Radio Resource Allocation Scheme Research Articles

Novel Radio Resource Allocation Scheme in 5G and Future Sharing Network via Multi-Dimensional Collaboration

Radio resource allocation schemes are critical to enhance user experience and spectrum efficiency. In the context of fifth-generation (5G) and future networks, co-construction and sharing among multiple telecom operators, which effectively mitigate challenges stemming from resource scarcity, energy consumption, and network construction costs, also attract wide attention. Therefore, optimal resource allocation techniques in sharing networks should be explored. Current resource allocation schemes primarily optimize for load balancing, single-user throughput, and fairness of multi-user whole network throughput, with minimal consideration for network-level user experience. Moreover, existing approaches predominantly concentrate on specific resource domains, seldom considering holistic collaboration across all domains, which limits the user experience of the whole network. This paper introduces an innovative resource allocation method grounded in the Shannon theorem, incorporating time-frequency-spatial domain multi-dimensional collaboration. More importantly, by constructing an optimization model, we strive to attain optimal network-level user experience. Furthermore, we provide a smart grid technology based on the Artificial Intelligence (AI) method to predict inter-frequency information, including Received Signal Reference Power (RSRP), beam ID, and spectral efficiency, which are modeled as air interface utilization, channel bandwidth, and signal-to-noise ratio, respectively, providing input for the optimization algorithm, which seeks to achieve the optimal time-frequency-space resource allocation scheme. Extensive experimentation validates the effectiveness and superiority of our proposed methodology.

Downlink resource allocation for NOMA-based hybrid spectrum access in cognitive network

To solve the contradiction between limited spectrum resources and increasing communication demand, this paper proposes a wireless resource allocation scheme based on the Deep Q Network (DQN) to allocate radio resources in a downlink multi-user cognitive radio (CR) network with slicing. Secondary users (SUs) are multiplexed using non-orthogonal multiple access (NOMA). The SUs use the hybrid spectrum access mode to improve the spectral efficiency (SE). Considering the demand for multiple services, the enhanced mobile broadband (eMBB) slice and ultrareliable low-latency communication (URLLC) slice were established. The proposed scheme can maximize the SE while ensuring Quality of Service (QoS) for the users. This study established a mapping relationship between resource allocation and the DQN algorithm in the CR-NOMA network. According to the signal-to-interference-plus-noise ratio (SINR) of the primary users (PUs), the proposed scheme can output the optimal channel selection and power allocation. The simulation results reveal that the proposed scheme can converge faster and obtain higher rewards compared with the Q-Learning scheme. Additionally, the proposed scheme has better SE than both the overlay and underlay only modes.

Multi-agent deep reinforcement learning based resource management in heterogeneous V2X networks

In Heterogeneous Vehicle-to-Everything Networks (HVNs), multiple users such as vehicles and handheld devices and infrastructure can communicate with each other to obtain more advanced services. However, the increasing number of entities accessing HVNs presents a huge technical challenge to allocate the limited wireless resources. Traditional model-driven resource allocation approaches are no longer applicable because of rich data and the interference problem of multiple communication modes reusing resources in HVNs. In this paper, we investigate a wireless resource allocation scheme including power control and spectrum allocation based on the resource block reuse strategy. To meet the high capacity of cellular users and the high reliability of Vehicle-to-Vehicle (V2V) user pairs, we propose a data-driven Multi-Agent Deep Reinforcement Learning (MADRL) resource allocation scheme for the HVN. Simulation results demonstrate that compared to existing algorithms, the proposed MADRL-based scheme achieves a high sum capacity and probability of successful V2V transmission, while providing close-to-limit performance.

Targeted Dissemination of Incident Information With Combinatorial Traffic-Communication Optimization

The dissemination of traffic incident information (TII) will greatly help to decrease fuel consumption and congestion under future Internet of Vehicles (IoV) environments. Compared with the current semi-targeted dissemination strategies of TII that focus on communication performance, we propose a complete targeted-dissemination strategy by jointly considering the impact of the dissemination on the route planning of connected vehicles and the communication performance of information dissemination in the IoV environment. This strategy further alleviates the considerable challenges caused by the increasing number of vehicles and limited radio resources in dissemination, while reducing the additional fuel consumption caused by excessive and meaningless dissemination by selectively distributing traffic information to connected vehicles. Specifically, the proposed strategy consists of a radio resource allocation strategy guaranteeing communication quality and a traffic-influencing targeted dissemination strategy selecting the targets. Simulation results validate the effectiveness of the proposed strategy in ensuring communication performance, reducing total cost, and decreasing the carbon dioxide emission rate.

