Resource Block Allocation Research Articles

An Effective Resource Allocation Framework for D2D Communication in 5G Wireless Personal Area Network With Hybrid Optimizer

ABSTRACTAllocating resources in an energy‐effective method for the development of 5G networks. Energy efficiency in D2D communication is another challenge. Less energy usage is needed for devices to communicate with one another. D2D communication frequently uses more energy than conventional communication techniques. This is one of the major challenges of D2D transmission that needs to be resolved. To address these challenges, a new energy‐effective resource allocation system for D2D transmission in 5G WPAN is presented in this paper. The primary goal of the presented task is to enhance the average power effectiveness of the entire links of D2D, thus ensuring the service quality of the cellular user equipments (CUEs) and the constraints of energy harvesting (EH) in the D2D connections. Problems such as D2D user equipments (DUEs), spectrum, resource block allocation, and time slot allocation are used in D2D communication. To rectify these problems, this work derived an average energy efficiency issue. Moreover, this work joins the spectrum and power block allocation. Then, the EH time slot allocation is executed based on the Hybrid Gazelle with Walrus Optimization (HGWO). The extensive analysis outcomes illustrate that the implemented HGWO optimization‐aided model attains more energy efficacy for distinct network attribute settings.

Delay aware resource allocation in ORAN through network optimization

AbstractA multi variable resource allocation problem is investigated in network environments, specifically focusing on the consideration of quality of service in open radio access network. The main objective is to minimise the combined latency of various servers while complying with network limitations. The delay of each server is represented by a non‐linear function that has exponentially based. This characteristic inherently brings non‐convexity into the objective function. In contrast, the constraints comprise various linear combinations of network variables, including resource block allocations, power consumption, and number of virtual machines. The purpose of these constraints is to guarantee that the allocation of resources adheres to practical limitations and upholds fairness among servers. Nevertheless, the inclusion of a non‐convex objective function significantly adds complexity to the optimisation problem and non‐convex behaviour, requiring specialised algorithms and techniques to identify solutions. Subsequently, the Lagrange multiplier method has been used to solve this problem mathematically. Numerically, three algorithms have been utilised and compared to solve the problem, these are active‐set, interior point and sequential quadratic programming. Note that the total delay as an objective function is based on the total power consumption of the servers. Previous to optimising the total delay, a delay model is proposed and compared with two research works that are based on experimental and real time data. The proposed model shows data matching with the other works and permits for more adaptation/integration with any other works that uses different servers’ characteristics and network parameters.

Modified Best Channel Quality Indicator Scheduler for Heterogeneous LTE-A Network

In this paper, a Long-Term Evolution-Advanced (LTE-A) Modified Best Channel Quality Indicator (M-BCQI) Scheduler is proposed for enhanced cell edge throughput performance. The scheduling algorithm utilizes two time slots, each of 0.5 ms in length, with the First Time Slot (FTS) implemented with the Best Channel Quality Indicator (BCQI) algorithm has a CQI-focused Resource Block (RB) allocation mechanism, while the Robin (RR) algorithm is the mechanism used in the Second Time Slot (STS) for RB allocation. Vienna LTE system level simulator was used for the proposed scheduling algorithm assessment. The results obtained showed significant improvement, indicating 44% superior cell edge performance and 56 % better fairness index in comparison to BCQI. However, M-BCQI slightly underperformed by 4.2% in terms of average throughput in comparison to BCQI and by 7% in comparison to RR in terms of fairness index.

Federated learning-based trajectory prediction for dynamic resource allocation in moving small cell networks

With the evolution of fifth generation (5G) of mobile communication, vehicular edge computing (VEC) and moving small cell (MSC) network are gaining attention because of their capability to provide improved quality-of-service (QoS) to vehicular users. The ultimate goal of VEC-enabled MSC network is to diminish the vehicular penetration effect and path loss resulting in improved network performance for vehicular users. In this paper, we explore distributed resource block (RB) allocation for fronthaul links of MSCs with probabilistic mobility in VEC-enabled MSC network. The proposed work exploits the computational power of road side units (RSUs) deployed with VEC servers present along the road sides to allocate the resources to MSCs in a distributed manner by exchanging limited information, with the objective of maximizing the data rate achieved by MSC network. Moreover, we propose federated learning (FL)-based position prediction of MSCs to predict the trajectory of MSCs in advance for efficient prediction of resource allocation in MSC network. Simulations results are presented to compare the position prediction dependent distributed and centralized time interval dependent interference graph-based resource allocation to MSCs in terms of RB utilization and average achievable data rate of MSC network. For comparison, we further investigated centralized as well as distributed threshold time dependent interference graph-based allocation of resources to MSC network.

