term “Quantum particles” as used herein exist in a superposition of states which represents a weighted linear combination of wave functions of different possible states. For example, the wave function |?a> of a quantum particle a (such as a photon or an electron) with two possible states |0> and |1> (such as up / down spin states or different polarization states) can be described as a superposition of these two states given by
|?a> = a1 |0> + a2|1> (1)
where a1 and a2 are complex amplitudes such that a_1^2 + a_2^2 =1. Here a_1^2 and a_2^2 represent probabilities associated with the states |0> and |1> respectively, and these probabilities are equivalent to the relative energies associated with the two states. The orthogonal basis column vectors (10)T and (01)T can be used to represent the states |0> and |1> respectively. If the two states are equally probable, then a1 = a2 = 1/v2 . Equation (1) represents a single qubit system in a superposition of two states.
[0038] The measurement of the qubit state results in a collapse of the qubit state to one of the possible states in the system. A two-qubit system for a pair of quantum particles a and b is represented as a tensor product of states associated with each particle leading to a representation based on a superposition of 4 states |00>, |01>, |10> and |11> given by
|?ab> = ß1 |00> + ß2 |01> + ß3 |10> + ß4 |11> (2)
where ß_1^2 + ß_2^2 + ß_3^2 + ß_4^2 = 1. The orthogonal basis column vectors (1000)T , (0100)T , (0010)T , and (0001)T can be used to represent the states |00>, |01>, |10> and |11> respectively. In general, a k-qubit system can be represented as a superposition of m states |s1>, |s2>, ….., |sm> given by |?> = a1|s1> + a2|s2> + ….. + am|sm> (3)
where m = 2k, and the basis states can be represented as orthogonal column vectors of length m.
[0039] The proposed embodiments of the system and method utilize probabilistic state information in quantum state variables (QSVs), or entangled QSVs to achieve resource optimization in wireless networks. QSVs can be realized as physical quantum bit registers and/or realized virtually in software and evolved in time, to reflect different possible states and their respective amplitudes/probabilities. Resource management problems such as energy optimization, PCI optimization with proposed virtual cell identifiers, and load balancing optimization are proposed in the description.
[0040] Radio Access Network (RAN) Intelligent Controllers are being explored in emerging programmable networks that operate across multiple cells and across available licensed/unlicensed wireless networks, which interact with centralized units (CUs) and/or distributed units (DUs) and/or Wi-Fi access points, where the suggested algorithms could be deployed. Additional techniques can be utilized as well to explore the application for quantum-inspired approaches to solve the above-mentioned problems. For example, for the load balancing optimization problem, amplitude magnification or modification techniques can be attempted to increase the likelihood of a node with a lighter load being selected as the access node that serves a given user. In another embodiment, other resource optimization problems such as coverage and capacity optimization or inter-cell interference coordination problems can be solved in a similar manner.
[0041] Further, as research in the sixth generation (6G) domain begins to advance, the telecom industry is entering an inflection point to pursue interesting challenges to work on newer network architecture paradigms and to explore new technologies. The increased heterogeneity of technologies for accessing networks such as based on 4G/5G/6G or WiFi6 or 5G-NR-Unlicensed, will lead to processing of information in an increasingly distributed manner.
[0042] FIG. 1 illustrates exemplary network architecture (100) in which or with which proposed system may be implemented, in accordance with an embodiment of the present disclosure. The example network architecture (100) shown in FIG. 1 includes one or more computing devices (102-1, 102-2…102-N) (also referred collectively computing devices (102) and individually as computing device (102)) associated with radio access network (106) (RAN 106) and a plurality of nodes (104-1, 104-2…104-N). A computing system (100) may include additional or different features, and the components of a computing system may operate as described with respect to FIG. 1 or in another manner such as computing devices associated with disaggregated RAN (114). The computing devices (102) shown in FIG. 1 may further correspond to a centralized server (112), a RAN controller unit (110) associated with a quantum processor unit (108). In an exemplary embodiment, the plurality of nodes can be any or a combination of enodes (116) and gnodes (118) and access nodes (120). In an exemplary embodiment, the disaggregated RAN (114) may include one or more central units (122) (CUs 122), one or more distributed units (124) (DUs 124), and remote units (126) (RUs 126).