Developing capacity sharing strategy for vehicular networks with integrated use of licensed and unlicensed spectrum

A widely deployed cellular network, supported by direct connections, can offer a promising solution that supports new services with strict requirements on access availability, reliability, and end-to-end (E2E) latency. The communications between vehicles can be made using different radio interfaces: One for cellular communication (i.e., cellular communication over the cellular network based on uplink (UL)/downlink (DL) connections) and the other for direct communication (i.e., D2D-based direct communications between vehicles which allows vehicular users (V-UEs) to communicate directly with others). Common cellular systems with licensed spectrum backed by direct communication using unlicensed spectrum can ensure high quality of service requirements for new intelligent transportation systems (ITS) services, increase network capacity and reduce overall delays. However, selecting a convenient radio interface and allocating radio resources to users according to the quality of service (QoS) requirements becomes a challenge. In this regard, let’s introduce a new radio resource allocation strategy to determine when it’s appropriate to establish the communication between the vehicles over a cellular network using licensed spectrum resources or D2D-based direct connections over unlicensed spectrum sharing with Wi-Fi. The proposed strategy aims at meeting the quality of service requirements of users, including reducing the possibility of exceeding the maximum delay restrictions and enhancing network capacity utilization in order to avoid service interruption. The proposed solution is evaluated by highlighting different conditions for the considered scenario, and it is demonstrated that the proposed strategy improves network performance in terms of transmitted data rate, packet success rate, latency, and resource usage

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%.

Efficient radio resource allocation scheme for 5G networks with device-to-device communication

<p>A vital technology in the next-generation cellular network is device-to-device (D2D) communication. Cellular user enabled with D2D communication provides high spectral efficiency and further increases the coverage area of the cell, especially for the end-cell users and blind spot areas. However, the implementation of D2D communication increases interference among the cellular and D2D users. In this paper, we proposed a radio resource allocation (RRA) algorithm to manage the interference using fractional frequency reuse (FFR) scheme and Hungarian algorithm. The proposed algorithm is divided into three parts. First, the FFR scheme allocates different frequency bands among the cell (inner and outer region) for both the cellular and the D2D users to reduce the interference. Second, the Hungarian weighted bipartite matching algorithm is used to allocate the resources to D2D users with the minimum total system interference, while maintaining the total system sum rate. The cellular users share the resources with more than one D2D pair. Lastly, the local search technique of swapping is used for further allocation to minimize the interference. We implemented two types of assignments, fair multiple assignment, and restricted multiple assignment. We compared our results with existing algorithms which verified that our proposed algorithm provides outstanding results in aspects like interference reduction and system sum rate. For restricted multiple assignment, 60-70% of the D2D users are allocated in average cases.</p>

A Deep Learning Based Automatic Interference Avoidance Resource Allocation Scheme for SCMA Systems

Sparse code multiple access (SCMA) is able to provide high spectral efficiency and massive connectivity, hence it is considered as a promising scheme for the fifth generation (5G) systems. This paper proposed a radio resource allocation scheme based on deep learning for SCMA systems, with the aim to automatically avoid the inter-cell interference. A long short term memory (LSTM) network is adopted to learn the past interference characteristics and predict the interference power in the current subframe. Radio resource blocks with less predicted interference power are then selected for users to transmit signals. Simulation results show that the proposed scheme outperforms the moving average prediction method and has significant gains over the random radio resource block allocation in terms of achievable bit error rate in SCMA systems.

