Radio Resource Allocation Algorithm Research Articles

Uplink resource shared interference mitigation scheme for in-band D2D underlay 5G networks

The benefits of Device-to-Device (D2D) communication often include increased overall system throughput, reduced latency, and increased spectrum efficiency. These advantages usually come from improved spatial reuse of time and frequency resources and the last but not least, the proximity of users engaged into D2D communication. Radio Resource allocation is a critical issue in D2D model and if not dealt with carefully, it can lead to significant interference to both pure cellular and D2D users, thereby proving detrimental to the desired outcomes. This paper presents novel Uplink Resource Shared Interference Mitigation Scheme called URSIMS. URSIMS adopts guard zones, further it implements radio resource allocation algorithm (RRAA) and distance dependent dynamic power control algorithm (DDDPA) with the goal to mitigate interference and achieve increased overall system throughput and spectral efficiency. URSIMS has been compared with other similar state of the art both for static and user mobility based scenarios; it is found that URSIMS responded adequately well and outperformed Random and Greedy schemes in terms of salient performance indicators including overall system throughput, pure cellular user throughput, spectral efficiency and D2D pair throughout. URSIMS did provide sustained higher system throughput, which significantly improved further in case of increased user mobility while compared to Random and Greedy schemes respectively.

Cross-Layer Wireless Resource Allocation Method Based on Environment-Awareness in High-Speed Mobile Networks

In high-speed mobile scenarios characteristic of the Fifth Generation Mobile Networks (5G) environment, the user video experience may be compromised due to concurrent access by numerous users and frequent base station transitions. Addressing this issue, this study introduces a cross-layer resource allocation model that integrates environmental awareness and is tailored for the exigencies of high-speed mobile networks. The paper delves into the challenges engendered by rapid mobility and extensive user access within the 5G environment and critiques the constraints of prevalent resource allocation methodologies. The model delineated herein is conceptualized as an optimization challenge and characterized as a nonlinear, NP-hard problem. In response to this challenge, this study advocates a novel streaming media transmission algorithm underpinned by edge computing, in tandem with an environment-aware wireless resource allocation algorithm. The article articulates the foundational principles and operational modalities of these algorithms, underscoring the significance of environmental cognizance in resource distribution and the efficacy of edge computing in increasing video transmission efficiency. Empirical validation, achieved through simulation experiments, corroborates the efficacy of the proposed approach. Comparative analysis reveals that, relative to conventional methodologies, the proposed framework significantly improves video transmission quality and user experience, particularly in contexts characterized by frequent network fluctuations and high user densities. This research contributes novel insights and pragmatic solutions to optimize video transmission in existing 5G and prospective network paradigms.

Intelligent Resource Allocation for V2V Communication with Spectrum-Energy Efficiency Maximization.

Aiming to address the limitations of traditional resource allocation algorithms in the Internet of Vehicles (IoV), whereby they cannot meet the stringent demands for ultra-low latency and high reliability in vehicle-to-vehicle (V2V) communication, this paper proposes a wireless resource allocation algorithm for V2V communication based on the multi-agent deep Q-network (MDQN). The system model utilizes 5G network slicing technology as its fundamental feature and maximizes the weighted spectrum-energy efficiency (SEE) while satisfying reliability and latency constraints. In this approach, each V2V link is treated as an agent, and the state space, action, and reward function of MDQN are specifically designed. Through centralized training, the neural network parameters of MDQN are determined, and the optimal resource allocation strategy is achieved through distributed execution. Simulation results demonstrate the effectiveness of the proposed scheme in significantly improving the SEE of the network while maintaining a certain success rate for V2V link load transmission.

