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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. [ABSTRACT FROM AUTHOR]

In this paper, the joint radio resource management issues in a cognitive radio network driven by radio frequency energy harvesting (CRN-RF-EH) functionalities are investigated. For the CRN-RF-EH, the cognitive radio (CR) node first harvests its required energy directly from the transmitter of spectrum licensed user for its data communication and consequently transmits its data on the licensed frequency of the legacy user using the underlay accessing technique. Thus, RF-EH is an exciting innovation for energizing low-powered next-generation wireless networks (NGWNs). Consequently, due to CRN-RF-EH'd low power limitations, the resource allocation for CRN-RF-EH has to be optimized considering the trade-off among spectral efficiency, energy efficiency, and RF energy supply. Equal allocation of transmission time and/or transmission power may not be efficient for CRN-RF-EH with limited transmission time and power resources. A joint optimal time and power allocation (OTPA) strategy for CRN-RF-EH is proposed to maximise the total spectral efficiency of the CRN-RF-EH. The coupled variables in the formulated joint resource allocation problems create a non-convex optimization problem formulation. For analytical tractability, the non-convex optimization formulation is initially converted to its equivalent standard convex optimization formulation using proper variables and next, it is then solved using the convex optimization technique. The CONOPT solver, a powerful optimization-solving tool for solving convex optimization problems, is utilized to resolve the equivalent standard convex optimization problem formulation. When compared with the baseline biased random time optimum power allocation (BRTOPA) scheme, numerical simulation results show that the OTPA strategy dramatically improves the total spectral efficiency performance. In a severe radio propagation environment with a path loss exponent (PLE) equal to 3.5 such as in urban areas and less severe radio propagation environment with a path loss exponent (PLE) equal to 2.0, such as in rural areas, the OTPA outperformed the BROTPA with a mean performance improvement of approximately 23.96% and 42.94%, respectively. [ABSTRACT FROM AUTHOR]

Next Generation Wireless Networks (NGWNs) have two main components: Network Slicing and Open Radio Access Networks (O-RAN). NS is needed to handle various Quality of Services (QoS). O-RAN adopts an open environment for network vendors and Mobile Network Operators (MNOs). In recent years, Deep Reinforcement Learning (DRL) approaches have been proposed to solve some key issues in NGWNs. The primary obstacles preventing the DRL deployment are being slowly converged and unstable. Additionally, these algorithms have enormous carbon emissions that negatively impact climate change. This paper tackles the dynamic allocation problem of O-RAN radio resources for better QoS, faster convergence, stability, lower energy and power consumption, and reduced carbon emissions. Firstly, we develop an agent with a newly designed latency-based reward function and a top-k filtration mechanism for actions. Then, we propose a policy Transfer Learning approach to accelerate agent convergence. We compared our model to another two models. [ABSTRACT FROM AUTHOR]

Industry applications that support massive mobile nodes are an important part of 5G Narrow Band Internet of Things (NB-IoT). The mobile node is abstracted as a terminal scene with many shapes and complex behaviors in the cellular internet of things, which makes the network need a better radio resource management strategy to meet more mobility, larger connection requests and higher access rate services. Therefore, the feature extraction of the node trajectories will greatly facilitate the development of optimal algorithms for radio resource management in the large mobile scene of 5G NB-IoT networks. This paper presents a feature mining of the real trajectories from the urban operating vehicles in the city of Shenzhen, China. Meanwhile, the generated trajectories by four common mobility models are also handled as a comparison. The self-similarity, hot-spots, long-tails and travel time are evaluated due to the widely recognized four features of human traces. Mining results show that the vehicles to serve the daily trip of human in the city always take a short travel and activate in several hot-spots with randomly roaming, while the vehicles to serve the goods are showing the opposite characteristics. Moreover, the trajectory generated by the models does not have the characteristics of short travel and roaming in multiple hot spots, nor does it have different movement trends in various time periods. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The enormous increase in mobile data traffic and the heterogeneity and stringent Quality of Service (QoS) requirements of different applications have placed a substantial strain on the underlying network infrastructure and represent a challenge for Cellular Vehicle-to-Everything (Cellular V2X). V2X communication is a key enabler for the realization of smart and connected transportation systems, offering a wide range of applications, such as enhanced road safety, traffic management, and autonomous driving. In this context, the best way to provide great flexibility and address both the present and future QoS concerns is to use intelligent Radio Resource Management (RRM) in general and creative packet scheduling in particular. The diverse QoS requirements of multiple application classes under dynamic network conditions present substantial challenges for conventional scheduling algorithms given the increasing demand for bandwidth-hungry applications. This study proposes a scheduling system for V2X communications based on traffic prioritization that manages QoS provisioning for different types of traffic considering channel quality, remaining payload, and delay. Simulation results demonstrate the highly promising performance of the proposed New Scheduling V2X Communications (NSVC) algorithm that leads to significantly lower latencies, as the average delay scheme did not exceed 0.001 ms for 100 users. [ABSTRACT FROM AUTHOR]

In this paper, the particle swarm optimization (PSO) method with dynamic generation of biasing factors is used to determine the optimal particle size, maximize cell spectral efficiency (CSE) and balance the load in 5G networks. This work studies two distinct interference scenarios: in the first approach, CSE is calculated with varying numbers of users, when different radio services are used by each tier (when several radio access technologies are used), and when interference is received by the consumer only from the same tier base stations (BSs). In the second approach, interference is created when all levels use the same radio services and interference from BSs belonging to the same tier and other tiers is received by the consumer. Simulation results show that the cell-less network performs better than the cellular network in terms of maximizing CSE and balancing the load.

