Radio Resource Utilization Research Articles

An intelligent resource allocation strategy with slicing and auction for private edge cloud systems

The convergence of transformative technologies, including the Internet of Things (IoT), Big Data, and Artificial Intelligence (AI), has driven private edge cloud systems to the forefront of research efforts. The access to massive terminals and the emergence of personalized services pose serious challenges for efficient resource management in power private edge cloud systems. To address the challenge of inequitable resource allocation in the private edge cloud, this work proposes an intelligent resource allocation strategy with a slicing and auction approach. By formalizing the resource allocation problem as a Mixed Integer Nonlinear Programming (MINLP) puzzle, the method transforms it into a hierarchical allocation challenge for Mobile Network Operators (MNOs), Mobile Virtual Network Operators (MVNOs), and power terminals. The proposed Multi-hop Progressive Auction Algorithm (MPAA) addresses the sliced resource allocation problem between MNOs and MVNOs. Furthermore, a Terminal Resource Allocation Strategy (TRAS) based on improved particle swarm optimization is proposed to solve the spectrum resource allocation problem between MVNOs and power terminals. Extensive simulation results show that the bidding overhead of MPAA is reduced by 6.12% and the average terminal satisfaction of TRAS is improved by about 1.3% compared to conventional methods, thus improving the utilization of wireless resources within the power AIoT.

Real-Time Implementation of a Cognitive Radio Node for Video Transmission with Dynamic Channel Selection

This paper endeavors to exploit the extent to which the radio spectrum can be utilized by almagating and enhancing various methods proposed by previous researchers in the field. Cognitive radio research addresses the challenge of spectrum scarcity and underutilization through cognitive radio technology. The realm of cognitive radio research encompasses diverse aspects of its architecture, ranging from spectrum sensing to higher-level strategies. Notably, the navigation strategy employed by cognitive radios constitutes a pivotal layer in these implementations. Thus, this paper focuses on the real-time implementation of a cognitive radio node in a spectrum of licensed users. Leveraging the capabilities of software-defined radios, GNU Radio, GStreamer and Ubuntu OS computers, this investigation introduces a streamlined channel strategy. This strategy uses both short-term and long-term historical data, along with a buffering mechanism, to facilitate the real-time transmission of a high-definition video signal. The successful implementation of this system yields a promising enhancement of approximately 32% in radio resource utilization. Furthermore, the sensing time of the cognitive radio experiences a significant reduction. This research contributes to the advancement of cognitive radio technology and demonstrates the consolidated strategies' practical implications, underscoring their potential to overcome spectrum-related challenges.

An Overview of MAC Scheduling techniques in 5G-NR

The advent of 5G technology has brought about a paradigm shift in wireless communication, enabling unprecedented data rates, ultra-low latency, and massive connectivity. At the heart of this technological revolution lies the 5G New Radio (5G-NR) standard, a key component that facilitates the seamless interaction between devices and networks. Within the 5G-NR architecture, the Medium Access Control (MAC) layer plays a pivotal role in optimizing the utilization of radio resources and ensuring efficient data transmission. This comprehensive overview delves into the intricate realm of MAC scheduling techniques within the context of 5G-NR. Scheduling, a fundamental aspect of wireless communication, is the process through which resources are allocated to different user devices to enable simultaneous and efficient data transmission. NR is essentially a scheduled system where the scheduler in the gNB controls downlink and uplink transmissions. This paper describes the details around dynamic scheduling, including associated functionality such as buffer-status report and power-headroom reports.

