Radio Resource Management Problem Research Articles

Hierarchical Slicing: A New Paradigm of Radio Resource Management for Mobile Networks

Radio access network slicing is considered to be highly challenging due to the complexity of mobile environments and the diversity of mobile services. Existing works in this area mainly decompose the radio resource management problem into slice-level resource management and user-level resource scheduling. In this paper, we propose a hierarchical three-tier slicing architecture, where an additional tier is introduced to merge the gap between the conventional two tiers in time and spatial scales. Specifically, the medium-scale tier provides intercell and inter-slice coordination to address short-term dynamics caused by local user mobility and traffic variations, which highly simplifies the upper and lower tiers by allowing them to focus on long-term traffic distribution and instant user demands, respectively. Thus, it allows each independent iter to apply more flexible and more efficient radio resource management methods with appropriate time and spatial scales. A proof of concept is provided to show that the proposed three-tier architecture can achieve a better tradeoff between slice isolation and slice capacity as compared to the state-of-the-art two-tier solution.

A joint cluster-based RRM and Low-latency framework using the full-duplex mechanism for NR-V2X networks

The radio resource management (RRM) problem in new radio vehicle-to-everything (NR-V2X) communication systems is a combinatorial optimization problem, and it is hard to achieve an optimum result in polynomial time. To reduce the complexity, linear algorithms or meta-heuristics can solve the problem. In this study, we proposed a joint cluster-based resource management and low-latency framework using a full-duplex mechanism (JCRRM-FD) for NR-V2X networks, which is used for vehicle-to-vehicle (V2V)/ device-to-device (D2D) and vehicle-to-infrastructure (V2I) transmission in NR-V2X networks. The delay, resource management, and system throughput analysis of the full-duplex mechanism in this framework was performed to deal with resource utilization and latency constraints. Moreover, joint cluster-based radio resource management and ant colony optimization (ACO) algorithms were proposed to manage and optimize resources efficiently to achieve user separation. The swarm intelligence algorithm is a standard meta-heuristic algorithm employed to deal with the optimization concern by exploiting the V2X communication network’s unlimited speed (maximizing the data transfer capacity and overall network performance) while considering the quality-of-service (QoS) requirements. A comprehensive experiment analysis was enacted to evaluate the efficiency of the developed JCRRM-FD framework with baseline approaches. Based on the simulation results, the proposed JCRRM-FD framework enhances the fairness index, average delay, packet drop rate, best cost value, CDF, and throughput compared to the benchmark approaches.

Radio Resource Management for Cellular-Connected UAV: A Learning Approach

Integrating unmanned aerial vehicles (UAVs) into existing cellular networks encounters lots of challenges, among which one of the most striking concerns is how to achieve harmonious coexistence of aerial transceivers, inter alia, UAVs, and terrestrial user equipments (UEs). In this paper, a cellular-connected UAV network is focused, where multiple UAVs receive messages from base stations (BSs) in the down-link, while BSs are serving ground UEs in their cells. For effectively managing inter-cell interferences (ICIs) among UEs due to intense reuse of time-frequency resource block (RB) resource, a first p-tier based RB coordination criterion is proposed and adopted. Then, to enhance wireless transmission quality for UAVs while protecting terrestrial UEs from being interfered by ground-to-air (G2A) transmissions, a radio resource management (RRM) problem of joint dynamic RB coordination and time-varying beamforming design minimizing UAV’s ergodic outage duration (EOD) is investigated. To cope with conventional optimization techniques’ inefficiency in solving the formulated RRM problem, a deep reinforcement learning (DRL)-aided solution is initiated, where deep double duelling Q network (D3QN) and twin delayed deep deterministic policy gradient (TD3) are invoked to deal with RB coordination in discrete action domain and beamforming design in continuous action regime, respectively. The hybrid D3QN-TD3 solution is trained via interacting with the considered outer and inner environments in an online centralized manner so that it can then help achieve the suboptimal EOD minimization performance during its offline decentralized exploitation. Simulation results have illustrated the effectiveness of the proposed hybrid D3QN-TD3 algorithm, compared to several representative baselines.