Retracted: Radio Resource Allocation Algorithm of the M2M Communication System in 5G Era

Retracted: Radio Resource Allocation Algorithm of the M2M Communication System in 5G Era

A Packet Collision Reduction Based on Reselection for LTE V2X Mode 4

Vehicle to Everything (V2X) is a technology that includes communication between the vehicles and everything such as Vehicle to Vehicle (V2V), Vehicle to Infrastructure (V2I), and Vehicle to Pedestrian (V2P). Long Term Evolution (LTE) V2X based on LTE supports a sidelink communication in which User Equipment (UE) communicates with each other. In Mode 4 of the sidelink communication, the UE autonomously selects a radio resource that is not expected to be used by other UEs based on sensing information. However, a resource can be selected by simultaneous UEs and packet collisions occur because of the periodic resource reselection. In this paper, we propose two resource selection methods for the reselection using information originally included in the control information. Through computer simulations, we show that the proposed methods can improve the packet reception rate without requiring restrictions such as additional information. The main strength of this method is that it effectively utilizes the information contained in Resource Reservation Interval (RRI), which is used in the Semi-Persistent Scheduling wireless resource allocation algorithm. In this research, the value of RRI, which has been used in standards, is utilized to improve performance while maintaining compatibility. Since our method is designed under conditions that maintain compatibility with existing standards, it may or may not have a significant effect, but it does not degrade performance.

An Efficient Radio Resource Allocation Scheme considering Terminal Mobility in Dense mmWave Cellular Networks

In millimeter wave (mmWave) communication systems, beamforming-enabled directional transmission and network densification are commonly used to reduce high path loss and improve signal coverage quality. The combination of the two approaches will pose a challenge to radio resource allocation, which is especially true when terminals move frequently. The existing works presented some effective solutions for resource allocation in dense mmWave cellular networks, but they assumed that terminals move infrequently. So, these works cannot be directly applied to the dense mmWave cellular networks where terminals move frequently. In this paper, based on the results of the existing beamforming training (BFT) information-aided radio resource allocation algorithm, we propose a relay selection method to select a set of reasonable relays to take over the terminals whose performance deteriorates due to movement, which can ensure that each selected relay is as close as possible to the original performance of the corresponding moved terminal. Then, the resource allocation problem between the Device to Device (D2D) links from the selected relays to the corresponding moved terminals is formulated as a potential game model. By designing the utility function reasonably, the resource allocation results on the D2D links can converge to a Nash equilibrium solution. The simulation results show that the proposed scheme adapts to the scenario with frequent terminal movement, restrains the sharp performance decline caused by terminal movement, and outperforms the existing related algorithms in terms of average energy efficiency and throughput per link.

Joint wireless resource allocation and service function chaining scheduling for Tactile Internet

With the growing popularity of immersive interaction applications, e.g., industrial teleoperation and remote-surgery, the service demand of communication network is switching from packet delivery to remote control-based communication. The Tactile Internet (TI) is a promising paradigm of remote control-based wireless communication service, which enables tactile users to perceive, manipulate, or control real and virtual objects in perceived real-time. To support TI, the ultra-reliable and low latency communication service is required. However, the multi-tactile to multi-teleoperator interactive property, in-network computing demand, ordered service function chaining (SFC) requirement, as well as other features of TI, challenge the ultra-low latency provisioning. This paper studies the joint wireless resource allocation and SFC routing and scheduling problem for end-to-end delay reduction. We first formulate the problem as an end-to-end delay minimization problem constrained to SFC and wireless resource. Then, a distributed and cooperative scheme, which consists of the min–max (MM) wireless resource allocation algorithm and the delay-aware scheduling (DAS) of SFC algorithm, thus called MM-DAS, is proposed to address the problem. In MM-DAS, we use the MM algorithm for uplink/downlink communication at wireless edges, while DAS is proposed to solve the virtual network function (VNF) mapping and scheduling at the wireless core, for achieving the goal of providing low end-to-end delay to tactile–teleoperator pairs. The simulation results have illustrated the efficiency of the proposal for low end-to-end delay performance provisioning in TI environments.

