Radio Resource Management Research Articles

Radio Resource Management for C-V2X Using Graph Matching and Actor–Critic Learning

We propose a hybrid centralized-distributed radio resource management (RRM) scheme for cellular vehicle-to-everything (C-V2X), which is to mitigate the interference caused by radio resource sharing between vehicle-to-infrastructure (V2I) links and vehicle-to-vehicle (V2V) links. Specifically, it contains two decoupled aspects: centralized channel allocation and distributed power control. The former is conducted by graph matching to decide spectrum sharing strategy; the latter is performed via actor-critic learning, a deep deterministic policy gradient (DDPG)-based algorithm, to increase the total system capacity. Finally, the proposed scheme is numerically evaluated and outperforms other deep Q-network (DQN)-based schemes in terms of the system capacity.

Radio resource management for device to device communication using S and V shaped transfer functions

Radio resource management for device to device communication using S and V shaped transfer functions

Active 5G radio resource management measurements using a multiple CATR reflector system

AbstractThis paper presents results for active and passive measurements using a novel method based on multiple compact antenna test range (CATR) reflectors to perform simultaneous multiple angle measurements in order to characterize the beam-forming characteristics in a real environment of the 5G devices operating in the millimeter wave frequency band: 24–44 GHz. The over-the-air (OTA) system generates four planar wavefronts with different incidence angles, realizing up to five pairs of angular spreads or four switched/simultaneous angles of arrival. The initial target application is radio resource management (RRM) testing, where the execution of mobility procedures and radio link monitoring of a 5G millimeter wave device are evaluated. The applicability of the multi-reflector approach to RRM testing is measured with commercial 5G handsets, through three test scenarios. The paper demonstrates that baseband (non OTA) testing is not sufficient for RRM FR2, as the results are influenced by the direction of arrival of the signal. It is further shown that OTA testing in a multi-reflector CATR system and a careful selection of a representative set of test directions is critical for full characterization of the performance of a wireless device operating in the millimeter wave bands.

Quality of Service Based Radio Resources Scheduling for 5G eMBB Use Case

Several use cases appear with 5G and beyond networks such as enhanced mobile broadband (eMBB), where ultra-high data rates and low-latency connections become essential demands for asymmetric services, e.g., 8K video streaming and virtual reality (VR). The millimeter-wave (mmWave) band can be a promising player to handle these applications under the condition of efficient implementation of radio resource management (RRM) schemes, which distribute resources among user equipment (UEs) in the network. Firstly, mmWave UE channels are highly affected by the distance between the access point (AP) and UEs. Secondly, static and dynamic obstacles can easily block the AP-UE line-of-sight (LOS) link; hence, it highly attenuates mmWave signals. Moreover, eMBB applications lack symmetry in their data rate requirements, from 75 Mbps up to 300 Mbps; consequently, UE quality of service (QoS) should be considered in designing RRM schemes. In this paper, we study possible scheduling schemes that can be implemented for the 5G eMBB use case. Moreover, we propose a new demand-based proportional fairness (DPF) scheduling algorithm that first depends on both UE channel conditions and quality-of-service demands, then, if certain UEs reach the requirement, the algorithm prioritizes it only based on their channel quality. Furthermore, in this work, we consider a real model to simulate the effect of blockage occurrence on the performance of scheduling schemes. Results prove that the proposed DPF scheduling scheme outperforms conventional algorithms in terms of UE satisfaction while maintaining high total system throughput and fairness among UEs. For example, assuming blockage occurrence with 16 and 32 UEs, it guarantees satisfaction for more than 99% and 60% of UEs and, at the same time, obtains 3.29 and 4.24 Gbps system throughput and maintains fairness between UEs at 0.99 and 0.82, respectively. In contrast, conventional proportional fairness highly degrades satisfaction to only 74% and 30% to achieve total throughput equal to 3.1 and 4.3 Gbps, respectively.

Quality-driven video streaming for ultra-dense OFDMA heterogeneous networks

The explosive growth of mobile video traffic has brought enormous traffic pressure to the existing bandwidth-limited cellular network. Ultra-dense heterogeneous networks (HetNets) are deemed as one of the most promising 5G technologies for increasing the cellular network capacity. In this paper, we study quality-driven multiuser video streaming algorithm for two-tier orthogonal frequency division multiple access (OFDMA) HetNets. Specifically, we formulate a stochastic optimization problem for the quality-driven multiuser video streaming in two-tier OFDMA HetNets to maximize the average perceived video quality of small cell users while meeting the average data rate demand of macrocell users. We then decompose the original stochastic problem into a sequence of per-slot deterministic subproblems of adaptive bitrate selection and radio resource management which have no requirement of prior channel knowledge by employing Lyapunov optimization technique. We accordingly propose an online quality-driven video streaming algorithm. In this algorithm, we derive the optimal bitrate decision for the adaptive bitrate selection subproblem, and design an iterative scheduling algorithm of joint subchannel assignment and power control by utilizing alternating optimization and successive convex approximation techniques to solve the radio management subproblem. Extensive simulation results validate the high efficiency of the proposed quality-driven video streaming algorithm in OFDMA HetNets.

