Radio Resource Management Scheme Research Articles

Radio resource management for OFDM-based dual-function radar-communication: sum-rate and fairness

AbstractThis paper focuses on radio resource management (RRM) in multi-user dual-function radar communication (DFRC) systems using orthogonal frequency division multiplexing (OFDM) waveforms. We propose two RRM schemes, one from the perspective of sum rate maximization and the other from the perspective of user fairness maximization. These optimization problems are non-convex due to the presence of mixed integer terms, making them difficult to solve. To address these challenges, we have employed a decomposition approach to transform these two complex problems into separate, more readily solvable ones. In addressing the sum rate maximization problem, we initially introduce a heuristic greedy algorithm to obtain a resource allocation scheme that satisfies radar performance requirements. Subsequently, we utilize a cyclic iterative method along with KKT conditions to solve the sum rate maximization problem for communication users. Concerning the fairness maximization problem for communication users, we similarly employ a heuristic greedy algorithm to obtain a resource allocation scheme that meets radar performance constraints. Then utilize the Lagrangian dual method to solve the multi-user fairness maximization problem for communication users. Our experimental results confirm the effectiveness of the proposed algorithms.

Radio resource management scheme in radar and communication spectral coexistence platform

In vehicular environments, the cooperation of radar and communications has been recognized as a promising solution to alleviate the shortage of spectrum resources. In this paper, we investigate the joint radar sensing and communication (JSC) system based on the cooperative spectrum sharing strategy. Via the full cooperation between both radar sensing and wireless communication functionalities, we can avoid the under-utilization of spectral resource while improving system efficiency. To implement our JSC control scheme, we focus on coalitional bargaining solutions according to the cooperative game theory. Especially, the coalitional Nash bargaining solution (CNBS) and coalitional Kalai-Smorodinsky bargaining (CKSBS) are employed to get a mutually desirable solution in the radar-communication coexistence platform. The main ides of CNBS is adopted to distribute the spectrum resource among roadside units, and both radar and communication functions in each individual vehicle. The concept of CKSBS is applied to allocate the spectrum resource for vehicles in order to maximize their payoffs. In the proposed scheme, the CNBS and CKSBS work together to share the surplus of a coalition with the fairness and efficiency properties. The main contribution of our proposed approach is the reciprocal combination of the CNBS and CKSBS to obtain the synergy effect. Finally, numerical results are provided to verify the effectiveness of the proposed scheme for the JSC system operations.

Radio Resource Management for C-V2X: From a Hybrid Centralized-Distributed Scheme to a Distributed Scheme

Spectrum sharing in cellular vehicle-to-everything (C-V2X) has been conceived as a promising solution to improve spectrum efficiency. However, the co-channel interference incurred with it may cause severe performance degradation to vehicular links. Thereby, radio resource management (RRM) is motivated and designed to ensure communication reliability and increase system capacity. One challenge is that RRM involves channel allocation and power control, which are tightly coupled and hard to optimize simultaneously. Another challenge for this is the difficulty adapting centralized RRM schemes, requiring global channel state information (CSI) and causing high signaling overhead. To tackle these challenges, we propose the hybrid centralized-distributed RRM scheme and the distributed RRM scheme. Specifically, we prove a decoupling method that provides a theoretical lower bound so that channel allocation and power control can be optimized independently. Given the decoupling method, the hybrid centralized-distributed RRM scheme is based on graph matching and reinforcement learning (GMRL) to maximize system capacity and guarantee reliability requirements. Further, to decrease computation complexity and signaling overhead, the distributed RRM scheme that only requires local CSI with hybrid-framework reinforcement learning (HFRL) is exploited. Finally, both schemes are numerically evaluated through experiments and outperform other deep Q-network (DQN)-based schemes.

