METHOD AND SYSTEM FOR DSS BETWEEN LTE CELL AND NR CELL IN WIRELESS NETWORK

The disclosure relates to a 5th generation (5G) or 6th generation (6G) communication system for supporting a higher data transmission rate. A method and a network entity for dynamic spectrum sharing (DSS) between long term evolution (LTE) cell and new radio (NR) cell in a wireless network are provided. The method includes determining a plurality of DSS parameter, determining a resource split between at least one bearer of a plurality of bearers of the LTE cell and at least one bearer of a plurality of bearers of the NR cell based on the plurality of DSS parameters, determining optimal key performance indicator (KPI) parameters for an overlapped LTE cell and NR cell combination based the resource split, and applying optimal tuning parameters on the overlapped LTE cell and NR cell combination in the wireless network based on the optimal KPI parameters.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is based on and claims priority under 35 U.S.C. § 119(a) of an Indian provisional patent application number 202241029255, filed on May 20, 2022, in the Indian Patent Office, and of an Indian non-provisional patent application number 202241029255, filed on Apr. 19, 2023, in the Indian Intellectual Property Office, the disclosure of each of which is incorporated by reference herein in its entirety.

BACKGROUND

The disclosure relates to a method and a system for dynamic spectrum sharing (DSS) between a long-term evolution (LTE) cell and a new radio (NR) cell in a wireless network.

2. Description of Related Art

In general, a NR system needs to use low-frequency spectrum in order to provide a wide area coverage. The low-frequency spectrum is scarcely available as the low-frequency spectrum is mostly occupied by a long term evolution (LTE) system. In such scenario, efficient spectrum sharing is a good alternative to spectrum re-farming from the LTE system to the NR system, which helps in a smooth migration over a period of time from the LTE system to the NR system as a usage and a popularity for the NR starts to increase. LTE/NR spectrum sharing enables sharing of resources in a same carrier with dynamic adaptation based on load, users, or the like, thereby achieving high spectrum utilization efficiency, which is not currently available in methods and systems of the related art.

Spectrum sharing system of the related art dynamically configures a number of subframes to support communications via an LTE air interface and a 5thgeneration (5G) NR-LTE interface. However, the spectrum sharing system of the related art does not dynamically control DSS tuning parameters and does not perform optimal split between control and data symbols for slot pattern to achieve high spectrum utilization efficiency.

SUMMARY

Aspects of the disclosure are to address at least the above-mentioned problems and/or disadvantages and to provide at least the advantages described below. Accordingly, an aspect of the disclosure is to provide a method and a system for DSS between a LTE cell and a NR cell in a wireless network. The method includes determining, by a network entity, a resource split between at least one bearer of a plurality of bearers of the LTE cell and at least one bearer of a plurality of bearers of the NR cell based on a plurality of DSS parameters.

Another aspect of the disclosure is to determine optimal key performance indicator (KPI) parameters for an overlapped LTE cell and NR cell combination based the resource split.

Another aspect of the disclosure is to apply optimal tuning parameters on the overlapped LTE cell and NR cell combination in the wireless network based on the optimal KPI parameters.

Another aspect of the disclosure is to provide a method that discloses an LTE/NR spectrum sharing which enables sharing of resources in a same carrier with dynamic adaptation based on load, users, or the like, thereby achieving high spectrum utilization efficiency.

In accordance with an aspect of the disclosure, a method for DSS between an LTE cell and a NR cell in a wireless network is provided. The method includes determining, by a network entity, a plurality of DSS parameters. The method includes determining a resource split between at least one bearer of a plurality of bearers of the LTE cell and at least one bearer of a plurality of bearers of the NR cell based on the plurality of DSS parameters. The method also includes determining optimal KPI parameters for an overlapped LTE cell and NR cell combination based the resource split. The method includes applying optimal tuning parameters on the overlapped LTE cell and NR cell combination in the wireless network based on the optimal KPI parameters.

In an embodiment of the disclosure, the plurality of DSS parameters includes a cell identifier (ID), a radio network temporary identifier (RNTI), a bandwidth, total number of resource blocks, a user equipment (UE) configuration, access and mobility management related parameters of the UE, a UE ID, channel model parameters, buffer occupancy (BO), modulation and coding scheme (MCS), a packet delay budget (PDB), a signal-to-noise ratio and a block error rate.