Radio Resource Allocation Improvements in Cognitive Radio Sensor Network for Smart Grid: Investigative Study and Solutions

Background: A cognitive radio sensor network (CRSN)-based Smart Grid (SG) is a new paradigm for a modern SG. It is totally different from the traditional power grid and conventional SG. Currently, an SG uses a static resource allocation technique to allocate resources to sensor nodes in the SG network. Static resource allocation is not efficient due to the heterogeneous nature of CRSN-based SGs. Hence, an appropriate mechanism such as dynamic radio resource allocation (RRA) is required for efficient resource allocation in CRSNs for SGs. Objective: The objective of this paper is to investigate and propose suitable dynamic RRA for efficient resource allocation in CRSNs-based SGs. This involves a proposal for appropriate strategy that will address poor throughput and excessive errors in resource allocation. Methods: In this paper, the dynamic RRA approach is used to allocate resources such as frequency, energy, channels and spectrum to the sensor nodes. This is because of the heterogeneity in a CRSN which differs for SG applications. The dynamic RRA approach is based on optimization of resource allocation criteria such as energy efficiency, throughput maximization, QoS guarantee, etc. The methods include an introduced model called “guaranteed network connectivity channel allocation for throughput maximization” (GNC-TM). Also used, is an optimal spectrum-band determination in RRA for improved throughput. Results: The results show that the model outperforms the existing protocol of channel allocation in terms of throughput and error probability. Conclusion: This study explores RRA schemes for CRSNs for SGs. The paper proposed a GNC-TM model, including demonstration of suitable spectrum band operation in CRSNs for SGs.

Intelligent multi-branch allocation of spectrum slices for inter-numerology interference minimization

Network slicing and mixed-numerology access schemes cover a central role to enable the flexible multi-service connectivity that characterizes 5G radio access networks (RAN). However, the interference generated by the simultaneous multiplexing of radio slices having heterogeneous subcarrier spacing can hinder the isolation of the different slices sharing the RAN and their effectiveness in meeting the application requirements. To overcome these issues, we design a radio resource allocation scheme that accounts for the inter-numerology interference and maximizes the aggregate network throughput. To overcome the computationally complexity of the optimal formulation, we leverage deep reinforcement learning (DRL) to design an agent capable of approximating the optimal solution exploiting a model-free environment formulation. We propose a multi-branch agent architecture, based on Branching Dueling Q-networks (BDQ), which ensures the agent scalability as the number of spectrum resources and network slices increases. In addition, we augment the agent learning performance by including an action mapping procedure designed to enforce the selection of feasible actions. We compare the agent performance to several benchmarks schemes. Results show that the proposed solution provides a good approximation of the optimal allocation in most scenarios.

Slice Function Placement Impact on the Performance of URLLC with Multi-Connectivity

Network slicing has emerged as a promising technical solution to ensure the coexistence of various 5G services. While the 5G architecture evolution for supporting slicing has been exhaustively studied, the architectural option impacts on RAN resource allocation efficiency remain unclear. This article fills a gap in this area by evaluating the impact of architecture choices on the quality of service of different services in the new 5G ecosystem, focusing on ultra-reliable low-latency communication applications. We propose architectural options based on the placement of the entities responsible for implementing these functions. We then assess their impact on the radio resource allocation flexibility when slices span two radio access technologies with redundant coverage. Our numerical experiments showed that the slice management function placement plays a pivotal role in choosing an adequate radio resource allocation scheme for URLLC slices.

Communication-efficient federated learning

Federated learning (FL) enables edge devices, such as Internet of Things devices (e.g., sensors), servers, and institutions (e.g., hospitals), to collaboratively train a machine learning (ML) model without sharing their private data. FL requires devices to exchange their ML parameters iteratively, and thus the time it requires to jointly learn a reliable model depends not only on the number of training steps but also on the ML parameter transmission time per step. In practice, FL parameter transmissions are often carried out by a multitude of participating devices over resource-limited communication networks, for example, wireless networks with limited bandwidth and power. Therefore, the repeated FL parameter transmission from edge devices induces a notable delay, which can be larger than the ML model training time by orders of magnitude. Hence, communication delay constitutes a major bottleneck in FL. Here, a communication-efficient FL framework is proposed to jointly improve the FL convergence time and the training loss. In this framework, a probabilistic device selection scheme is designed such that the devices that can significantly improve the convergence speed and training loss have higher probabilities of being selected for ML model transmission. To further reduce the FL convergence time, a quantization method is proposed to reduce the volume of the model parameters exchanged among devices, and an efficient wireless resource allocation scheme is developed. Simulation results show that the proposed FL framework can improve the identification accuracy and convergence time by up to 3.6% and 87% compared to standard FL.