[0043] In an embodiment, the RAN architecture may be Open RAN (interchangeably referred to as O-RAN) but not limited to it and may include capabilities for a non-real-time RAN Interface Controller (Non RT-RIC) in a service and management orchestration layer associated with the RAN. The RAN architecture may also include a centralized server (112), and a near-real-time RAN Interface Controller (Near RT-RIC) in the RAN that interacts the CUs or DUs in the network.
[0044] In an embodiment, the RAN controller unit (110) (interchangeably referred to as RAN intelligent controller unit) associated with the quantum processor unit (108) may receive from the one or more computing device (102) a set of data packets, each data packet corresponding to a microservice. A microservice may be referred to as an architectural and organizational approach to software development where software is composed of small independent (micro) services that communicate over well-defined APIs. Such microservices are owned by small, self-contained teams in an embodiment. Microservices architectures make applications easier to scale and faster to develop, enabling innovation and accelerating time-to-market for new features. In an embodiment, the set of data packets may include status data that includes information associated with the microservice. For example, set of data packets can include UE/network state information such as, link quality, number of UEs connected to access nodes, energy associated for load operations, time stamp associated with a data packet, node states such as PCI value associated with a node. In an embodiment, two or more of the above-mentioned information may be combined across nodes to determine the overall distributed QS of the network.
[0045] The quantum processor unit (108) may cause the RAN controller unit (110) to run an executable first set of instructions associated with an optimized allocation of the plurality of microservices to the plurality of nodes. Based on the executed first set of instructions, the RAN controller unit (110) may distribute the set of data packets to the plurality of nodes (104).
[0046] In an embodiment, the plurality of microservices may include several SON-related functions in cellular standards including but not limited to, automatic inventory, automatic software download, automatic neighbor relation (ANR), automatic physical cell ID (PCI) assignment, mobility robustness/handover (MRO), random access channel (RACH), load balancing, Inter-Cell Interference Coordination (ICIC), coverage and capacity, enhanced Inter-Cell Interference Coordination (eICIC), cell outage detection and compensation, minimization of drive testing, energy savings, handover, and self-healing functions coordination between various Self-Organizing Network (SON) functions.
[0047] In the emergent O-RAN architecture, SON features will be typically supported utilizing optimization microservices that realize specific SON functions. These microservices can be executed at the non-real-time RIC for delay-tolerant optimization requirements, whereas a near-real-time RIC can execute optimization microservices to address more latency-sensitive optimizations in the network.
[0048] In an embodiment, the first set of executable instructions (interchangeably referred to as “quantum algorithm”) may be executed on the one or more computing devices (102) that may include a quantum, a classical computer, and a hybrid mode using a combination of quantum and classical computers. The first set of executable instructions may include a quantum state variable (QSV) that can be associated with one or more physical qubits, or one or more virtual qubits. If the QSV is associated with a physical qubit, the state updates may be processed by the quantum processing unit that is associated with a node (e.g., computing device 102) in the network. In the case of a physical qubit, the state of the qubit will not be fixed and will remain in a state of superposition, until it is measured. Prior to measurement, when a physical qubit is in a state of superposition, the amplitudes associated with the different possible states will not be known. If the state of a physical qubit is measured, then it will collapse to one of the possible states.
[0049] FIG. 2 illustrates an exemplary representation (200) of a RAN controller unit (110), in accordance with an embodiment of the present disclosure. The RAN controller unit (110) implements the quantum processing unit (108). As illustrated, the quantum processing unit (108) may include one or more processors (202) that may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that manipulate data based on operational instructions. Among other capabilities, the one or more processor(s) (202) may be configured to fetch and execute computer-readable instructions stored in a memory (204) of the computing device (104). The memory (204) may store one or more computer-readable instructions or routines, which may be fetched and executed to create or share the data units over a network service. The memory (204) may comprise any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, and the like.