Abstract This 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.

This 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. [ABSTRACT FROM AUTHOR]

RPL—Routing Protocol for Low-Power and Lossy Networks (usually pronounced "ripple")—is the de facto standard for IoT networks. However, it neglects to exploit IoT devices' full capacity to optimize their transmission power, mainly because it is quite challenging to do so in parallel with the routing strategy, given the dynamic nature of wireless links and the typically constrained resources of IoT devices. Adapting the transmission power requires dynamically assessing many parameters, such as the probability of packet collisions, energy consumption, the number of hops, and interference. This paper introduces Adaptive Control of Transmission Power for RPL (ACTOR) for the dynamic optimization of transmission power. ACTOR aims to improve throughput in dense networks by passively exploring different transmission power levels. The classic solutions of bandit theory, including the Upper Confidence Bound (UCB) and Discounted UCB, accelerate the convergence of the exploration and guarantee its optimality. ACTOR is also enhanced via mechanisms to blacklist undesirable transmission power levels and stabilize the topology of parent–child negotiations. The results of the experiments conducted on our 40-node, 12-node testbed demonstrate that ACTOR achieves a higher packet delivery ratio by almost 20%, reduces the transmission power of nodes by up to 10 dBm, and maintains a stable topology with significantly fewer parent switches compared to the standard RPL and the selected benchmarks. These findings are consistent with simulations conducted across 7 different scenarios, where improvements in end-to-end delay, packet delivery, and energy consumption were observed by up to 50%. [ABSTRACT FROM AUTHOR]

The paradigm of the Next Generation cellular network (6G) and beyond is machine-type communications (MTCs), where numerous Internet of Things (IoT) devices operate autonomously without human intervention over wireless channels. IoT's autonomous and energy-intensive characteristics highlight effective energy efficiency (EEE) as a crucial key performance indicator (KPI) of 6G. However, there is a lack of investigation on the EEE of random arrival traffic, which is the underlying platform for MTCs. In this work, we explore the distinct characteristics of F -composite fading channels, which specify the combined impact of multipath fading and shadowing. Furthermore, we evaluate the EEE over such fading under a finite blocklength regime and QoS constraints where IoT applications generate constant and sporadic traffic. We consider a point-to-point buffer-aided communication system model, where (1) an uplink transmission under a finite blocklength regime is examined; (2) we make realistic assumptions regarding the perfect channel state information (CSI) available at the receiver, and the channel is characterized by the F -composite fading model; and (3) due to its effectiveness and tractability, application data are found to have an average arrival rate calculated using Markovian sources models. To this end, we derive an exact closed-form expression for outage probability and the effective rate, which provides an accurate approximation for our analysis. Moreover, we determine the arrival and required service rates that satisfy the QoS constraints by applying effective bandwidth and capacity theories. The EEE is shown to be quasiconcave, with a trade-off between the transmit power and the rate for maximising the EEE. Measuring the impact of transmission power or rate individually is quite complex, but this complexity is further intensified when both variables are considered simultaneously. Thus, we formulate power allocation (PA) and rate allocation (RA) optimisation problems individually and jointly to maximise the EEE under a QoS constraint and solve such a problem numerically through a particle swarm optimization (PSO) algorithm. Finally, we examine the EEE performance in the context of line-of-sight and shadowing parameters. [ABSTRACT FROM AUTHOR]

A hybrid wireless-wireline access network (HWWAN) is an integrated network infrastructure that combines wireless and wired technologies to offer users seamless and reliable connectivity. However, the existing resource management schemes for HWWANs have largely overlooked the stringent quality of service (QoS) requirements of latency-critical users. Moreover, the surge in the demands for ultra-broadband applications with extremely strict real-time requirements has resulted in an explosive growth of data traffic, rendering network resource allocation challenging for HWWANs. This paper proposes an efficient radio resource allocation scheme for HWWANs that effectively addresses the requirements of both latency-critical user equipment (UEs) and hybrid customer premises equipment (HCPEs) with throughput gap (TG) requirements. Specifically, we formulate a resource optimization problem to maximize the effective capacity (EC) of UEs while considering the TG requirements of HCPEs, and the availability of physical resource blocks (PRBs) at the base station. The problem is solved using convex optimization. Simulation results indicate the efficacy of the proposed scheme in improving the percentages of satisfied UEs and HCPEs, the sum EC of UEs, and the aggregate throughput of all the HCPEs.