Active RIS Assisted Spectrum Sharing: Able to Achieve Energy-Efficient Notable Detection Performance Gains

Efficient radio resource utilization is crucially important for future wireless communication systems. The advanced reconfigurable intelligent surface (RIS) has recently been incorporated into the spectrum sharing mechanism, allowing for both spectrum efficiency (SE) and energy efficiency (EE). Spectrum sensing is the cornerstone of spectrum sharing. Nevertheless, due to the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">doubling fading</i> effect, the path loss experienced from the reflection link is thousands of times that from the direct link. Consequently, the detection performance gains obtained through passive RIS are negligible, especially in typical communication scenarios where the direct link is not weak. To address this issue, this paper proposes an active RIS assisted spectrum sensing mechanism that can notably improve the detection performance gains. Multiple active RISs are introduced and the closed-form expressions of the detection performance metrics are derived in this paper. More importantly, due to the extra energy consumption in amplifying reflected signals, it's necessary to investigate the EE maximization problem for the active RIS assisted mechanism by jointly optimizing the detection parameter and the reflecting precoding. Alternating optimization (AO) algorithm and quadratic transform (QT) method are utilized to decouple the multidimensional <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fractional programming</i> (FP) problem. Then, the optimal detection parameter is obtained with a closed-form solution based on the convex optimization theory. The phase shift and amplification coefficient of each active element are optimized through the sequential rotation (SR) method and an improved differential evolutionary (DE) algorithm, respectively. Simulation results show that the proposed active-RIS mechanism could outperform the conventional passive-RIS mechanism in achieving notably enhanced detection performance gains and higher EE by optimizing the reflecting precoding.

A DRL-Based Joint Scheduling and Resource Allocation Scheme for Mixed Unicast–Broadcast Transmission in 5G

With the ever-increasing demand for data services, the spectrum becomes precious resource. The cooperative transmission mechanism, which allows broadcast and unicast to share the same spectrum, can effectively alleviate this situation. The success of LDM in ATSC 3.0 has inspired many scholars to superimpose broadcast and unicast services with a fixed power injection ratio. To further improve radio resource utilization, A flexible LDM scheme with a variable power injection ratio is proposed. The scheduling policy in Medium Access Control layer is formulated as a sequential decision problem, and it proved to have Markov property. A DRL-based algorithm is proposed to obtain the optimal scheduling scheme. Considering the requirements of broadcasting service and unicast services, the Lyapunov optimization method is used to convert the problem to the Lyapunov penalty form that is more conducive for the agent to learn the optimal strategy. Simulation results show that the proposed scheme can achieve much improved performance in system throughput when guaranteeing the quality of the broadcast service.

LoRaWAN Meets ML: A Survey on Enhancing Performance with Machine Learning.

The Internet of Things is rapidly growing with the demand for low-power, long-range wireless communication technologies. Long Range Wide Area Network (LoRaWAN) is one such technology that has gained significant attention in recent years due to its ability to provide long-range communication with low power consumption. One of the main issues in LoRaWAN is the efficient utilization of radio resources (e.g., spreading factor and transmission power) by the end devices. To solve the resource allocation issue, machine learning (ML) methods have been used to improve the LoRaWAN network performance. The primary aim of this survey paper is to study and examine the issue of resource management in LoRaWAN that has been resolved through state-of-the-art ML methods. Further, this survey presents the publicly available LoRaWAN frameworks that could be utilized for dataset collection, discusses the required features for efficient resource management with suggested ML methods, and highlights the existing publicly available datasets. The survey also explores and evaluates the Network Simulator-3-based ML frameworks that can be leveraged for efficient resource management. Finally, future recommendations regarding the applicability of the ML applications for resource management in LoRaWAN are illustrated, providing a comprehensive guide for researchers and practitioners interested in applying ML to improve the performance of the LoRaWAN network.

Reliability-Guaranteed Uplink Resource Management in Proactive Mobile Network for Minimal Latency Communications

Proactive Mobile Network (PMN) has been proposed to support extremely low latency communications with multi-tier computing architectures, machine-centricity, and data-driven operation features. Nevertheless, the communication reliability of the PMN introduces new substantial technological challenges. As PMN employs unique proactive open-loop communication, any feedback-based control is avoided to enhance end-to-end latency. This paper focuses on machine-initiated uplink transmission in PMN and proposes a reliability-guaranteed resource management scheme. Without requiring feedback control information, our scheme uniquely decomposes conventional resource management into two collaborative decision processes: predictive resource allocation suggested by network anchor nodes (ANs) and proactive smart resource utilization by smart equipment (SE). These two decision-making processes are constructed as independent reinforcement learning (RL) problems, but implicitly share the states of radio resources according to operating environments. Different algorithms for different operating scenarios have been investigated for this dual-decision solution. Simulation results in various scenarios show that our scheme enables PMN’s reliability close to the theoretical optimal value and successfully serves radio resource utilization in the PMNs.

