基于不完美CSI的D2D通信网络鲁棒能效资源分配算法

针对设备到设备(D2D)直连通信网络传统最优资源分配算法在随机信道时延、信道估计误差影响下鲁棒性弱的问题,该文在考虑参数不确定性影响的条件下,提出D2D用户总能效最大的鲁棒资源分配算法。考虑干扰功率门限、用户最小速率需求、最大传输功率和子信道分配约束,建立了下垫式频谱共享模式下多用户D2D网络资源分配模型。基于有界信道不确定性模型,利用最坏准则方法将原非凸鲁棒资源分配问题转换为确定性的凸优化问题。然后利用拉格朗日对偶理论求得资源分配的解析解。仿真结果表明所提出的算法具有很好的鲁棒性。     

基于不完美CSI的D2D通信网络鲁棒能效资源分配算法

针对设备到设备(D2D)直连通信网络传统最优资源分配算法在随机信道时延、信道估计误差影响下鲁棒性弱的问题,该文在考虑参数不确定性影响的条件下,提出D2D用户总能效最大的鲁棒资源分配算法.考虑干扰功率门限、用户最小速率需求、最大传输功率和子信道分配约束,建立了下垫式频谱共享模式下多用户D2D网络资源分配模型.基于有界信道不确定性模型,利用最坏准则方法将原非凸鲁棒资源分配问题转换为确定性的凸优化问题.然后利用拉格朗日对偶理论求得资源分配的解析解.仿真结果表明所提出的算法具有很好的鲁棒性.     

基于多智能体深度强化学习的D2D通信资源联合分配方法

设备对设备(D2D)通信作为一种短距离通信技术,能够极大地减轻蜂窝基站的负载压力和提高频谱利用率。然而将D2D直接部署在授权频段或者免授权频段必然导致与现有用户的严重干扰。当前联合部署在授权和免授权频段的D2D通信的资源分配通常被建模为混合整数非线性约束的组合优化问题,传统优化方法难以解决。针对这个挑战性问题,该文提出一种基于多智能体深度强化学习的D2D通信资源联合分配方法。在该算法中,将蜂窝网络中的每个D2D发射端作为智能体,智能体能够通过深度强化学习方法智能地选择接入免授权信道或者最优的授权信道并发射功率。通过选择使用免授权信道的D2D对(基于“先听后说”机制)向蜂窝基站的信息反馈,蜂窝基站能够在非协作的情况下获得WiFi网络吞吐量信息,使得算法能够在异构环境中执行并能够确保WiFi用户的QoS。与多智能体深度Q网络(MADQN)、多智能体Q学习(MAQL)和随机算法相比,所提算法在保证WiFi用户和蜂窝用户的QoS的情况下能够获得最大的吞吐量。     

异构云无线接入网场景下D2D通信资源共享与分配

针对异构云无线接入网中的前向链路受限问题,提出了一种基于干扰阈值的设备到设备(Device-to-Device,D2D)多用户分簇方案,并对系统信道和功率资源的分配进行优化。首先根据D2D用户之间的干扰级别,利用着色图理论对多用户进行分簇;然后,在满足D2D用户和蜂窝用户服务质量约束下,建立了基于D2D用户和速率最大化的资源分配模型,并进一步采用二分法对已分簇的D2D用户进行功率优化分配。仿真实验结果验证了所提方案相比传统方案,系统频谱利用率提高了55%以上。     

D2D通信中联合链路共享与功率分配算法研究

针对D2D （Device-to-Device,D2D）通信过程中的资源分配问题,提出一种联合链路共享和功率分配算法.在保证系统内蜂窝用户服务质量（Quality of Service,QoS）需求的前提下,利用系统的信道状态信息,为D2D用户生成一个由蜂窝用户组成的通信链路的候选集合;在通信链路候选集合内使用凸优化方法得到D2D用户最优功率分配策略;最后利用（Kuhn-Munkres,KM）算法求解最大加权二部图匹配（Maximum Weight Bipartite Matching,MWBM）问题,为D2D用户选择最优的蜂窝用户进行资源共享.仿真结果表明该算法能有效的提升通信网络的吞吐量,可以为D2D用户选择最优的资源分配策略.     

基于资源分配和功率控制的D2D中继选择

为优化蜂窝用户通信与设备直传(D2D)中继通信共存下的同频干扰问题,满足蜂窝用户容量要求,提出了一种基于能效的联合资源分配和功率控制的D2 D中继选择算法.该算法首先对等效D2 D中继链路进行资源分配,减小算法复杂度的同时使得D2 D链路对蜂窝链路产生的干扰最小;然后以资源分配结果和功率控制算法为依据进行中继选择.该方案不仅考虑了D2 D中继链路的能效问题,而且还同时考虑到了对蜂窝链路的干扰问题.通过仿真验证,所提算法不仅能有效提升D2 D中继链路的能效值,同时降低了对蜂窝用户的干扰.     

