带缓存的云接入网络中最小化传输时延设计

为了更好适应下一代通信网络以内容为中心的特性，在云接入网络（Cloud Radio Access Network，Cloud-RAN）中考虑射频拉远头（Remote Radio Heads，RRHs）具备缓存功能也变得必要。本文考虑在Cloud-RAN中设计优化算法，并通过有效设计缓存方案减少系统传输时延。基于混合式自动重传请求(hybrid automatic repeat request, HARQ)的重传机制，前程链路与下行链路频谱信道的正交性，系统采用马尔可夫链理论建立了最小化系统传输时延的优化问题。考虑只能通过递归方式得到优化目标函数表达式，头脑风暴优化(brain storm optimization，BSO)算法被引入解决非凸问题，获得最优缓存方案。仿真结果表明，比起其他缓存方案，本文提出的优化算法可以有效地减少系统传输时延，满足未来通信需求。      

密集异构网络中基于多目标优化的资源分配策略

密集异构网络(Dense Heterogeneous Network, DHN)通过部署小基站可以提升网络容量和用户速率,但小基站的密集部署会产生巨大的能耗和严重的干扰,进而影响系统的能量效率(Energy Efficiency, EE)和频谱效率(Spectral Efficiency, SE)。在保证用户服务质量(Quality of Service, QoS)需求的前提下,为了联合优化系统的能量效率和频谱效率,研究了密集异构网络中下行链路的资源分配(Resource Allocation, RA)问题。首先,将频谱和小基站发射功率分配问题建模为联合优化系统能量效率和频谱效率的多目标优化问题;其次,提出了基于单策略多目标强化学习(Single-strategy Multi-objective Reinforcement Learning, SMRL)的资源分配算法求解所建立的多目标优化问题。仿真结果表明,与基于单目标强化学习的资源分配算法相比,所提算法可以实现系统能量效率和频谱效率的联合优化,与基于群体智能算法的资源分配算法相比,所提算法的系统能量效率提高了1%～1.5%,频谱效率...     

多小区5G认知无线电网络能量有效资源优化

为提高5G通信系统中能量利用效率,本文提出一种资源配置算法来解决多小区5G认知无线电网络中资源配置问题。针对需要优化的载波分配变量和功率变量,该算法采用交替优化的方式分别对上述变量进行优化。对于载波分配,所提算法依据最大化信噪比原则来分配载波;对于功率分配,本文将其转变为另外一个等效问题,然后利用连续凸近似方法求解。由于传统正交频分复用调制（OFDM）具有严重的频谱泄露,其他几种具有较低频谱泄露特性的5G候选调制方式,例如滤波器组多载波调制（FBMC）、通用滤波多载波调制（UFMC）、广义频分复用调制（GFDM）等,也被分析比较。仿真结果表明本文所提算法相比干扰受限算法具有更高的能量效率,并且证明具有较低频谱泄露的调制方式能取得更高的能量效率。      

面向数字孪生边缘网络的区块链分片及资源自适应优化机制

为了解决数字孪生边缘网络数据共享面临的隐私和安全问题,提出一种基于区块链分片的数字孪生边缘网络数据安全共享机制。考虑动态时变的数字孪生边缘网络和边缘网络孪生模型与物理网络映射误差,建立联合多播簇头选择、本地基站（BS）共识接入选择、频谱和计算共识资源分配的自适应资源优化模型,实现最大化区块链分片交易吞吐量的目标。在数字孪生边缘网络环境下,提出双层近端策略优化（PPO）算法,求解自适应资源优化问题。仿真结果表明,所提算法可以有效改善区块链分片交易吞吐量。同时,在适应映射误差方面优于传统深度强化学习算法。     

HSDPA数据业务优化案例分析

HSDPA（高速下行分组接入）技术是WCDMA系统R5协议中引入的无线增强技术,它可以为下行提供高达13.9Mbps的峰值速率,提高了小区吞吐量、服务质量,改善了下行分组数据业务的频谱效率。然而新的应用对无线网络运营产生了很高的要求,有效的网络优化可以解决网络的通信忙点和盲点,保证网络稳定高效运行。文章给出HSDPA数据业务优化的实际案例,对案例中的现象进行分析和优化,使得HSDPA数据业务性能得以改善。     