A Novel Machine Learning-Based Scheme for Spectrum Sharing in Virtualized 5G Networks

Network virtualization allows the coexistence of multiple network slices over a shared physical infrastructure, each delivering a service with own requirements in terms of quality of service, coverage, and time span. Spectrum is an expensive commodity, so it must be managed among these slices in the most efficient way. However, current spectrum sharing techniques are too rigid to address all combinations of service requirements and properly exploit the new air interface flexibility and network deployment options introduced by the Fifth Generation (5G) mobile networks. In this paper, we extend the state of the art first by proposing a novel spectrum sharing scheme that supports an unlimited number of radio networks. Second, we propose a 5G compliant novel service-based network management architecture to integrate Machine Learning (ML) algorithms for Radio Resource Management (RRM), including spectrum sharing. Finally, we propose a new three-stage ML framework that exploits forecasting, clustering and reinforcement learning algorithms to implement the proposed spectrum sharing scheme. Our proposed solution can allow an arbitrary number of 5G network slices to share spectrum more effectively and more dynamically either with each other or with other radio networks.

Scheduling Out-of-Coverage Vehicular Communications Using Reinforcement Learning

Performance of vehicle-to-vehicle (V2V) communications depends highly on the employed scheduling approach. While centralized network schedulers offer high V2V communication reliability, their operation is conventionally restricted to areas with full cellular network coverage. In contrast, in out-of-cellular-coverage areas, comparatively inefficient distributed radio resource management is used. To exploit the benefits of the centralized approach for enhancing the reliability of V2V communications on roads lacking cellular coverage, we propose VRLS (Vehicular Reinforcement Learning Scheduler), a centralized scheduler that proactively assigns resources for out-of-coverage V2V communications \textit{before} vehicles leave the cellular network coverage. By training in simulated vehicular environments, VRLS can learn a scheduling policy that is robust and adaptable to environmental changes, thus eliminating the need for targeted (re-)training in complex real-life environments. We evaluate the performance of VRLS under varying mobility, network load, wireless channel, and resource configurations. VRLS outperforms the state-of-the-art distributed scheduling algorithm in zones without cellular network coverage by reducing the packet error rate by half in highly loaded conditions and achieving near-maximum reliability in low-load scenarios.

Joint Content and Radio Access for the Internet of Things: A Smart-Contract-Based Trusted Framework

Access control plays a fundamental role in securing the Internet of Things (IoT) systems. Recently, blockchain-enabled secure and trusted access control has attracted remarkable attention since it alleviates the centralization concern in traditional frameworks. However, existing designs only deal with access request validation problems whereas overlooks content delivery issues. Considering that radio resources are increasingly stretched with the rapid growth of IoT devices in the 5G era, this may give rise to unacceptably high latencies of legal requests in peak hours. This article addresses this limitation by presenting JCRA, a Joint Content and Radio Access control framework for IoT applications. JCRA deploys access point contracts (APCs) and device contracts (DCs) to manage radio access points (APs) and IoT devices, respectively. The atom access contract (AAC) is responsible for conducting access control according to access policies in DCs as well as radio resource states in APCs. To enable flexible radio resource management that often involves solving optimization models in AAC, we introduce a task offloading scheme, which recruits specialized executor nodes to complete the computation-heavy part off-chain. A Proof-of-Solution-Quality rule is proposed for executor solution validation which ensures the off-chain calculation is trusted. We implement a prototype of JCRA to demonstrate its feasibility. The costs and performance of JCRA are also evaluated in detail.

Primal–Dual Learning for Cross-Layer Resource Management in Cell-Free Massive MIMO IIoT

The use of cell-free massive multiple-input–multiple-output (MIMO) is regarded as a novel technique in the Industrial Internet of Things (IIoT) networks, and many studies have been reported on its cross-layer optimization, including random access and power allocation. Nevertheless, the cooperation of deep reinforcement learning (DRL) and cell-free massive lacks of deep study. In this article, a primal–dual deep deterministic policy gradient (DDPG) algorithm is designed to obtain cross-layer radio resource management, including power allocation in the physical layer and random access in the medium access layer. Different from the current studies, the random access and power allocation is formulated in cell-free massive MIMO IIoT networks, utilized by the stochastic ergodic optimization. In contrast to the stochastic policy gradient algorithm, a primal–dual DDPG algorithm is designed for the cross-layer optimization. Moreover, a multiagent primal–dual DDPG algorithm is proposed to different scenarios in the cell-free massive MIMO IIoT networks. Simulations are presented to verify the effectiveness of the primal–dual DDPG algorithm for random access and power allocation in the cell-free massive MIMO IIoT networks.