Survey on radio resource allocation in long‐term evolution‐vehicle

AbstractThe main goal behind radio resource management (RRM), in any conventional wireless networks, is to efficiently utilize the available resources. Critical and safety communications have very stringent quality of service requirements on reliability and availability. Consequently, the efficient use of resources becomes more compelling when it deals with this type of application. In this context, cellular vehicle‐to‐everything (C‐V2X) communications, enabled by cellular device‐to‐device (D2D), have recently drawn much attention. This is due to their applicability to road safety and traffic efficiency applications, which require information exchange among road users and road‐side entities. This emerging technology, referred hereinafter as LTE‐V or C‐V2X, supports two different communication modes: 3 and 4. Both of the two modes enable direct vehicle‐to‐vehicle (V2V) communications via PC5 interface. However, they differ on how the radio resources are allocated. Several approaches and algorithms are proposed in the literature to address the resource allocation issue. In this paper, a state of the art of the radio resource allocation strategies was made for LTE‐V technology and a qualitative description was carried out of these different schemes. We proposed a classification of these strategies into different categories. This classification identified the different strengths and weaknesses of each proposal. Up to our knowledge, several surveys concerning resource allocation are published in the context of LTE but very few of them focuses on both RRM and vehicular networks.

Radio Resource Allocation and Power Control Scheme in V2V Communications Network

Reasonable resource management scheme is the premise to ensure system reliability and practicability of vehicle-to-vehicle (V2V) communications network. In this paper, spectrum resource allocation and power control scheme is studied. Firstly, the problem of resource block (RB) sharing is considered. Assuming that each V2V link occupies at most one resource block while multiple links can share the same RB. In this work, a factor graph model is proposed corresponding to the RB allocation problem through function mapping. Therefore, the RB allocation problem is equivalent to studying the message update and belief inference problem on the factor graph model. We propose a Belief Propagation based on Real-time Update of Messages (BPRUM) algorithm, in which the message of each node is calculated based on the new messages of other nodes rather than the messages calculated in the last iteration. Secondly, after the RB allocation is completed, a power control scheme under the maximum power limit is proposed by utilizing the Lagrange dual decomposition. And the total throughput of the system is maximized by using the proposed power control scheme. Simulation results show that the resource allocation and power control scheme can achieve a significant increment in spectrum utilization and throughput of the V2V communications network.

Comprehensive Survey of Radio Resource Allocation Schemes for 5G V2X Communications

In recent years, intelligent transportation systems, which provide safety and autonomous services on the road using the Internet and cellular technologies, have become increasingly important. Vehicles are equipped with sensors that can transmit data to other vehicles or infrastructure according to different types of services. In order to ensure robust connectivity for safety services, the transmission among vehicles requires low latency, high reliability, and high packet delivery ratio. With the increased number of vehicles, more frequency resources are needed to establish fast and reliable services. In the area of high vehicle density, the demand of frequency resources is increasing to establish stable connectivity in a short time. However, the amount of frequency resources is limited; the resource allocation scheme (RAS) should effectively assign a resource block to each user to ensure the quality of service. In this paper, we present a comprehensive survey of RASs for the 5G-based vehicular networks known as 5G V2X. We also discuss the challenges and opportunities for resource allocation in modern vehicular networks and present numerous promising future research directions.

Cellular-Assisted Device-to-Device Communications for Healthcare Monitoring Wireless Body Area Networks

We envisage each person will wear an individual wireless body area network (WBAN) for healthcare monitoring. Two WBANs in proximity may collide. Such WBAN collisions can significantly degrade communication quality of service (QoS), in terms of throughput and latency of healthcare applications. To avoid collisions, we propose to use cellular-assisted device-to-device (D2D) communications to connect on-body sensors to a hub within WBAN. These D2D transmissions are performed concurrently with cellular transmissions and thus, interference is imposed on each other. A radio resource allocation scheme has been developed to manage the interference. The scheme uses a combination of difference of convex optimization and Hungarian algorithm to control transmit power and to assign communication channel to both cellular and D2D transmissions. The objective is to maximize the sum of cellular throughput while avoiding collisions among co-located WBANs and satisfying QoS requirements of healthcare applications. Evaluation results confirm that WBAN collisions can be avoided at a crowded pedestrian walkway, even when there are as many as 6 pedestrian arrivals per second.