Flexible numerology in 5G New Radio (NR) helps to reduce End-to-End (E2E) delay by supporting different slot duration options. But, higher numerologies can increase signaling overhead (in terms of Scheduling Requests (SRs) in uplink (UL)) for applications which are UL heavy in nature; as a consequence, the E2E delay of such applications could be impacted. This sudden increase in SRs in UL happens when the timing of the Constant-Bit-Rate (CBR) UL transmission is not properly aligned with the slot duration of the numerology employed and aggressive emptying of the buffered data at the User Equipments (UEs) by the UL scheduler. In this work, to minimize SRs while meeting the latency requirements of the UL heavy applications like vehicular applications, we propose a novel UL MAC scheduling algorithm named RETALIN. The proposed RETALIN is a queue-aware radio resource scheduler that estimates the probability of SR with respect to each UE (vehicle) and anatomizes the transient queue behavior by controlling backlogs of the UEs in order to reduce SRs and thereby mitigating an adverse impact of numerology in the UL traffic while keeping a bound on E2E delay of the vehicular applications. Extensive NS-3 simulations are performed to evaluate the performance of the proposed RETALIN scheme with mobility traces taken from Simulation of Urban Mobility (SUMO) using OpenStreetMap. Simulation results show that the proposed RETALIN scheduling scheme significantly reduces link delay for different numerologies when compared with a state-of-art QoS scheduler. Further, RETALIN increases Packet Delivery Ratio (PDR) while reducing signaling overhead due to SRs for UL scheduling. In the case of a High Definition Map (HD Map) vehicular application, RETALIN assists in increasing the Offloading Success Rate (OSR) over the QoS scheduler.

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%. [ABSTRACT FROM AUTHOR]

With increasing evolution of applications and services in internet-of-things (IoT), there is an increasing concern of offering superior quality of service to its ever-increasing user base. This demand can be fulfilled by harnessing the potential of cognitive radio network (CRN) where better accessibility of services and resources can be achieved. However, existing review of literature shows that there are still open-end issues in this regard and hence, the proposed system offers a solution to address this problem. This paper presents a model which is capable of performing an optimization of resources when CRN is integrated in IoT using five generation (5G) network. The implementation uses analytical modeling to frame up the process of topology construction for IoT and optimizing the resources by introducing a simplified data transmission mechanism in IoT environment. The study outcome shows proposed system to excel better performance with respect to throughput and response time in comparison to existing schemes. [ABSTRACT FROM AUTHOR]

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.

Recent times have seen a significant rise in interest from mobile operators, vendors, and research projects toward achieving more energy-efficient and sustainable networks. Not surprisingly, it comes at a time when higher traffic demand and more stringent and diverse network requirements result in diminishing benefits for operators using complex AI-driven network optimization solutions. In this paper, we propose the idea of tower companies that facilitate radio access network (RAN) infrastructure sharing between operators and evaluate the additional energy savings obtained in this process. In particular, we focus on the RAN-as-a-Service (RANaaS) implementation, wherein each operator leases and controls an independent logical RAN instance running on the shared infrastructure. We show how an AI system can assist operators in optimizing their share of resources under multiple constraints. This paper aims to provide a vision, a quantitative and qualitative analysis of the RANaaS paradigm, and its benefits in terms of energy efficiency. Through simulations, we show the possibility to achieve up to 75 percent energy savings per operator over 24 h compared to the scenario where none of the energy-saving features are activated. This is an additional 55 percent energy savings from sharing the RAN infrastructure compared to the baseline scenario where the operators use independent hardware.

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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. [ABSTRACT FROM AUTHOR]

Machine learning offers advanced tools for efficient management of radio resources in modern wireless networks. In this study, we leverage a multi-agent deep reinforcement learning (DRL) approach, specifically the Parameterized Deep Q-Network (DQN), to address the challenging problem of power allocation and user association in massive multiple-input multiple-output (M-MIMO) communication networks. Our approach tackles a multi-objective optimization problem aiming to maximize network utility while meeting stringent quality of service requirements in M-MIMO networks. To address the non-convex and nonlinear nature of this problem, we introduce a novel multi-agent DQN framework. This framework defines a large action space, state space, and reward functions, enabling us to learn a near-optimal policy. Simulation results demonstrate the superiority of our Parameterized Deep DQN (PD-DQN) approach when compared to traditional DQN and RL methods. Specifically, we show that our approach outperforms traditional DQN methods in terms of convergence speed and final performance. Additionally, our approach shows 72.2% and 108.5% improvement over DQN methods and the RL method, respectively, in handling large-scale multi-agent problems in M-MIMO networks. [ABSTRACT FROM AUTHOR]