异构网络中基于能效优化的D2D资源分配机制

针对异构网络中D2D通信复用蜂窝用户频谱时存在的频谱分配问题,该文提出一种基于改进离散鸽群优化(PIO)算法的D2D通信资源分配机制。通过设置信干噪比(SINR)门限值来保证用户的通信服务质量(QoS),采用功率控制算法为用户设置发射功率,使用基于运动权值的二进制离散鸽群优化(MWBPIO)算法为D2D用户进行资源分配,并将D2D通信技术与中继技术进行有效结合,为边缘用户建立D2D中继链路,保证边缘用户的通信质量,最大化系统性能目标。仿真结果表明,该方案有效抑制了异构通信系统中引入D2D用户后导致的干扰问题,提高了边缘用户的通信质量和系统的频谱利用率以及系统的能效。     

基于安全中断概率的D2D安全接入策略

针对D2D蜂窝系统通信安全性受资源限制的问题,考虑到蜂窝链路和D2D链路的同频干扰能够为两者带来安全增益,基于此,提出一种基于安全中断概率的D2D用户接入策略。首先理论分析了蜂窝用户和D2D用户的安全中断概率,并给出了基于安全中断概率最小化的D2D用户功率优化算法。在上述分析的基础上,选择安全中断概率最小的D2D用户接入复用蜂窝用户的无线资源,同时提高D2D通信链路和蜂窝上行链路的安全性。最后,仿真结果证明了所提算法的有效性。     

D2D网络中信道选择与功率控制策略研究

针对D2D通信的资源分配问题,该文研究了D2D信道选择与功率控制策略。在保证蜂窝用户服务质量(QoS)的前提下,提出一种基于启发式的D2D信道选择算法,为系统内的D2D用户找到合适的信道复用资源。同时,利用拉格朗日对偶方法求解得到D2D用户最优传输功率。仿真结果表明当蜂窝用户与多对D2D用户共享信道资源时能够大幅度提升系统平均吞吐量。在相同条件下,该算法的性能要明显优于现有算法。     

基于用户中断概率的D2D通信干扰协调与资源分配

在LTE系统中引入设备直传(D2D)通信技术,会因为D2D用户复用蜂窝用户资源进行通信而产生同频干扰.在现有的干扰协调与资源分配研究中,都需要基站获取各个通信链路的信道状态信息(CSI),但这样无疑会增加基站的信令负担.为减小干扰与基站的信令负担,提出了一种基于用户中断概率的干扰协调与资源分配算法,首先在保证蜂窝用户正常通信的情况下,通过限制D2D用户到基站间的距离来降低干扰;其次通过遍历所有蜂窝用户的频谱资源,选择能使D2D用户的总中断概率最低的频谱资源进行复用.仿真结果表明,所提算法能够在保证蜂窝用户正常通信的情况下,明显降低D2D用户的平均中断概率,同时还能够降低基站信令负担.     

Resource allocation for NOMA based D2D underlaid cellular networks

This paper puts forward a user clustering and power allocation algorithm for non-orthogonal multiple access (NOMA) based device-to-device (D2D) cellular system. Firstly, an optimization problem aimed at maximizing the sum-rate of the system is constructed. Since the optimization problem is a mixed-integer non-convex optimization, it is decomposed into two subproblems, namely user clustering and power allocation subproblem. In the subproblem of user clustering, the clustering algorithms of cellular user and D2D pair are proposed respectively. In the power allocation subproblem, the gradient assisted binary search (GABS) algorithm and logarithmic approximation in successive convex approximation (SCA) are used to optimize the power of subchannel (SC) and D2D transmitted power respectively. Finally, an efficient joint iterative algorithm is proposed for the original mixed inter non-convex non-deterministic polynomial (NP)-hard problem. The simulation results show that the proposed algorithm can effectively improve the total system rate and the larger the ratio of cellular users (CUs) to total users, the larger the total system rate.     

Energy efficient joint radio resource management in D2D assisted cellular communication

Device-to-device (D2D) is one of the promising technology that can be used in association with cellular networks for the provision of high data rates, which is the foremost demand of today’s cellular users. The climatic changes due to combustion are posing another big challenge that needs to be addressed by today’s technologists to strive for existence of humanity. Thus developing energy efficient schemes for the deployment of such joint venture is a challenging task due to increased system complexity. This paper addresses the mode selection, user admission control and the resource allocation issues related to optimizing the overall Energy Efficiency (EE) of the cellular systems. The contributions that have been made in this paper are (1) Development of a pooled D2D and cellular network optimization model for efficient resource allocation (2) Development of an algorithm for maximization of EE for the proposed joint D2D cellular network . The problem formulated is a Mixed Integer Nonlinear Problem, whereas such problems belong to a class of optimization problems whose computational complexity is quite high that is NP-hard. In such hard problems the computational complexity increases with increasing the number of users that make these problems more challenging. Finding the optimal solution of such hard problems by the brute force and searching the feasible space is also challenging since the complexity increases exponentially by increasing the admitted users. A Mesh Adaptive Direct Search algorithm has been proposed to find the EE resource allocation for this user admission controlled D2D based cellular network. Based on analysis of our simulation results, it can be clearly designated that our proposed solution for increasing the EE for this joint cellular and D2D venture is very much close to optimal solution provided by Exhaustive Search Algorithm.     