[0050] The RAN controller unit (110) may also comprise an interface(s) (206). The interface(s) (206) may comprise a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, SCADA, Sensors and the like. The interface(s) (206) may facilitate communication of the computing device (102) with various devices coupled to it. The interface(s) (206) may also provide a communication pathway for one or more components of the computing device (102). Examples of such components include, but are not limited to, quantum processing engine(s) (202) and database (230).
[0051] The one or more processors (202) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the one or more processors (202). In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the one or more processors (202) may be processor executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the one or more processors (202) may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the one or more processors (202). In such examples, the RAN controller unit (110) may comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the RAN controller unit (110) and the processing resource. In other examples, the one or more processors (202) may be implemented by electronic circuitry. In an aspect, the database (230) may comprise data that may be either stored or generated as a result of functionalities implemented by any of the components of the processor (202) or the quantum processing engines (208).
[0052] In an exemplary embodiment, the quantum processing engine(s) (208) of the RAN controller unit (110) may include, a data acquisition engine (212), a quasi-quantum processing engine (214) and other engines (216). The other engines (216) may further include, without limitation, storage engine, computing engine, or signal generation engine. The computing device (102) can be implemented using any or a combination of hardware components and software components.
[0053] In an embodiment, the memory (204) includes one or more instructions which when executed causes the processor (202) to receive status data from a plurality of nodes, wherein the status data comprises information associated with one or more parameters associated with the plurality of nodes (e.g., 104, 116, 118, 120) configured to communicate with one or more computing devices (e.g., 102). The memory (204) further includes instructions which when executed causes the processor (202) to determine, based at least in part on the status data, one or more actionable insights for optimized allocation of network resources to the plurality of nodes, wherein the status data is indicative of a quantum-based representation of the node characteristics.
[0054] In an embodiment, the one or more parameters includes a state of the communication network, a state of one or more of the plurality of nodes, a state of a microservice associated with one or more of the plurality of nodes, a state of network resources associated with the communication network, and the like. In an embodiment, the plurality of nodes corresponds one or more of a base station (BS), an access points (AP), or a distributed unit (DU). In an embodiment, the node characteristics includes a current load, a current energy consumption, and a current set of device identifier allocation associated with each of the plurality of nodes.
[0055] In an embodiment, the one or more actionable insights correspond to one or more of optimizing PCI, saving energy, and balancing load associated with the communication network. In an embodiment, the status data comprises a Quantum State Variable (QSV) associated with one or more physical qubits or one or more virtual qubits. In an embodiment, the memory (204) comprises one or more instructions which when executed further causes the processor (202) to determine probabilistic state information in the quantum state variables (QSVs) or entangled QSVs.
[0056] FIG. 3 illustrates exemplary flow diagram of the proposed method, in accordance with an embodiment of the present disclosure.
[0057] In an embodiment, the method (300) may include at 302 the step of receiving, by the processor 202, a set of data packets, each data packet corresponding to a microservice and at 304 the step of executing, a first set of instructions by the processor 202 in quantum processor unit (108), wherein the first set of instructions may be associated with an optimized allocation of the plurality of microservices to the plurality of nodes. Based on the first set of instructions, the method may further include at 306, the step of distributing the set of data packets to the plurality of nodes (104) by the processor 202 in the RAN controller unit (110).
[0058] In an embodiment, a quantum-based algorithm (i.e., the first set of instructions) can be utilized to represent a qubit. For example, but not as a limitation, a physical QSV JSON data structure can be represented as physical QSV as shown below
{
“QSV”: {
“Name”: “phyQSV-A”,
“Type”: “physical”,
“creationTimestamp”: “T1”,
“numStates”: 4,
“states”: {
“state0”: “|00>”,
“state1”: “|01>”,
“state2”: “|10>”,
“state3”: “|11>”,
}
“collapsedState”: “no”,
“collapsedStateValue”: “NA”,
}
}
[0059] In the case of a physical qubit, the state of the qubit is in a state of superposition, until it is measured. Prior to measurement, when a physical qubit is in a state of superposition, the amplitudes associated with the different possible states are not known. If the state of a physical qubit is measured, then it collapses to one of the possible states. In an embodiment, each of the states can correspond to a state of the network, a state of the node, a state of the microservice, a state of the network resources, and the like.