A novel method for spectrum sensing in cognitive radio networks using fractional GWO[sbnd]CS optimization

The co-channel interference and radio resources in a wireless communication system are often systematically managed by employing Radio Resource Management (RRM) strategies. The parameters of radio resources such as transmit power, data rate, error coding, and user allocation can be controlled via the RRM techniques whose main aim is to better exploit the limited radio frequency spectrum resources. The unallocated spectrum resources become spectrum holes and the effective utilization of the spectrum holes is made possible through cognitive radio by improving the network throughput. To enhance the effective energy utilization capacity of the spectrum holes by considering different sensing states, this paper integrates the grey wolf optimization (GWO) with the Cuckoo search (CS) algorithm to form the Fractional GWOCS Optimization model. The Fractional Optimization Model (FOM) allows the Secondary User (SU) for performing periodic sensing and data transfer. The main aim of the Fractional GWOCS Optimization model is to optimize different parameters like power spectral density (PSD), transmission power, and sensing bandwidth (SB). When computing energy efficiency concerning detection probability, SB, PSD, mutual optimization of PSD and SB, and convergence, the proposed methodology offers improved outcomes when compared to the existing techniques.

Optimal Learning Paradigm and Clustering for Effective Radio Resource Management in 5G HetNets

Ultra-dense heterogeneous networks (UDHN) based on small cells are a requisite part of the future cellular networks as they are proposed as one of the enabling technologies to handle coverage and capacity problems. But co-tier and cross-tier interferences in UDHN severely degrade the quality of service due to K-tiered architecture. Machine learning based radio resource management either through independent learning or cooperative learning is a proven efficient scheme for interference mitigation and quality of service provision in UDHN in a both distributive and cooperative manner. However, an optimal learning paradigm selection, i.e., either independent or cooperative learning and optimal cooperative cluster size in cooperative learning for efficient radio resource management in UDHN is still an open research problem. In this article, a Q-learning based radio resource management scheme is proposed and evaluated for both distributive and cooperative schemes using independent and cooperative learning. The proposed Q-learning solution follows the ϵ-greedy policy for optimal convergence. The simulation results for the UDHN in an urban setup show that in comparison to the independent learning paradigm, cooperative learning has no significant impact on macro cell user capacity. However, there is a significant improvement in small cell user capacity and the sum capacity of the cooperating small cells in the cluster. A significant increase of 48.57% and 37.9% is observed in the small cell user capacity, and sum capacity of the cooperating small cells, respectively, using cooperative learning as compared to independent learning which sets cooperative learning as an optimal learning strategy in UDHN. The improvement in small cell user capacity is at cost of increased computational time which is directly proportional to the number of cooperating small cells. To solve the issue of computational time in cooperative learning, an optimal clustering algorithm is proposed. The proposed optimal clustering reduced the computational time by four times in cooperative Q-learning.

Radio Resource Management Scheme for URLLC and eMBB Coexistence in a Cell-Less Radio Access Network

We address the latency challenges in a high-density and high-load scenario for an ultra-reliable and low-latency communication (URLLC) network which may coexist with enhanced mobile broadband (eMBB) services in the evolving wireless communication networks. We propose a new radio resource management (RRM) scheme consisting of a combination of time domain (TD) and frequency domain (FD) schedulers specific for URLLC and eMBB users. We also develop a user ranking algorithm from a radio unit (RU) perspective, which is employed by the TD scheduler to increase the efficiency of scheduling in terms of resource consumption in large-scale networks. Therefore, the optimized and novel resource scheduling scheme reduces latency for the URLLC users (requesting a URLLC service) in an efficient resource utilization manner to support scenarios with high user density. At the same time, this RRM scheme, while minimizing the latency, it also overcomes another important challenge of eMBB users (requesting an eMBB service), namely the throughput of those who coexist in such highly loaded scenario with URLLC users. The effectiveness of our proposed scheme including time and frequency domain (TD and FD) schedulers is analyzed. Simulation results show that the proposed scheme improves the latency of URLLC users and throughput of the eMBB users compared to the baseline scheme. The proposed scheme has a 29% latency improvement for URLLC and 90% signal-to-interference-plus-noise ratio (SINR) improvement for eMBB users as compared with conventional scheduling policies.

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.

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.

