Radio Resource Management Research Articles

Comparison of energy conservation strategies for 5G NR RedCap service in industrial environment

The recently standardized reduced capability (RedCap) type of user equipment (UE) for 5G New Radio (NR) systems offers a useful option for energy-constrained devices. By utilizing a combination of discontinuous reception (DRX), wake-up signal (WUS), and radio resource management (RRM) relaxation functions, RedCap UE may provide excellent power efficiency for a large set of applications. In this study, we investigate power efficiency and battery lifetime for RedCap UEs for different types of applications and various combinations of energy conservation mechanisms. We utilize extended virtual reality (X-VR) and web browsing as applications of interest. To this aim, we develop a versatile mathematical framework representing the sought metrics as a function of time, accounting for specifics of millimeter wave (mmWave) propagation, micro- and macro-mobility of UEs, human body blockage, and type of application. Numerically, we show that higher micro- and macro-mobility speeds lead to worse power efficiency and the loss can be quite substantial amounting up to 30%. Antenna arrays with worse directivity show better performance in the presence of micro- and macro-mobilities when RRM Relaxation is utilized, with a difference between 15×15 and 4×4 arrays reaching 1.5 bit/J/KHz, which is approximately 40%. The energy conservation mechanisms produce no noticeable impact on the power efficiency and battery lifetime for rate-greedy applications such as X-VR. Low-data-rate applications with long pauses between transmission cycles, such as web browsing, may benefit from utilizing WUS and RRM Relaxation in addition to conventional DRX. However, their impact is rather small at a scale of 5%–10%.

A Cascaded Multi-Agent Reinforcement Learning-Based Resource Allocation for Cellular-V2X Vehicular Platooning Networks.

The platooning of cars and trucks is a pertinent approach for autonomous driving due to the effective utilization of roadways. The decreased gas consumption levels are an added merit owing to sustainability. Conventional platooning depended on Dedicated Short-Range Communication (DSRC)-based vehicle-to-vehicle communications. The computations were executed by the platoon members with their constrained capabilities. The advent of 5G has favored Intelligent Transportation Systems (ITS) to adopt Multi-access Edge Computing (MEC) in platooning paradigms by offloading the computational tasks to the edge server. In this research, vital parameters in vehicular platooning systems, viz. latency-sensitive radio resource management schemes, and Age of Information (AoI) are investigated. In addition, the delivery rates of Cooperative Awareness Messages (CAM) that ensure expeditious reception of safety-critical messages at the roadside units (RSU) are also examined. However, for latency-sensitive applications like vehicular networks, it is essential to address multiple and correlated objectives. To solve such objectives effectively and simultaneously, the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework necessitates a better and more sophisticated model to enhance its ability. In this paper, a novel Cascaded MADDPG framework, CMADDPG, is proposed to train cascaded target critics, which aims at achieving expected rewards through the collaborative conduct of agents. The estimation bias phenomenon, which hinders a system's overall performance, is vividly circumvented in this cascaded algorithm. Eventually, experimental analysis also demonstrates the potential of the proposed algorithm by evaluating the convergence factor, which stabilizes quickly with minimum distortions, and reliable CAM message dissemination with 99% probability. The average AoI quantity is maintained within the 5-10 ms range, guaranteeing better QoS. This technique has proven its robustness in decentralized resource allocation against channel uncertainties caused by higher mobility in the environment. Most importantly, the performance of the proposed algorithm remains unaffected by increasing platoon size and leading channel uncertainties.

Fairness-Aware Radio Resource Management in OFDMA-Based Wimax Networks with Comparison Between Three Downlink Scheduling Algorithms

Fairness-Aware Radio Resource Management in OFDMA-Based Wimax Networks with Comparison Between Three Downlink Scheduling Algorithms

Adaptive Radio Access Technology Selection Algorithm for Heterogeneous Wireless Networks

In Heterogeneous Wireless Networks (HWNs), Radio Access Technologies (RAT) can only consider the situation of one particular Radio Resource Management (RRM) which is unsuitable for managing multiple RATs. This study deployed an adaptive RAT selection scheme model to allocate users to the best RAT with the use of the cost function variable. The adopted model uses different input criteria like signal strength, network loads, service type and QoS requirement for the best access network selections. The adaptive RAT selection algorithm was executed in different service mixes (voice and data service) to access model suitability for users in Global System for Mobile Communications with Enhanced Data Rates for Global Evolution Radio Access Network (GERAN) and Universal Mobile Telecommunications System Radio Access Network (UTRAN). The proposed algorithm resulted in the call blocking probability reduction by 0.03 for GERAN and 0.14 for UTRAN as validated with the existing algorithm based on load balancing, service-based and priority-based. The drop implied an increased probability of ensuring session stability and high quality of the active service, leading to a high load distribution.