[0060] In an embodiment, quasi-quantum processing may utilize virtual qubits simulated in software. If the QSV is associated with one or more virtual qubits, then the state updates may be processed through software emulation of a quantum algorithm on a classical computer. A data structure can be managed in the system 200 to represent not only the existence of the qubit, but also to explicitly manage the amplitudes of the states in software. For example, but not as a limitation, a virtual QSV JSON data structure can be written as
{
“QSV”: {
“Name”: “virtQSV-B”,
“Type”: “virtual”,
“creationTimestamp”: “T2”,
“numStates”: 4,
“numStates”: 4,
“states”: {
“state0”: “|00>”,
“state1”: “|01>”,
“state2”: “|10>”,
“state3”: “|11>”,
}
“stateAmplitudes”: {
“amplitude0001”: 0.2,
“amplitude0010”: 0.8,
“amplitude0100”: 0.4,
“amplitude1000”: 0.4,
}
“collapsedState”: “no”,
“collapsedStateValue”: “NA”,
}
}
[0061] In an embodiment, when using quantum computers, physical qubits may be used, and when using a classical computer, virtual qubits may be used but not limited to it. A collapse of the quantum state can be accomplished in software if desired. Depending on whether the quantum states may be physical or virtual, the appropriate data structure can be utilized when executing a quantum algorithm on a quantum or a classical computer respectively, to implement quasi-quantum algorithms.
[0062] In an embodiment, a virtual QSV for a k-qubit system can thus be used to represent a probabilistic state vector over the state space for the network architecture 100. A quasi-quantum algorithm leverages such virtual QSVs to evolve such a probabilistic state vector in the pursuit of a solution to a problem that is a function of such a probabilistic system state.
[0063] Embodiments of a method for quantum-based resource management in a communication network are disclosed. In an embodiment, the method includes receiving, by the processor (202), status data from a plurality of nodes, wherein the status data comprises information associated with one or more parameters associated with the plurality of nodes (e.g., 104, 116,118, 120) configured to communicate with one or more computing devices (102). The method further includes determining, by the processor (202), based at least in part on the status data, one or more actionable insights for optimized allocation of network resources to the plurality of nodes, wherein the status data is indicative of a quantum-based representation of the node characteristics.
[0064] FIG. 4 illustrates exemplary representation of a heterogeneous RAN Controller, in accordance with an embodiment of the present disclosure. The illustrated RAN Controller is an example implementation of system 200 described with reference to FIG. 2.
[0065] As illustrated, in an aspect, to optimize resources in the RAN domain, the (Open RAN (O-RAN) architecture may include capabilities for a non-real-time RAN Interface Controller (Non-RT-RIC) in a Service and Management Orchestration layer associated with the RAN. The O-RAN architecture also includes a near-real-time RAN Interface Controller (Near RT-RIC) in the RAN that interacts with Central Units (CUs), or Distributed Units (DUs) in the network. A heterogeneous RIC (RAN Intelligent Controller) is disclosed that manages other radio access technologies and systems as well such as traditional Wi-Fi access points (APs), 4G eNodeBs and 5G gNodeBs, in addition to disaggregated components such as CUs, DUs, and RUs, in emerging RAN architecture as shown in FIG. 4.
[0066] In an exemplary embodiment, the DUs provide support for lower layers of the protocol stack such as the physical layer, the MAC layer and the RLC layer. The CUs may provide support for upper layers of the protocol stack such as the RRC, PDCP, and the SDAP layers. System information metrics/parameters from the CUs and DUs may be monitored and aggregated at the RICs and stored in a system information model (typically a yang model) that can vary dynamically. Changes in the system model can be continuously monitored. The system can be designed to monitor changes in specific metrics that are related to a specific optimization microservice, and such changes can trigger the execution of relevant optimization microservices. In an embodiment, these optimization microservices can relate to SON functions being defined for the RAN and into other network segments at SDOs such as ETSI, NGMN, 3GPP and ITU-T.