Statistical Learning-Based Adaptive Network Access for the Industrial Internet of Things

Industrial Internet-of-Things (IIoT) applications generate data in varying amounts with diverse quality of service requirements. The adaptive network access approach and distributed resource management in IIoT networks can reduce the communication overheads caused by centralized resource management approaches. In this regard, statistical learning is a promising tool for addressing decision-making problems in a dynamic environment. This paper considers uplink dominant IIoT networks in which massive devices generate delay-sensitive and delay-tolerant data and communicate over shared radio resources. We propose a novel grant-free access scheme using a statistical learning approach that enables IIoT entities to perform delay-sensitive and delay-tolerant transmissions over dynamically partitioned resources in a prioritized manner. In order to improve utilization of available radio resources, we design an adaptive network access mechanism operating in a semi-distributed manner. This mechanism enables end devices to use their transmission history to choose between static and dynamic resource allocation-based grant-free schemes in a dynamic environment. Simulation results show that average latency and resource utilization vary in grant-free access schemes employing static and dynamic resource allocations. Thus, compared to a single transmission scheme, the proposed adaptive network access offers better channel utilization while meeting the application-specific latency bound in IIoT networks.

Dynamic packet duplication for reliable low latency communication under mobility in 5G NR-DC networks

The fifth generation (5G) networks and beyond are key to meeting the exponentially increasing demands of next generation services for high throughput and reliable low latency communication under various mobility scenarios. These promising features have critical gaps to be filled before they can be fully implemented for mobile applications in complex environments like smart cities. Millimeter wave (mmWave) communications is a key enabler for a significant increase in the performance of these networks. However, due to the extremely limited transmission range of mmWave frequencies, 5G network deployments are designed to have several small cells operating in the mmWave frequency range using Ultra-dense networking (UDN) techniques to provide continuous coverage. But, such deployments not only face challenges in terms of a higher number of handovers, higher latency, lower reliability, and higher interference levels but also in terms of site acquisition, logistics, unbalanced load distributions, and power requirements. Multi-connectivity can improve the performance of UDNs and provide better deployment strategies as it provides multiple simultaneous links between the User Equipment (UE) and base stations. In such systems, packet duplication can be used to meet the stringent reliability and latency requirements of modern cellular networks as data packets are duplicated and transmitted concurrently over two or more independent links. The downside to packet duplication, however, is the increased usage of resources like spectrum, power, etc. In this work, we explore and analyze different techniques that could aid in reducing radio resource utilization without sacrificing the improvements in reliability and latency observed through packet duplication. To perform this study, we develop a novel 5G deployment with new radio dual connectivity (NR-DC) and packet duplication to improve reliability. We then analyze possible enhancements in the system to improve radio resource utilization when packet duplication is implemented. In this article, we propose and evaluate this novel 5G network deployment with multi-connectivity using Simu5G network simulator for enabling future 5G systems. The proposed 5G architecture is shown to meet the requirements of next generation applications. Our simulations show that the proposed techniques improve the throughput by up to 165.72%, the latency by up to 91.22%, and the packet loss decreases to near zero compared to a single link system.

Hardware-accelerated Real-time Drift-awareness for Robust Deep Learning on Wireless RF Data

Proactive and intelligent management of network resource utilization (RU) using deep learning (DL) can significantly improve the efficiency and performance of the next generation of wireless networks. However, variations in wireless RU are often affected by uncertain events and change points due to the deviations of real data distribution from that of the original training data. Such deviations, which are known as dataset drifts, can subsequently lead to a shift in the corresponding decision boundary degrading the DL model prediction performance. To address these challenges, we present hardware-accelerated real-time radio frequency (RF) analytics and drift-awareness modules for robust DL predictions. We have prototyped the proposed design on a Zynq-7000 System-on-Chip that contains an FPGA and an embedded ARM processor. We have used Xilinx Vivado design suite for synthesis and analysis of the HDL design for the proposed solution. To detect dataset drifts, the proposed solution adopts a distance-based technique on FPGA to quantify in real-time the change between the prediction distribution obtained from DL predictions and data distribution of input streaming samples. Using various performance metrics, we have extensively evaluated the performance of the proposed solution and shown that it can significantly improve the DL model robustness in the presence of dataset drifts.

