Radio Resource Management Algorithms Research Articles

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.

Performance evaluation of opportunistic schedulers based on fairness and throughput in new-generation mobile networks

This paper discusses radio resource management in the telecommunications industry that utilizes opportunistic scheduling approaches and methods. It proposes a new scheduler called IFMR (Improved Fairness of MaxRate) to extend the opportunistic approach with the proposition of a new scheduling solution that enables to significantly maximize the system throughput. Our goal is to ensure better results that maximize system throughput, ensure perfect fairness at three levels and maximize the number of satisfied users who have achieved their desired throughput. Similarly, the performance of the proposed algorithm is evaluated and compared with reference algorithms: round robin, MaxSNR and proportional fair (PF). The focus of this study is to evaluate and analyze the quality of service guaranteed to each network subscriber, considering the heterogeneous data traffic based on simulations. The algorithms presented in this paper are programmed and simulated using the MATLAB simulation software. The study aims to effectively analyze the impact of radio resource management algorithms and schedulers, including the round robin algorithm, MaxSNR and PF, among others, with the main objective of evaluating the performance of the schedulers used by telecommunication and cellphone network operators. The scheduling phase, which precedes resource allocation and assignment, is a critical aspect of the study. The impact of these schedulers on throughput and fairness will also be evaluated and analyzed based on specific criteria.

A queuing theory based delay-efficient packet scheduler for machine type communication

Lately, there is a drastic change in the telecommunication industry due to emergence of Internet of Things (IoT). Machine to Machine (M2M) plays a very important role in enabling IoT. As the LTE is mainly suitable for Human-to-Human (H2H) communication, MTC faces some challenges in LTE like radio resource management and uplink-scheduling algorithms are not suitable for MTC. In this paper, we use a queuing theory and propose a packet scheduler, which would model the regular and event-driven traffic and schedule the jobs in queue for request processing. Then using the Markov Chain process, we calculate the queue length and determine the blocking probability. If there is high blocking probability and still there are some requests in queue, then those requests will be processed in virtual server for maximum network usage.

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.

Development of a mathematical model of radio resource management of special purpose radio communication systems based on an evolutionary approach

The object of research is a special-purpose radio communication system. A special purpose radio communication system is affected by many different destructive influences. The main ones are deliberate interference and cybernetic impact of various purposes. The above causes the search for new scientific approaches to identify and identify the destructive impact on special-purpose radio communications in order to increase the operational efficiency of special-purpose radio communications systems. In this work, the problems of developing a mathematical model for managing the radio resource of special-purpose radio communication systems based on the evolutionary approach are solved. In the course of the research, the authors of the work used the main provisions of the theory of artificial intelligence, the theory of automation, the theory of complex technical systems, as well as general scientific methods of cognition, namely analysis and synthesis. The proposed methodological approach was developed taking into account the practical experience of the authors of this work during military conflicts of the last decade. The research results will be useful for: – development of new radio resource management algorithms; – substantiation of recommendations for improving the efficiency of radio resource operational management; – analysis of the radio-electronic situation during the conduct of hostilities (operations); – when creating promising technologies for increasing the efficiency of radio resource operational management; – assessment of the adequacy, reliability, sensitivity of the scientific and methodological apparatus for the operational management of the radio resource; – development of new and improvement of existing radio resource management models. Directions for further research will be aimed at developing a methodology for intelligent control of the radio resource of special-purpose radio communication systems.

RNN-Based Radio Resource Management on Multicore RISC-V Accelerator Architectures

Radio resource management (RRM) is critical in 5G mobile communications due to its ubiquity on every radio device and its low latency constraints. The rapidly evolving RRM algorithms with low latency requirements combined with the dense and massive 5G base station deployment ask for an on-the-edge RRM acceleration system with a tradeoff between flexibility, efficiency, and cost-making application-specific instruction-set processors (ASIPs) an optimal choice. In this work, we start from a baseline, simple RISC-V core and introduce instruction extensions coupled with software optimizations for maximizing the throughput of a selected set of recently proposed RRM algorithms based on models using multilayer perceptrons (MLPs) and recurrent neural networks (RNNs). Furthermore, we scale from a single-ASIP to a multi-ASIP acceleration system to further improve RRM throughput. For the single-ASIP system, we demonstrate an energy efficiency of 218 GMAC/s/W and a throughput of 566 MMAC/s corresponding to an improvement of 10× and 10.6×, respectively, over the single-core system with a baseline RV32IMC core. For the multi-ASIP system, we analyze the parallel speedup dependency on the input and output feature map (FM) size for fully connected and LSTM layers, achieving up to 10.2× speedup with 16 cores over a single extended RI5CY core for single LSTM layers and a speedup of 13.8× for a single fully connected layer. On the full RRM benchmark suite, we achieve an average overall speedup of 16.4×, 25.2×, 31.9×, and 38.8× on two, four, eight, and 16 cores, respectively, compared to our single-core RV32IMC baseline implementation.