PCI optimization problem:
[0067] In an exemplary embodiment, a dynamic optimization may be done locally by the RAN controller 110, so that only a small subset of IDs can be allocated to a given geographical region to prevent conflicts across regions. Under such constraints, only a few colors (representing cells) may be utilized in a given region. In an embodiment, the color (equivalently the vPCI value) of a node “i” can take on one of any of K colors in the graph. The system 200 optimizes results in the determination of the value for the color of each of the cells such that PCI collision or confusion is avoided in the network. A collision occurs when two nodes have the same color or vPCI value in the same network. A confusion occurs when two neighbors of the same node have the same colour or vPCI value in the network. From the perspective of a quantum-inspired framework, it would be useful to model this problem as a graph with nodes whose colours (PCI values) are determined based on a quantum-state variable (QSVs) associated with that node, where different states correspond to different possible colour assignments to nodes.
[0068] In an embodiment, the state probabilities associated with the QSVs for the nodes in the graph can be refined when there are edge conflicts between nodes that have the same colour. To this end, a single Quantum State Variable (QSV) can be defined as below:
(4) with K states (or colours) that could be associated with a given cell, such that a collapse of the QSV to one of the K states results in the determination of the colour (or vPCI value) for that cell. The state of a QSV is given by a superposition of K states from the set { | >, | >, …….., | > } such that is the probability associated with state | >. When using a physical QSV, it may be possible that a state that has a relative higher probability (higher amplitude in the wave function relative to other states) may correspond to the resulting collapsed state after measurement. When using a virtual QSV, the collapse may be emulated on a classical computer. For a quantum-inspired software implementation, a probability of at least 10% for edge connectivity may be used to generate random graphs for different scenarios in an embodiment. The proposed algorithm may be emulated as a software program or a set of executable instructions on a traditional x86 dual-core laptop with 16GB of RAM and 2GHz processors but not limited to the like.
[0069] FIGs. 5A-5C illustrate exemplary representations collision conflicts with a predefined number of nodes, in accordance with an embodiment of the present disclosure.
[0070] As illustrated, FIG. 5A, FIG. 5B, and FIG. 5C, show the gradual reduction in vPCI collision conflicts with additional iterations for different number of available colours given a fixed number of nodes (250, 500, and 750 nodes respectively) in the network according to an embodiment. As can be observed from the Figures and from Table 1, as the number of colours was increased, the system required fewer iterations to converge as expected to eliminate conflicts, due to the greater flexibility afforded by a larger number of available colours. The collisions in the graph may be determined based on colour conflicts given an adjacency matrix A for the network graph as shown in TABLE 1.

[0071] In an embodiment, to avoid confusions in the network, the system 200 performs a similar study on the square of the adjacency graph A2 to determine two step-walks in the graph. This allows neighbours of a given node in a network graph with adjacency matrix A, to become neighbours of each other in a network graph with adjacency matrix A2. TABLE 2 shows similar results to determine vPCI values that eliminate both vPCI collisions and confusions. It can also be observed that as the number of nodes is increased, additional colours are useful to provide more degrees of freedom for allocation of colours to nodes to eliminate collisions and confusions in the system.
Energy Saving Problem:
[0072] In an embodiment, when the utilization of the network falls below a threshold such that the available capacity is large in different cells in the network, and if the corresponding access nodes (e.g., gNodeBs, eNodeBs, or WiFi-APs, or Distributed Units (DUs) associated with gNodeBs) are jointly covering the same geographical region, then a D-Wave based quantum optimization framework in the network architecture 100 can be relevant to determine which nodes can be kept active, and which nodes can be turned off for energy savings. A D-Wave system contains the Quantum Processing Unit (QPU) 108 operated at a temperature close to absolute zero, wherein the system 200 is designed to naturally execute annealing algorithms that attempt to minimize system energy. A D-Wave QUBO (Quadratic Unconstrained Binary Optimization) problem is defined with the goal of minimizing an energy function E (also known as the Hamiltonian for the system) given by
(5)
[0073] In an embodiment, the terms xi take on values 1 or 0 to indicate whether an access node is “on” or “off” in the network architecture. This is based on the Ising Model where the variables represent “spin up” or “spin down” states. Self-coupling coefficients Qii can be used to provide an opportunity for a node to provide service with the value depending on the size of the coverage area provided by the node in the region. The goal is to minimise the energy , and hence a more negative or a lower value of a self-coupling coefficient indicates higher likelihood that the node represented by the coefficient is turned on in the network. The cross-coupling coefficients Qij represent the degree of correlation between nodes in the network. Such a self-coupling coefficient can be used to provide an opportunity for a node to provide service where the value can depend on the size of the coverage area provided by the node in the region, or the cost of service from the node in the region. The coupling between two nodes can represent either a positive or negative correlation.