L‐RUBI: An efficient load‐based resource utilization algorithm for bi‐partite scatternet in wireless personal area networks

SummaryRecently, much of the wireless personal area network (WPAN) research concerns network protocols, scheduling, and security challenges but the major issue of resource utilization has been very rarely investigated. The design of resource sharing in a network gets more attention when the number of users increases. While optimizing performance, resource utilization plays a critical role. In this paper, the numerical performance of a wireless resource utilization algorithm for a bi‐partite scatternet is presented. This algorithm is focused to enhance the bandwidth allocation and power utilization of wireless scatternets. Every node can communicate with a single neighbor at a time with minimum resources. Finally, the performances of the RUBI algorithm are shown. This algorithm is compared with the existing algorithms such as the load adaptive scheduling algorithm and pseudorandom coordinated scheduling scheme in terms of various parametric metrics like reliability, throughput, collision probability, transmission probability, and signal‐to‐noise ratio (SINR). The proposed L‐RUBI achieves 93.4% of reliability, 93.6% of transmission probability, 91.4% of throughput, 76.8% of collision performance, and 72.2% SINR.

ICRAN: Intelligent Control for Self-Driving RAN Based on Deep Reinforcement Learning

Mobile networks are increasingly expected to support use cases with diverse performance expectations at a very high level of reliability. These expectations imply the need for approaches that timely detect and correct performance problems. However, current approaches often focus on optimizing a single performance metric. Here, we aim to address this gap by proposing a novel control framework that maximizes radio resources utilization and minimizes performance degradation in the most challenging part of cellular architecture that is the radio access network (RAN). We devise a method called Intelligent Control for Self-driving RAN (ICRAN) which involves two deep reinforcement learning based approaches that control the RAN in a centralized and a distributed way, respectively. ICRAN defines a dual-objective optimization goals that are achieved through a set of diverse control actions. Using extensive discrete event simulations, we confirm that ICRAN succeeds in achieving its design goals, showing a greater edge over competing approaches. We believe that ICRAN is implementable and can serve as an important point on the way to realizing self-driving mobile networks.

SmartCon: Deep Probabilistic Learning-Based Intelligent Link-Configuration in Narrowband-IoT Toward 5G and B5G

To enhance the coverage and transmission reliability, repetitions adopted by Narrowband Internet of Things (NB-IoT) allow repeating transmissions several times. However, this results in a waste of radio resources when the signal strength is high. In addition, in low signal quality, the selection of a higher modulation and coding scheme (MCS) level leads to a huge packet loss in the network. Moreover, the number of physical resource blocks (PRBs) per-user needs to be chosen dynamically, such that the utilization of radio resources can be improved on per-user basis. Therefore, in NB-IoT systems, dynamic adaptation of repetitions, MCS, and radio resources, known as auto link-configuration, is crucial. Accordingly, in this paper, we propose SmartCon which is a Generative Adversarial Network (GAN)-based deep learning approach for auto link-configuration during uplink or downlink scheduling, such that the packet loss rate is significantly reduced in NB-IoT networks. For the training purpose of the GAN, we use a Multi-Armed Bandit (MAB)-based reinforcement learning mechanism that intelligently tunes its output depending on the present network condition. The performance of SmartCon is thoroughly evaluated through simulations where it is shown to significantly improve the performance of NB-IoT systems compared to baseline schemes.