Dynamic SNR, Spectral Efficiency, and Rate Characterization in 5G/6G mmWave/sub-THz Systems with Macro- and Micro-Mobilities

The performance of 5G/6G cellular systems operating in millimeter wave (mmWave, 30–100 GHz) and sub-terahertz (sub-THz, 100–300 GHz) bands is conventionally assessed by utilizing the static distributions of user locations. The rationale is that the use of the beam tracking procedure allows for keeping the beams of a base station (BS) and user equipment (UE) aligned at all times. However, by introducing 3GPP Reduced Capability (RedCap) UEs utilizing the Radio Resource Management (RRM) Relaxation procedure, this may no longer be the case, as UEs are allowed to skip synchronization signal blocks (SSB) to improve energy efficiency. Thus, to characterize the performance of such UEs, methods explicitly accounting for UE mobility are needed. In this paper, we will utilize the tools of the stochastic geometry and random walk theory to derive signal-to-noise ratio (SNR), spectral efficiency, and rate as an explicit function of time by accounting for mmWave/sub-THZ specifics, including realistic directional antenna radiation patterns and micro- and macro-mobilities causing dynamic antenna misalignment. Different from other studies in the field that consider time-averaged performance measures, these metrics are obtained as an explicit function of time. Our numerical results illustrate that the macro-mobility specifies the overall trend of the time-dependent spectral efficiency, while local dynamics at 1–3 s scales are mainly governed by micro-mobility. The difference between spectral efficiency corresponding to perfectly synchronized UE and BS antennas and time-dependent spectral efficiency in a completely desynchronized system is rather negligible for realistic cell coverages and stays within approximately 5–10% for a wide range of system parameters. These conclusions are not affected by the utilized antenna array at the BS side. However, accounting for realistic radiation patterns is critical for a time-dependent performance analysis of 5G/6G mmWave/sub-THz systems.

Models for the optimization of radio signaling capabilities in 5G systems

Optimizing radio signaling capabilities in 5G systems involves various modeling techniques that can be systematized as follows - channel modeling, signal processing, optimization algorithms, queuing theory, machine learning, information theory, radio resource management, interference management, network topology and routing. The purpose of writing the article is to analyze mathematically the methods for optimizing radio signaling capabilities in 5G systems and to propose different models representing the behavior of the radio channel in variable environments and conditions. To consider algorithms and mathematical models for processing and analysis of radio signals received by gNB and user devices. To discuss mathematical models for analyzing and optimizing radio system performance in terms of delay, packet loss and throughput. Research and analysis of radio signaling capabilities in 5G systems, presentation of models related to the behavior of the radio channel in different environments and conditions, analysis of algorithms and mathematical models for processing and analysis of radio signals received from 5G base stations and end user devices can be noted as contributions to the development of the article.

Mobility Management in Next Generation Wireless Networks

Living in a modern society without smart devices is impossible now a days. Every sector related to human lifestyle is either smart or controlled devices which was rare a decade back. Expectations are not limited to network connection but extend to mobility as well. As a result, mobility management becomes an essential and challenging task to accomplish. The revolution in wireless technologies expects more scalability and flexibility in resource management. Handover is one of the vital parts of radio resource management. Execution with perfection and optimization of the handover technique increases the reliability of the system deployed to meet the requirement of high mobility. The cell became small as the wireless cell size adjusted with the revolution of relevant technologies like fifth generation (5G) and beyond. Traffic profile and its density are always in a growing trend. This pattern draws the attention of ultra-dense networks (UDN). The UDN of small cells requires an extra number of handovers with higher accuracy and less delay in execution. In this context, this paper proposed an algorithm where a cross-examination to reduce unnecessary handover that improves the handover performance in next-generation wireless networks.