Optimization of Image Transmission in Cooperative Semantic Communication Networks

In this paper, a semantic communication framework for image data transmission is developed. In the investigated framework, a set of servers cooperatively transmit image data to a set of users utilizing semantic communication techniques, which enable servers to transmit only the semantic information that accurately captures the meaning of images. To evaluate the performance of studied semantic communication system, a multimodal metric called image-to-graph semantic similarity (ISS) is proposed to measure the correlation between the extracted semantic information and the original image. To meet the ISS requirement of each user, each server must jointly determine the semantic information to be transmitted and the resource blocks (RBs) used for semantic information transmission. Due to the co-channel interference among users associated with different servers, each server must cooperate with other servers to find a globally optimal semantic oriented RB allocation. We formulate this problem as an optimization problem whose goal is to minimize the sum of the average transmission latency of each server while reaching the ISS requirement. To solve this problem, we propose a value decomposition based entropy-maximized multi-agent reinforcement learning (RL) algorithm. The proposed algorithm enables each server to coordinate with other servers in training stage and execute RB allocation in a distributed manner to approach to a globally optimal performance with less training iterations. Compared to traditional multi-agent RL algorithms, the proposed RL framework improves the exploration of valuable action of servers and the probability of finding a globally optimal RB allocation policy based on local observation of wireless and semantic communication environments. Simulation results show that the proposed algorithm can reduce the transmission delay by up to 16.1% and improve the convergence speed by up to 100% compared to the traditional multi-agent RL algorithms.

Bat algorithm based semi‐distributed resource allocation in ultra‐dense networks

AbstractThis paper addresses the resource allocation (RA) for ultra‐dense network (UDN), where base stations (BSs) are densely deployed to meet the demands of future wireless communications. However, the design of RA in UDN is challenging, as the RA problem is non‐convex and NP‐hard. Therefore, this paper considers and studies a semi‐distributed resource block (RB) allocation scheme, in order to achieve a well‐balanced trade‐off between performance and complexity. In the context of semi‐distributed RB allocation scheme, the problem can be decomposed into the subproblem of clustering and the subproblem of cluster‐based RB allocation. We first improve the K‐means clustering algorithm by employing the Gaussian modified method, which can significantly decrease the number of iterations for carrying out the K‐means algorithm as well as the failure possibility of clustering. Then, bat algorithm (BA) is introduced to attack the problem of cluster‐based RB allocation. In order to make the original BA applicable to the problem of RB allocation, chaotic sequences are adopted to discretize the initial position of the bats, and simultaneously increase the population diversity of the bats. Furthermore, in order to speed up the convergence of BA, the logarithmic decreasing inertia weight is employed for improving the original BA. Our studies and performance results show that the proposed approaches are capable of achieving a desirable trade‐off between the performance and the implementation complexity.

Joint MCS Adaptation and RB Allocation in Cellular Networks Based on Deep Reinforcement Learning With Stable Matching