Energy-Efficient Transmission Range and Duration for Cognitive Radio Sensor Networks

Cognitive Radio (CR) promises an efficient utilization of radio spectrum resources by enabling dynamic spectrum access to overcome the spectrum scarcity problem. Cognitive Radio Sensor Networks (CRSNs) are one type of Wireless Sensor Networks (WSNs) equipped with CR capabilities. CRSN nodes need to operate energy-efficiently to extend network lifetime due to their limited battery capacity. In this paper, for the first time in literature, we formulate the problem of finding a common energy-efficient transmission range and transmission duration for all CRSN nodes and network deployment that would minimize the energy consumed per goodput per meter toward the sink in a greedy forwarding scenario. Results reveal non-trivial relations for energy-efficient CRSN transmission range and duration as a function of nine critical network parameters such as primary user activity levels. These relations provide valuable insights for detailed CRSN designs prior to deployment.

Multi-Dimensional Multiple Access With Resource Utilization Cost Awareness for Individualized Service Provisioning in 6G

The increasingly diversified Quality-of-Service (QoS) requirements envisioned for future wireless networks call for more flexible and inclusive multiple access techniques in 6G for supporting emerging applications and communication scenarios. To achieve this, we propose a multi-dimensional multiple access (MDMA) protocol to meet individual User Equipment’s (UE’s) unique QoS demands while utilizing multi-dimensional radio resources cost-effectively. In detail, the proposed scheme consists of two novel aspects, i.e., selection of a tailored multiple access mode for each UE while considering the UE-specific radio resource utilization cost caused by non-orthogonal interference cancellation; and multi-dimensional radio resource allocation among coexisting UEs under dynamic network conditions. To reduce the UE-specific resource utilization cost, the base station (BS) organizes UEs with disparate multi-domain resource constraints as UE coalition by considering each UE’s specific resource availability, perceived quality, and utilization capability. Each UE within a coalition could utilize its preferred radio resources, which leads to low utilization cost while avoiding resource-sharing conflicts with remaining UEs. Furthermore, to meet UE-specific QoS requirements and varying resource conditions at the UE side, the multi-dimensional radio resource allocation among coexisting UEs is formulated as an optimization problem to maximize the summation of cost-aware utility functions of all UEs. A solution to solve this NP-hard problem with low complexity is developed using the successive convex approximation and the Lagrange dual decomposition methods. The effectiveness of our proposed scheme is validated by numerical simulation and performance comparison with state-of-the-art schemes. In particular, the simulation results demonstrate that our proposed scheme outperforms these benchmark schemes by large margins.

Device and Network Coordination for Opportunistic Utilization of Radio Resources in 3D Networks

Device and network coordination is critical for efficient radio resource (RR) utilization while meeting Quality of Service (QoS) requirements in heavily congested future heterogeneous wireless networks featured with 3-Dimensional (3D) small cells (SCs). Device and network coordination assisted opportunistic and coordinated use of RRs in distinct bands could dramatically improve the spectrum utilization in these networks. In this study, overall communication performance enhancement through better utilization of opportunistically available spatially distributed RRs in a 3D SC is addressed considering two co-located networks operated in licensed band (LB) and unlicensed band (UB) while jointly accounting for several related factors like 3D spatial positions and QoS requirements of the devices. To confront this problem, a device and network coordination assisted solution is developed using Q-learning and Slotted-ALOHA principles. Then, to maintain performance standards, device and network coordination aided scheduling, power control and access prioritization schemes are discussed. Subsequently, regret based learning assisted algorithm is presented for the UB to optimally utilize RRs. In these solutions, both device-network and network-network interactions are considered. In results, approximately 75% better overall coordination efficiency over conventional methods is shown at the initial iterations for the scenarios with the highest device density demonstrating attractive performance.

Machine Learning Enables Radio Resource Allocation in the Downlink of Ultra-Low Latency Vehicular Networks