A QoE-Aware Nonlinear Fuzzy Radio Resource Management Approach for Revenue Enhancement

In this article, the problem of radio resource management is addressed by adjusting the tradeoff between two key quality of experience (QoE) influencing factors, namely transmission rate, and service price. To this end, a piecewise utility function is employed to assess the user QoE in three different quality classes. It comprises the combination of two utility functions corresponding to the transmission rate and the service price. A low-complexity fuzzy-based approach is then introduced to reallocate additional resources to dissatisfied users to increase the number of satisfied users. The simulation results indicate the efficiency and applicability of the novel approach in terms of increasing the service provider revenue while providing approximately the same overall QoE and power consumption. The results also show proposed approach achieves an improvement of at least 15% in total service provider revenue on average compared to other methods.

Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless Networks

Deep reinforcement learning (DRL) algorithms have recently gained wide attention in the wireless networks domain. They are considered promising approaches for solving dynamic radio resource management (RRM) problems in next-generation networks. Given their capabilities to build an approximate and continuously updated model of the wireless network environments, DRL algorithms can deal with the multifaceted complexity of such environments. Nevertheless, several challenges hinder the practical adoption of DRL in commercial networks. In this article, we first discuss two key practical challenges that are faced but rarely tackled when developing DRL-based RRM solutions. We argue that it is inevitable to address these DRL-related challenges for DRL to find its way to RRM commercial solutions. In particular, we discuss the need to have safe and accelerated DRL-based RRM solutions that mitigate the slow convergence and performance instability exhibited by DRL algorithms. We then review and categorize the main approaches used in the RRM domain to develop safe and accelerated DRL-based solutions. Finally, a case study is conducted to demonstrate the importance of having safe and accelerated DRL-based RRM solutions. We employ multiple variants of transfer learning (TL) techniques to accelerate the convergence of intelligent radio access network (RAN) slicing DRL-based controllers. We also propose a hybrid TL-based approach and sigmoid function-based rewards as examples of safe exploration in DRL-based RAN slicing.

Multi-Agent Deep Reinforcement Learning for Enhancement of Distributed Resource Allocation in Vehicular Network

To solve a decentralized radio resource management problem in a 5G vehicular network, we propose a novel resource allocation algorithm based on a multiagent deep reinforcement learning (MARL). We let each vehicle act as an individual agent that can select a unique combination of transport block (TB) and transmission power to broadcast periodic packets. Agent explores the environment and collects observations that later will be used to find the best combination of TB and transmission power. We apply an actor-critic reinforcement learning technique to choose optimal TB for each agent. To eliminate a nonstationarity in a multiagent setting, we utilize a centralized training that allows all agents to share their observations over critic networks. The information shared through critic network can assist each agent to learn the policies of other agents. In a decentralized execution, each agent may only use its actor network and local observation to find the most appropriate TB in the given level of transmission power. While training, the actions taken by actor are evaluated by corresponding critic that maps Q-value for all feasible actions in the given state. Our method results in 18% higher packet reception ratio than a spectrum allocation scheme based on a double DQN. The proposed method achieves 33% higher reward than the previous state-of-the-art that is also based on MARL.

State-Augmented Learnable Algorithms for Resource Management in Wireless Networks

We consider resource management problems in multi-user wireless networks, which can be cast as optimizing a network-wide utility function, subject to constraints on the long-term average performance of users across the network. We propose a state-augmented algorithm for solving the aforementioned radio resource management (RRM) problems, where, alongside the instantaneous network state, the RRM policy takes as input the set of dual variables corresponding to the constraints, which evolve depending on how much the constraints are violated during execution. We theoretically show that the proposed state-augmented algorithm leads to feasible and near-optimal RRM decisions. Moreover, focusing on the problem of wireless power control using graph neural network (GNN) parameterizations, we demonstrate the superiority of the proposed RRM algorithm over baseline methods across a suite of numerical experiments.