Architectural, cognitive, and service layers are the three components that come together to form the system as a whole. The data that is acquired by the instruments at the application layer is processed by the system that is in charge of the network. The conceptual layer, which is where edge sensors are put, is responsible for managing radio resource management and intersensor connections in order to solve the issues raised by the physical layer about increasing power consumption and increased latency. In response to the processed data provided by the logical layer, the application layer will make judgements. The key objective is to lower prices so that they are more accessible to regular people. Patients will not only be able to maintain their financial stability, but they will also have easy access to private therapy. This research presents a solution based on the Internet of Things (IoT), which will simplify the usage of a generally complicated medical device while allowing you to do it at a reasonable cost and in the comfort of your own home. The Elephant Herding Optimizations using Convolutional Neural Networks (CNNs) method is discussed here in order to differentiate between healthy and unhealthy patterns of behavior. The scoring function, also known as fuzzy logic, is used in order to arrive at a conclusion on the severity of the irregularity. In the end, tests were carried out to see how well the recommended work fared in contrast to the existing approaches in terms of specificity, recall, f1-score, and ROC curve. These metrics were examined. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is a relevant technology for realizing the primary goals of nextgeneration wireless networks, such as high connectivity and stability. This paper focuses on the foundational principles of Power Domain NOMA (PD-NOMA), a promising technique for meeting the needs of fifth Generation (5G) in real-world applications. PD-NOMA offers flexibility in allocating radio resources to enhance user access performance. By enabling several users to utilize the same radio resources, PD-NOMA achieves improved spectrum efficiency. This paper considers the practical aspects of PD-NOMA system design, exploring various network scenarios. This paper primarily focuses on PD-NOMA that uses the superposition coding (SC) at the transmitter side in addition to the successive interference cancellation (SIC) at the receiver side. The paper also provides a theoretical formulation and solutions for downlink PD-NOMA and addresses potential areas for future research. Finally, the paper concludes by summarizing the key findings and implications of the study. [ABSTRACT FROM AUTHOR]

Fifth-generation (5G) networks require efficient radio resource management (RRM) which should dynamically adapt to the current network load and user needs. Monitoring and forecasting network performance requirements and metrics helps with this task. One of the parameters that highly influences radio resource management is the profile of user traffic generated by various 5G applications. Forecasting such mobile network profiles helps with numerous RRM tasks such as network slicing and load balancing. In this paper, we analyze a dataset from a mobile network operator in Portugal that contains information about volumes of traffic in download and upload directions in one-hour time slots. We apply two statistical models for forecasting download and upload traffic profiles, namely, seasonal autoregressive integrated moving average (SARIMA) and Holt-Winters models. We demonstrate that both models are suitable for forecasting mobile network traffic. Nevertheless, the SARIMA model is more appropriate for download traffic (e.g., MAPE [mean absolute percentage error] of 11.2% vs. 15% for Holt-Winters), while the Holt-Winters model is better suited for upload traffic (e.g., MAPE of 4.17% vs. 9.9% for SARIMA and Holt-Winters, respectively). [ABSTRACT FROM AUTHOR]

Abstract The Deployment of new techniques to accommodate high data throughput, significant reliability, and quality of service (QoS) delivery prompts the use of 4G and 5G networks in several applications. In these networks, Vehicle‐to‐Everything (V2X) communications authorised by device‐to‐device cellular links (D2D) have gained momentum because of their potential to improve road safety, efficiency, and convenience. This improvement is to be provided by different types of V2X communications, including Vehicle‐to‐Pedestrian (V2P), Vehicle‐to‐Vehicle (V2V), Vehicle‐to‐Infrastructure (V2I), and Vehicle‐to‐Network (V2N) communications. However, considering several limiting factors such as mobility and increasing number of vehicles, efficiency of using wireless resources is a challenging task. Indeed, different research works on V2X radio resource allocation have been studied and evaluated with respect to several criteria. In this study, a survey of recent works but in a different way synthesising radio resource allocation methods is presented. Resource allocation classification into different categories is proposed. This classification is related to three main factors. The first takes into account the selection mode factor. It highlights the use of different approaches in each of the centralised, distributed, and mixed modes. The second one takes into account the considered performance criteria, including capacity, priority, delay etc. The last one focuses on the methods and scenarios used to evaluate a resource allocation approach.

The internet of robotic things (IoRT) is a modern as well as fast-evolving technology employed in abundant socio-economical aspects which connect user equipment (UE) for communication and data transfer among each other. For ensuring the quality of service (QoS) in IoRT applications, radio resources, for example, transmitting power allocation (PA), interference management, throughput maximization etc., should be efficiently employed and allocated among UE. Traditionally, resource allocation has been formulated using optimization problems, which are then solved using mathematical computer techniques. However, those optimization problems are generally nonconvex as well as nondeterministic polynomial-time hardness (NP-hard). In this paper, one of the most crucial challenges in radio resource management is the emitting power of an antenna called PA, considering that the interfering multiple access channel (IMAC) has been considered. In addition, UE has a natural movement behavior that directly impacts the channel condition between remote radio head (RRH) and UE. Additionally, we have considered two well-known UE mobility models i) random walk and ii) modified Gauss-Markov (GM). As a result, the simulation environment is more realistic and complex. A data-driven as well as model-free continuous action based deep reinforcement learning algorithm called twin delayed deep deterministic policy gradient (TD3) has been proposed that is the combination of policy gradient, actor-critics, as well as double deep Q-learning (DDQL). It optimizes the PA for i) stationary UE, ii) the UE movements according to random walk model, and ii) the UE movement based on the modified GM model. Simulation results show that the proposed TD3 method outperforms model-based techniques like weighted MMSE (WMMSE) and fractional programming (FP) as well as model-free algorithms, for example, deep Q network (DQN) and DDPG in terms of average sum-rate performance.