Autonomous driving and intelligent transportation demand ultra-low latency and high reliability communication in future vehicular networks. Proactive wireless communication can facilitate minimal latency by open-loop communication, which discards traditional feedback control mechanisms. However, appropriate radio resource allocation in such proactive mobile networks has not been fully studied due to lacking channel state information (CSI) and the alleviation of multiple access interference (MAI) in multiple virtual cells. This paper aims to ensure the reliability of downlink communication by a novel radio resource allocation scheme in proactive vehicular networks with ultra-low latency. We regard data transmission success rate as the reliability indicator and propose a joint radio resource allocation model based on the “generalized closed-loop”, where anchor node (AN) uses the radio resource utilization information (RRUI) from the vehicle in the immediate past uplink as a guide to assist resource allocation. Subsequently, we study the radio resource allocation model solution on the vehicle side and the network side respectively. On the vehicle side, vehicles use the local or global data transmission experience to select the radio resource with the best quality as the RRUI. On the network side, according to the latest RRUI of vehicle and resource occupancy information, deep reinforcement learning is proposed to make appropriate radio resource allocation decisions. Simulations demonstrate the effectiveness of the intelligent joint radio resource allocation scheme under the cooperation between vehicles and AN. When the resource load rate reaches 40%, the joint radio resource allocation scheme can achieve a data transmission success rate of more than 98%.

Coordinated 3D spectrum utilization for B5G indoor HetNets: A collaborated crowdsensing approach

The 5G and beyond (B5G) networks are expected to provide significantly increased capacity for diverse services with limited spectrum resources. However, new aspects of B5G networks particularly the ultra-dense network deployment and the heterogeneous network structure, make spectrum resources highly distributed in three-dimension (3D), which brings unprecedented challenges for highly efficient spectrum utilization, especially in an indoor environment. To tackle the challenges on dynamic spectrum utilization and improving the volume capacity of indoor 3D networks, a collaborated crowdsensing approach is proposed for the coordinated 3D spectrum utilization through integrating the crowdsensing, data analytics, and software defined network (SDN) techniques. The integration of sensing, learning, and intelligent control provides critical capabilities for timely observing 3D radio resources and enabling the coordinated radio resource utilization among co-existing indoor heterogeneous networks (HetNets). Case study confirms the superiority of the proposed coordinating method on achieved volume capacity.

Energy Efficient Optimal Resource Allocation in Multi-RAT Heterogeneous Network

ABSTRACT In this paper, we address the frequent problem associated with user association and resource allocation along with optimal deployment of base station (BS) in multiple radio access technology (Multi-RAT)-assisted heterogeneous network (Het-Net). Considering real time user scenarios, optimal resource allocation in Het-Net while ensuring each user’s minimum required data rate is a challenging task to be performed. Here, we propose a novel algorithm with a well-known and efficient meta-heuristic optimization technique to resolve the aforementioned problem. We use hybrid memory-based dragonfly algorithm with differential evolution (DADE) for its excellent convergence characteristics. Extensive simulations are performed to determine the optimal network utility under the consideration of nonuniform user distribution and fine-tuning their respective service class and contract of association parameters. Simulation results depict that the proposed algorithm improves the overall network utility in terms of radio resource utilization and energy consumption while satisfying the user demands. Comparative analysis of the proposed technique with the other state-of-the-art algorithm depicts the superiority of the proposed algorithm in terms of accuracy and consistency. We also perform optimal multi-RAT cell planning under the above constraints including a network blackout scenario. The algorithm ensures each user coverage by optimally allocating the available resources.

Wireless for Control: Over-the-Air Controller

In closed-loop wireless control systems, the state-of-the-art approach prescribes that a controller receives by wireless communications the individual sensor measurements, and then sends the computed control signal to the actuators. We propose an over-the-air controller scheme where all sensors attached to the plant simultaneously transmit scaled sensing signals directly to the actuator; then the feedback control signal is computed partially over the air and partially by a scaling operation at the actuator. Such over-the-air controller essentially adopts the over-the-air computation concept to compute the control signal for closed-loop wireless control systems. In contrast to the state-of-the-art sensor-to-controller and controller-to-actuator communication approach, the over-the-air controller exploits the superposition properties of multiple-access wireless channels to complete the communication and computation of a large number of sensing signals in a single communication resource unit. Therefore, the proposed scheme can obtain significant benefits in terms of low actuation delay and low wireless resource utilization by a simple network architecture that does not require a dedicated controller. Numerical results show that our proposed over-the-air controller achieves a huge widening of the stability region in terms of sampling time and delay, and a significant reduction of the computation error of the control signal.