Radio Resource Allocation Algorithm of the M2M Communication System in 5G Era

M2M is a concept and a general term for all technologies that enhance the communication and network capabilities of machines and equipment. It is a combination of different types of communication technologies. M2M enables all devices and users to share information, saving operation time and improving work efficiency. M2M is not a simple data transmission, but it is an intelligent and interactive communication between machines. Even if no real-time signal is sent, the machine will actively communicate according to the established program and select intelligently according to the obtained data. The communication system refers to the general term of the technology that completes the information transmission function. The modern communication system is mainly realized by the propagation of electromagnetic waves in free space or the transmission mechanism in the guiding medium. When the wavelength of the electromagnetic wave reaches the light wave range, such a telecommunication system is called an optical communication system, and the communication system in the other electromagnetic wave range is called an electromagnetic communication system, abbreviated as a telecommunication system. The purpose of this paper is to explore the study of wireless data sharing method for M2M mobile telecommunication systems in the 5G era. It is expected that the new algorithm will make the wireless resource allocation more reasonable and improve the resource utilization rate. The performance of the deterministic measurement matrix proposed in this paper is close to or even better than that of the random measurement matrix. The M-SP algorithm greatly improves the performance of the SP algorithm. The experimental results of this paper show that when the number of microbase stations is 10, the energy consumption of the direct algorithm is 175000 W, the energy consumption of the indirect algorithm is 350000 W, and the energy consumption of the direct algorithm is much smaller than that of the indirect algorithm.

Edge intelligence based digital twins for internet of autonomous unmanned vehicles

AbstractIt aims to explore the efficient and reliable wireless transmission and cooperative communication mechanism of Internet of Vehicles (IoV) based on edge intelligence technology. It first proposes an intelligent network architecture for IoV services by combining network slicing and deep learning (DL) technology, and then began to study the key technologies needed to achieve the architecture. It designs the cooperative control mechanism of unmanned vehicle network based on the full study of wireless resource allocation algorithm from the micro level. Second, in order to improve the safety of vehicle driving, deep reinforcement learning is used to configure the wireless resources of IoV network to meet the needs of various IoV services. The research results show that the accuracy rate of the improved AlexNet algorithm model can reach 99.64%, the accuracy rate is more than 80%, the data transmission delay is less than 0.02 ms, and the data transmission packet loss rate is less than 0.05. The algorithm model has practical application value for solving the data transmission related problems of vehicular internet communication, providing an important reference value for the intelligent development of unmanned vehicle internet.

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>

Intelligent Resource Allocation Method for Wireless Communication Networks Based on Deep Learning Techniques

In this paper, a deep learning approach is used to conduct an in-depth study and analysis of intelligent resource allocation in wireless communication networks. Firstly, the concepts related to CSCN architecture are discussed and the throughput of small base stations (SBS) in CSCN architecture is analyzed; then, the long short-term memory network (LSTM) model is used to predict the mobile location of users, and the transmission conditions of users are scored based on two conditions, namely, the mobile location of users and whether the small base stations to which users are connected have their desired cache states, and the small base stations select the transmission. The small base station selects several users with optimal transmission conditions based on the scores; then, the concept of game theory is introduced to model the problem of maximizing network throughput as a multi-intelligent noncooperative game problem; finally, a deep augmented learning-based wireless resource allocation algorithm is proposed to enable the small base station to learn autonomously and select channel resources based on the network environment to maximize the network throughput. Simulation results show that the algorithm proposed in this paper leads to a significant improvement in network throughput compared to the traditional random-access algorithm and the algorithm proposed in the literature. In this paper, we apply it to the fine-grained resource control problem of user traffic allocation and find that the resource control technique based on the AC framework can obtain a performance very close to the local optimal solution of a matching-based proportional fair user dual connection algorithm with polynomial-level computational complexity. The resource allocation and task unloading decision policy optimization is implemented, and at the end of the training process, each intelligent body independently performs resource allocation and task unloading according to the current system state and policy. Finally, the simulation results show that the algorithm can effectively improve the quality of user experience and reduce latency and energy consumption.

DRL-Based Channel and Latency Aware Radio Resource Allocation for 5G Service-Oriented RoF-MmWave RAN

A channel and latency aware radio resource allocation algorithm based on deep reinforcement learning (DRL) is proposed and evaluated. The proposed scheme aims to optimize the uplink scheduling for service-oriented multi-user millimeter wave (mmWave) radio access networks (RAN) in the 5G era. In the DRL system, multiple application flows are implemented with various statistical models and the key function modules of the system are designed to reflect the operation and requirements of service-oriented RANs. In particular, the mmWave channel characteristics utilized in the system are collected experimentally and verified via a radio-over-fiber (RoF)-mmWave testbed with dynamic channel variations. Results show that the proposed DRL algorithm can operate adaptively to channel variations and achieve at least 12% average reward improvement compared to conventional single-rule schemes, providing joint improvement of bit error rate and latency performance.