Data-Importance Aware Radio Resource Allocation: Wireless Communication Helps Machine Learning

The rich mobile data and edge computing enabled wireless networks motivate to deploy artificial intelligence (AI) at network edge, known as \emph{edge AI}, which integrates wireless communication and machine learning. In communication, data bits are equally important, while in machine learning some data bits are more important. Therefore we can allocate more radio resources to the more important data and allocate less radio resources to the less important data, so as to efficiently utilize the limited radio resources. To this end, how to define "more or less important" of data is the key problem. In this article, we propose two importance criteria to differentiate data's importance based on their effects on machine learning, one for centralized edge machine learning and the other for distributed edge machine learning. Then, the corresponding radio resource allocation schemes are proposed to improve performance of machine learning. Extensive experiments are conducted for verifying the effectiveness of the proposed data-importance aware radio resource allocation schemes.

Delay-Optimized Resource Allocation in Fog-Based Vehicular Networks

As a typical and prominent component of the Internet of Things, vehicular communication and the corresponding vehicular networks (VNETs) are promising to improve spectral efficiency, decrease transmission delay, and increase reliability. The ever-increasing number of vehicles and the demand of passengers/drivers for rich multimedium services bring key challenges to VNETs, which requiring huge capacity, ultralow delay, and ultrahigh reliability. To meet these performance requirements, a fog computing-based VNET is presented in this article, where the resource allocation as the corresponding key technique is researched. In particular, joint optimization of user association and radio resource allocation scheme is investigated to minimize the transmission delay of the concerned VNET. The proposed optimization problem is formulated as a mixed-integer nonlinear program and transformed into a convex problem by Perron–Frobenius theory and a weighted minimum mean square error method. Numerical results show that the proposed solution can significantly reduce the transmission delay with fast convergence.

Artificial Intelligence Aided Joint Bit Rate Selection and Radio Resource Allocation for Adaptive Video Streaming over F-RANs

Recently, fog-computing-based radio access networks (F-RANs) have been conceptualized to provide high quality of experience (QoE) for adaptive bit rate (ABR) streaming, where additional computing capacity is supplemented on fog nodes to facilitate complicated cross-layer optimization (i.e., joint bit rate selection and radio resource allocation). However, finding an optimal global solution with acceptable complexity is still infeasible by the conventional optimization methods. In this work, we propose an artificial intelligence (AI) aided joint bit rate selection and radio resource allocation scheme referred to as iABR, which provides a new vision for handling the over-complicated optimization in F-RANs. Based on multi-agent hierarchy deep reinforcement learning, the proposed iABR can dynamically allocate radio resource and select bit rate in a multiuser scenario, by perceiving the network environment and clients' player information. Moreover, long short-term memory (LSTM) is employed by the iABR algorithm, which enables accurate prediction of the change of channel quality by learning the history of the wireless channel. Hence, iABR is able to adjust the action policy in advance to accommodate the future channel quality for avoiding bit rate fluctuation. According to the experimental results, the iABR exhibits higher QoE in terms of high average bit rate, low rebuffering ratio, and average bit rate variance. Last but not least, the QoE performance of all the clients are fairly guaranteed by the iABR algorithm, enhancing the practicality of AI-driven F-RANs for multimedia service delivery.

Scheduling Enhancements and Performance Evaluation of Downlink 5G Time-Sensitive Communications

Time-sensitive communications (TSC) in wireless networks is an emerging paradigm that gains research momentum as an enabler of the industrial Internet of Things. As compared to ultra-reliable low-latency communications (URLLC) in fifth-generation cellular networks, TSC has stricter requirements in terms of latency and reliability, and also demands absolute time-synchronization and on-time delivery of packets for deterministic and isochronous real-time applications. In this regard, a key question is how to schedule TSC traffic flows effectively. This paper presents a radio resource allocation strategy for deterministic downlink TSC flows leveraging traffic pattern knowledge. Taking physical layer control channel effects into account, a comparison of semi-persistent and dynamic packet scheduling methods is presented as well as necessary enhancements to link adaptation and interference coordination procedures. With the proposed methods, the network capacity in terms of number of supported TSC flows can be more than doubled compared to traditional dynamic scheduling methods as commonly assumed for URLLC applications.