The paradigm of the Next Generation cellular network (6G) and beyond is machine-type communications (MTCs), where numerous Internet of Things (IoT) devices operate autonomously without human intervention over wireless channels. IoT’s autonomous and energy-intensive characteristics highlight effective energy efficiency (EEE) as a crucial key performance indicator (KPI) of 6G. However, there is a lack of investigation on the EEE of random arrival traffic, which is the underlying platform for MTCs. In this work, we explore the distinct characteristics of F-composite fading channels, which specify the combined impact of multipath fading and shadowing. Furthermore, we evaluate the EEE over such fading under a finite blocklength regime and QoS constraints where IoT applications generate constant and sporadic traffic. We consider a point-to-point buffer-aided communication system model, where (1) an uplink transmission under a finite blocklength regime is examined; (2) we make realistic assumptions regarding the perfect channel state information (CSI) available at the receiver, and the channel is characterized by the F-composite fading model; and (3) due to its effectiveness and tractability, application data are found to have an average arrival rate calculated using Markovian sources models. To this end, we derive an exact closed-form expression for outage probability and the effective rate, which provides an accurate approximation for our analysis. Moreover, we determine the arrival and required service rates that satisfy the QoS constraints by applying effective bandwidth and capacity theories. The EEE is shown to be quasiconcave, with a trade-off between the transmit power and the rate for maximising the EEE. Measuring the impact of transmission power or rate individually is quite complex, but this complexity is further intensified when both variables are considered simultaneously. Thus, we formulate power allocation (PA) and rate allocation (RA) optimisation problems individually and jointly to maximise the EEE under a QoS constraint and solve such a problem numerically through a particle swarm optimization (PSO) algorithm. Finally, we examine the EEE performance in the context of line-of-sight and shadowing parameters.

RPL—Routing Protocol for Low-Power and Lossy Networks (usually pronounced “ripple”)—is the de facto standard for IoT networks. However, it neglects to exploit IoT devices’ full capacity to optimize their transmission power, mainly because it is quite challenging to do so in parallel with the routing strategy, given the dynamic nature of wireless links and the typically constrained resources of IoT devices. Adapting the transmission power requires dynamically assessing many parameters, such as the probability of packet collisions, energy consumption, the number of hops, and interference. This paper introduces Adaptive Control of Transmission Power for RPL (ACTOR) for the dynamic optimization of transmission power. ACTOR aims to improve throughput in dense networks by passively exploring different transmission power levels. The classic solutions of bandit theory, including the Upper Confidence Bound (UCB) and Discounted UCB, accelerate the convergence of the exploration and guarantee its optimality. ACTOR is also enhanced via mechanisms to blacklist undesirable transmission power levels and stabilize the topology of parent–child negotiations. The results of the experiments conducted on our 40-node, 12-node testbed demonstrate that ACTOR achieves a higher packet delivery ratio by almost 20%, reduces the transmission power of nodes by up to 10 dBm, and maintains a stable topology with significantly fewer parent switches compared to the standard RPL and the selected benchmarks. These findings are consistent with simulations conducted across 7 different scenarios, where improvements in end-to-end delay, packet delivery, and energy consumption were observed by up to 50%.

In the field of communication networks, the management of resource allocation is a crucial research area. To achieve efficient radio resource management, mobile telecommunications operators utilize opportunistic scheduling algorithms. This study presents a novel opportunistic scheduler, called MMR (Min–Max Rate), which aims to ensure equal treatment of users, good quality of service, and maximum overall system throughput simultaneously and improve network throughput and fairness by up to 70% compared to traditional and classical scheduling algorithms. To evaluate the performance of MMR, we compared it with other reference algorithms such as Round Robin, MaxSNR, and Proportional Fair, known for their scheduling efficiency and robustness. The simulation results obtained by the MATLAB software and analysis focused on fairness criteria and system throughput for different user groups, and we found that our proposed algorithm outperforms the existing algorithms. [ABSTRACT FROM AUTHOR]

Hybrid wireless–wireline access networks (HWWANs), which allow coordinated and simultaneous use of wireless and wireline interfaces, are a promising network architecture for achieving more coverage, better reliability, and higher throughput, particularly for underserved wireline networks. Existing resource management schemes lack the flexibility and scalability to cope with the dynamic traffic fluctuations from users with diverse quality of service (QoS) requirements. Moreover, these schemes lack resource isolation capability and are incompatible with the HWWAN architecture. This paper proposes a hierarchical radio resource allocation framework for a network slicing-enabled HWWAN, which consists of an upper inter-slice layer and a lower intra-slice layer. Firstly, an inter-slice radio resource allocation problem is formulated to maximize the aggregate network throughput by allocating a pool of resource blocks among the slices, subject to slice-specific QoS requirements. An efficient, low-complexity, demand-oriented greedy resource allocation algorithm is then developed to solve this problem. Secondly, an intra-slice radio resource allocation problem is formulated to maximize the total throughput of individual slices by reallocating the resource blocks secured by individual slices in the upper inter-slice layer to their respective users, subject to their QoS requirements. This problem is decomposed into two smaller sub-problems and solved using convex optimization. Simulation results show that the proposed scheme outperforms other baseline schemes in terms of average throughput, fairness, and customer satisfaction rate, thereby providing service providers with a flexible and efficient resource allocation solution in the era of next-generation fixed mobile convergence. [ABSTRACT FROM AUTHOR]