Stackelberg Game-Based Radio Resource Management Algorithm in an Urban Rail Transit Communication System

Train-to-wayside (T2W) and train-to-train (T2T) communication modes may coexist in future train-centric communication-based train control (CBTC) systems. The feasibility of T2T communication in urban rail transit is analyzed first. Referring to the device-to-device (D2D) communication scenario in the general cellular network, this paper establishes a radio resource optimization model for the coexistence of train-to-train communication and train-to-wayside communication. With the aim of more efficient scheduling of radio time-frequency resources in the dedicated frequency band, we propose a Stackelberg game-based radio resource management algorithm based on the consideration of different service priorities of trains. The analysis and simulation results show that the proposed algorithm can effectively guarantee the performance of the system and improve the reliability of the CBTC system.

Heuristic Radio Resource Management for Massive MIMO in Satellite Broadband Communication Networks

The advent of technologies allowing the implementation of flexible satellite payloads is opening up great opportunities to efficiently cope with non-uniform traffic conditions. The latter is one of the “crux” of modern satellite broadband access service economic provision. In this paper we derive affordable (linear) complexity radio resource management (RRM) techniques for satellite multi-beam broadband communication networks that are closely approaching the optimum solution which has an exponential complexity with the number of users. The proposed heuristic RRM solution is applied to the most demanding Massive Multiple Input Multiple Output (M-MIMO) case. It is shown that the low-complexity payload pragmatic M-MIMO solution combined with heuristic RRM is able to efficiently cope with very different traffic conditions with a small penalty in terms of throughput compared to the much more complex ideal Minimum Mean Square Error (MMSE) M-MIMO adopting the same RRM algorithm. Instead, the pragmatic M-MIMO with heuristic RRM is outperforming MMSE M-MIMO exploiting simple RRM.

Trajectory Mining‐Based City‐Level Mobility Model for 5G NB‐IoT Networks

Due to the large coverage of 5G NB‐IoT networks, a more realistic mobility model for a macroscopic scene will greatly facilitate the development of optimal radio resource management algorithms. However, models devised for a random motion scene are no longer applicable in circumstances. Therefore, in this paper, a city‐level mobility model is proposed based on the feature mining of the real trajectory of vehicles in the city of Shenzhen. The proposed model is separately designed in the motion trajectory to reduce the mutual influence between the time and spatial sequence. Simulation results show that it can better present specific node motions with the physical constraints of the city layout, which are motivated with a high degree of fit in terms of self‐similarity, hotspots, and long‐tail features.

Radio resource management for large constellations in a spectrum sharing environment

SummaryIn the last five years, the satellite community has witnessed growing interest in the delivery of Broadband Services from non‐geostationary orbit (NGSO) systems. However, accessing the entire spectrum available without having to share it among the different operators is key for these systems to have a chance of breaking even, which makes for a challenging business case. This paper focuses on the challenges of spectrum coexistence of multiple satellite constellations for broadband communications and proposes a radio resource management algorithm to enable such coexistence.

Resource Allocation in Full-Duplex UAV Enabled Multismall Cell Networks

Flying platforms, such as unmanned aerial vehicles (UAVs) are a promising solution for future small cell networks. UAVs can be used as aerial base stations (BSs) to enhance coverage, capacity and reliability of wireless networks. Also, with recent advances of self interference cancellation (SIC) techniques in full-duplex (FD) systems, practical implementation of FD BSs is feasible. In this paper, we investigate the problem of resource allocation for multi-small cell networks with FD-UAVs as aerial BSs with imperfect SIC. We consider three different scenarios: a) maximizing the DL sum-rate; b) maximizing the UL sum-rate; and finally c) maximizing the sum of UL and DL sum-rates. The aforementioned problems result in non-convex optimization problems, therefore, successive convex approximation algorithms are developed by leveraging D.C. (Difference of Convex functions) programming to find sub-optimal solutions. Simulation results illustrated validity and effectiveness of the proposed radio resource management algorithms in comparison with ground BSs, in both FD mode and its half-duplex (HD) counterpart. The results also indicate those situations where using aerial BS is advantageous over ground BS and reveal how FD transmission enhances the network performance in comparison with HD one.