[0074] In an embodiment, a negative value of a cross-coupling coefficient increases the likelihood of two nodes jointly serving a network thus lowering energy in the system, whereas a positive value of the cross-coupling coefficients has the opposite effect, reducing the likelihood of two nodes jointly serving the network. A positive value of the cross-coupling coefficient can be used if two nodes have a common area that they are covering, so that the magnitude of the coupling reflects the degree of overlap. Similarly, a negative value could be used if two nodes cover different areas, with a higher degree of negative coupling if the nodes are highly separated from each other.
[0075] In an embodiment, if the cross-coupling coefficient Qij between two nodes has a positive value, and yet if that cost is lower in magnitude relative to the magnitude of the sum of the negative self-coupling costs Qii and Qjj for the two nodes “i” and “j”, then the overall cost/energy to jointly utilize both access nodes is negative. In an alternative embodiment, if a positive cross-coupling coefficient is higher in magnitude relative to the negative self-coupling costs for the nodes, then the joint utilization of the nodes results in a higher energy system. In general, the costs associated with utilization of all access nodes needs to be jointly considered, so that a joint optimization across all nodes is needed for an overall determination of which access nodes can be kept on at any given time.
[0076] The disclosed technique was implemented in the D-Wave QUBO simulator. For example, when the self-coupling values and cross-coupling values as listed in Table 1 are utilized, and the cross-coupling terms are utilized only once for any pair of nodes as shown in equation 2, then the system settles down to a low energy state of -14 units with an allocation of 10011 across the nodes, enabling nodes 1 and 2 to go to sleep, while keeping nodes 0, 3 and 4 active in the system as shown in Table 3. Such a formulation can thus be used for energy optimization across a network of base-stations or access points, by utilizing a quantum-system representing the system state to evolve, anneal, and settle down to a low energy state that minimizes system energy.
Table 3 Iterative Quasi-Quantum vPCI optimization (Collisions and Confusions)
Node 0 Node 1 Node 2 Node 3 Node 4
Node 0 -4 5 3 -3 2
Node 1 5 -4 4 4 3
Node 2 3 4 -4 2 2
Node 3 -3 4 2 -4 -1
Node 4 2 3 2 -1 -4

Load Balancing Problem:
[0077] FIG. 6 illustrates exemplary representation of a plurality of access nodes serving a mobile device, in accordance with an embodiment of the present disclosure. As illustrated, N access nodes in the network collaborating together to provide access to different users in a network in a common geographical area as shown in FIG. 6 may be considered. The nodes may be operating in different frequency bands in the same geographical region so that the load on access nodes is utilized to determine which node provides access to a particular user in that region. In that regard, the system 200 can create a QSV that is entangled across different access nodes in the network, where each access node has a state | si(t)> where i = 1, 2,…., N.
[0078] In an exemplary embodiment, as an example but not as a limitation, a two-qubit system can be directly entangled such that the measurement of the state of one qubit in the system collapses the state of both qubits to either | s0(t)> or | s1(t)>. In an embodiment, the entanglement can be accomplished in a quantum computer using quantum gates such as by applying a Hadamard gate to one of the qubits followed by the application of a CNOT gate on the two-qubit system. A two-qubit system can be inversely entangled as well. This is possible for example, when particles of opposite spins are created, so that their entanglement has an opposite correlation. The measurement of the state of a qubit in an inversely entangled 2-qubit system collapses the state of that qubit to either | s0(t)> or | s1(t)> and the state of the other qubit collapses to | s1(t)> or | s0(t)> respectively. Multiple quantum particles can be directly entangled with each other to create an entangled state across these particles, such that after observation/measurement all entangled particles collapse to either the | s0(t)> or | s1(t)> state respectively. A hybrid combination of direct or inverse correlated states could be represented for multiple entangled photons in general if desired. In an embodiment, the state may correspond to load on a particular node.