Joint modulation-coding scheme (MCS) adaptation and resource block (RB) allocation is an effective approach to guarantee different quality of service (QoS) requirements of all UEs under dynamic network environments. In this paper, we consider a fifth generation (5 G) cellular network with time-varying wireless channels, in which the BS serves multiple user equipments (UEs) under limited available RBs. We aim to minimize the total RB consumption subject to the rigorous constraints of each UE's QoS requirement. To attain this objective, this paper puts forth an online learning technique, referred to as integrated Deep Reinforcement learning and stable Matching (DeepRM), in the sense that the MCS adaptation and RB allocation decisions are conducted without acquiring the real-time channel quality indicator (CQI) feedback. DeepRM is a closed-loop framework, where the output of deep reinforcement learning (DRL) is imported into the stable matching to guide optimal RB allocation whilst the output of stable matching is fed into the DRL framework to assist efficient MCS decision-making. Specifically, in DeepRM, we first develop a powerful DRL algorithm, termed as Action-and-Reward Branching Deep Q-network (ARBDQ), by incorporating the action branch architecture into conventional DRL and modifying the traditional deep neural network training mechanism, to perform judicious MCS decisions on different links in parallel. Then, a new many-to-one stable matching algorithm, called adaptive deferred acceptance, is exploited to dynamically adjust the RB quota of each UE in a computationally efficient fashion. Simulation results demonstrate that compared with ACO-HM, OLLA-ADA, and ARBDQ-Random algorithms, DeepRM induces much less RB consumption while guaranteeing the QoS requirements of all UEs in various network scenarios. Furthermore, under miscellaneous QoS requirement, number of UEs, and CQI reporting period setups, DeepRM is more robust than other baselines.

Coalitional Game Based Resource Allocation in D2D-Enabled V2V Communication

The joint resource block (RB) allocation and power optimization problem is studied to maximize the sum-rate of the vehicle-to-vehicle (V2V) links in the device-to-device (D2D)-enabled V2V communication system, where one feasible cellular user (FCU) can share its RB with multiple V2V pairs. The problem is first formulated as a nonconvex mixed-integer nonlinear programming (MINLP) problem with constraint of the maximum interference power in the FCU links. Using the game theory, two coalition formation algorithms are proposed to accomplish V2V link partitioning and FCU selection, where the transferable utility functions are introduced to minimize the interference among the V2V links and the FCU links for the optimal RB allocation. The successive convex approximation (SCA) is used to transform the original problem into a convex one and the Lagrangian dual method is further applied to obtain the optimal transmit power of the V2V links. Finally, numerical results demonstrate the efficiency of the proposed resource allocation algorithm in terms of the system sum-rate.

An Efficient Resource Block Allocation Method in Distributed MIMO

An Efficient Resource Block Allocation Method in Distributed MIMO

Federated learning for resource allocation in vehicular edge computing-enabled moving small cell networks

Moving networks comprising of moving small cells (MoSCs) is an emerging technology that provide ubiquitous connectivity to the cellular users in vehicular environment. MoSCs are the small cells deployed on the top of vehicles (city buses, trains, trams etc.) to support the vehicular users with improved quality-of-service (QoS). However, the deployment of small cells in vehicular environment demands for an efficient resource allocation mechanism. This is due to high mobility of MoSCs resulting in dynamic interference between MoSCs, high computational cost, privacy issues, latency issues, and high data transmission requirement. To overcome these issues, gated recurrent unit (GRU)-based federated learning (FL) model is proposed for resource allocation in moving networks. In our proposed work, we investigate resource allocation in vehicular edge computing (VEC)-enabled MoSC network with MoSCs deployed on trams travelling with deterministic mobility. In the proposed MoSC network, road side units (RSUs) equipped with VEC servers use their computational power to train the resource allocation model in a distributed manner, in which each RSU exploits the training data of its associated MoSCs to generate a shared model. The proposed GRU-based FL model enables RSUs to cooperatively train a global model that can predict resource block (RB) allocation to MoSCs without transmitting the historical data to the central server. We have conducted extensive system level simulations to determine key performance comparison between FL and centralized learning-based resource allocation in MoSC network.

AI-Based Resource Allocation in E2E Network Slicing with Both Public and Non-Public Slices

Network slicing is a key technology for 5G networks, which divides the traditional physical network into multiple independent logical networks to meet the diverse requirements of end-users. This paper focuses on the resource allocation problem in the scenario where public and non-public network slices coexist. There are two kinds of resources to be allocated: one is the resource blocks (RBs) allocated to the users in the radio access network, and the other is the server resources in the core network. We first formulate the above resource allocation problem as a nonlinear integer programming problem by maximizing the operator profit as the objective function. Then, a combination of deep reinforcement learning (DRL) and machine learning (ML) algorithms are used to solve this problem. DRL, more specifically, independent proximal policy optimization (IPPO), is employed to provide the RB allocation scheme that makes the objective function as large as possible. ML, more specifically, random forest (RF), assists DRL agents in receiving fast reward feedback by determining whether the allocation scheme is feasible. The simulation results show that the IPPO-RF algorithm has good performance, i.e., not only are all the constraints satisfied, but the requirements of the non-public network slices are ensured.