Wireless networks are burdened with the demand of providing higher data rates for the user devices connected to the wireless networks. This demand presents a huge challenge for the traditional radio resource constrained wireless communication networks. The cognitive radios and cooperative communication techniques have emerged to offer promising solutions to the burden of higher data rate demands. This paper addresses the problem of radio resource management in cooperative cognitive radio relay networks (CCRRN). In resource-constrained networks, a uniform bandwidth allocation scheme may not be efficient for the connected wireless multi-users. In this paper, a convex optimization analytical framework is used to investigate the problem of radio resource management in CCRRN. A joint optimal resource allocation (JORA) for power and bandwidth allocation strategy for the maximization of the total throughput capacity of all users in the networks is proposed. The formulated radio resource management problem is proved to be a standard convex optimization problem. The convex optimization problem is solved efficiently using the SNOPT solver package. The SNOPT solver is implemented in the MATLAB/TOMLAB software environment. The numerical results showed that the JORA scheme significantly outperformed the equal bandwidth (EQBW) scheme in terms of throughput capacity. [ABSTRACT FROM AUTHOR]

Recent times have seen a significant rise in interest from mobile operators, vendors, and research projects toward achieving more energy-efficient and sustainable networks. Not surprisingly, it comes at a time when higher traffic demand and more stringent and diverse network requirements result in diminishing benefits for operators using complex AI-driven network optimization solutions. In this paper, we propose the idea of tower companies that facilitate radio access network (RAN) infrastructure sharing between operators and evaluate the additional energy savings obtained in this process. In particular, we focus on the RAN-as-a-Service (RANaaS) implementation, wherein each operator leases and controls an independent logical RAN instance running on the shared infrastructure. We show how an AI system can assist operators in optimizing their share of resources under multiple constraints. This paper aims to provide a vision, a quantitative and qualitative analysis of the RANaaS paradigm, and its benefits in terms of energy efficiency. Through simulations, we show the possibility to achieve up to 75 percent energy savings per operator over 24 h compared to the scenario where none of the energy-saving features are activated. This is an additional 55 percent energy savings from sharing the RAN infrastructure compared to the baseline scenario where the operators use independent hardware. [ABSTRACT FROM AUTHOR]

Decentralized vehicle-to-everything (V2X) networks (i.e., C-V2X Mode-4 and NR-V2X Mode-2) utilize sensing-based semi-persistent scheduling (SPS) where vehicles sense and reserve suitable radio resources for Basic Safety Message (BSM) transmissions at prespecified periodic intervals termed as Resource Reservation Interval (RRI). Vehicles rely on these received periodic BSMs to localize nearby (transmitting) vehicles and infrastructure, referred to as cooperative awareness. Cooperative awareness enables line of sight and non-line of sight localization, extending a vehicle’s sensing and perception range. In this work, we first show that under high vehicle density scenarios, existing SPS (with prespecified RRIs) suffer from poor cooperative awareness, quantified as tracking error. Tracking error is defined as the difference between a vehicle’s true and estimated location as measured by its neighbors. To address the issues of static RRI SPS and improve cooperative awareness, we propose two novel RRI selection algorithms– namely, Channel-aware RRI (Ch-RRI) selection and Age of Information (AoI)-aware RRI (AoI-RRI) selection. Ch-RRI dynamically selects an RRI based on channel resource availability depending upon the (sparse or dense) vehicle densities, whereas AoI-RRI utilizes a novel information freshness metric, called Age of Information (AoI) to select a suitable RRI. Both adaptive RRI algorithms use SPS for selecting transmission opportunities for timely BSM transmissions at the chosen RRI. System-level simulations demonstrate that both proposed schemes outperform the SPS with fixed RRI in terms of improved cooperative awareness. Furthermore, AoI-RRI SPS outperforms Ch-RRI SPS in high densities, whereas Ch-RRI SPS is slightly better than AoI-RRI SPS in low densities.

Future factories will rely on highly reliable wireless communication among the plenty of devices and the network. The 5G networks enable ultra-reliable low-latency communication (URLLC) service to address the reliability and latency requirements in factories, where device-to-device (D2D) connections can provide additional means for improving communication reliability. To this aim, we have proposed a sidelink (SL)-assisted cooperative retransmissions (CoRe) scheme in our previous work, where retransmissions via SL for unsuccessful downlink (DL) transmissions are used to improve communication reliability under strict latency constraints. In this paper, we evaluate the CoRe scheme for a realistic factory scenario using system-level simulations, where we consider interference coordination and an optimal power control (PC) scheme. Inspired by the outcome of small transmit powers needed for the SL-assisted retransmissions, we propose a novel resource management scheme named “flipped-underlay”, which is realized by underlay communication. While in conventional underlay, a D2D communication with small transmit power is assigned to resources already allocated for uplink (UL) communication and thus underlaid, the D2D resources are allocated firstly in our scheme, and hence called flipped-underlay. Results demonstrate that the gains from CoRe scheme, are three-fold: showing a significant reduction in the total number of SL-assisted retransmissions compared to conventional retransmissions via DL while maintaining the desired reliability and latency performance, reduced transmit power consumption by virtue of the optimal power allocation for retransmissions, and finally yet importantly, reuse of resources in our new flipped-underlay resource allocation (FURA) algorithm substantially reduces the total amount of resources occupied by the system.