基于能量效率的分布式星群下行链路功率分配方法研究

针对多波束卫星通信系统星上资源稀缺和能量利用效率不高的问题,本文提出了分布式星群网络下行链路中兼顾系统功耗和数据速率的功率分配方法,通过合理的资源分配来优化系统的能量效率.首先建立分布式星群功率分配模型,将复杂的分式问题转化为易于求解的减法形式问题,然后基于凸优化理论,提出功耗-数据速率权衡功率分配算法,并讨论了能量效率（energy efficiency）与频谱效率（spectral efficiency）之间的权衡关系.仿真结果验证了提出算法的有效性和EE-SE权衡关系,并分析了电路功耗对系统性能的影响.     

3D MIMO-OFDMA系统中基于垂直波束成形的能效优化算法

针对三维多输入多输出（3D MIMO）正交频分多址（OFDMA）系统,提出了一种能效优化算法。该算法在垂直波束成形技术下,以能量效率最大化为目标,通过调整资源分配、功率分配、天线的波束下倾角来提高系统能量效率。根据分数优化理论,将复杂的分数优化问题转化为较易求解的整式优化问题,然后引入拉格朗日乘子通过不断迭代得到能量效率的最优值。仿真结果表明,所提算法在较少迭代次数下可以获得更高的能量效率。     

射频能量捕获传感网移动能量源的布置策略研究

射频能量捕获传感网（RF Energy Harvesting Wireless Sensor Network,RFEH-WSN）由专用射频能量源设备（Energy Transmitter,ET）和具有射频能量捕获功能的传感器节点（Energy Harvesting Recevier,简称EHR）组成.该网络解决了传感器网络中电池不易更换与节点能量容易耗尽的问题,使得RFEH-WSN应用前景更加广阔.RFEH-WSN应用中一个值得研究的问题是如何布置ET的充电位置,降低ET能耗且提高覆盖率.已有的工作主要考虑ET布置中单目标优化问题,如最小充电时间、最小功耗、最大覆盖率等.本文以时间最小和覆盖率最大为目标建立多目标优化模型,并提出利用粒子群算法（Particle Swarm Optimization,PSO）求解多目标函数（Multiple Object Program,MOP）的低复杂度近似算法,获得了最优Pareto解集.仿真结果表明,多目标优化可以满足不同情况的需求,提高充电效用.     

基于深度强化学习的能量采集认知无线电动态频谱接入

为解决认知无线电(Cognitive Radio, CR)中频谱和能量短缺的问题，提出一种基于深度Q网络(Deep Q-Network, DQN)的动态频谱接入算法。次级用户(Secondary User, SU)通过基站射频信号采集能量，并在频谱感知后实现信道的自主接入。模型通过DQN训练，并使用奖励机制和训练算法优化，SU能够根据环境信息作出合适的接入策略。仿真结果表明，提出的深度强化学习(Deep Reinforcement Learning, DRL)模型性能优于无学习模型，提高了频谱感知准确率及用户吞吐量，对比结果证明了模型的适用性及合理的虚警率可以提升模型的学习性能。     

基于图神经网络的联合用户调度与波束成形优化算法

协作多点（Co MP）传输技术具有降低同频干扰和提高频谱效率的特点。对于Co MP,用户调度与波束成形是2个分别位于媒体访问接入层和物理层的基本研究问题。在考虑用户服务质量需求下,重点研究用户调度与波束成形的联合优化问题,并以网络吞吐量最大化为目标。为了克服传统优化算法计算开销大且未有效利用网络历史数据信息的问题,提出了一种基于图神经网络联合用户调度与功率分配模型,并结合波束向量的解析公式,以实现联合用户调度与波束成形优化。仿真分析表明,所提算法能够以较低的计算开销实现与传统优化算法相匹配,甚至更优的性能表现。     

Energy-Spectral-Efficiency Tradeoff in Interference-Limited Wireless Networks

Spectral efficiency (SE) is an important metric in traditional wireless network design. However, as the development of high-data rate services and rapid increase of energy consumption, energy efficiency (EE) has received more and more attention. In this paper, we investigate the EE–SE tradeoff problem in interference-limited wireless networks. Different from previous researches, we try to optimize EE and SE simultaneously. Firstly, the problem is formulated as a multi-objective optimization problem (MOP), with the constraint of transmit power limit. Then, we convert the MOP to a single-objective optimization problem by the weighted linear sum method. We present an algorithm utilizing difference between two convex functions programming (DCP) to handle with SE optimization problem (SD). EE optimization problem can be solved by an algorithm (EFD) consists of fractional programming embedded with DCP. While for EE–SE tradeoff problem, a particle swarm optimization algorithm is proposed (ESTP) to deal with it. Simulation results validate that the proposed algorithm can efficiently balance EE and SE by adjusting the value of weighted coefficient, which could be used to design a flexible energy efficient network in the future.

