无人机辅助反向散射通信计算任务卸载与资源分配

针对边缘计算网络中用户能量短缺问题，该文提出一种无人机(UAV)辅助的反向散射通信网络计算任务卸载和资源分配方案。首先，通过联合考虑飞行轨迹、用户的计算频率、任务卸载比例、无人机及用户的发射功率、反向散射时间分配以及主动通信时间分配，构建最小化无人机总能耗优化问题。其次，利用交替优化算法，将原非凸问题分解为两个子问题，并通过连续凸逼近方法将原问题转化为凸问题进行求解。仿真结果表明，所提算法使得无人机能耗显著减少，且具有良好的收敛性。     

无线供能移动边缘网络中计算时延最小化资源分配方法研究

针对无线供能移动边缘计算(MEC)网络,该文将计算时延定义为数据卸载与计算所消耗的时间,并提出一种节点计算时延之和最小化的多维资源分配方法。首先,在节点能量因果约束下,通过联合优化专用能量站工作时长、任务分割系数、节点计算频率和发射功率来建立一个计算时延之和最小化的多维资源分配问题。由于存在优化变量耦合与max-max函数,所建问题非凸且无法采用凸优化工具获取最优解。为此,通过引入一系列松弛变量和辅助变量来进行优化问题简化以及优化变量解耦,并在此基础上,通过深入分析简化问题的结构特性,提出一种基于二分法的迭代算法来求解原问题的最优解。最后,计算机仿真验证了所提迭代算法的正确性以及所提资源分配方法在计算时延方面的优越性。     

基于无人机无线能量传输的边缘计算系统能耗优化方法研究

无线能量传输(WPT)和移动边缘计算(MEC)可以为无线设备提供能量供应和任务计算,有效提高设备的能量效率.该文提出一种基于无人机无线能量传输的边缘计算系统能耗优化方法,在所提方法中,通过联合优化能量收集(EH)时间、用户发射功率和卸载决策,最小化系统总能耗.利用块坐标下降法(BCD),将优化问题分解为两个子问题,通过...     

多标签无线供电反向散射通信网络能效优化算法

为了提高物联网(IoT)节点的运行周期和能量利用率,该文提出一种多标签无线供电反向散射通信网络能效最大化资源分配算法。考虑传输速率约束、能量收集约束以及发射功率约束,建立了基于系统能效最大化的资源分配模型。利用Dinkelbach理论、2次变换以及变量替换法,将原分式非凸问题转化为可求解的凸优化问题。通过拉格朗日对偶理论求得优化问题的全局最优解。仿真结果表明,该算法具有较好的收敛性和能效。     

雷达-通信网络频谱资源动态协同算法

在雷达-通信一体网络中各时刻间的决策变量具有时间相关性时,以往追求某一时刻性能最优的资源分配算法不再适用。本文基于马尔可夫决策过程理论,构建面向雷达-通信一体网络的资源分配决策问题,其目标在于最小化各用频设备的长期平均发射功率。该马尔可夫决策过程问题状态-动作空间维度随用频设备数量呈指数增长,易陷入“维度诅咒”。为提升运行效率,本文提出一种分布式相对值迭代算法,通过对每个用频设备进行资源预分配处理,将原问题分解为多个可并行迭代的低维子问题,其中每个子问题可通过传统的相对值迭代法快速求解。仿真结果表明所提算法与追求单一时刻性能最优的贪婪策略比较,其性能可得到明显提升。      

基于边缘计算的无人机通感融合网络波束成形与资源优化

为了解决传统通信-感知融合网络模式对地面基础设施的依赖,针对复杂场景下通感融合网络系统功耗较大、信号阻塞、覆盖盲区等问题,提出了一种无人机搭载边缘计算服务器与雷达收发器辅助通感融合网络。首先,在满足用户传输功率、雷达估计信息率、任务卸载比例限制的条件下,通过联合优化无人机雷达波束成形、计算资源分配问题、任务卸载量划分、终端用户发射功率和无人机飞行轨迹,建立系统总能耗最小化问题；其次,将该非凸优化问题重新构建为一个马尔可夫决策过程,使用深度强化学习中的近端策略优化算法实现系统的优化决策。仿真结果表明,所提算法训练速度较快,能够在保证应用的感知与计算时延需求的同时有效降低系统能耗。     

工业物联网中基于任务紧急程度的资源分配算法

工业物联网中任务的生成通常具有连续性和周期性,并且任务对时延要求很高,这给系统成本带来了挑战。为应对这一挑战,提出了一种基于任务紧急程度的成本最小化资源分配算法。通过遗传算法优化任务的卸载策略和系统的资源分配策略,对于卸载的任务,根据任务的紧急程度进行调度,并在满足时延要求的前提下计算任务的最优发射功率。仿真结果表明,所提算法有效改善了系统总能耗成本。     