This paper demonstrates the use of deep learning and time series data generated from user equipment (UE) beam measurements and positions collected by the base station (BS) to enable handoffs between beams that belong to the same or different BSs. We propose the use of long short-term memory (LSTM) recurrent neural networks with three different approaches and vary the number of lookbacks of the beam measurements to study the performance of the prediction used for the proactive beam handoff. Simulations show that while UE positions can improve the prediction performance, it is only up to a certain point. At a sufficiently large number of lookbacks, the UE positions become irrelevant to the prediction accuracy since the LSTMs are able to learn the optimal beam based on implicitly defined positions from the time-defined trajectories.

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 $\epsilon -$ 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.

With the rapid development of wireless technologies, wireless access networks have entered their Fifth-Generation (5G) system phase. The heterogeneous and complex nature of a 5G system, with its numerous technological scenarios, poses significant challenges to wireless resource management, making radio resource optimization an important aspect of Device-to-Device (D2D) communication in such systems. Cellular D2D communication can improve spectrum efficiency, increase system capacity, and reduce base station communication burdens by sharing authorized cell resources; however, can also cause serious interference. Therefore, research focusing on reducing this interference by optimizing the configuration of shared cellular resources has also grown in importance. This paper proposes a novel algorithm to address the problems of co-channel interference and energy efficiency optimization in a long-term evolution network. The proposed algorithm uses the fuzzy clustering method, which employs minimum outage probability to divide D2D users into several groups in order to improve system throughput and reduce interference between users. An efficient power control algorithm based on game theory is also proposed to optimize user transmission power within each group and thereby improve user energy efficiency. Simulation results show that these proposed algorithms can effectively improve system throughput, reduce co-channel interference, and enhance energy efficiency.

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

Summary: Internet of Vehicles (IoVs), the emerging trend of Internet of Things (IoTs), has undoubtedly become a promising trend to improve communication among vehicles on the roads. Vehicle‐to‐everything (V2X) communication that is based on 5G technology enables vehicle users to communicate and collaborate with each other to enhance road traffic efficiency and safety. Owing to the increased traffic load and restricted resources of existing network substructure, a channel that responds to the latency and reliability needs of V2X communication must be designed. Thereby, several intelligent spectrum allocation techniques have been proposed to improve the system's overall effectiveness. In this paper, we discuss the spectrum sharing issue of V2X communication in Device‐to‐Device (D2D)‐based cellular networks. We propose a new multi‐layer neural network (MLNN)‐based Resource Allocation and sharing approach (MNNRA) for D2D‐based V2X communications. According to the main advantages of MLNN, the proposed algorithm takes several profits by improving system performance while reducing computational complexity. Numerical analysis is presented to approve the effectiveness of our proposed solution in terms of network sum rate, packet reception ratio, resource utilization ratio, and time complexity. [ABSTRACT FROM AUTHOR]

Keywords: antennas; bandwidth allocation; microwave propagation EN antennas bandwidth allocation microwave propagation 415 418 4 06/01/23 20230501 NES 230501 INTRODUCTION A revolutionary enhancement of transmission data rates is required to meet the challenge of an exponential capacity increase for the sixth generation (6G) and beyond mobile communication and sensing systems in the era of 2030 and beyond. They can be clustered into three main categories, namely antennas at the THz band, antennas at the mmWave band, and propagation at mmWave and THz bands. SUMMARY All of the papers selected for this Special Issue provide the first-hand information and the state of the art of advanced antenna technologies and propagation modelling at mmWave, sub-mmWave, and THz bands. [Extracted from the article]

In millimetre wave (mmWave) frequency band, radio resource management (RRM) performance is characterised in plane wave conditions using radiated over‐the‐air test method, which is a challenging combination of protocol and radio frequency performance measurement. This article presents a novel design of multiple lens‐based compact antenna test ranges (CATRs) to simulate multiple angles of arrival from different base‐stations in RRM measurement. Only four lens‐based CATRs are proposed to create plane wave conditions at the device under test with six different angles of arrival and capable of accurately constructing the wireless propagation condition and minimising the diffraction and scattering between adjacent CATRs. The lens‐based CATR is the key element in the RRM measurement system, which is composed of a metasurface lens for phase alignment and a 2 × 2 feed array antenna for the emission of electromagnetic signals. The function and capability of the proposed lens‐based CATR is validated through simulation and measurement, that the worst‐case amplitude variation in the quiet zone region is lower than ±0.8 dB at 26 GHz, and phase variation is lower than ±7.5° in the designed quiet zone, which could be used in the fifth‐generation mmWave RRM measurement. [ABSTRACT FROM AUTHOR]