A Resource Allocation Algorithm for Ultra-Dense Networks Based on Deep Reinforcement Learning

The resource optimization of ultra-dense networks (UDNs) is critical to meet the huge demand of users for wireless data traffic. But the mainstream optimization algorithms have many problems, such as the poor optimization effect, and high computing load. This paper puts forward a wireless resource allocation algorithm based on deep reinforcement learning (DRL), which aims to maximize the total throughput of the entire network and transform the resource allocation problem into a deep Q-learning process. To effectively allocate resources in UDNs, the DRL algorithm was introduced to improve the allocation efficiency of wireless resources; the authors adopted the resource allocation strategy of the deep Q-network (DQN), and employed empirical repetition and target network to overcome the instability and divergence of the results caused by the previous network state, and to solve the overestimation of the Q value. Simulation results show that the proposed algorithm can maximize the total throughput of the network, while making the network more energy-efficient and stable. Thus, it is very meaningful to introduce the DRL to the research of UDN resource allocation.

Greedy Based Radio Resource Allocation Algorithm with Water Filling Power Control Scheme in D2D Underlaying Communication

Device to Device (D2D) is communication between two devices directly without the intervention of eNodeB.This communication can improve sum-rate, spectral efficiency, and decrease the workload of eNodeBbecause using the same spectrum frequency with Cellular User Equipment (CUE). But this communicationshould use the same resource simultaneously with CUE which is called D2D underlaying. This sharingresources also causes interference and should be managed using the resource allocation algorithm. In thiswork, the resource allocation is allocated in a single cell and uplink communication using joint greedyalgorithm with water filling power control scheme. This algorithm is compared with greedy, joint greedy,and greedy algorithm with water filling power control scheme. Joint greedy algorithm works based on thecapacity of eNodeB and D2D pair. While in water filling power control, the power of the user is managedbased on the channel condition and impact to energy efficiency. After all the resource is allocated, theparameter performance of the system is calculated, such as spectral efficiency, energy efficiency, and D2Dfairness. From the simulation result, joint greedy algorithm with water filling power control scheme result29,34 bps/Hz in spectral efficiency, 0.939 x 107 bps/watt in energy efficiency, and 0,996 in D2D fairness.

An Evasive Scheduling Enhancement Against Packet Dropping Attacks in C-V2X Communication

Third Generation Partnership Project (3GPP) has standardized the Sensing-Based Semi-Persistent Scheduling (SB-SPS) as the radio resource allocation algorithm for Cellular V2X (C-V2X) sidelink communication Mode 4. The algorithm leverages on past resource use pattern and reservation information of neighbor vehicles to reduce the chances of packet collisions. However, the very predictability can be exploited by potential attackers to intentionally cause packet collisions in the predicted transmission locations in the resource plane. In this letter, we demonstrate that such packet dropping attack can cause the victim to become a “ghost vehicle” to neighbors for a long duration. If used in highly safety-critical situations, the attack can push the victim into a situation of higher crash risk, which would be worse off than not having the C-V2X safety communication. Therefore, we propose an enhancement to the SB-SPS algorithm to guard against the attack by randomizing actually used resource locations to a certain degree. We demonstrate the effectiveness of the proposed enhancement through analysis and simulation, and discuss its cost aspect as well.

Greedy Based Radio Resource Allocation Algorithm with SARSA Power Control Scheme in D2D Underlaying Communication

Device-to-Device (D2D) underlaying communication system is a solution in reducing the workload of eNodeB and increasing the system data rate. This communication system consists of two users, namely Cellular User Equipment (CUE) and D2D pair, where CUE will share its resources with the D2D pair. This sharing resources also causes interference and should be managed using the resource allocation algorithm. In this work, the resource allocation scheme occurs in a single cell with an uplink communication direction. The resource allocation process uses greedy and joint greedy algorithms. After CUE allocates all of its resources, SARSA algorithm performs the power allocation process. The resource allocation process involves the scheduled CUE and D2D pair. After all the resource and power are allocated, parameter performance of the system is calculated. Based on the work results, joint greedy algorithm with power allocation using SARSA algorithm have performance results 1.375 × 107 bps/Watt in energy efficiency, 43.105 bps/Hz in spectral efficiency, and 0.993 in D2D fairness index.