Federated Learning Empowered Computation Offloading and Resource Management in 6G-V2X

Humankind's urbanization and luxuriousneed increase the number of vehicles day by day. Alongside the advancement of technologies, autonomous vehicles have now come into a greater existence. Due to the resource constraints for obtaining a high processing rate in performing the intelligent tasks, Vehicular Edge Computing (VEC) has been introduced. However, a delay occurs in transferring the information due to the traits of automotive tasks. In this paper, a Federated Learning empowered computation Offloading and Resource management (FLOR) is proposed, which offloads and manages the resources with low latency. The FLOR framework operates over heterogeneous networks that include Vehicle-to-Everything (V2X) communication, Dedicated Short Range Communication (DSRC), 5G millimeter-wave (5G-mmWave), and 6G Vehicle-to-Vehicle (6G-V2V) communication. FLOR employs the stochastic network calculus mechanism for computing the upper bound delay of the heterogeneous communication and obtains the probability of offloading mechanism. Upon the detected probability value, the FLOR offloads the computation task to the optimal network. Further, to avoid the increase in resource cost and utilize the available resources efficiently, an effective Radio Resource Management (e-RRM) based on Federated Q-Learning is proposed in FLOR, for allocating and using the available resources optimally. The simulation result shows that the proposed FLOR framework effectively offloads the computation between the available heterogeneous networks with optimal resource allocation by 20.69% than the existing solutions.

Toward Programmability of Radio Resource Control Based on O-RAN

Abstract Open Radio Access Network (O-RAN) is a concept that aims at embedding intelligence at the network edge and at disaggregating of network functionality from the hardware. The paper studies how the O-RAN concept can be used for optimization of radio resource management. The research focuses on adaptive radio resource allocation based on predictions of device activity. For narrowband devices which send sporadically small volumes of data, a feature is defined which enables a device with no activity for a short time to suspend its session and to resume it moving in active state. Dynamic configuration of the inactivity timer based on prediction of device activity may further optimize radio resource allocation. The paper studies an O-RAN use case for dynamic radio resource control and presents the results of emulation of the RESTful interface defined between the O-RAN non-real-time and near real-time functions.

Radio Resource Management for Eicic, Comp, Relaying and Back-hauls Techniques in LTE-advanced Networks: Concepts and a Literature Survey

Frequency reuse in cells is one of the strategies that LTE (Long Term Evolution) uses to maximize the spectrum efficiency. However, it leads to an interference among the cells, especially at the cell edges where the probability for a cell-edge user to be scheduled on a resource block that is being transmitted by the neighbouring cell is high; consequently, the interference is high. In-order to mitigate Inter-Cell Interference (ICI), Inter-Cell Interference Coordination (ICIC) was proposed by the Third Generation Partnership Project (3GPP) standards for the LTE network, and later on, the enhanced Inter-Cell Interference Coordination (eICIC) was proposed for the LTE-Advanced network. ICIC reduces cell-edge interference on traffic channels from neighbouring cells by the use of three interference reduction schemes that works in the power and frequency domain, and they are based on lowering the power of some channels to limit their reception to the users that are close to the base station, and by reducing the chance of frequency overlap. eICIC was proposed to handle ICI in Heterogeneous Network (HetNet) deployments. It reduces the interference on both the traffic and control channels. It uses power, frequency and also time domain to mitigate intra-frequency interference in HetNets. Another technique that was proposed by the 3GPP for the LTE-Advanced network that can reduce the ICI and improve the cell average and cell-edge user throughput is the Coordinated Multi-Point (CoMP) transmission/reception technique. CoMP can increase the cell average and cell edge user throughput in both the uplink and downlink transmission by joint scheduling and data processing in multiple cells/eNBs. Another technique that was proposed by the 3GPP for the LTE-Advanced network that can improve the LTE network coverage in difficult conditions is to deploy Relay Nodes (RNs). In this paper, we survey Radio Resource Management (RRM) for some of the techniques that are used with LTE-A. The included techniques in this paper are; the ICIC, eICIC, CoMP, Relaying and Back-hauls. We start by explaining the concepts of these techniques. Then, by summarizing the radio resource management approaches that were proposed in the literature for these techniques. And finally, we provide some concluding remarks in the last section.