[0079] In an exemplary embodiment, an access node i that may provide network access to a new user. In this regard, the system 200 can create a quantum state ?i(t) which is a superposition of the states |si(t)> for these N access nodes, where the state |si(t)> represents node i serving a new user at time t, given by
?i(t) = Si ai(t) |si(t)> (6)
[0080] The state probability may be given by the square of the amplitude ai(t) representing the likelihood of access node i serving a new user at time t. In the simple case, when all states are equally likely with probability 1/N, then the amplitudes ai(t) have values 1/ .
[0081] In an embodiment, an access node that has a lighter load relative to other nodes has a higher amplitude in the QSV, so that the access node would have a higher likelihood of being selected to serve a new user in the network. Since the nodes share an entangled QSV periodically, a collapse of the QSV helps in determining which node provides access across all the nodes. To maintain an updated version of the state, each access node i would have to periodically exchange its current load Li(t) and their total capacity Ci(t), with other nodes in the network, to enable its available capacities Ai (t) = Ci(t) – Li(t) to be computed by other nodes. Alternatively, nodes could merely exchange their dynamic available capacity Ai (t) periodically. This allows the state probabilities to be computed so that they are proportional to the dynamic available capacities of each of the nodes i, to then determine the state amplitudes for the distributed probabilistic quantum state vector across the nodes.
[0082] In an exemplary embodiment, a blockchain network can be considered across these nodes interacting with each other to exchange information between each other to determine state probabilities. Subsequently, a QSV collapse can determine which node provides access to a new user to balance the network load. The disclosed technique was simulated starting with a random initial load across 10 access nodes. The process was iterated at least 4 times where during each iteration at least100 users were admitted into the network consisting of the at least 10 access nodes but not limited to it.
[0083] FIG. 7 illustrates exemplary representations of Dynamic Quantum Load Optimization, in accordance with an embodiment of the present disclosure. As illustrated, Dynamic Quantum Load Optimization across at least 10 access nodes serving mobile nodes in a network is shown. The load may get progressively balanced across the network as new users join the network utilizing the suggested quantum collapse on a QSV across the nodes to admit such new users. Each curve shows the distribution of load across the nodes after each iteration starting with the lowermost initial load distribution curve in the graph. It can be seen that as new nodes join the network, the load across the network is distributed equitably progressively across the access nodes in the network.
[0084] FIG. 8 illustrates an exemplary computer system in which or with which embodiments of the present invention can be utilized in accordance with embodiments of the present disclosure. As shown in FIG. 8, computer system (800) can include an external storage device (810), a bus (820), a main memory (830), a read only memory (840), a mass storage device (870), communication port (860), and a processor (870). A person skilled in the art will appreciate that the computer system may include more than one processor and communication ports. Examples of processor (870) include, but are not limited to, an Intel® Itanium® or Itanium 2 processor(s), or AMD® Opteron® or Athlon MP® processor(s), Motorola® lines of processors, FortiSOC™ system on chip processors or other future processors. Processor (870) may include various modules associated with embodiments of the present invention. Communication port (880 can be any of an RS-232 port for use with a modem based dialup connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. Communication port (880 may be chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which computer system connects. Memory 830 can be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. Read-only memory (840) can be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or BIOS instructions for processor (870). Mass storage (850) may be any current or future mass storage solution, which can be used to store information and/or instructions. Exemplary mass storage solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or Firewire interfaces), e.g. those available from Seagate (e.g., the Seagate Barracuda 7102 family) or Hitachi (e.g., the Hitachi Deskstar 7K1000), one or more optical discs, Redundant Array of Independent Disks (RAID) storage, e.g. an array of disks (e.g., SATA arrays), available from various vendors including Dot Hill Systems Corp., LaCie, Nexsan Technologies, Inc. and Enhance Technology, Inc.
