Energy efficiency maximization for buffer-aided multi-UAV relaying communications

This paper studies a multiple unmanned aerial vehicle(UAV)relaying communication system,where multiple UAV re-lays assist the blocked communication between a group of ground users(GUs)and a base station(BS).Since the UAVs only have limited-energy in practice,our design aims to maximize the energy efficiency(EE)through jointly designing the communica-tion scheduling,the transmit power allocation,as well as UAV trajectory under the buffer constraint over a given flight period.Actually,the formulated fractional optimization problem is diffi-cult to be solved in general because of non-convexity.To re-solve this difficulty,an efficient iterative algorithm is proposed based on the block coordinate descent(BCD)and successive convex approximation(SCA)techniques,as well as the Dinkel-bach’s algorithm.Specifically,the optimization variables of the formulated problem are divided into three blocks and we alter-nately optimize each block of the variables over iteration.Numeri-cal results verify the convergence of the proposed iterative al-gorithm and show that the proposed designs achieve significant EE gain,which outperform other benchmark schemes.

Constant envelope FrFT OFDM: spectral and energy efficiency analysis

Constant envelope with a fractional Fourier transformorthogonal frequency division multiplexing(CE-FrFT-OFDM)is a special case of a constant envelope OFDM(CE-OFDM),both being energy efficient wireless communication techniques with a 0 dB peak to average power ratio(PAPR).However,with the proper selection of fractional order,the first technique has a high bit error rate(BER)performance in the frequency-time selective channels.This paper performs further analysis of CE-FrFT-OFDM by examining its spectral efficiency(SE)and energy efficiency(EE)and compare to the famous OFDM and FrFT-OFDM techniques.Analytical and comprehensive simulations conducted show that,the CE-FrFT-OFDM has five times the EE of OFDM and FrFT-OFDM systems with a slightly less SE.Increasing CE-FrFT-OFDM’s transmission power by increasing its amplitude to 1.7 increases its SE to match that of the OFDM and FrFT-OFDM systems while slightly reducing its EE by 20%to be four times that of OFDM and FrFTOFDM systems.OFDM and FrFT-OFDM’s amplitude fluctuations cause rapid changing output back-off(OBO)power requirements and further reduce power amplifier(PA)efficiency while CE-FrFTOFDM stable operational linear range makes it a better candidate and outperforms the other techniques when their OBO exceeds 1.7.Higher EE and low BER in time-frequency selective channel are attracting features for CE-FrFT-OFDM deployment in mobile devices.

Spectral and Energy Efficiency of Line-of-Sight OAM-MIMO Communication Systems

Not only high spectral efficiency(SE)but also high energy efficiency(EE)are required for future wireless communication systems.Radio orbital angular momentum(OAM)provides a new perspective of mode multiplexing to improve SE.However,there are few studies on the EE performance of OAM mode multiplexing.In this paper,we investigate the SE and EE of a misaligned uniform concentric circle array(UCCA)-based multi-carrier multimode OAM and multiple-input multiple-output(MCMM-OAM-MIMO)system in the line-ofsight(LoS)channel,in which two transceiver architectures implemented by radio frequency(RF)analog synthesis and baseband digital synthesis are considered.The distance and angle of arrival(AoA)estimation are utilized for channel estimation and signal detection,whose training overhead is much less than that of traditional MIMO systems.Simulation results validate that the UCCA-based MCMM-OAM-MIMO system is superior to conventional MIMOOFDM system in the EE and SE performances.

Optimization of energy efficiency for the full-duplex massive MIMO systems

The energy efficiency（EE） for the full-duplex massive multi-input multi-output（MIMO） system is investigated. Given the transmit powers of both the uplink and the downlink, the closed-form solutions of the optimal number of antennas and the maximum EE are achieved in the high regime of the signal-to-noise ratio（SNR）. It is shown that the optimal number of antennas and the maximum EE gets larger with the increase in user numbers. To further improve the EE, an optimization algorithm with low complexity is proposed to jointly determine the number of antennas and the transmit powers of both the uplink and the downlink. It is shown that, the proposed algorithm can achieve the system performance very close to the exhaustive search.