Advanced NOMA-Based RRM Schemes for Broadcasting in 5G mmWave Frequency Bands

A relevant solution for the high demand for new multimedia applications and services is millimeter wave (mmWave) frequency band in 5G. However, in order to face the technological challenges of the present and those that will appear in the short-term future, it is necessary to improve the spectral efficiency of 5G systems. In particular, the Radio Resource Management (RRM) module is considered an essential component. Nevertheless, resource allocation techniques that combine orthogonal multiplexing (OMA) schemes, such as Time Division Multiple Access (TDMA), with Non-Orthogonal Multiple Access (NOMA) techniques have not been studied in-depth. Therefore, this article designs and evaluates different RRM models that combine TDMA with NOMA for innovative applications in the 5G mmWave frequency bands. To this end, the advantages and challenges associated with mmWave frequency bands and potential future applications are introduced. An innovative use case has been designed to test the RRM models. The use case is based on the on-demand distribution of multimedia content in high-density environments, such as museum halls. The on-demand content aims at offering Augmented Reality (AR) services to unicast users in order to provide personalized services. The models have been evaluated in terms of throughput, capacity, and availability. According to the results, the combined RRM techniques offer up to 50% more capacity than the single multiplexing technology models and can provide video-on-demand service to practically the entire cell.

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.

Cluster-Based Radio Resource Management in Dynamic Vehicular Networks

With the rapid growth of traffic demand for vehicular data transmissions, the limited cellular band becomes a constraint to meet the needs of all cellular-vehicle-to-everything (C-V2X) users. In this paper, we investigate an efficient clustering-based spectrum resource management scheme for dynamic heterogeneous vehicular networks. Due to the frequent changes of channel state information (CSI) caused by high mobility of vehicles, the traditional radio resource management scheme based on complete CSI is no longer applicable. To solve this problem, we consider different quality of service (QoS) requirements for vehicular communications and propose an optimization problem for C-V2X networks. The proposed dynamic vehicular optimization strategy is carried out on the vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) links, which can construct a more realistic two-way lane in actual communication environments. We also propose a low-complexity vehicle matching algorithm based on large-scale fading to improve the stability of communication links. The desirable performance is confirmed by computer simulations.

Improving the QoS in 5G HetNets Through Cooperative Q-Learning

Heterogeneous networks are an integral part of the 5G cellular networks as they are one of the important enabling technologies for increased coverage and capacity. However, interferences in multi-tiered architecture bottleneck its performance. Although multiple schemes have been proposed for efficient radio resource management to handle the interferences in heterogeneous networks but provision of quality of service to macrocell and small cell user equipment simultaneously, is still an open research problem. Intelligent schemes for radio resource management in heterogeneous networks have proved their effectiveness due to their self-optimization capabilities. In this research article, a cooperative Q-Learning, algorithm is proposed for efficient joint radio resource management in ultra-dense heterogeneous networks to handle interferences by adaptive power allocation to small cell base stations while considering the minimum quality of service requirements. In this proposed cooperative Q-Learning algorithm, small cell base stations interacts with the neighboring small cell base stations to exchange information and performs self-optimization based on a joint reward function. The proposed solution not only provided significant improvement in the capacity of macrocell and small cell user equipment as compared to other state of art Q-Learning based radio resource management schemes but also ensure the provision of quality of service to all macrocell and small cell user equipment simultaneously in the cluster of 16 small cells. The proposed solution provided a minimum capacity of 2 b/s/Hz to macrocell and small cell user equipment which is 100% higher than the minimum quality of service requirements defined in literature where none of recently proposed solution could meet minimum quality of service requirements. The results analysis shows that cooperation among the small cells yields a significant improvement of 48% in capacity of small cell user equipment at the cost of a slight increase in computational time as compared to independent learning.