ML-Based Radio Resource Management in 5G and Beyond Networks: A Survey

In this survey, a comprehensive study is provided, regarding the use of machine learning (ML) algorithms for effective resource management in fifth-generation and beyond (5G/B5G) wireless cellular networks. The ever-increasing user requirements, their diverse nature in terms of performance metrics and the use of various novel technologies, such as millimeter wave transmission, massive multiple-input-multiple-output configurations and non-orthogonal multiple access, render the multi-constraint nature of the radio resource management (RRM) problem. In this context, ML and mobile edge computing (MEC) constitute a promising framework to provide improved quality of service (QoS) for end users, since they can relax the RMM-associated computational burden. In our work, a state-of-the-art analysis of ML-based RRM algorithms, categorized in terms of learning type and potential applications as well as MEC implementations,is presented, to define the best-performing solutions for various RRM sub-problems. To demonstrate the capabilities and efficiency of ML-based algorithms in RRM, we apply and compare different ML approaches for throughput prediction, as an indicative RRM task. We investigate the problem, either as a classification or as a regression one, using the corresponding metrics in each occasion. Finally, open issues, challenges and limitations concerning AI/ML approaches in RRM for 5G and B5G networks, are discussed in detail.

A Deep Learning-Based Transmission Scheme Using Reduced Feedback for D2D Networks

In this study, we investigate frequency division duplex (FDD)-based overlay device-to-device (D2D) communication networks. In overlay D2D networks, D2D communication uses a dedicated radio resource to eliminate the cross-interference with cellular communication and multiple D2D devices share the dedicated radio resource to resolve the scarcity of radio spectrum, thereby causing co-channel interference, one of the challenging problems in D2D communication networks. Various radio resource management problems for D2D communication networks can’t be solved by conventional optimization methods because they are modelled by non-convex optimization. Recently, various studies have relied on deep reinforcement learning (DRL) as an alternative method to maximize the performance of D2D communication networks overcoming co-channel interference. These studies showed that DRL-based radio resource management schemes can achieve almost optimal performance, and even outperform the state-of-art schemes based on non-convex optimization. Most of DRL-based transmission schemes inevitably require feedback information from D2D receivers to build input states, especially in FDD networks where the channel reciprocity between uplink and downlink is not valid. However, the effect of feedback overhead has not been well investigated in previous studies using DRL, and none of the studies reported on reducing the feedback overhead of DRL-based transmission schemes for FDD-based D2D networks. In this study, we propose a DRL-based transmission scheme for FDD-based D2D networks where input states are built by using reduced feedback information, thereby reducing feedback overhead. The proposed DRL-based transmission scheme using reduced feedback information achieves the same average sum-rates as that using full feedback, while reducing the feedback overhead significantly.

Embedding Model-Based Fast Meta Learning for Downlink Beamforming Adaptation

This paper studies the fast adaptive beamforming for the multiuser multiple-input single-output downlink. Existing deep learning-based approaches assume that training and testing channels follow the same distribution which causes task mismatch, when the testing environment changes. Although meta learning can deal with the task mismatch, it relies on labelled data and incurs high complexity in the pre-training and fine tuning stages. We propose a simple yet effective adaptive framework to solve the mismatch issue, which trains an embedding model as a transferable feature extractor, followed by fitting the support vector regression. Compared to the existing meta learning algorithm, our method does not necessarily need labelled data in the pre-training and does not need fine-tuning of the pre-trained model in the adaptation. The effectiveness of the proposed method is verified through two well-known applications, i.e., the signal to interference plus noise ratio balancing problem and the sum rate maximization problem. Furthermore, we extend our proposed method to online scenarios in non-stationary environments. Simulation results demonstrate the advantages of the proposed algorithm in terms of both performance and complexity. The proposed framework can also be applied to general radio resource management problems.