Energy Efficiency Optimization for D2D Communications Based on SCA and GP Method

In this paper, we propose an energy-efficient power control scheme for device-to-device(D2D) communications underlaying cellular networks, where multiple D2D pairs reuse the same resource blocks allocated to one cellular user. Taking the maximum allowed transmit power and the minimum data rate requirement into consideration, we formulate the energy efficiency maximization problem as a non-concave fractional programming(FP) problem and then develop a two-loop iterative algorithm to solve it. In the outer loop, we adopt Dinkelbach method to equivalently transform the FP problem into a series of parametric subtractive-form problems, and in the inner loop we solve the parametric subtractive problems based on successive convex approximation and geometric programming method to obtain the solutions satisfying the KarushKuhn-Tucker conditions. Simulation results demonstrate the validity and efficiency of the proposed scheme, and illustrate the impact of different parameters on system performance.

Exploiting Spectral-Energy Efficiency Tradeoff with Fairness in Non-Orthogonal Multiple Access Systems

The spectral efficiency(SE)and energy efficiency(EE)tradeoff while ensuring rate fairness among users in non-orthogonal multiple access(NOMA)systems is investigated.In order to characterize the SE-EE tradeoff with rate fairness,a multi-objective optimization(MOO)problem is first formulated,where the rate fairness is represented with theα-fair utility function.Then,the MOO problem is converted into a single-objective optimization(SOO)problem by the weighted sum method.To solve the converted non-convex SOO problem,we apply sequential convex programming,which helps to propose a general power allocation algorithm to realize the SE-EE tradeoff with rate fairness.We prove the convergence of the proposed algorithm and the convergent solution satisfies the KKT conditions.Simulation results demonstrate the proposed power allocation algorithm can achieve various levels of rate fairness,and higher fairness results in degraded performance of SE-EE tradeoff.A pivotal conclusion is reached that NOMA systems significantly outperform orthogonal multiple access systems in terms of SE-EE tradeoff with the same level of rate fairness.

Optimal Size for Maximal Energy Efficiency in Information Processing of Biological Systems Due to Bistability

Energy efficiency is closely related to the evolution of biological systems and is important to their information processing. In this work, we calculate the excitation probability of a simple model of a bistable biological unit in response to pulsatile inputs, and its spontaneous excitation rate due to noise perturbation. Then we analytically calculate the mutual information, energy cost, and energy efficiency of an array of these bistable units. We find that the optimal number of units could maximize this array＇s energy efficiency in encoding pulse inputs, which depends on the fixed energy cost. We conclude that demand for energy efficiency in biological systems may strongly influence the size of these systems under the pressure of natural selection.

Energy efficiency optimization of relay-assisted D2D two-way communications with SWIPT

Aiming at the energy consumption of long-distance device-to-device(D2D) devices for two-way communications in a cellular network,this paper proposes a strategy that combines two-way relay technology(TWRT) and simultaneous wireless information and power transfer(SWIPT) technology to achieve high energy efficiency(EE) communication.The scheme first establishes a fractional programming problem to maximize EE of D2D,and transforms it into a non-fractional optimization problem that can be solved easily.Then the problem is divided into three sub-problems:power control,power splitting ratios optimization,and relay selection.In order to maximize EE of the D2D pair,the Dinkelbach iterative algorithm is used to optimize the transmitted power of two D2D devices simultaneously;the one-dimensional search algorithm is proposed to optimize power splitting ratios;an improved optimal relay selection scheme based on EE is proposed to select relay.Finally,experiments are carried out on the Matlab simulation platform.The simulation results show that the proposed algorithm has faster convergence.Compared with the one-way relay transmission and fixed relay algorithms,the proposed scheme has higher EE.