     

Energy efficiency and spectral efficiency tradeoff in downlink OFDMA systems with imperfect CSI

In this paper, we investigate the tradeoff between energy efficiency (EE) and spectral efficiency (SE) in downlink orthogonal frequency division multiplexing access (OFDMA) systems, whilst considering the channel estimation cost and the corresponding effect of imperfect channel state information (CSI) on SE and EE. The problem is formulated as a multi-objective optimization to determine the optimal pilot transmission power, data transmission power and subcarrier assignment, and then transformed into a single-objective optimization problem, which is a non-convex mixed-integer nonlinear programming (NCMINP) and NP-hard. To address it, we propose an efficient algorithm by adopting alternating optimization and convex optimization methods in lower power region as well as approximate conversion and branch-and-bound methods in high power region. Simulation results analyze and validate the performance of EE-SE tradeoff.     

An energy-efficient power allocation scheme for millimeter-wave MIMO-NOMA systems

There are many challenges in fifth generation (5G) telecommunication systems, due to the increasing demands and applications. The most important of which are need to have higher energy efficiency (EE) and spectral efficiency (SE). They are critical in the practical multiple-input multiple-output (MIMO) telecommunication systems. Non-orthogonal multiple access (NOMA) methods and millimeter-waves can be used in conjunction with MIMO systems to improve their EE and SE performance. In this paper, we investigate the application of NOMA and mm-Wave transmission in the downlink of MIMO systems. Then, we formulate the optimization problem for users in MIMO-NOMA systems to maximize the EE that is subject to minimum data rate to satisfy required quality of service (QoS) and maximum transmission power. To achieve the optimal power allocation for users, we reach a problem for the EE maximization that is non-convex and solution of the optimization problem is not trivial. We exploit a lower bound of the data rate and the Lagrange dual function to convert it to a convex and unconstrained problem, which is easy to solve. In the next step, we derive a relation for determining the optimal power allocation of users. In addition, a numerical algorithm is presented that can be used to solve the problem. According to the simulation results of the proposed algorithm, our method performs better and provides higher EE than both orthogonal multiple access and equal power allocation schemes.     

On the Ergodic Capacity and Energy Efficiency for Fading MIMO NOMA Systems

Non-orthogonal multiple access (NOMA) is expected to be a promising multiple access techniques for 5G networks due to its superior spectral efficiency (SE). Previous research mainly focus on the design to improve the SE performance with instantaneous channel state information (CSI). In this paper, we consider the fading MIMO channels with only statistical CSI at the transmitter, and explore the potential gains of MIMO NOMA scheme in terms of both ergodic capacity and energy efficiency (EE). The ergodic capacity maximization problem is first studied for the fading multiple-input multiple-output (MIMO) NOMA systems. We derive the optimal input covariance structure and propose both optimal and low complexity suboptimal power allocation schemes to maximize the ergodic capacity of MIMO NOMA system. For the EE maximization, the optimization problem is formulated to maximize the system EE (defined by ergodic capacity under unit power consumption) under the total transmit power constraint and the minimum rate constraint of the weak user. By transforming the EE maximization problem into an equivalent one-dimensional optimization problem, the optimal power allocation for EE design is proposed. To further reduce the computation complexity, a near-optimal solution based on golden section search and suboptimal closed form solution are proposed as well. Numerical results show that the proposed NOMA schemes significantly outperform the traditional orthogonal multiple access scheme with traditional orthogonal multiple access transmission in terms of both SE and EE.     