Experimental Evaluation of Transmitted Signal Distortion Caused by Power Allocation in Inter-Cell Interference Coordination Techniques for LTE/LTE-A and 5G Systems

Error vector magnitude (EVM) and out-of-band emissions are key metrics for evaluating in-band and out-band distortions introduced by all potential non-idealities in the transmitters of wireless systems. As EVM is a measure of the quality of the modulated signal/symbols, LTE/LTE-A and 5G systems specify mandatory EVM requirements in transmission for each modulation scheme. This paper analyzes the influence of the mandatory satisfaction of EVM requirements on the design of radio resource management strategies (RRM) (link adaptation, inter-cell interference coordination), specifically in the downlink (DL). EVM depends on the non-idealities of the transmitter implementations, on the allocated power variations between the subcarriers and on the selected modulations. In the DL of LTE, link adaptation is usually executed by adaptive modulation and coding (AMC) instead of power control, but some flexibility in power allocation remains being used. LTE specifies some limits in the power dynamic ranges depending on the allocated modulation, which ensures the satisfaction of EVM requirements. However, the required recommendations concerning the allowed power dynamic range when inter-cell interference coordination (ICIC) and enhanced ICIC (eICIC) mechanisms (through power coordination) are out of specification, even though the EVM performance should be known to obtain the maximum benefit of these strategies. We perform an experimental characterization of the EVM in the DL under real and widely known ICIC implementation schemes. These studies demonstrate that an accurate analysis of EVM is required. It allows a better adjustment of the design parameters of these strategies, and also allows the redefinition of the main criteria to be considered in the implementation of the scheduler/link adaptation concerning the allocable modulation coding scheme (MCS) in each resource block.

A Comprehensive Survey on Radio Resource Management in 5G HetNets: Current Solutions, Future Trends and Open Issues

The 5G network technologies are intended to accommodate innovative services with a large influx of data traffic with lower energy consumption and increased quality of service and user quality of experience levels. In order to meet 5G expectations, heterogeneous networks (HetNets) have been introduced. They involve deployment of additional low power nodes within the coverage area of conventional high power nodes and their placement closer to user underlay HetNets. Due to the increased density of small-cell networks and radio access technologies, radio resource management (RRM) for potential 5G HetNets has emerged as a critical avenue. It plays a pivotal role in enhancing spectrum utilization, load balancing, and network energy efficiency. In this paper, we summarize the key challenges i.e. cross-tier interference, co-tier interference, and user association-resource-power allocation (UA-RA-PA) emerging in 5G HetNets and highlight their significance. In addition, we present a comprehensive survey of RRM schemes based on interference management (IM), UA-RA-PA and combined approaches (UA-RA-PA + IM). We introduce a taxonomy for individual (IM, UA-RA-PA) and combined approaches as a framework for systematically studying the existing schemes. These schemes are also qualitatively analyzed and compared to each other. Finally, challenges and opportunities for RRM in 5G are outlined, and design guidelines along with possible solutions for advanced mechanisms are presented.

Distributed and Scalable Radio Resource Management for mmWave V2V Relays towards Safe Automated Driving.

The millimeter-wave (mmWave) Vehicle-to-Vehicle (V2V) communication system has drawn attention as a critical technology to extend the restricted perception of onboard sensors and upgrade the level of vehicular safety that requires a high data rate. However, co-channel inter-link interference presents significant challenges for scalable V2V communications. To overcome such limitations, this paper firstly analyzes the required data rate ensuring maneuver safety via mmWave V2V relays in an overtaking traffic scenario. Based on these preparations, we propose a distributed radio resource management scheme that integrates spatial, frequency, and power domains for two transmission ranges (short/long). In the spatial domain, ZigZag antenna configuration is utilized to mitigate the interference, which plays a decisive role in the short inter-vehicle distance. In frequency and power domains, two resource blocks are allocated alternately, and transmit power is controlled to suppress the interference, which has a decisive impact on interference mitigation in the long inter-vehicle distance. Simulation results reveal that the achievable End-to-End (E2E) throughput maintains consistently higher than the required data rate for all vehicles. Most importantly, it works effectively in scalable mmWave V2V topology.