Internet of Vehicles Based On Cellular-Vehicle-To-Everything (C-V2X)

In line with the development of automotive and traffic systems, high mobility and density in different road topologies cause scalability and delay issues due to frequent disconnection between communication nodes. From a safety aspect, Cellular-V2X (C-V2X) wireless technology was introduced by the Third Generation Partnership Project Organization (3GPP) to realise the transmission of emergency messages at critical times, anywhere. Specifically, Mode 4 C-V2X supports side-link communication without relying on a base station to provide network coverage. However, Mode 4 is susceptible to several limitations, which include half-duplex transmission, packet collision, and propagation errors that will cause intermittent connectivity issues. It is also difficult to determine appropriate parameter configurations that can increase the spectrum efficiency of dense networks to facilitate reliable and low-latency networks. The objective of this paper is to investigate the effectiveness of a Mode 4 C-V2X system under different road topologies and traffic scenarios. The study adopts a Krauss vehicular mobility model based on SUMO software to model normal and dense networks in a highway and a road intersection scenario, then perform simulation using OMNET++ software to analyse the impact of different physical layer (PHY) configurations such as modulation and coding scheme, packet size, number of resource block allocation, as well as the probability of resource reservation. The results show that the optimal configuration of parameters depends on the scenario. For highway scenarios, a lower MCS and a higher number of RBs are recommended. For road intersection scenarios, a higher MCS and a lower number of RBs are recommended. The packet size should also be in accordance with the requirements of the application used. The findings of this study can be used to assist in the design of an optimal intelligent transportation system using adaptive C-V2X parameters that can be automatically adjusted under different scenarios and network conditions.

Enabling Grant-Free URLLC for AoI Minimization in RAN-Coordinated 5G Health Monitoring System

Age of information (AoI) is used to evaluate the performance of 5G health monitoring systems because stale data can be fatal for patients with serious illness. Recently, grant-free ultra-reliable and low latency communications (URLLC) have shown greater potential of minimizing AoI than conventional grant-based approaches; however, existing grant-free schedulers cannot provide guaranteed performance in 5G health monitoring systems because they involve two fundamental problems in time and frequency domains, namely the joint scheduling problem and physical resource block (PRB) allocation. In this study, we investigate two resource allocation problems for the first time, aiming to enable grant-free URLLC to minimize AoI in 5G health monitoring systems. Specifically, we propose two adaptive solutions based on an open radio access network-coordinated wireless system: 1) a joint scheduling algorithm and 2) an adaptive PRB allocation algorithm. To verify the effectiveness of the proposed solutions, we built a simulation environment similar to a real health monitoring system and captured the performance variations under realistic deployment scenarios.

Large-Scale Service Function Chaining Management and Orchestration in Smart City

In the core networking of smart cities, mobile network operators need solutions to reflect service function chaining (SFC) orchestration policies while ensuring efficient resource utilization and preserving quality of service (QoS) in large-scale networking congestion states. To offer this solution, we observe the standardized QoS class identifiers of smart city scenarios. Then, we reflect the service criticalities via cloning virtual network function (VNF) with reserved resources for ensuring effective scheduling of request queue management. We employ graph neural networks (GNN) with a message-passing mechanism to iteratively update hidden states of VNF nodes with the objectives of enhancing allocation of resource blocks, accurate detection of availability statuses, and duplication of heavily congested instances. The deployment properties of smart city use cases are presented along with their intelligent service functions, and we aim to activate a modular architecture with multi-purpose VNFs and chaining isolation for generalizing global instances. Experimental simulation is conducted to illustrate how the proposed scheme performs under different congestion levels of SFC request rates, while capturing the key performance metrics of average delay, acceptance ratios, and completion ratios.