In an embodiment of the disclosure, determining, by the network entity, the optimal KPI parameters for the overlapped LTE cell and NR cell combination includes determining fairness index for maintaining fairness among the plurality of bearers of the LTE cell and the plurality of bearers of the NR cell based on the resource split, determining throughput in a system and across the neighboring cells of the LTE cell and the neighboring cells of the NR cell based on the fairness index, determining interference across neighboring cells of the LTE cell and neighboring cells of the NR cell based on the plurality of DSS parameters, and determining the optimal KPI parameters for the overlapped LTE cell and NR cell combination based on the fairness index, the interference and the throughput in the system and across the neighboring cells of the LTE cell and the neighboring cells of the NR cell.

In an embodiment of the disclosure, determining, by the network entity, the fairness index for maintaining fairness among the plurality of bearers of the LTE cell and the plurality of bearers of the NR cell includes determining priority metrics including an accumulated transport block size and a guaranteed bit rate (GBR) of each bearer of the plurality of bearers of the LTE cell, and priority metrics including an accumulated transport block size and an GBR of each bearer of the plurality of bearers of the NR cell based on the plurality of DSS parameters, and determining the fairness index for maintaining fairness among the plurality of bearers of the LTE cell and the plurality of bearers of the NR cell using the priority metrics including the accumulated transport block size and the GBR of each bearer of the plurality of bearers of the LTE cell and the priority metrics including the accumulated transport block size and the GBR of each bearer of the plurality of bearers of the NR cell.

In an embodiment of the disclosure, applying, by the network entity, the optimal tuning parameters on the overlapped LTE cell and NR cell combination in the wireless network includes determining optimal resource split between the at least one bearer of the plurality of bearers of the LTE cell and the at least one bearer of the plurality of bearers of the NR cell is based on a frequency-division multiplexing (FDM) mode, determining a slot for allocating at least one optimal resource to the at least one bearer of the plurality of bearers of the LTE cell and at least one optimal resource to the at least one bearer of the plurality of bearers of the NR cell is based on the FDM mode or a time-division multiplexing (TDM) mode, and allocating at least one optimal resource to the at least one bearer of the plurality of bearers of the LTE cell and at least one optimal resource to the at least one bearer of the plurality of bearers of the NR cell in response to determining the slot.

In an embodiment of the disclosure, when the slot for allocating the at least one optimal resource to the at least one bearer of the plurality of bearers of the LTE cell and the at least one optimal resource to the at least one bearer of the plurality of bearers of the NR cell is based on the FDM, the method includes selecting candidate bearers from the plurality of bearers of the LTE cell and the plurality of bearers of the NR cell based on the priority metrics including the accumulated transport block size and the GBR of each bearer of the plurality of bearers of the LTE cell and the priority metrics including the accumulated transport block size and the GBR of each bearer of the plurality of bearers of the NR cell, separating the candidate bearers per cell of the LTE cell and the NR cell based on the priority metrics including the accumulated transport block size and the GBR of each bearer of the plurality of bearers of the LTE cell and the priority metrics including the accumulated transport block size and the GBR of each bearer of the plurality of bearers of the NR cell, and allocating the at least one optimal resource to the at least one bearer of the plurality of bearers of the LTE cell and the at least one optimal resource to the at least one bearer of the plurality of bearers of the NR cell using the slot based on the FDM.

In an embodiment of the disclosure, when the slot for allocating the at least one optimal resource to the at least one bearer of the plurality of bearers of the LTE cell and the at least one optimal resource to the at least one bearer of the plurality of bearers of the NR cell is based on the TDM, the method includes determining the candidate bearers per cell of the LTE cell and the NR cell based on the priority metrics including the accumulated transport block size and the GBR of each bearer of the plurality of bearers of the LTE cell and the priority metrics including the accumulated transport block size and the GBR of each bearer of the plurality of bearers of the NR cell, and allocating the at least one optimal resource to the at least one bearer of the plurality of bearers of the LTE cell and the at least one optimal resource to the at least one bearer of the plurality of bearers of the NR cell using the slot based on the TDM.