An Energy-Efficient UAV Deployment Scheme for Emergency Communications in Air-Ground Networks with Joint Trajectory and Power Optimization

The space-air-ground integrated network(SAGIN)has gained widespread attention from academia and industry in recent years.It is widely applied in many practical fields such as global observation and mapping,intelligent transportation systems,and military missions.As an information carrier of air platforms,the deployment strategy of unmanned aerial vehicles(UAVs)is essential for communication systems’performance.In this paper,we discuss a UAV broadcast coverage strategy that can maximize energy efficiency(EE)under terrestrial users’requirements.Due to the non-convexity of this issue,conventional approaches often solve with heuristics algorithms or alternate optimization.To this end,we propose an iterative algorithm by optimizing trajectory and power allocation jointly.Firstly,we discrete the UAV trajectory into several stop points and propose a user grouping strategy based on the traveling salesman problem(TSP)to acquire the number of stop points and the optimization range.Then,we use the Dinkelbach method to dispose of the fractional form and transform the original problem into an iteratively solvable convex optimization problem by variable substitution and Taylor approximation.Numerical results validate our proposed solution and outperform the benchmark schemes in EE and mission completion time.

Collaborative Multiple Access And Energy-Efficient Resource Allocation in Distributed Maritime Wireless Networks

With the rapid increasing of maritime activities, maritime wireless networks(MWNs) with high reliability, high energy efficiency, and low delay are required. However, the centralized networking with fixed resource scheduling is not suitable for MWNs due to the special environment. In this paper,we introduce the collaborative relay communication in distributed MWNs to improve the link reliability, and propose an orthogonal time-frequency resource block reservation based multiple access(RRMA) scheme for both one-hop direct link and two-hop collaborative relay link to reduce the interference. To further improve the network performance, we formulate an energy efficiency(EE) maximization resource allocation problem and solve it by an iterative algorithm based on the Dinkelbach method. Finally, numerical results are provided to investigate the proposed RRMA scheme and resource allocation algorithm, showing that the low outage probability and transmission delay can be attained by the proposed RRMA scheme. Moreover,the proposed resource allocation algorithm is capable of achieving high EE in distributed MWNs.

Convolutional Neural Network-Based Deep Q-Network (CNN-DQN) Resource Management in Cloud Radio Access Network

The recent surge of mobile subscribers and user data traffic has accelerated the telecommunication sector towards the adoption of the fifth-generation (5G) mobile networks. Cloud radio access network (CRAN) is a prominent framework in the 5G mobile network to meet the above requirements by deploying low-cost and intelligent multiple distributed antennas known as remote radio heads (RRHs). However, achieving the optimal resource allocation (RA) in CRAN using the traditional approach is still challenging due to the complex structure. In this paper, we introduce the convolutional neural network-based deep Q-network (CNN-DQN) to balance the energy consumption and guarantee the user quality of service (QoS) demand in downlink CRAN. We first formulate the Markov decision process (MDP) for energy efficiency (EE) and build up a 3-layer CNN to capture the environment feature as an input state space. We then use DQN to turn on/off the RRHs dynamically based on the user QoS demand and energy consumption in the CRAN. Finally, we solve the RA problem based on the user constraint and transmit power to guarantee the user QoS demand and maximize the EE with a minimum number of active RRHs. In the end, we conduct the simulation to compare our proposed scheme with nature DQN and the traditional approach.

Adaptive Energy Efficient Power Allocation Scheme for DAS with Multiple Receive Antennas

Energy efficiency(EE)of downlink distributed antenna system(DAS)with multiple receive antennas is investigated over composite Rayleigh fading channel that takes the path loss and lognormal shadow fading into account.Our aim is to maximize EE which is defined as the ratio of the transmission rate to the total consumed power under the constraints of the maximum transmit power of each remote antenna.According to the definition of EE,the optimized objective function is formulated with the help of Lagrangian method.By using the Karush-KuhnTucker(KKT)conditions and numerical calculation,considering both the static and dynamic circuit power consumptions,an adaptive energy efficient power allocation(PA)scheme is derived.This scheme is different from the conventional iterative PA schemes based on EE maximization since it can provide closed-form expression of PA coefficients.Moreover,it can obtain the EE performance close to the conventional iterative scheme and exhaustive search method while reducing the computation complexity greatly.Simulation results verify the effectiveness of the proposed scheme.