Predicting Device-to-Device Channels From Cellular Channel Measurements: A Learning Approach

Device-to-device (D2D) communication, which ena- bles a direct connection between users while bypassing the cellular channels to base stations (BSs), is a promising way to offload the traffic from conventional cellular networks. In D2D communication, optimizing the resource allocation requires the knowledge of D2D channel gains. However, such knowledge is hard to obtain at reasonable signaling costs. In this paper, we show this problem can be circumvented by tapping into the information provided by the estimated cellular channels between the users and surrounding BSs as these channels are estimated anyway for a normal operation of the network. While the cellular and D2D channel gains exhibit independent fast fading behavior, we show that average gains of the cellular and D2D channels share a non-explicit relation, which is rooted into the network topology, terrain, and buildings setup. We propose a deep learning approach to predict the D2D channel gains from seemingly independent cellular channels. Our results show a high degree of convergence between the true and predicted D2D channel gains. Moreover, we demonstrate the robustness of the proposed scheme against environment changes and inaccuracies during the offline training. The predicted gains allow to reach a near-optimal capacity in many radio resource management algorithms.

Energy Efficiency for MISO-OFDMA-Based User-Relay Assisted Cellular Networks

The concept of improving energy efficiency (EE) without sacrificing the service quality has become important nowadays. The combination of orthogonal frequency-division multiple-access (OFDMA) multiantenna transmission technology and relaying is one of the key technologies to deliver the promise of reliable and high data rate coverage in the most cost-effective manner. In this article, EE is studied for the downlink multiple-input single-output OFDMA-based user-relay assisted cellular networks. EE maximization is formulated for the decode and forward relaying scheme with the consideration of both transmit and circuit power consumption as well as the data rate requirements for the mobile users. The quality of service constrained EE maximization, which is defined for multicarrier, multiuser, multirelay, and multiantenna networks, is a nonconvex and combinatorial problem so it is hard to tackle. To solve this difficult problem, a radio resource management algorithm that solves the subcarrier allocation, mode selection, and power allocation separately is proposed. The efficiency of the proposed algorithm is demonstrated by numerical results for different system parameters.

Uplink Low Power Based Radio Resource Management in Wireless Heterogeneous Networks

Due to the rapid rise of energy consumption and limited battery power of wireless user equipment (UE), low power communication becomes very important. Heterogeneous network deployment can significantly reduce regional power consumption. For heterogeneous network scenarios, this paper proposes an uplink low-power resource allocation scheme considering battery power limitation and user QoS requirements. Based on the assumption that each user has access to at most one base station (BS), the scheme is modeled as a mixed integer nonlinear programming (MINLP) problem. Since each user has access to one BS at most, the transmitting power matrix from the UE to the BSs (UE-to-BS matrix) is a sparse matrix. The MINLP problem can be transformed into an optimization problem searching sparse solution. In this paper, the reweighted L1 norm is introduced as a penalty function to remove the integer constraint and to regulate the sparsity of the UE-to-BS transmitting power matrix. In order to obtain the optimal resource allocation scheme, a reweighted L1 norm penalty based radio resource management (RRM) algorithm is proposed. Simulation results show that the reweighted L1 norm penalty based RRM algorithm has good convergence performance and the results are very close to optimal solutions. The proposed resource management scheme can prolong the working time of low-energy users and guarantee the QoS requirements of UE in the system.

Unified Scheduling Framework for Dual Connectivity in Multi-RAT NSA 5G Networks

Supporting the dual connectivity technology in non-stand-alone 5G scenarios involves three main functionalities: a user device (UD)-base station (BS) association (UBA) mechanism, a flow control algorithm (FCA) to determine the data split transmitted by each BS and radio resource management (RRM) algorithms. However, previous works only focused on the design of at most two of these functionalities. In thisletter, we go beyond and design a unified scheduling framework including the three aforementioned functionalities. The proposed framework targets the user satisfaction maximization and runs in a completely decentralized fashion, where the UBA algorithm runs independently at each UD, the FCA is executed at the master LTE BS and the RRM is performed individually by each BS. Simulations show the incremental gains of each proposed functionality as well as the total gain of the unified framework.