Data-Driven Spectrum Allocation and Power Control for NOMA HetNets

Towards a green communication discipline, more energy-efficient wireless networks are needed. In this paper, an energy-efficient interference management technique is proposed for heterogeneous networks (HetNets) with downlink nonorthogonal multiple access (NOMA) operating in millimeter (mm) waves. The proposed technique utilizes data-driven-based power and resource blocks (RBs) allocation scheme to maximize the energy efficiency of the entire network while managing inter-user, co-tier, and cross-tier interference. The studied interference management problem is mathematically formulated as maximizing the energy efficiency of NOMA-HetNets under constraints; quality of service (QoS), successive interference cancelation condition (SIC), maximum power allocation, and the maximum capacity of each RB, which is a non-convex and combinatorial NP-hard problem that needs exhaustive search for an optimum solution. The formulated problem is relaxed into a minimization of a linear <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm problem. Then, a lowcomplex model-free data-driven based approach is proposed to find a near-optimum allocation for power and RBs based on a dynamic linearization representation of a time-varying pseudogradient parameter estimation procedure. Numerical analyses show that the proposed scheme achieves higher network energy efficiency with lower computational complexity compared to the benchmark schemes.

Edge aggregation placement for semi-decentralized federated learning in Industrial Internet of Things

Rapid technological advancements have resulted in more smart devices with an increased volume of data in Industrial Internet of Things (IIoT) systems. These data often contain significant levels of heterogeneity and privacy in accordance with the various requirements of industrial applications, therefore controlling for these data in an intelligent and secure way is still an outstanding problem. Federated edge learning (FEEL), a distributed machine learning paradigm, has become a viable option for developing learning models while maintaining data privacy. In this paper, we concentrate on a semi-decentralized FEEL (SD-FEEL) framework, taking into account the limited training data in a single central cluster, where the shared edge models is trained among devices and edge servers. An edge aggregation optimization problem with joint device association, resource block allocation, and edge server placement is then formulated based on the evaluated upper bound of the convergence performance for SD-FEEL, with the goal of minimizing the training loss while taking into account the cost budget of edge servers. Based on the calculation of training loss degradation, the proposed optimization issue can be transformed into a dynamic optimum problem. A Trilateral Matching-based association (TMA) method is then proposed to discover solutions to the sub-problems of device association and resource block allocation. Additionally, a Tabu Search-based Service location (TSP) method is suggested to optimize the placement of the edge servers. To fully address the optimization problem, a combination iterative approach including the TMA and TSP is proposed. According to simulation data, the proposed algorithm can greatly beat the baselines in terms of test accuracy with limited cost budget.

Dual-Band Aerial Networks for Priority-Based Traffic

Unmanned aerial vehicle networks suffer from lackluster performance due to line-of-sight issues as well as resource scarcity. Network slicing, multi-band transmission, and numerology show great potential in mitigating such limitations. In this work, we propose the use of the sub-6 GHz and mmWave bands, the latter of which requires careful consideration of line-of-sight, with numerology for aerial network slicing to provision time-critical services and broadband access. Accordingly, we formulate a user admission control policy to regulate band access after which we formulate a joint resource block and power allocation problem, a mixed-integer non-linear programming problem, to minimize the quality-of-service gap of the throughput-dependent broadband users as a best-effort service and meet the time-critical service requirements. We propose a low-complexity algorithm, PREDICT, to tackle the formulated problem and present extensive simulation results to validate the advantages of our scheme.

Reconfigurable intelligent surface-enabled integrated sensing and communication: a sensing-assisted communication framework