ENGNN: A General Edge-Update Empowered GNN Architecture for Radio Resource Management in Wireless Networks

ENGNN: A General Edge-Update Empowered GNN Architecture for Radio Resource Management in Wireless Networks

AI-Based Radio Resource Management and Trajectory Design for IRS-UAV-Assisted PD-NOMA Communication

AI-Based Radio Resource Management and Trajectory Design for IRS-UAV-Assisted PD-NOMA Communication

Underlay Cognitive Radio Resource Management with Hybrid Meta-Loss Learning

Underlay Cognitive Radio Resource Management with Hybrid Meta-Loss Learning

Secure Radio Resource Management in Cloud Computing using CRN

Limited processing power in Cognitive Radio Networks (CRNs) creates challenges for secure radio resource management, particularly in dynamic spectrum access environments. This paper proposes a novel cloud-assisted CRN framework that addresses this limitation. By offloading spectrum sensing and allocation tasks to the cloud's vast computational resources, CR users can achieve a more secure and efficient spectrum utilization. The cloud platform prioritizes robust security protocols to safeguard spectrum information and communication, ensuring confidentiality, integrity, and user trust. This innovative approach optimizes spectrum usage, improves Quality of Service (QoS) for CR users, and strengthens the CRN's defense against unauthorized access and malicious attacks

Joint Task Offloading and Radio Resource Management in Stochastic MEC Systems

Joint Task Offloading and Radio Resource Management in Stochastic MEC Systems

Cost-efficient RAN slicing for service provisioning in 5G/B5G

Network slicing represents a substantial technological advance in 5G mobile network, greatly expanding the variety and manifoldness of network services to be supported. Additionally, 3GPP 5G New Radio (NR) has introduced novel features such as mixed numerology and mini-slots, which can be harnessed by network slicing to cater to the diverse requirements of 5G services. While however the co-existence of multiple network slices leads to a challenging resource allocation problem, these new features also severely complicate the management of radio resources. As a further point of attention, the virtualization of radio functions may exact a significant toll from the, already limited, computing resources at the network edge. It follows that a cost-efficient resource allocation across all the slices becomes crucial. In this paper, we address the above-mentioned issues by modeling a cost-efficient radio resource management in 5G NR featuring network slicing, named CERS, through a Mixed Integer Quadratically constrained Program (MIQCP). We maximize the profit of all slices simultaneously guaranteeing the target data rate and delay specified in the service level agreements (SLAs) fo the different traffic flows. To reduce the complexity of the MIQCP problem, we decompose it into two sub-problems, namely, the scheduling problem of enhanced Mobile Broadband (eMBB) user equipments (UEs) on a time-slot basis and of Ultra-Reliable Low Latency Communications (uRLLC) UEs on a mini-slot basis, while keeping the objective unchanged. To address the scheduling issue of eMBB UEs, we employ a heuristic technique, and, by leveraging the outcome of this heuristic, we derive an optimal solution for the problem of uRLLC UEs. The significance of the proposed approach over a baseline approach is evaluated through extensive numerical simulations in terms of the number of allocated uRLLC resource blocks (RBs) per mini-slot. We also assess our approach by measuring the impact of the uRLLC slice changes on the eMBB slice, and vice versa, including delay for uRLLC users and data rates for eMBB users.

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.

Digital twin enabled cellular network management and prediction

Digital twin (DT) technologies have been increasingly important and useful for wireless communications. In particular, to support soaring wireless traffic with limited frequency spectrum assigned to legacy cellular systems, efficient operation and management of wireless resources as well as preemptively prediction of future spectrum usage are crucially important. For such purpose, DT networks for current fourth-generation (4G) and fifth-generation (5G) networks are constructed in this paper, by simultaneously utilizing measurement data from user equipments (UEs) and geographical information and characteristics of 4G and 5G base stations (BSs) within a specific observation area. Representative case studies are provided to demonstrate the usefulness of DT enabled cellular network management and prediction. As a real or near real time DT application, the impact and benefit of dual connectivity and dynamic spectrum sharing between 4G and 5G networks are analyzed. As a non-real time DT application, long-term improvement of 5G networks such as densification of BSs, implementation of advanced multiple input and multiple output technologies, and assignment of additional spectrum are analyzed and compared.

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.

SliceOptiAI: Smart Resource Allocation for Seamless Network Slicing

An AI-driven model for resource allocation in network slicing is examined in this research paper. The model’s algorithm comprises three stages, each with its specific algorithm. Resource allocation begins with reservation, where the controller reserves minimum resources for each slice. The second stage is autonomous radio resource management, mainly focusing on AI model training and decision engines. The last stage is physical resource allocation, where resources are distributed to the slices and users. Simulations on MATLAB software indicated this model to be effective in enhancing resource allocation.