In accordance with another aspect of the disclosure, a network entity for DSS between the LTE cell and the NR cell in the wireless network is provided. The network entity includes a memory, a processor coupled to the memory, a communicator coupled to the memory and the processor, and a centralized controller coupled to the memory, the processor and the communicator. The centralized controller configured to determine the plurality of DSS parameters, determine the resource split between the at least one bearer of the plurality of bearers of the LTE cell and the at least one bearer of the plurality of bearers of the NR cell based on the plurality of DSS parameters, determine the optimal KPI parameters for the overlapped LTE cell and NR cell combination based the resource split, and apply the optimal tuning parameters on the overlapped LTE cell and NR cell combination in the wireless network based on the optimal KPI parameters.

DETAILED DESCRIPTION

Accordingly, the embodiments herein disclose a method for a method for DSS between a LTE cell and a NR cell in a wireless network. The method includes determining, by a network entity, a plurality of DSS parameters. The method includes determining a resource split between at least one bearer of a plurality of bearers of the LTE cell and at least one bearer of a plurality of bearers of the NR cell based on the plurality of DSS parameters. The method also includes determining optimal KPI parameters for an overlapped LTE cell and NR cell combination based the resource split. The method includes applying optimal tuning parameters on the overlapped LTE cell and NR cell combination in the wireless network based on the optimal KPI parameters.

Accordingly, the embodiments herein disclose a system for DSS between the LTE cell and the NR cell in the wireless network. The method includes a memory, a processor coupled to the memory, a communicator coupled to the memory and the processor, and the network entity coupled to the memory, the processor and the communicator. The network entity configured to determine the plurality of DSS parameters, determine the resource split between the at least one bearer of the plurality of bearers of the LTE cell and the at least one bearer of the plurality of bearers of the NR cell based on the plurality of DSS parameters, determine the optimal KPI parameters for the overlapped LTE cell and NR cell combination based the resource split, and apply the optimal tuning parameters on the overlapped LTE cell and NR cell combination in the wireless network based on the optimal KPI parameters.

Spectrum sharing system of the related art dynamically configures a number of subframes to support communications via an LTE air interface and a 5G NR-LTE interface. However, the spectrum sharing system of the related art does not dynamically control DSS tuning parameters and does not perform optimal split between control and data symbols for slot pattern to achieve high spectrum utilization efficiency.

FIG.3illustrates a step-by step procedure for allocating resources according to the related art.

Referring toFIG.3, methods and systems of the related art allocate resources using a rule-based mechanism. The methods and systems of the related art allocate the resources by following operations:

At operation301, the network entity determines cell parameters, UE parameters and a channel model parameters.

At operation302, BO is determined from one of the cell parameters, UE parameters and a channel model parameters for the at least one bearer of the plurality of bearers of the LTE cell and the at least one bearer of the plurality of bearers of the NR cell.

At operation303, a media access control (MAC) layer pre-processes the cell parameters, the UE parameters and the channel model parameters and output a list of bearers per cell to a central controller.

At operation304, the central controller receives the list of bearers per cell and determines whether a slot for allocating an optimal resource to the bearer of the LTE cell and an optimal resource to the bearer of the NR cell is based on a FDM mode or a TDM mode. Further at operation304, the central controller directly shares candidate bearers to a resource allocation controller based on the pre-processed parameters.

At operation305, the central controller shares the list of bearer of each cell to a scheduler if the slot for allocating the optimal resource to the bearer of the LTE cell and the optimal resource to the bearer of the NR cell is based on the FDM mode.

At operation306, the scheduler determines priority metrics and a GBR of each bearer of the LTE cell and the NR cell and chooses candidate bearers (or configured values) based on the priority metrics and the GBR of each bearer of the LTE cell and the NR cell. The scheduler shares the candidate bearers (or configured values) to the central controller in priority order of the GBR.

At operation307, the central controller performs resource split between the LTE bearers and NR bearers based on simplistic parameters for example ratio of number of bearers and shares resources to a resource allocation controller.