[0085] Bus (820) communicatively couples processor(s) (870) with the other memory, storage and communication blocks. Bus (820) can be, e.g. a Peripheral Component Interconnect (PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), USB or the like, for connecting expansion cards, drives and other subsystems as well as other buses, such a front side bus (FSB), which connects processor (870) to software system.
[0086] Optionally, operator and administrative interfaces, e.g., a display, keyboard, joystick and a cursor control device, may also be coupled to bus (820) to support direct operator interaction with a computer system. Other operator and administrative interfaces can be provided through network connections connected through communication port (860). The external storage device (810) can be any kind of external hard-drives, floppy drives, IOMEGA® Zip Drives, Compact Disc - Read Only Memory (CD-ROM), Compact Disc-Re-Writable (CD-RW), Digital Video Disk-Read Only Memory (DVD-ROM). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system limit the scope of the present disclosure.
[0087] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.
,CLAIMS:1. A system for quantum-based resource management in a communication network, the system comprising:
a processor coupled to a memory, the memory comprising one or more instructions which when executed causes the processor to:
receive a set of data packets, each data packet corresponding to a microservice;
execute a first set of instructions, the first set of instructions associated with an optimized allocation of a plurality of microservices to the plurality of nodes; and
based on the executed first set of instructions, distribute the set of data packets to the plurality of nodes.
2. The system as claimed in claim 1, wherein the microservice corresponds to one or more of automatic inventory, automatic software download, automatic neighbor relation (ANR), automatic physical cell ID (PCI) assignment, mobility robustness/handover (MRO), random access channel (RACH), load balancing, Inter-Cell Interference Coordination (ICIC), coverage and capacity, enhanced Inter-Cell Interference Coordination (eICIC), cell outage detection and compensation, minimization of drive testing, energy savings, handover, and self-healing coordination between various Self-Organizing Network (SON) functions.
3. The system as claimed in claim 1, wherein the plurality of nodes corresponds to any or a combination of enodes and gnodes and access nodes.
4. A system for quantum-based resource management in a communication network, the system comprising:
a processor coupled to a memory, the memory comprising one or more instructions which when executed causes the processor to:
receive status data from a plurality of nodes, wherein the status data comprises information associated with one or more parameters associated with the plurality of nodes configured to communicate with one or more computing devices; and
determine, based at least in part on the status data, one or more actionable insights for optimized allocation of network resources to the plurality of nodes, wherein the status data is indicative of a quantum-based representation of the node characteristics.
5. The system as claimed in claim 4, wherein the one or more parameters comprises a state of the communication network, a state of one or more of the plurality of nodes, a state of a microservice associated with one or more of the plurality of nodes, a state of network resources associated with the communication network, and the like.
6. The system as claimed in claim 4, wherein the plurality of nodes correspond one or more of a base stations (BS), an access points (AP), or a distributed units (DU).
7. The system as claimed in claim 4, wherein the node characteristics comprises a current load, a current energy consumption, and a current set of device identifier allocation associated with each of the plurality of nodes.
8. The system as claimed in claim 4, wherein the one or more actionable insights correspond to one or more of optimizing PCI, saving energy, and balancing load associated with the communication network.
9. The system as claimed in claim 4, wherein the status data comprises a Quantum State Variable (QSV) associated with one or more physical qubits or one or more virtual qubits.
10. The system as claimed in claim 9, wherein the memory comprises one or more instructions which when executed further causes the processor to determine probabilistic state information in the quantum state variables (QSVs) or entangled QSVs.
11. A method for quantum-based resource management in a communication network, the method comprising:
receiving, by a processor, status data from a plurality of nodes, wherein the status data comprises information associated with one or more parameters associated with the plurality of nodes configured to communicate with one or more computing devices; and
determining, by a processor, based at least in part on the status data, one or more actionable insights for optimized allocation of network resources to the plurality of nodes, wherein the status data is indicative of a quantum-based representation of the node characteristics.