Design of Ideological and Political Multimedia Network Teaching Resources Integration System Based on Wireless Network

The main purpose of the integration of network media and middle school ideological and political course is to better realize the moral education function of middle school ideological and political development. The use of resource integration technology to manage distributed teaching resources is conducive to improving the level of information construction in colleges and universities, and so on. This paper puts forward the integration system of ideological and political multimedia network teaching resources based on wireless network. Firstly, it presents an overview of the Radio Resource Management (RRM) problem and projected strategies within wireless network framework, where several Radio Access Technologies (RATs) are collectively controlled and identify the different activities relating to the CRRM issues. Secondly, this paper targets providing a summary of the RRM problem and projected solutions within wireless network. Thirdly, the theoretical elements of the design and the teaching resource management assessment process are clarified by using XML as the data exchange carrier to realize the automatic integration of teaching resources. Fourthly, we have used dSPACE platform, by calling its API function, which then call the subscribe function of dSPACE framework and store the subscription information in the database. Whenever new teaching resources enter the subscribed field, the dSPACE framework will automatically send e-mail to the subscribers. Finally, during the operation of the system, all functions operate normally and have strong security, so as to avoid the illegal theft or leakage of the data about the education system.

Bayesian Optimization for Radio Resource Management: Open Loop Power Control

We provide the reader with an accessible yet rigorous introduction to Bayesian optimisation with Gaussian processes (BOGP) for the purpose of solving a wide variety of radio resource management (RRM) problems. We believe that BOGP is a powerful tool that has been somewhat overlooked in RRM research, although it elegantly addresses pressing requirements for fast convergence, safe exploration, and interpretability. BOGP also provides a natural way to exploit prior knowledge during optimization. After explaining the nuts and bolts of BOGP, we delve into more advanced topics, such as the choice of the acquisition function and the optimization of dynamic performance functions. Finally, we put the theory into practice for the RRM problem of uplink open-loop power control (OLPC) in 5G cellular networks, for which BOGP is able to converge to almost optimal solutions in tens of iterations without significant performance drops during exploration.

Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis

Deep learning has recently emerged as a disruptive technology to solve challenging radio resource management problems in wireless networks. However, the neural network architectures adopted by existing works suffer from poor scalability and generalization, and lack of interpretability. A long-standing approach to improve scalability and generalization is to incorporate the structures of the target task into the neural network architecture. In this paper, we propose to apply graph neural networks (GNNs) to solve large-scale radio resource management problems, supported by effective neural network architecture design and theoretical analysis. Specifically, we first demonstrate that radio resource management problems can be formulated as graph optimization problems that enjoy a universal permutation equivariance property. We then identify a family of neural networks, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">message passing graph neural networks</i> (MPGNNs). It is demonstrated that they not only satisfy the permutation equivariance property, but also can generalize to large-scale problems, while enjoying a high computational efficiency. For interpretablity and theoretical guarantees, we prove the equivalence between MPGNNs and a family of distributed optimization algorithms, which is then used to analyze the performance and generalization of MPGNN-based methods. Extensive simulations, with power control and beamforming as two examples, demonstrate that the proposed method, trained in an unsupervised manner with unlabeled samples, matches or even outperforms classic optimization-based algorithms without domain-specific knowledge. Remarkably, the proposed method is highly scalable and can solve the beamforming problem in an interference channel with 1000 transceiver pairs within 6 milliseconds on a single GPU.