Greedy Based Radio Resource Allocation Algorithm with Sectoring Scheme in D2D Underlaying Communication

D2D communication is a communication that allows users to communicate without passing through the Base Station (BS). The D2D Underlaying communication system can use the same Resource Block (RB) as the Cellular User Equipment (CUE). Implementing this communication system can ease the burden on BS and can transmit data with higher throughput in low power. However, using RB simultaneously can cause interference and therefore an RB allocation scheme is needed. The algorithm used in the scheduling process of the uplink RB owned by CUE to the D2D pair is the joint greedy algorithm with sectoring scheme. This work used a scenario where the value of the D2D pair are varied. The parameters measured in this simulation were sum-rate, spectral efficiency and fairness. The simulation results show that the joint greedy with sectoring allocation scheme has good energy efficiency and spectral efficiency values of 6.063× 106 bps/watt and 16.982 bps/Hz. On the other hand, the D2D fairness value in the joint greedy with sectoring allocation scheme is 0.886.

Downlink scheduling algorithm over LTE networks using genetic algorithm

ABSTRACT The Third Generation Partnership Project (3GPP) has introduced the Long-Term Evolution (LTE) to handles the increasing demand for the wireless network setup. In the LTE setup, radio resource management scheduling algorithms are employed to distribute radio resources to users that have diverse Quality of Service (QoS) requirements. QoS Class identifier radio resource allocation algorithm for LTE downlink uses QoS requirements and channel conditions to decide the transmission order of users. However, this resulted in poor network performance by deteriorating the network data throughput and an increase in average delay due to the lack of proffer use of genetic algorithms (GA). In this paper, a new scheduling algorithm is proposed to improve the poor performance degradation of the network by introducing the idea of GA before radio resources are allowed to users. Simulation is used to test the performance of the proposed algorithm and the results obtained show that it considerably beats the other algorithm according to the measured metrics. An improvement of 65.76% was observed in terms of spectral efficiency, an increase of throughput and the average delay was minimised.

An Efficient Radio Access Resource Management Scheme Based on Priority Strategy in Dense mmWave Cellular Networks

In millimeter wave (mmWave) communication systems, beamforming-enabled directional transmission and network densification are usually used to overcome severe signal path loss problem and improve signal coverage quality. The combination of directional transmission and network densification poses a challenge to radio access resource management. The existing work presented an effective solution for dense mmWave wireless local area networks (WLANs). However, this scheme cannot adapt to network expansion when it is applied directly to dense mmWave cellular networks. In addition, there is still room for improvement in terms of energy efficiency and throughput. Therefore, we firstly propose an efficient hierarchical beamforming training (BFT) mechanism to establish directional links, which allows all the small cell base stations (SBSs) to participate in the merging of training frames to adapt to network expansion. Then, we design a BFT information-aided radio access resource allocation algorithm to improve the downlink energy efficiency of the entire mmWave cellular network by reasonably selecting beam directions and optimizing transmission powers and beam widths. Simulation results show that the proposed hierarchical BFT mechanism has the smaller overhead of BFT than the existing BFT mechanism, and the proposed BFT information-aided radio access resource allocation algorithm outperforms the existing corresponding algorithm in terms of average energy efficiency and throughput per link.

Fine-Grained Management in 5G: DQL Based Intelligent Resource Allocation for Network Function Virtualization in C-RAN

Recently, the installation of 5G networks offers a variety of real-time, high-performance and human-oriented customized services. However, the current laying 5G structure is unable to meet all of the growing communication needs by these new emerging services. In this paper, we propose a DQL (Deep Q-learning Network) based intelligent resource management method for 5G architecture, to improve the quality of service (QoS) under limited communication resources. In the environment of network function virtualization (NFV), we aim at improving the efficient usage of spectrum resources. In this two-step solution, our first goal is to guarantee the maximum communication quality with the smallest number of infrastructures. Then, a DQL-based wireless resource allocation algorithm is designed to realize the elaborate operation. Unlike previous studies, our system can provide the allocation policy in a more subdivided way and finally maximize the usage of bandwidth resources. The simulation also shows that our proposed MSIO improves 3.12% in the performance of the maximum coverage importance problem and the ARODQ algorithm improves 4.05% than other standard solutions.