A Federated Learning-Based Resource Allocation Scheme for Relaying-Assisted Communications in Multicellular Next Generation Network Topologies

Growing and diverse user needs, along with the need for continuous access with minimal delay in densely populated machine-type networks, have led to a significant overhaul of modern mobile communication systems. Within this realm, the integration of advanced physical layer techniques such as relaying-assisted transmission in beyond fifth-generation (B5G) networks aims to not only enhance network performance but also extend coverage across multicellular orientations. However, in cellular environments, the increased interference levels and the complex channel representations introduce a notable rise in the computational complexity associated with radio resource management (RRM) tasks. Machine and deep learning (ML/DL) have been proposed as an efficient way to support the enhanced user demands in densely populated environments since ML/DL models can relax the traffic load that is associated with RRM tasks. There is, however, in these solutions the need for distributed execution of training tasks to accelerate the decision-making process in RRM tasks. For this purpose, federated learning (FL) schemes are considered a promising field of research for next-generation (NG) networks’ RRM. This paper proposes an FL approach to tackle the joint relay node (RN) selection and resource allocation problem subject to power management constraints when in B5G networks. The optimization objective of this approach is to jointly elevate energy (EE) and spectral efficiency (SE) levels. The performance of the proposed approach is evaluated for various relaying-assisted transmission topologies and through comparison with other state-of-the-art ones (both ML and non-ML). In particular, the total system energy efficiency (EE) and spectral efficiency (SE) can be improved by up to approximately 10–20% compared to a state-of-the-art centralized ML scheme. Moreover, achieved accuracy can be improved by up to 10% compared to state-of-the-art non-ML solutions, while training time is reduced by approximately 50%.

Intelligent wireless resource management in industrial camera systems: Reinforcement Learning-based AI-extension for efficient network utilization

In this paper, we present an innovative method for replacing wired communication within an operational agile production cell with wireless communication, using a minimal number of access points. Our approach utilizes Reinforcement Learning (RL) to optimize the layout and minimize the required number of access points. We introduce an Artificial Intelligence (AI) agent that facilitates cooperative interaction between cameras and access points, optimizing camera stream utilization and radio coverage. This solution benefits from the recent standardization of The fifth generation of mobile communications (5G) Network Resource Management (NRM) features of the User Equipment (UE) attached to the cameras. To accommodate the dynamic nature of the production cell, our optimization algorithms run during cell operation, necessitating extended training times.Our contributions extend beyond the state-of-the-art by offering a Digital Twin (DT) environment equipped with physics and radio propagation capabilities, fast raytracing for radio propagation modeling, curriculum-learning-based reinforcement learning, and a Physical Twin (PT) of the radio-connected camera with self-adjusting scene setup capabilities. This comprehensive proposal combines the efficiency of 5G radio utilization with precise modeling and high-resolution simulations, while also significantly improving training speed.In the Agile Robotics for Industrial Automation Competition (ARIAC) 2020 environment, our methods showed compelling results, with curriculum learning achieving a remarkable 3x speed-up compared to non-curriculum approaches. We also demonstrated that high Quality of Service (QoS) demands can be reduced to just 2% of total time while maintaining consistent productivity Key Performance Indicators (KPIs). Furthermore, we explored the integration of these methods into Internet of Things (IoT) devices with limited performance capabilities. In conclusion, our approach offers significant benefits and savings for IoT devices, networks, edge clouds, and industry players.

Machine learning-inspired intelligent optimization for smart radio resource management in satellite communication networks to improve quality of service

Satellite communication networks are seeing a significant surge in traffic requirements. Nevertheless, the rise in traffic requirements is inconsistent across the service region because of the unequal distribution of consumers and fluctuations in traffic requirements during the day. Variable payload designs solve this issue by enabling the uneven allocation of payload resources to match the traffic requirement of each beam. Optimization-based Radio Resource Management (ORRM) has its high substantial efficiency demonstrated computational difficulty hinders its real-world deployment. This work explores the structure, execution, and uses of Machine Learning (ML) for resource allocation in satellite systems. The primary emphasis is on two systems: one that offers power, capacity, and beamwidth adaptability and provides temporal flexibility via beam hopping. The research examines and contrasts several ML methods suggested for these structures. The research determines whether training must be done online or offline depending on the features and needs of each ML method. The study analyzes the most suitable system structure and the pros and cons of each strategy.