Radio resource management: approaches and implementations from 4G to 5G and beyond

Radio resource and its management is one of the key areas of research where technologies, infrastructure and challenges are rapidly changing as 5G system architecture demands a paradigm shift. The previous generation communication technologies require customizations and upgrades as 5G will remain inclusive for significantly long duration. Radio resource management (RRM) schemes that are evolved during LTE/LTE-A network environment period will remain relevant for 5G, however, these schemes must become more intelligent and adaptive for future as features and requirements of network and users will be diverse and highly demanding. In this paper, a comprehensive view is provided upon various aspects of RRM, its challenges and existing schemes. The existing RRM schemes are presented with their respective architecture which has significant impact on the approaches. The problem of RRM is multi-dimensional and different dimensions are presented with their respective solutions such as interference or energy management. In this paper study of legacy and state of the art RRM schemes is presented with their features and inefficiencies in the modern telecommunication era of heterogeneous, ultra-dense, very low latency and highly reliable mobile network. In addition to this various comparison among approaches and schemes are presented for analyzing the solutions. The need of RRM solution is critical and this paper aim is to outline the challenges, existing solutions and directions for research to find and develop smarter and more adaptive schemes for future.

Radio Resource Management Techniques for Multibeam Satellite Systems

Next–generation of satellite communication (SatCom) networks are expected to support extremely high data rates for a seamless integration into future large satellite-terrestrial networks. In view of the coming spectral limitations, the main challenge is to reduce the cost (satellite launch and operation) per bit, which can be achieved by enhancing the spectral efficiencies. In addition, the capability to quickly and flexibly assign radio resources according to the traffic demand distribution has become a must for future multibeam broadband satellite systems. This letter presents the radio resource management problems encountered in the design of future broadband SatComs and provides a comprehensive overview of the available techniques to address such challenges. Firstly, we focus on the demand-matching formulation of the power and bandwidth assignment. Secondly, we present the scheduling design in practical multibeam satellite systems. Finally, a number of future challenges and the respective open research topics are described.

On the Multiplexing of Data and Metadata for Ultra-Reliable Low-Latency Communications in 5G

This paper addresses the problem of downlink radio resource management for ultra-reliable low-latency communications (URLLC) in fifth generation (5G) systems. To support low-latency communications, we study performance of two multiplexing schemes namely in-resource control signalling and joint encoding of data and metadata . In the former, the metadata and data are separately encoded and the metadata is sent at the beginning of transmission time prior to the data. Thus, it benefits from a low-complexity receiver structure to decode the data block. The latter takes advantages of transmitting a larger blocklength to enhance the reliability and improve spectrum efficiency by jointly encoding data and metadata as a single codeword. Dealing with small URLLC payloads, the overhead and error of sending metadata are not negligible and have a significant impact on the system performance in terms of resource usage the reliability of transmission. For each scheme, we derive expressions for the outage probability and resource usage by taking into account impacts of the finite blocklength payloads, overhead and error of sending metadata, and probability of error in uplink feedback channel. We propose a novel framework for joint data and metadata link adaptation to minimize the average number of allocated resources, while ensuring the stringent URLLC quality of service requirements. An optimization problem is formulated for each scheme that is mixed-integer non-convex problem, difficult to solve in polynomial time. Solutions based on successive convex optimization are proposed. Numerical evaluations show that the proposed algorithms perform close to the optimal solution and demonstrate remarkable gains of up to $\mathbf {27\%}$ improvement in resource usage. Finally, we present sensitivity analysis of the results for various network parameters.

Green Deep Reinforcement Learning for Radio Resource Management: Architecture, Algorithm Compression, and Challenges

Artificial intelligence (AI) heralds a step-change in wireless networks but may also cause irreversible environmental damage due to its high energy consumption. Here, we address this challenge in the context of 5G and beyond, where there is a complexity explosion in radio resource management (RRM). For high-dimensional RRM problems in a dynamic environment, deep reinforcement learning (DRL) provides a powerful tool for scalable optimization, but it consumes a large amount of energy over time and risks compromising progress made in green radio research. This article reviews and analyzes how to achieve green DRL for RRM via both architecture and algorithm innovations. Architecturally, a cloudbased training and distributed decision-making DRL scheme is proposed, where RRM entities can make lightweight, deep, local decisions while being assisted by on-cloud training and updating. At the algorithm level, compression approaches are introduced for both deep neural networks (DNNs) and the underlying Markov decision processes (MDPs), enabling accurate lowdimensional representations of challenges. To scale learning across geographic areas, a spatial transfer learning scheme is proposed to further promote the learning efficiency of distributed DRL entities by exploiting the traffic demand correlations. Together, our proposed architecture and algorithms provide a vision for green and on-demand DRL capability.