A flexible scheduling algorithm for the 5th-generation networks

At present, the 5th-Generation (5G) wireless mobile communication standard has been released. 5G networks efficiently support enhanced mobile broadband traffic, ultra-reliable low-latency communication traffic, and massive machine-type communication. However, a major challenge for 5G networks is to achieve effective Radio Resource Management (RRM) strategies and scheduling algorithms to meet quality of service requirements. The Proportional Fair (PF) algorithm is widely used in the existing 5G scheduling technology. In the PF algorithm, RRC assigns a priority to each user which is served by gNodeB. The existing metrics of priority mainly focus on the flow rate. The purpose of this study is to explore how to improve the throughput of 5G networks and propose new scheduling schemes. In this study, the package delay of the data flow is included ic the metrics of priority. The Vienna 5G System-Level (SL) simulated is a iCATLAB-based SL simulation platform which is used to facilitate the research and development of 5G and beyond mobile communications. This paper presents a new scheduling algorithm based on the analysis of different scheduling schemes for radio resources using the Vienna 5G SL simulator.

A selective neighbour channels in Wi-Fi networks based on adaptive machine learning techniques

Due to the growing commercial exploitation of WiFi-based technologies in recent years and the lack of solutions for effective WiFi orchestration, spectrum utilization and user performance are often sub-optimal. The integration of WiFi-based radio resource management (RRM) and radio environmental maps (REMs) may create a cost-effective Smart-WiFi solution that optimizes underlying spectrum utilisation and network performance. The REM enables effective use of radio environmental data such as device location, estimated channel models, real-time network interference levels, WiFi channel occupancies, and so on. In WiFi-related settings, this information may be used to make intelligent and optimum RRM decisions. This study provides a new REM-based RRM strategy for managing and optimizing commercial WiFi devices that makes use of the underlying radio environmental data. The research uses a commercially available platform to illustrate the suggested solution, which includes on-the-fly radio environmental data gathering and optimal WiFi RRM allocation. In comparison to traditional WiFi networks, the simulation study findings reveal that the proposed Smart-WiFi leverages considerable performance advantages for large-scale situations.

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.

Downlink Radio Resource Management Through CoMP and Carrier Aggregation for LTE-Advanced Network

Long Term Evolution-Advanced (LTE-A) offers several new technologies to improve the performance of the user. However, poor received signal and interference from adjacent cells in the cell-edge area can reduce the efficiency of using individual technology. Therefore, the cell-edge users have lower throughput compared to the other users in the cell and LTE-A standard. An efficient downlink radio resource management scheme is proposed in this paper by combining the coordinated multipoint transmission and reception technique along with carrier aggregation technique to achieve higher throughput for the cell-edge user and better overall performance. The proposed method jointly transmits multiple component carriers to the cell-edge user from different cells to increase the bandwidth, strengthen the received signal, and reduce the interference while it satisfies several constraints. Modified largest weighted delay first packet scheduling algorithm is deployed for resource allocation, which takes into account the delay parameters, the probability of packet loss, and data rates of the user. The obtained system-level simulation results show that the proposed method significantly enhances the throughput performances, spectral efficiency, and fairness index, compared with the existing conventional methods.

Broadcasting Services Over 5G NR Enabled Multi-Beam Non-Terrestrial Networks

The era of the fifth-generation (5G) New Radio (NR) technology has just begun, and its promises to substantially improve the system performance have turned into reality. The 5G NR enabled Non-Terrestrial Network (NTN) is almost upon us and will represent an effective solution to provide services anytime, anywhere, and over wider coverage areas. In this context, high throughput satellite systems with advanced multi-beam transmissions have gained significant attention owing to their ability to boost the system capacity through beam frequency re-use, increased user data rates, and system spectral efficiency. In this paper, we propose a novel radio resource management scheme, named Single-Frequency Multi-Beam Transmission (SF-MBT), for efficient delivery of the enhanced Mobile Broadband (eMBB) services over the 5G NR multi-beam NTN systems. The main thinking is to group beams into the dedicated Beam Areas, wherein a certain content flow is delivered via simultaneous multi-beam transmissions over the same radio resources without causing inter-beam interference. A simulation campaign is conducted under different scenarios to assess the effectiveness of the proposed SF-MBT algorithm as compared to the current schemes based on frequency reuse. System-wide performance is evaluated in terms of the aggregate data rate, mean throughput, and system spectral efficiency.