Energy spectral efficiency tradeoff in downlink OFDMA network

Spectral efficiency (SE) is an important metric in traditional wireless network design. However, as the development of high‐data rate services and rapidly increase of energy consumption, energy efficiency (EE) has received more and more attention. In this paper, we investigate the EE–SE tradeoff in downlink OFDMA network. Different from previous researches, we try to optimize EE and SE simultaneously. First, the problem is formulated as a multiobjective optimization problem (MOP), and its Pareto optimal set is characterized. Then, we convert the MOP to a single‐objective optimization problem (SOP) by the weighted linear sum method and show that it is neither quasi‐convex nor quasi‐concave. After that, a novel algorithm using particle swarm optimization is proposed to solve the SOP. Simulation results validate that the proposed algorithm can efficiently reduce total transmit power and improve EE, although the cost is sacrificing some SE, which could be used to design an flexible energy efficient network in the future.Copyright © 2014 John Wiley & Sons, Ltd.     

多无人机协作的认知通信网络中能/谱效折中优化

在无人机（Unmanned Aerial Vehicle,UAV）认知通信网络中,其能量受限和通信高吞吐量问题备受关注。然而,能量效率（Energy Efficiency,EE）的提升可能会导致频谱效率（Spectrum Efficiency,SE）的下降。针对此问题,对UAV协作认知通信网络中EE和SE的折中优化进行了研究。首先,进行了感知时间、UAV通信的发射功率和判决门限各自对SE与EE两者的优化;其次,通过二分法求解使得EE和SE最大化的感知时间值,并通过穷尽搜索法分别求解感知时间、UAV通信的发射功率和判决门限对EE和SE折中优化问题的最优参数值。在此基础上,提出一种联合参数迭代优化算法,求解EE和SE的折中优化问题。仿真实验表明,SE和EE之间存在折中的权衡,并验证了所提优化方案的有效性。     

Power allocation scheme for maximizing spectrum efficiency and energy efficiency tradeoff for multi-cluster NOMA system

In this paper, a power allocation to maximize tradeoff between spectrum efficiency (SE) and energy efficiency (EE) is considered for the downlink non-orthogonal multiple access (NOMA) system with arbitrarily clusters and arbitrarily users, where the subcarriers of clusters are mutually orthogonal to each other. Specifically, an optimization problem of maximizing SE-EE tradeoff is formulated by optimizing power allocation among users under the constraints of user rate requirements. Then, the optimization problem is decomposed into a group of sub-problems with the aim of maximizing SE-EE tradeoff for each cluster, which is solved by using bisection method and monotonicity of function. Finally, the power allocation optimization problem among users is transformed into that between clusters, and a two steps inter-cluster power allocation algorithm is developed to solve this problem. Simulation results show that SE-EE tradeoff of the proposed scheme is better than that of the existing schemes.     

Energy efficient power allocation strategy for downlink MU-MIMO with massive antennas

With the increasing energy consumption, energy efficiency （EE） has been considered as an important metric for wireless communication networks as spectrum efficiency （SE）. In this paper, EE optimization problem for downlink multi-user multiple-input multiple-output （MU-MIMO） system with massive antennas is investigated. According to the convex optimization theory, there exists a unique globally optimal power allocation achieving the optimal EE, and the closed-form of the optimal EE only related to channel state information is derived analytically. Then both the approximate and accurate power allocation algorithms with different complexity are proposed to achieve the optimal EE. Simulation results show that the optimal EE obtained by the approximate algorithm coincides to that achieved by the accurate algorithm within the controllable error limitation, and these proposed algorithms perform better than the existing equal power allocation algorithm. The optimal EE and corresponding SE increase with the number of antennas at base station, which is promising for the next generation wireless communication networks.     

An Algorithm for Traffic Grooming in WDM Optical Mesh Networks with Multiple Objectives

This paper studies a traffic grooming in wavelength-division multiplexing (WDM) mesh networks for the SONET/SDH streams requested between node pairs. The traffic could be groomed at the access node before converting to an optical signal carried in the all-optical network. We design a virtual topology with a given physical topology to satisfy multiple objectives and constraints. The grooming problem of a static demand is considered as an optimization problem. The traditional algorithms found in the literatures mostly focus on a single objective either to maximize the performance or to minimize the cost. We propose a multi-objective evolutionary algorithm to solve a grooming problem that optimizes multiple objectives all together at the same time. In this paper we consider the optimization of three objectives: maximize the traffic throughput, minimize the number of transceivers, and minimize the average propagation delay or average hop counts. The simulation results show that our approach is superior to an existing heuristic approaches in an acceptable running time.