Deep Learning-Based Beam Management and Interference Coordination in Dense mmWave Networks

Due to severe signal pathloss in millimeter wave (mmWave) band, beamforming enabled directional transmission is critical to overcome the attenuation challenge in future mmWave communication systems. Furthermore, in order to improve signal coverage of mmWave networks, network densification has to be used at the same time. However, the concurrent use of directional transmission and network densification will make the radio resource management (RRM) of dense mmWave network dramatically more complicated than that of microwave network. In order to maximize the sum-rate of the entire network, tedious and complex RRM algorithms are usually needed to obtain good results, which require high complexity of computation. To address this challenge, we proposed a deep learning-based beam management and interference coordination (BM-IC) method in dense mmWave network, through which the conventional complex BM-IC algorithm is transformed into a deep neural network (DNN)-based approximation. Because DNN only requires a series of simple calculations (e.g., some additions and multiplications), the complexity of computation is greatly reduced. Simulation results show that the proposed deep learning-based BM-IC approach can obtain comparable sum-rate to conventional BM-IC algorithm, but with much less computation time. Thus, deep learning could be a powerful tool to mitigate the complexity of RRM problems in dense mmWave networks.

Trading off Network Density with Frequency Spectrum for Resource Optimization in 5G Ultra-Dense Networks

To effectively increase the capacity in 5G wireless networks requires more spectrum and denser network deployments. However, due to the increasing network density, the coordination of network and spectrum management becomes a challenging task both within a single operator’s network and among multiple operators’ networks. In this article, we develop new radio resource management (RRM) algorithms for adapting the frequency spectrum and the density of active access nodes in 5G ultra-dense networks (UDNs) to the traffic load and the user density in different geographical areas of the network. To this end, we formulate a network optimization problem where the allocation of spectrum bandwidth and the density of active access nodes are optimized to minimize a joint cost function, and we exploit Lagrange duality techniques to develop provably optimal network-scheduling algorithms. In particular, we develop density algorithms for two application scenarios. The first scenario solves the resource management problem for an operator of an ultra-dense network with exclusive access to a pool of frequency resources, while the second scenario applies to the management of the network density of collocated UDNs that belong to multiple operators sharing the same frequency spectrum. Simulation results demonstrate how effectively the algorithms can adapt the allocation of the spectrum allocation and the density of active access nodes over space and time.

On Designing Energy-Efficient Heterogeneous Cloud Radio Access Networks

Heterogeneous cloud radio access network (H-CRAN) promises higher energy efficiency (EE) than the conventional cellular networks by centralizing the baseband signal processing into the baseband unit (BBU) pool hosted by cloud computing platforms. Because of the difference between H-CRAN and conventional cellular networks, existing energy-efficient networking mechanisms designed for conventional cellular networks cannot fully leverage H-CRAN in terms of reducing the network energy consumption. In this paper, we bridge this gap by proposing a radio resource management scheme to optimize the network EE (NEE) of H-CRAN. We develop a network energy consumption model that characterizes the energy consumption of radio access points, fronthaul, and the BBU pool in H-CRAN. Based on the network energy consumption model, we formulate the NEE optimization problem with the consideration of the capacity constrained fronthaul. The NEE optimization problem is a mixed integer non-linear programming problem. We propose the H-CRAN energy-efficient radio resource management (HERM) algorithm to solve the NEE optimization problem efficiently. Various properties of the proposed solution are derived and extensive simulations are conducted. The simulation results show that the HERM algorithm significantly improves the NEE of H-CRAN. As compared with a baseline algorithm in which the radio resource management is not optimized, HERM boosts the NEE by 59% under the dynamic network traffic. As compared to an energy-efficient radio resource allocation (ERA) algorithm which does not optimize the energy consumption of the BBU pool, the NEE of H-CRAN achieved by the HERM algorithm is up to 51% better than that by the ERA algorithm with network traffic dynamics.

Learning Radio Resource Management in RANs: Framework, Opportunities, and Challenges

In the fifth generation (5G) of mobile broadband systems, radio resource management (RRM) will reach unprecedented levels of complexity. To cope with the ever more sophisticated RRM functionalities and the growing variety of scenarios, while carrying out the prompt decisions required in 5G, this manuscript presents a lean RRM architecture that capitalizes on recent advances in the field of machine learning in combination with the large amount of data readily available in the network from measurements and system observations. The architecture consists of a learner (or a few), which learns RRM policies directly from the data gathered in the network using a single general-purpose learning framework, and a set of distributed actors, which execute RRM policies issued by the learner and repeatedly generate samples of experience. Thus, the complexity of RRM is shifted to the design of the learning framework, while the RRM algorithms derived from this framework are executed in a computationally efficient distributed manner at the radio access nodes. The potential of this approach is verified in a pair of pertinent scenarios, and future directions on applications of machine learning to RRM are discussed. Although we focus on a mobile broadband context, the concepts proposed hereafter extend to any radio access network technology where one can conceive the idea of a central learning unit gathering data from distributed actors.