Integrated sensing and communication (ISAC) is an essential technology in the upcoming 6G network, and its performance can be effectively enhanced by the reconfigurable intelligent surface (RIS). Among the various RIS-enabled ISAC techniques, RIS-enabled sensing-assisted communication has attracted growing attention because it can effectively improve communication performance by focusing the energy on the sensed locations of the users and is easy to be integrated into existing communication systems. However, existing RIS-enabled sensing-assisted communication systems rely on antenna arrays for the acquisition of angular information to localize the users, making the system more complex and expensive. To handle this problem, in this paper, we propose an RIS-enabled multi-user sensing-assisted communication framework where a single antenna access point (AP) first senses the locations of the single-antenna users with the help of the RIS, and then the energy of the communication signal is focused on the sensed locations of the users to provide them with a higher sum-rate by adjusting the phase shifts of the RIS. However, the selection of the RIS phase shifts heavily influences the resource block and power allocation at the AP, which makes the joint design of the RIS phase shifts and the resource allocation very challenging. In order to address this challenge, we formulate an RIS-enabled multi-user communication optimization problem and design a two-stage optimization algorithm based on the genetic and Lagrangian duality methods to jointly optimize the RIS phase shifts and the resource allocation. Simulation and experimental results show that compared with the scheme without RIS, the proposed RIS-enabled multi-user communication system can achieve a higher sum-rate and lower localization error.

Information freshness-oriented trajectory planning and resource allocation for UAV-assisted vehicular networks

In this paper, multi-UAV trajectory planning and resource allocation are jointly investigated to improve the information freshness for vehicular networks, where the vehicles collect time-critical traffic information by on-board sensors and upload to the UAVs through their allocated spectrum resource. We adopt the expected sum age of information (ESAoI) to measure the network-wide information freshness. ESAoI is jointly affected by both the UAVs trajectory and the resource allocation, which are coupled with each other and make the analysis of ESAoI challenging. To tackle this challenge, we introduce a joint trajectory planning and resource allocation procedure, where the UAVs firstly fly to their destinations and then hover to allocate resource blocks (RBs) during a time-slot. Based on this procedure, we formulate a trajectory planning and resource allocation problem for ESAoI minimization. To solve the mixed integer nonlinear programming (MINLP) problem with hybrid decision variables, we propose a TD3 trajectory planning and Round-robin resource allocation (TTP-RRA). Specifically, we exploit the exploration and learning ability of the twin delayed deep deterministic policy gradient algorithm (TD3) for UAVs trajectory planning, and utilize Round Robin rule for the optimal resource allocation. With TTP-RRA, the UAVs obtain their flight velocities by sensing the locations and the age of information (AoI) of the vehicles, then allocate the RBs to the vehicles in a descending order of AoI until the remaining RBs are not sufficient to support another successful uploading. Simulation results demonstrate that TTP-RRA outperforms the baseline approaches in terms of ESAoI and average AoI (AAoI).

DRL-assisted joint allocation of antenna, radio, and front-haul resources in TWDM-PON-based NG-RANs with mMIMO-enabled beamforming

The high spectrum efficiency of massive multiple input multiple output (mMIMO)-enabled beamforming is attractive for solving the massive number of connections in next-generation radio access networks (NG-RANs). However, the beamforming based on multiple antenna sub-arrays to support multi-connections would introduce extra 2D antenna sub-array selection and radio resource block (RB) allocation; meanwhile, considering the front-haul bandwidth consumption would reduce as much as possible the redundant data transmitting over front-haul in the NG-RANs. Therefore, a key issue is how to coordinate 2D antenna sub-array selection and radio RB allocation to minimize the front-haul bandwidth and maximize the radio RB utilization when accommodating a set of beam antenna array (BAA) requests. In this paper, we first establish a 3D BAA mapping model to jointly optimize 2D antenna sub-array selection and radio RB allocation, which would in turn affect the allocation of the front-haul wavelength bandwidth in a time and wavelength division multiplexed passive optical network (TWDM-PON)-based front-haul with the mMIMO antenna system. An integer linear programming mathematical model is formulated, and a novel deep reinforcement learning (DRL)-based algorithm is proposed to optimize the front-haul bandwidth and radio RB utilization. Besides, two heuristic algorithms are developed with different antenna sub-array selection policies as the benchmarks. The extensive simulation results show that our proposed DRL-based algorithm can attain the lowest average cost (AC) for different numbers of BAA requests considered by seeking the optimal trade-off between the front-haul bandwidth consumption and the number of the used antennas. Compared with the inter-sub-array benchmark, the DRL-based algorithm can achieve up to 7.5% AC reduction, which is mainly attributed to the 21.2% reduction of the front-haul bandwidth without increasing the number of used antennas.