Mesh adaptive direct search approach for D2D resource management

Device‐to‐device (D2D) communications are being considered a way forward to achieve higher data rate targets for futuristic wireless networks. D2D introduces interference among cellular users and D2D users. A joint resource allocation (JRA) strategy in cellular network with D2D functionality can definitely enhance overall data rate. The strategy under consideration maximizes the overall data rate of cellular network besides meeting threshold of power and interference constraints. The JRA is a class of mixed integer non‐linear constraint optimization problems and is NP hard. Because of discrete nature of variables in the problem, optimal solution performs extensive search of integer variables, and problem becomes exponentially complex with the increasing number of user pairs. In this paper, mesh adaptive direct search algorithm is applied to solve the aforementioned problem. The algorithm is suitable for complex problems of combinatorial nature to solve the JRA strategy in D2D. The proposed algorithm converges to optimal solution within acceptable computational iterations. Simulation results of system capacity and interference also demonstrate the suitability of the proposed approach viz‐a‐viz other algorithms. Copyright © 2016 John Wiley & Sons, Ltd.     

An efficient resource optimization scheme for D2D communication

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.     

Deep reinforcement learning-based resource allocation for D2D communications in heterogeneous cellular networks

Device-to-Device (D2D) communication-enabled Heterogeneous Cellular Networks (HCNs) have been a promising technology for satisfying the growing demands of smart mobile devices in fifth-generation mobile networks. The introduction of Millimeter Wave (mm-wave) communications into D2D-enabled HCNs allows higher system capacity and user data rates to be achieved. However, interference among cellular and D2D links remains severe due to spectrum sharing. In this paper, to guarantee user Quality of Service (QoS) requirements and effectively manage the interference among users, we focus on investigating the joint optimization problem of mode selection and channel allocation in D2D-enabled HCNs with mm-wave and cellular bands. The optimization problem is formulated as the maximization of the system sum-rate under QoS constraints of both cellular and D2D users in HCNs. To solve it, a distributed multiagent deep Q-network algorithm is proposed, where the reward function is redefined according to the optimization objective. In addition, to reduce signaling overhead, a partial information sharing strategy that does not observe global information is proposed for D2D agents to select the optimal mode and channel through learning. Simulation results illustrate that the proposed joint optimization algorithm possesses good convergence and achieves better system performance compared with other existing schemes.     

协作D2D异构网络的联合资源分配和功率控制

针对下行协作D2D(Device-to-Device)异构网络中复用蜂窝用户的联合资源分配和功率控制问题,提出了一种量子珊瑚礁优化算法(Quantum Coral Reef Optimization Algorithm,QCROA).首先,构建异构网络模型并推导得到整个网络总吞吐量的数学表达式;其次,基于QCROA算法...     

D2D/蜂窝通信模式切换与联合功率控制方案

设备直连（Device-to-Device, D2D）通信技术通过复用蜂窝系统的频谱资源提高频谱利用率,但D2D的引入会给蜂窝系统带来干扰。如何合理地选择D2D/蜂窝通信模式并进行功率优化控制,成为减小D2D和蜂窝系统间干扰、提升网络性能的关键。本文考虑D2D用户复用蜂窝上行链路场景,提出了一种基于距离和联合功率控制的通信模式选择方案。在该方案中,D2D用户和蜂窝用户与基站距离的比值决定了D2D用户是否采用Underlay模式进行通信,进而在约束蜂窝用户和D2D用户发射功率的条件下实现D2D链路和蜂窝链路的联合功率控制,最终推导出能够最大化系统总吞吐量的最优用户功率分配方案。根据仿真结果,本文提出的联合功率控制方案能够在降低系统间干扰的同时有效提高D2D和蜂窝系统的总吞吐量,进而提高了系统的性能。      

基于注意力机制ConvLSTM的UAV节能预部署策略

无人机(UAV)可以作为空中基站而凭借其移动性灵活地实现热点区域的覆盖.如何预测流量的分布而优化UAV部署是运营商面临的挑战.针对此问题,该文提出一种基于注意力机制卷积长短期记忆网络(A-ConvL-STM)的UAV节能预部署策略:提出一种融合注意力机制的卷积长短期记忆深度时空网络模型A-ConvLSTM,用于预测用户...     

System utility based resource allocation for D2D multicast communication in software-defined cellular networks

Device-to-device (D2D) multicast communication is a useful way to improve the communication efficiency of local services. This study considers a scenario of D2D multicast communication in software defined cellular network and investigates the frequency resource allocation problem. Firstly, we build the system model and formulate the optimization problem. Secondly, a hierarchical scheme to achieve a suboptimal solution is proposed. To select appropriate user equipments (UEs) as potential D2D transmitters (PDTs), a social aware PDT selection method is proposed. Then, a resource allocation algorithm considering users’ priorities is proposed. Furthermore, to study the resource allocation for general system that UEs without priorities, a non-priority considered allocation algorithm is proposed also. Numerical simulation results show that the proposed schemes are effective in improving the system utility and reducing the resource consuming for D2D communications.