At operation308, the resource allocation controller allocates the resources accordingly based on the ratio of number of bearers.

At operations309and310, the resource allocation controller shares scheduled UE information and/or resources allocation information to a physical layer. The physical layer gets feedback values of the priority metrics, such as a bit/block error rate for a given signal to interference and noise ratio (SINR) and shares with the MAC layer.

From the above operations, the methods and systems of the related art clearly states that the resources are allocated using the rule based mechanisms in which spectrum re-farming from a LTE system to a NR system is not efficient and spectrum utilization efficiency is decreased. Hence, there is a need for a centralized controller orchestrating the NR system and the LTE system when both the NR system and the LTE system are deployed in an overlapping coverage area sharing the same spectrum resources.

Unlike the methods and systems of the related art, the proposed method uses a reinforcement learning model based approach to dynamically control DSS tuning parameters. The DSS tuning parameters are dynamically controlled by optimally splitting the resources between control and data symbols based on slot patterns, and dynamically allocating resources across the LTE cells and the NR cells based on the slot patterns. Moreover, the proposed method includes the centralized controller orchestrating the NR system and the LTE system when both the NR system and the LTE system are deployed in the overlapping coverage area sharing the same spectrum resources.

Referring now to the drawings and more particularly toFIGS.1to4, and5A to5C, where similar reference characters denote corresponding features consistently throughout the figure, these are shown preferred embodiments.

FIG.1is a block diagram of a network entity for DSS between a LTE cell and a NR cell in a wireless network according to an embodiment of the disclosure.

Referring toFIG.1, the network entity100includes a centralized controller140, such as for example but not limited to a centralized virtual RAN (CvRAN) controller, a RAN controller which is virtual or non-virtual, a single centralized controller and a software-defined networking controller.

In an embodiment of the disclosure, the network entity100includes a memory110, a processor120, a communicator130, and the centralized controller140.

The memory110is configured to store the plurality of DSS parameters including a cell ID, a RNTI, a bandwidth, total number of resource blocks, a UE configuration, an access and mobility management related parameters of the UE, a UE ID, channel model parameters, BO, MCS, a PDB, a signal-to-noise ratio and a block error rate. The memory110includes non-volatile storage elements. Examples of such non-volatile storage elements include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. In addition, the memory110, in some examples, is considered a non-transitory storage medium. The term “non-transitory” indicates that the storage medium is not embodied in a carrier wave or a propagated signal. However, the term “non-transitory” is not interpreted that the memory110is non-movable. In some examples, the memory110is configured to store larger amounts of information. In certain examples, a non-transitory storage medium stores data that changes over time (e.g., in a random access memory (RAM) or cache).

The processor120includes one or a plurality of processors. The one or the plurality of processors is a general-purpose processor, such as a central processing unit (CPU), an application processor (AP), or the like, a graphics-only processing unit, such as a graphics processing unit (GPU), a visual processing unit (VPU), and/or an AI-dedicated processor, such as a neural processing unit (NPU). The processor120includes multiple cores and is configured to determine the plurality of DSS parameters stored in the memory110.

In an embodiment of the disclosure, the communicator130includes an electronic circuit specific to a standard that enables wired or wireless communication. The communicator130is configured to communicate internally between internal hardware components of the network entity100and with external devices via one or more networks.

In an embodiment of the disclosure, the centralized controller140includes a receiver141, a DSS parameters determination controller42, a resource split controller143and a resource allocation controller144.

In an embodiment of the disclosure, the receiver141is configured to receive a plurality of UE parameters from a UE, a plurality of cell parameters from the LTE cell and the NR cell, and the channel model parameters. The plurality of UE parameters include but not limited to the UE configuration, the access and mobility management related parameters of the UE and the UE ID. The plurality of cell parameters include but not limited to the cell ID, the RNTI, the bandwidth and the total number of resource blocks. The channel model parameters include but not limited to a coherence bandwidth, a ratio of power in a dominant path and a scattered path, and a set of key/value pairs.