Differential Privacy Enabled Deep Neural Networks for Wireless Resource Management

Differential Privacy Enabled Deep Neural Networks for Wireless Resource Management

Cooperative evolution of support vector machine empowered knowledge-based radio resource management for 5G C-RAN

Cloud-radio access network (C-RAN) is an innovative approach in 5th Generation communication networks, where baseband processing units (BBUs) are dissociated from the remote radio heads (RRHs), and a remote cloud-based centralized pool of BBUs is formed. This paper provides an efficient multi-class classification radio resources management (RRM) scheme based on the cooperative evolution of support vector machine (SVM) to assign the spectrum resources of macrocellular users (MUE) i.e. sub-channel of variable bandwidth, to remote-head users (RUEs) and D2D pairs, such that sub-channels can be reused without compromising the quality of service (QoS). First, the C-RAN resource allocation problem is modeled as a NILP problem, with CUs and D2D nodes sharing the same channel. Following that, the proposed strategy is used to allocate resources. A realistic dataset is built for assessing machine learning-based techniques using 5G experimental prototype based on Open Air Interface (OAI). Finally, experimental results demonstrate the effectiveness of the proposed cooperative evolution of SVM-based, multi-class classification RRM over alternative schemes in terms of network throughput, prediction performance, and system utilization.

Radio Resource Management for Eicic, Comp, Relaying and Back-hauls Techniques in LTE-advanced Networks: Concepts and a Literature Survey

Radio Resource Management for Eicic, Comp, Relaying and Back-hauls Techniques in LTE-advanced Networks: Concepts and a Literature Survey

Joint Wireless and Edge Computing Resource Management With Dynamic Network Slice Selection

Network slicing is a promising approach for enabling low latency computation offloading in edge computing systems. In this paper, we consider an edge computing system under network slicing in which the wireless devices generate latency sensitive computational tasks. We address the problem of joint dynamic assignment of computational tasks to slices, management of radio resources across slices and management of radio and computing resources within slices. We formulate the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Joint Slice Selection and Edge Resource Management</i> (JSS-ERM) problem as a mixed-integer problem with the objective to minimize the completion time of computational tasks. We show that the JSS-ERM problem is NP-hard and develop an approximation algorithm with bounded approximation ratio based on a game theoretic treatment of the problem. We use extensive simulations to provide insight into the performance of the proposed solution from the perspective of the whole system and from the perspective of individual slices. Our results show that the proposed slicing policy can achieve significant gains compared to the equal slicing policy, and that the computational complexity of the proposed task placement algorithm is approximately linear in the number of devices.

Performance Evaluation of Call Admission Control Schemes in 3GPP Long Term Evolution Networks

Radio resource management (RRM) techniques are employed for resource utilization in wireless broadband networks. One of the major RRM techniques that are aimed at controlling the admission and rejection of new or handoff calls/connections in a network is known as Call Admission Control (CAC). CAC accepts or rejects a connection request based on a certain pre-determined criterion(s). Performance Evaluation of some existing CAC schemes was presented in this paper. The schemes evaluated are; Adaptive Call Admission Control Scheme with Bandwidth reservation termed as (ACAC), QoS-Aware Call Admission Control Scheme termed as (QA-CAC) and Enhanced Adaptive Call Admission Control Scheme with bandwidth reservation termed (EA-CAC). Vienna LTE system level simulator was used for the simulation experiments conducted. After several experiments, the results show that the QA-CAC and EA-CAC schemes perform better in terms of increasing the throughput of real-time (RT) traffic while all the three schemes almost have the same performance in terms of admitting the non-real-time (NRT) traffic.

Radio and Energy Resource Management in Renewable Energy-Powered Wireless Networks With Deep Reinforcement Learning

In this paper, we study radio and energy resource management in renewable energy-powered wireless networks, where base stations (BSs) are powered by both on-grid and renewable energy sources and can share their harvested energy with each other. To efficiently manage those resources, we propose a hierarchical and distributed resource management framework based on deep reinforcement learning. The proposed framework minimizes the on-grid energy consumption while satisfying the data rate requirement of each user. It is composed of three different policies in a distributed and hierarchical way. An intercell interference coordination policy constrains the transmission power at each BS to coordinate the intercell interference among the BSs. Under the power constraints, a distributed radio resource allocation policy of each BS determines its own user scheduling and power control. Lastly, an energy sharing policy manages the energy resources of the BSs by sharing the harvested energy via power lines between them. Through the simulation, we demonstrate that the proposed framework can effectively reduce the on-grid energy consumption while satisfying the data rate requirements.

Spatial radio resources management for Heterogeneous 5G Millimeter wave Networks

spatial radio resources management for Heterogeneous 5G Millimeter wave Networks

Simulation of IEEE 802.11(e) QoS Networks Using Nctuns

In a multi-service IP network, it is a key challenge to provide Quality of Service (QoS) to end-user applications while effectively using network resources.This paper describes Significant differences between 802.11 and 80.11e and Simulates 802.11e using NCTUns, an innovative network simulator and emulator for wireless and mobile networks. Effects of various radio resource management and quality of service (QoS) schemes on higher-layer protocols and real-world applications can be easily studied using NCTUns.