加权能耗最小化的无人机辅助移动边缘计算资源分配策略

针对无人机(UAV)辅助的移动边缘计算(MEC)系统,考虑到无人机能耗与地面设备能耗不在一个数量级,该文提出通过给地面设备能耗增加一个权重因子以平衡无人机能耗与地面设备能耗。同时在满足地面设备的任务需求下,通过联合优化无人机轨迹、系统资源分配以最小化无人机和地面设备的加权能耗。该问题高度非凸,为此提出一个基于交替优化算法的两阶段资源分配策略解决该非凸问题。第1阶段在给定地面设备的卸载功率下,利用连续凸逼近(SCA)方法求解无人机轨迹规划、CPU频率资源分配及卸载时间分配;第2阶段求解地面设备的卸载功率分配。通过两阶段的交替和迭代优化找到原问题的次优解。仿真结果验证了所提算法在降低系统能耗方面的有效性。     

基于Message Passing的认知无线电快速资源分配

针对多用户的OFDM认知无线电系统上行链路,提出一种基于Message Passing的分布式快速资源分配算法。该算法以认知系统总发射功率最小化为优化目标,综合考虑了认知用户对授权用户的干扰、总发射功率预算以及认知用户之间的比例公平性等约束条件,将资源分配分为子信道分配与功率分配相继2个步骤,构建资源分配的因子图,通过在节点间迭代地传递信息直至最终完成分布式的资源分配。分析和仿真结果表明,该算法在保证系统通信性能及资源分配公平性的前提下降低了系统总发射功率,并且运算效率得到了明显提升。     

多目标速度估计的分布式MIMO雷达资源分配算法

为了提高分布式MIMO雷达的多目标速度估计精度,该文分析了发射功率和信号有效时宽对估计精度的影响,进而提出一种将两者联合优化的资源分配算法。首先,以最小化目标速度估计的克拉美罗界(CRLB)最大值为目标函数,建立了包含发射功率和信号有效时宽两个优化变量的优化模型,然后采用连续参数凸估计(Sequential Parametric Convex Approximation, SPCA)算法对这个非凸的优化模型进行求解。最后,仿真结果表明利用所提算法进行资源分配能明显提高目标速度的估计精度。此外,仿真结果表明信号有效时宽对目标速度估计精度的影响大于发射功率。     

Energy efficiency maximization resource allocation algorithm in wireless-powered backscatter communication network

In order to alleviate the energy consumption problem caused by the increasing number of Internet of things (IoT) nodes,an energy-efficient (EE) maximization based resource allocation algorithm was proposed for multi-carrier wireless-powered backscatter communication network.Firstly,a multivariable and nonlinear resource allocation model was formulated to jointly optimize transmit power,transmission time,reflection coefficient,and energy-harvesting allocation coefficient,where the maximum transmit power constraint of the power station and the minimum harvested energy constraint at the backscatter device were considered.Then,the original non-convex optimization problem was transformed into a convex one which was solved by using Dinkelbach’s method and the variable substitution approach.Furthermore,the analytical solution of the resource allocation problem was obtained based on Lagrange dual theory.Simulation results verify that the proposed algorithm has better EE by comparing it with the existing algorithm under pure backscatter mode and algorithm under the harvested-then-transmit mode.     

Computation energy efficiency maximization based resource allocation scheme in wireless powered mobile edge computing network

For wireless powered mobile edge computing (MEC) network,a system computation energy efficiency (CEE) maximization scheme by considering the limited computation capacity at the MEC server side was proposed.Specifically,a CEE maximization optimization problem was formulated by jointly optimizing the computing frequencies and execution time of the MEC server and the edge user(EU),the transmit power and offloading time of each EU,the energy harvesting time and the transmit power of the power beacon.Since the formulated optimization problem was a non-convex fractional optimization problem and hard to solve,the formulated problem was firstly transformed into a non-convex subtraction problem by means of the generalized fractional programming theory and then transform the subtraction problem into an equivalent convex problem by introducing a series of auxiliary variables.On this basis,an iterative algorithm to obtain the optimal solutions was proposed.Simulation results verify the fast convergence of the proposed algorithm and show that the proposed resource allocation scheme can achieve a higher CEE by comparing with other schemes.     

Power allocation algorithm of full duplex cognitive relay network based on energy harvesting

To alleviate the shortage of spectrum resources and improve the power utilization of cognitive radio networks,a resource allocation algorithm of full duplex cognitive relay networks with energy harvesting was proposed.In the algorithm,the coefficient for power splitting of the relay and the transmit power of the secondary users were jointly optimized to maximize the throughput of the secondary users under the interference to primary users and energy harvesting constraints.Since the optimization of the algorithm was non-convex,it was transformed into two sub-optimizations,the sub-optimization of the coefficient for power splitting and the sub-optimization of the power transmitted of secondary users,which were the solvable convex sub-optimizations.Then,the final solution of the original optimization was obtained with the iterative algorithm.Simulation results show that the throughput of the proposed algorithm,can obtain 2 times throughput of the networks with half-duplex power splitting algorithm and 1.5 times throughput of the networks with full-duplex time switching algorithm.     