Secrecy Rate Maximization via Radio Resource Allocation in Cellular Underlaying V2V Communications

In device-to-device (D2D) underlaying vehicle-to-vehicle (V2V) communications, radio resource blocks (RBs) are allocated to primary cellular users, which yields sub-optimal rates and a long access latency for secondary D2D-enabled vehicles. This work investigates a joint radio resource and power management (RRPM) problem for secure cellular underlaying V2V communications, where cellular users and vehicles have the same priority, giving vehicles more opportunities to access the RBs. Specifically, we aim to maximize secrecy rates of V2V channels under the condition that eavesdropper has an adaptive receiving detection vector to maximize the received signal-to-interference-plus-noise ratio (SINR) from vehicles. For a single pair of a vehicle and a cellular user, the closed-form optimal power allocation expressions are derived for the interference-limited scenario and the noise-limited scenario, respectively. Moreover, for multiple pairs of vehicles and cellular users, a 3-partite hypergraph based 3-dimensional matching approach is proposed to solve a mixed-integer and non-convex problem, which achieves a near-optimal result with an O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> ) time complexity. Simulations in different scenarios show that the secrecy rate of the proposed scheme can be improved by 50% if compared to existing schemes.

Radio resource management for vehicular communication via cellular device to device links: review and challenges

The vehicle-to-everything (V2X) communications, as enabled by cellular device-to-device (D2D) links, has recently gained greater attention owing to the remarkable capacity it displays in enhancing traffic safety, effectiveness, and comfort. It is in this context that the present paper can be set, whereby a new technology is advanced, relating to the fifth generation (5G) associated D2D based vehicular communication, as lately approved by the 3GPP standardization community. In effect, V2X communication appears to raise a number of critical issues that are closely inter-related, mainly, those attached to stringent latency and reliability requirements. In this respect, an exhaustive investigation of the state of the art is put forward D2D based vehicular communication is detailed, throughout the scope of the present work. In the first place, a thorough examination of the D2D associated works, treating such a study area from different perspectives, is displayed. In a second place, the major reason lying behind the application of the D2D communication for vehicular service purposes, involving the possibilities it provides, along with the expected prospective potentials, it could offer, are investigated. In addition, a special emphasis is laid on the particular restrictions associated with the implementation of the V2X Radio Resource Management problem formulation. In a last step, and on reviewing a number of papers dealing with the area of D2D based vehicular communication, some disciplines that do not see to have been satisfactorily and exhaustively elaborated are depicted, while highlighting potential work associated challenges, mainly concerning the D2D effective integration for vehicular communication purposes.

A Deep Learning Based Transmission Algorithm for Mobile Device-to-Device Networks

Recently, device-to-device (D2D) communications have been attracting substantial attention because they can greatly improve coverage, spectral efficiency, and energy efficiency, compared to conventional cellular communications. They are also indispensable for the mobile caching network, which is an emerging technology for next-generation mobile networks. We investigate a cellular overlay D2D network where a dedicated radio resource is allocated for D2D communications to remove cross-interference with cellular communications and all D2D devices share the dedicated radio resource to improve the spectral efficiency. More specifically, we study a problem of radio resource management for D2D networks, which is one of the most challenging problems in D2D networks, and we also propose a new transmission algorithm for D2D networks based on deep learning with a convolutional neural network (CNN). A CNN is formulated to yield a binary vector indicating whether to allow each D2D pair to transmit data. In order to train the CNN and verify the trained CNN, we obtain data samples from a suboptimal algorithm. Our numerical results show that the accuracies of the proposed deep learning based transmission algorithm reach about 85%∼95% in spite of its simple structure due to the limitation in computing power.