In an embodiment of the disclosure, the DSS parameters determination controller142is configured to determine the plurality of DSS parameters from the plurality of UE parameters, the plurality of cell parameters, the channel model parameters and a plurality of network parameters. The plurality of network parameters include but not limited to a bandwidth, a throughput, latency, a packet loss, BO, MCS, a PDB, a signal-to-noise ratio, a block error rate and jitter. The plurality of DSS parameters includes but not limited to the cell ID, the RNTI, the bandwidth, the total number of resource blocks, the UE configuration, the access and mobility management related parameters of the UE, the UE ID, the channel model parameters, the BO, the MCS, the PDB, the signal-to-noise ratio and the block error rate.

In an embodiment of the disclosure, the resource split controller143is configured to determine a resource split between at least one bearer of a plurality of bearers of the LTE cell and at least one bearer of a plurality of bearers of the NR cell based on the plurality of DSS parameters. Further, the resource split controller143is configured to determine optimal KPI parameters for an overlapped LTE cell and NR cell combination based the resource split.

In an embodiment of the disclosure, the optimal KPI parameters for the overlapped LTE cell and NR cell combination are determined by:determining fairness index for maintaining fairness among the plurality of bearers of the LTE cell and the plurality of bearers of the NR cell based on the resource split,determining throughput in the network entity100and across neighboring cells of the LTE cell and neighboring cells of the NR cell based on the fairness index, anddetermining interference across the neighboring cells of the LTE cell and the neighboring cells of the NR cell based on the plurality of DSS parameters.

In an embodiment of the disclosure, the fairness index is determined by determining priority metrics including an accumulated transport block size and a guaranteed bit rate (GBR) of each bearer of the plurality of bearers of the LTE cell, and priority metrics including an accumulated transport block size and an GBR of each bearer of the plurality of bearers of the NR cell based on the plurality of DSS parameters.

In an embodiment of the disclosure, the resource allocation controller144is configured to apply optimal tuning parameters on the overlapped LTE cell and NR cell combination in the wireless network based on the optimal KPI parameters. Further, the resource allocation controller144is configured to allocate at least one optimal resource to the at least one bearer of the plurality of bearers of the LTE cell and at least one optimal resource to the at least one bearer of the plurality of bearers of the NR cell to apply the optimal tuning parameters on the overlapped LTE cell and NR cell combination in the wireless network.

The centralized controller140is implemented by processing circuitry, such as logic gates, integrated circuits, microprocessors, microcontrollers, memory circuits, passive electronic components, active electronic components, optical components, hardwired circuits, or the like, and may optionally be driven by firmware. The circuits, for example, are embodied in one or more semiconductor chips, or on substrate supports, such as printed circuit boards, and the like.

At least one of the plurality of modules/components of the centralized controller140is implemented through an AI model. A function associated with the AI model is performed through the memory110and the processor120. The one or a plurality of processors controls the processing of the input data in accordance with a predefined operating rule or the AI model stored in the non-volatile memory and the volatile memory. The predefined operating rule or artificial intelligence model is provided through training or learning.

Here, being provided through learning means that, by applying a learning process to a plurality of learning data, a predefined operating rule or AI model of a desired characteristic is made. The learning is performed in a device itself in which AI according to an embodiment is performed, and/or is implemented through a separate server/system.

The learning process is a method for training a predetermined target device (for example, a robot) using a plurality of learning data to cause, allow, or control the target device to make a determination or prediction. Examples of learning processes include but are not limited to supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.

Although theFIG.1show the hardware elements of the network entity100but it is to be understood that other embodiments are not limited thereon. In other embodiments of the disclosure, the network entity100includes less or more number of elements. Further, the labels or names of the elements are used only for illustrative purpose and does not limit the scope of the disclosure. One or more components are combined together to perform same or substantially similar function.

FIG.2is a flowchart200illustrating a method for DSS between an LTE cell and an NR cell in a wireless network by a network entity according to an embodiment of the disclosure.

Referring toFIG.2, at operation202, the method includes the network entity100determining the plurality of DSS parameters. For example, in the network entity100as illustrated in theFIG.1, the centralized controller140is configured to determine the plurality of DSS parameters.