The need for a strong system to access radio resources demands a change in operating frequency in wireless networks as a part of Radio Resource Management (RRM). In the fifth-generation (5G) wireless networks, the capacity of the system is expected to be enhanced by Device-to-Device (D2D) communication. The cooperation and Resources Allocation (RA) in the development of Internet of Things (IoT) enabled 5G wireless networks are investigated in this paper. Developing radio RA methods for D2D communication while not affecting any Mobile Users' (MU) communication is the main challenge of this research. Distinct performance goals such as practising equality in the rates of user data, increasing Network Throughput (NT), and reducing End-to-End Delay (EED) are achieved by RA. The study undertaken on optimising performance for various wireless networks is focused on in this research work. Proposing a polynomialtime Proportional Fair Resource Allocation Method (PFRAM), which considers the MU's rate requirements, is the prime objective of this paper. Any Resource Allocation Method (RAM) can be used by the proposed method for MU, and the time and differing location channel conditions are the factors to be adapted with. Allotting more than one resource block is allowed by our PFRAM to a D2D pair. The automatic maintenance of battery-less IoT wireless devices' energy level is done potentially using an Extensible Energy Management System (EEMS). Finally, the device's Node Transmission Power (NTP) can be managed using an Energy-Saving Algorithm (ESA) designed in this work for Node Uplink Data Transmission (NUDT). The trade-off between the Packet Loss Rate (PLR) and NTP is balanced by the algorithm. The cost of NUDT's average Energy Consumption (EC) is reduced by locating the optical NTP. In order to free much bandwidth for wireless information, NUDT conserves the harvested energy for minimising Radio Frequency (RF) Energy Transmission (ET). MATLAB simulations are used to assess the proposed EEMS. The IoT device's NTP is managed using ESA designed for NUDT. The significant minimisation of channel hopping EED and the selection of the premium quality communication channel by the proposed framework are indications of the simulation results. 67.19% is the bandwidth to transmit DPs with the Bandwidth Allocation Algorithm (BAA), which is greater than the cases in its absence. [ABSTRACT FROM AUTHOR]

Summary: In Release 17, 3GPP introduced adaptations and enhancements to the 5G new radio (NR) specification to support non‐terrestrial networks (NTNs) operation. The main challenges were due to long propagation delays, especially in GEO deployments, and the movement of the satellites in LEO deployments. In this paper, we give an overview of the protocol adaptations to support NTNs. The main user plane protocol adaptations include changes to random access and hybrid automatic repeat request to due long propagation delays. The control plane protocol adaptations include a variety of mobility related enhancements for user equipment. [ABSTRACT FROM AUTHOR]

The Deployment of new techniques to accommodate high data throughput, significant reliability, and quality of service (QoS) delivery prompts the use of 4G and 5G networks in several applications. In these networks, Vehicle‐to‐Everything (V2X) communications authorised by device‐to‐device cellular links (D2D) have gained momentum because of their potential to improve road safety, efficiency, and convenience. This improvement is to be provided by different types of V2X communications, including Vehicle‐to‐Pedestrian (V2P), Vehicle‐to‐Vehicle (V2V), Vehicle‐to‐Infrastructure (V2I), and Vehicle‐to‐Network (V2N) communications. However, considering several limiting factors such as mobility and increasing number of vehicles, efficiency of using wireless resources is a challenging task. Indeed, different research works on V2X radio resource allocation have been studied and evaluated with respect to several criteria. In this study, a survey of recent works but in a different way synthesising radio resource allocation methods is presented. Resource allocation classification into different categories is proposed. This classification is related to three main factors. The first takes into account the selection mode factor. It highlights the use of different approaches in each of the centralised, distributed, and mixed modes. The second one takes into account the considered performance criteria, including capacity, priority, delay etc. The last one focuses on the methods and scenarios used to evaluate a resource allocation approach. [ABSTRACT FROM AUTHOR]

Radio Resource Management (RRM) is the key component that influences the Quality of Service (QoS) in emerging mobile systems. The harsh requirements of recent multimedia applications press on the behavior of the resource allocation model at the Medium Access Control (MAC). This, under certain conditions, deteriorates awareness of the scheduler in satisfying QoS characteristics for the diverse attending traffic flows. The trade-off between QoS metrics may be a viable remedy to this problem. However, if the tradeoff is not meticulously addressed, it definitely increases the delay beyond the allowed threshold and plummets the data rates of the involved flows. In this article, a Delay-oriented Resource Allocation (DoRA) scheduler is proposed for the downlink channel to transmit multimedia applications. The main aim of DoRA is to schedule flows of different volumes with guaranteed low delay values and acceptable throughput under harsh network conditions (i.e., high traffic load and mobility). An efficient priority weight function is formulated based on a delay-oriented rule considering the buffer delay of the diverse flows. Eventually, a greedy algorithm assigns channel resources to the prioritized flows with high weights. DoRA is compared to reference schedulers using system-level simulation over two scenarios. The performance results indicate that DoRA maintains a low end-to-end delay with robust throughput and efficient spectrum efficiency for different flow volumes over congested network states. [ABSTRACT FROM AUTHOR]

This paper addresses the traffic’s grade of service indicators: call blocking and dropping rates as well as the optimization of their mutual relation, corresponding to the call admission control procedure configuration. In the presented results of simulations authors showed opportunities for the CAC load threshold adaptation according to the traffic volume and user mobility changes observed in the mobile radio network.