携能网络中保障能效的缓存队列中继选择与能量分配方案

在无线携能（SWIPT）网络中,带有缓存队列的一对半双工SWIPT中继可以在一个时隙内完成信息同时收发,实现虚拟全双工通信。针对SWIPT虚拟全双工网络的资源分配问题,该文研究了缓存队列机制的最佳SWIPT中继选择与子载波能量分配方案。首先建立数学模型,参考注水因子辅助搜索算法,以最大化能效为目标,在满足能量、信息传输速率、最佳接收中继干扰等多个约束条件下,通过求解优化问题选择SWIPT最佳转发中继,同时得到最佳转发中继的子载波能量分配最优解。仿真结果表明,与最大化端到端可达速率为目标的联合资源分配算法相比,所提方案考虑了SWIPT网络中继间干扰。且当源端发送功率较大时,所提方案可以获得较高的能效。      

Energy-efficiency resource allocation of very large multi-user MIMO systems

With increasing demand in multimedia applications and high data rate services, energy consumption of wireless devices has become a problem. At the user equipment side, high-level energy consumption brings much inconvenience, especially for mobile terminals that cannot connect an external charger, due to an exponentially increasing gap between the available and required battery capacity. Motivated by this, in this paper we consider uplink energy-efficient resource allocation in very large multi-user MIMO systems. Specifically, both the number of antenna arrays at BS and the transmit data rate at the user are adjusted to maximize the energy efficiency, in which the power consumption accounts for both transmit power and circuit power. We proposed two algorithms. Algorithm1, we demonstrate the existence of a unique globally optimal data rate and the number of antenna arrays by exploiting the properties of objective function, then we develop an iterative algorithm to obtain this optimal solution. Algorithm2, we transform the considered nonconvex optimization problem into a convex optimization problem by exploiting the properties of fractional programming, then we develop an efficient iterative resource allocation algorithm to obtain this optimal solution. Our simulation results did not only show that the the proposed two algorithms converge to the solution within a small number of iterations, but demonstrated also the performances of the proposed two algorithms are close to the optimum. Meanwhile, it also shows that with a given number iterations the performance of proposed algorithm1 is superior to proposed algorithm2 under small p _C . On the contrary, the performance of proposed algorithm2 is superior to proposed algorithm1 under large p _C .     

Energy‐efficient resource allocation in D2D communications for energy harvesting–enabled NOMA‐based cellular networks

In this paper, we propose an energy‐efficient power control and harvesting time scheduling scheme for resource allocation of the subchannels in a nonorthogonal multiple access (NOMA)–based device‐to‐device (D2D) communications in cellular networks. In these networks, D2D users can communicate by sharing the radio resources assigned to cellular users (CUs). Device‐to‐device users harvest energy from the base station (BS) in the downlink and transmit information to their receivers. Using NOMA, more than one user can access the same frequency‐time resource simultaneously, and the signals of the multiusers can be separated successfully using successive interference cancellation (SIC). In fact, NOMA, unlike orthogonal multiple access (OMA) methods, allows sharing the same frequency resources at the same time by implementing adaptive power allocation. Our aim is to maximize the energy efficiency of the D2D pairs, which is the ratio of the achievable throughput of the D2D pairs to their energy consumption by allocating the proper subchannel of each cell to each device user equipment (DUE), managing their transmission power, and setting the harvesting and transmission time. The constraints of the problem are the quality of service of the CUs, minimum required throughput of the subchannels, and energy harvesting of DUEs. We formulate the problem and propose a low‐complexity iterative algorithm on the basis of the convex optimization method and Karush‐Kuhn‐Tucker conditions to obtain the optimal solution of the problem. Simulation results validate the performance of our proposed algorithm for different values of the system parameters.     

Capacity Optimizing Power Allocation in Interference Channels

The interference channel is an essential model in both wireline and wireless communication systems. This article addresses transmit power allocation in interference channels with total transmit power constraint. The optimum power allocation maximizing the sum rate in two user interference channels can be derived analytically. However, the non-convexity of the optimization problem makes it prohibitively complex to obtain the optimum solution either analytically or numerically in general K user scenarios. After reviewing several conventional suboptimum power allocation schemes including equal power allocation, greedy power allocation and waterfilling power allocation, an iterative waterfilling algorithm is proposed and discussed. The performance of various power allocation schemes is evaluated through simulations, which suggests that the proposed iterative waterfilling outperforms other suboptimum power allocation schemes in certain scenarios.     

Secure resource allocation in hybrid energy-harvesting relay and full-duplex receiver

Taking into account the wireless physical layer security in energy-constrained relaying systems,a secure resource allocation scheme was proposed under simultaneous wireless information and power transfer (SWIPT) protocol.The utility optimization problem was considered aiming to maximize the secrecy rate by jointly optimizing the power splitting (PS) ratio and the transmit powers under the constraint of the transmit powers of the nodes and the harvested energy of the relay.The objective problem,which is non-convex,was decoupled into two subproblems.One was to optimize the PS ratio,another was to optimize the transmit powers.The optimal solution of the subproblems can be obtained in the closed-form.Then,the suboptimal solution is obtained with the proposed convergent iterative algorithm.Simulation results show the effects of artificial noise signal,residual self-interference signal,transmit power of nodes,amplification factor of relay and other factors on the security performance.Compared with the traditional gradient descent algorithm,the proposed algorithm can reduce more than 80% of the computational load,while the algorithm has the slightly better performance.