At operation204, the method includes the network entity100determining the resource split between the at least one bearer of the plurality of bearers of the LTE cell and the at least one bearer of the plurality of bearers of the NR cell based on the plurality of DSS parameters. For example, in the network entity100as illustrated in theFIG.1, the centralized controller140is configured to determine the resource split between the at least one bearer of the plurality of bearers of the LTE cell and the at least one bearer of the plurality of bearers of the NR cell based on the plurality of DSS parameters.

At operation206, the method includes the network entity100determining the optimal KPI parameters for the overlapped LTE cell and NR cell combination based the resource split. For example, in the network entity100as illustrated in theFIG.1, the centralized controller140is configured to determine the optimal KPI parameters for the overlapped LTE cell and NR cell combination based the resource split.

At operation208, the method includes the network entity100applying the optimal tuning parameters on the overlapped LTE cell and NR cell combination in the wireless network based on the optimal KPI parameters. For example, in the network entity100as illustrated in theFIG.1, the centralized controller140is configured to apply the optimal tuning parameters on the overlapped LTE cell and NR cell combination in the wireless network based on the optimal KPI parameters.

FIG.4illustrates a step-by step procedure for DSS between an LTE cell and an NR cell in a wireless network according to an embodiment of the disclosure.

Referring toFIG.4, at operation401, the network entity receives the cell parameters from the LTE cell and the NR cell, the UE parameters from the UE and the channel model parameters.

At operation402, the BO configured in the network entity100is determined based on one of the cell parameters, UE parameters, the channel model parameters for the at least one bearer of the plurality of bearers of the LTE cell and the at least one bearer of the plurality of bearers of the NR cell.

At operation404, the RL model41receives the list of bearers per cell via an interface40and determines whether a slot for allocating at least one optimal resource to the at least one bearer of the LTE cell and at least one optimal resource to the at least one bearer of the NR cell is based on a FDM mode or a TDM mode.

At operation405, the RL model41shares the list of bearers of each cell to a scheduler42.

At operation406, when the RL model41determines that the slot for allocating the at least one optimal resource to the at least one bearer of the LTE cell and the at least one optimal resource to the at least one bearer of the NR cell is based on the TDM mode, the scheduler42:determines priority metrics including an accumulated transport block size and a GBR of each bearer of the plurality of bearers of the LTE cell and priority metrics including an accumulated transport block size and a GBR of each bearer of the plurality of bearers of the NR cell,determines candidate bearers per cell of the LTE cell and the NR cell, andshares the candidate bearers per cell of the LTE cell and the NR cell to the RL model41.

When the RL model41determines resource blocks (RB s) of the slot for allocating the at least one optimal resource to the at least one bearer of the LTE cell and the at least one optimal resource to the at least one bearer of the NR cell is based on the FDM mode, the scheduler42:determines the priority metrics including the accumulated transport block size and the GBR of each bearer of the plurality of bearers of the LTE cell and the priority metrics including the accumulated transport block size and the GBR of each bearer of the plurality of bearers of the NR cell,selects candidate bearers from the plurality of bearers of the LTE cell and the plurality of bearers of the NR cell,separates the candidate bearers per cell of the LTE cell and the NR cell, andshares the candidate bearers per cell of the LTE cell and the NR cell to the RL model41.

At operation407, the RL model41prioritizes the candidate bearers per cell of the LTE cell and the NR cell and shares the candidate bearers per cell of the LTE cell and the NR cell to the resource allocation controller144in priority order.

At operation408, the resource allocation controller144determines optimal resource split between the at least one bearer of the plurality of bearers of the LTE cell and the at least one bearer of the plurality of bearers of the NR. The resource allocation controller144determines the slot for allocating the at least one optimal resource to the at least one bearer of the plurality of bearers of the LTE cell and the at least one optimal resource to the at least one bearer of the plurality of bearers of the NR cell. In response to determining the slot, the resource allocation controller144allocates the at least one optimal resource to the at least one bearer of the plurality of bearers of the LTE cell and the at least one optimal resource to the at least one bearer of the plurality of bearers of the NR cell.

At operations409and410, the resource allocation controller144shares scheduled UE information and/or resources allocation information to a physical layer. The physical layer gets feedback values of the priority metrics, such as a bit/block error rate for a given SINR and shares with the MAC layer.

In an embodiment of the disclosure, for each cell of the LTE cell and the NR cell, the following are recorded:Delay Histogram: For all of the bearers of the LTE cell and the NR cell, a head value of a line packet's delay is taken. The head value is normalized by the PDB of the at least one bearer. Bins are made where width of the bins and number of the bins are based upon a user's choice. A normalized delay value is determined in a corresponding bin to generate the delay histogram.MCS_BO Histogram: For all of the bearers of the LTE cell and the NR cell, an outstanding BO and MCS are taken. A two-dimensional (2D) histogram is generated where one dimension is for the BO and other dimension is for the MCS. Bins are made for the BO where the width of the bins and the number of the bins are based upon the user's choice. For the MCS, each bin has a width of 1 and so the number of the bins is equal to a maximum MCS supported in a system+1. Based upon BO value and MCS value, the corresponding bin to which the values correspond to in the 2D histogram is determined.Action Space for RL model41: The action space for the RL model41involves decision making by the RL model41where the RL model41decides about how many resource blocks groups (RBGs) has to be made available to one of the LTE cell and the NR cell, say for example the NR cell. Then, the number of RBGs to be made available to the other cell, say for example the LTE cell, is maxRBGs. The maxRBGs are the total RBGs available in the system per slot based upon the bandwidth.

Action is taken by the RL model41at every slot. An output layer have a dimension of size (maxRBGs+1), i.e., the action of the RL model41takes a value anywhere between 0 to maxRBGs.

Reward for the RL model41: The reward for the RL model41is defined as follows:

Reward=T#(X*T+Y*F)/100where, T=(summation of TB size that gets allocated to each of the bearers among both of the cells only in the current slot)/(111*Num_RB). Here 111 is the maximum TB size per RB and Num_RB is the number of RBs in the system. This normalization is done so that the effective value of T belongs to [0, 1].X and Y are proportion ratios between the throughput and the fairness for the reward of the RL model.F is the fairness of the system, which is calculated by fairness Index. The fairness is determined on the accumulated Tb size for each bearer from the beginning of the simulation until the current slot. The value of fairness also lies between 0 and 1.

Reward is updated to include PDB divergence and resource utilization.

Therefore, the proposed method applies the RL based learning model at the centralized controller140co-ordinating across multiple cells and provides most optimal balance between resource utilization maximization and interference minimization efficiently.

FIG.5Aillustrates different slot patterns for allocating resources according to an embodiment of the disclosure.

Referring toFIG.5A, a slot pattern of a LTE system510, a slot pattern of a NR system520, and a slot pattern of a combination of LTE and NR system530are depicted. The proposed method dynamically allocates the resources across the LTE system510, the NR system520, or the combination of LTE and NR system530in response to determining the slot patterns i.e., based on the TDM mode or the FDM mode.

FIG.5Billustrates a slot pattern configuration according to an embodiment of the disclosure.

In an embodiment of the disclosure, an example slot pattern configuration is depicted. The slot pattern configuration is determined for optimal resource split and for data split between the LTE system510and the NR system520. Further, the slot pattern configuration is determined for optimal resource split between the control symbols and the data symbols.

FIG.5Cillustrates a centralized controller according to an embodiment of the disclosure.

Referring toFIG.5C, the centralized controller140receives the plurality of parameters from the LTE system510and the NR system520. The plurality of parameters received from the LTE system510and the NR system520are pre-processed and shared with the RL model41for determining the resource split between the plurality of bearers of the LTE cell and the plurality of bearers of the NR cell based on the plurality of parameters. The RL model41performs optimal resource split based on the plurality of parameters, and determines the number of bearers across both the LTE cell and the NR cell, load distribution across both the LTE cell and the NR cell, the MCS/SINR distribution of the UEs and observed Vs. configured PDB. The RL model41determines and reports the optimal KPI parameters for the overlapped LTE cell and NR cell combination based the resource split and the number of bearers across both the LTE cell and the NR cell, load distribution across both the LTE cell and the NR cell, the MCS/SINR distribution of the UEs and the observed Vs. configured PDB.