Joint UAV 3D deployment and sensor power allocation for energy-efficient and secure data collection

Unmanned aerial vehicles(UAVs) are advantageous for data collection in wireless sensor networks(WSNs) due to its low cost of use,flexible deployment,controllable mobility,etc. However,how to cope with the inherent issues of energy limitation and data security in the WSNs is challenging in such an application paradigm. To this end,based on the framework of physical layer security,an optimization problem for maximizing secrecy energy efficiency(EE) of data collection is formulated,which focuses on optimizing the UAV’s positions and the sensors’ transmit power. To overcome the difficulties in solving the optimization problem,the methods of fractional programming and successive convex approximation are then adopted to gradually transform the original problem into a series of tractable subproblems which are solved in an iterative manner. As shown in simulation results,by the joint designs in the spatial domain of UAV and the power domain of sensors,the proposed algorithm achieves a significant improvement of secrecy EE and rate.

耦合相移下有源同时反射和透射智能反射面辅助的多用户安全通信

无源智能反射面在增强无线通信系统和提高物理层安全方面极具潜力,但是其存在严重的“双衰落”和半区域覆盖的缺点。为此,该文研究了一种有源同时反射和透射智能反射面(STAR-RIS),并在考虑反射和透射相移互耦合条件下,建立一个联合优化基站波束和有源STAR-RIS波束的安全能效最大化问题。为求解所形成的非凸优化问题,利用连续凸近似、罚函数法、半正定松弛、交替优化技术将原问题转化为凸问题,并提出一种基于惩罚对偶分解算法。仿真结果验证了该文所提算法的有效性。

STAR-RIS辅助通信感知一体化的多用户安全通信

智能反射面在提高通信感知一体化系统性能和增强物理层安全方面具有极大潜力,但是其存在半空间覆盖的缺点。为此,研究了一种同时反射和透射智能反射面,并在考虑感知目标的信噪比约束条件下,建立一个联合优化基站发射波束赋形和智能反射面相移的安全能效最大化问题。为求解所赋形的非凸优化问题,用Dinkelbach方法将目标函数转化为减法形式,并将该为题转化为两个子问题来解耦发射波束赋形和反射面相移,进而提出基于连续凸近似、半正定松弛的交替优化算法来求解。仿真结果表明,与传统的分组智能反射面场景相比,该方案能够提升25.6%的系统能效。

非线性能量采集的工业物联网非线性信息年龄分析

针对工业物联网(IIoT)中对信息新鲜性日益增长的需求,提出一种基于非线性能量采集(EH)的IIoT监控系统,配备电池容量大小为B的传感器节点,从无线供电基站(WPS)使用非线性EH技术采集射频(RF)能量进行充电,电池充满后将采集到的数据发送给数据采集节点。此外,针对在复杂场景中需要设置多个非线性信息年龄(AoI)算法所存在系统较为复杂的问题,提出1个普适函数来描述不同的非线性AoI,并通过调节参数来控制函数图像形状。在推导系统非线性AoI封闭表达式的同时引入系统能量效率(EE),以两者的比值Ψ作为系统的性能指标。鉴于问题较为复杂,采用一维搜索方法来寻找最优电池容量。通过系统仿真和数值分析,证明了存在最优的电池容量B使系统Ψ最小化。仿真结果表明,信道衰落参数、状态更新大小、WPS发射功率等参数都会对系统的性能产生影响,在相关系统的优化设计时都需要考虑。当WPS发射功率为0.5 W时,指数型非线性AoI下的Ψ是对数型非线性的2倍,是线性的1.8倍。

混合智能反射面辅助的通信感知一体化:高能效波束成形设计

能量效率(EE)是5G+/6G无线通信的重要设计指标,而智能反射面(RIS)被普遍认为是改善EE的潜在手段。不同于被动RIS,混合RIS由有源和无源元件组成,对来波移相的同时可放大信号强度,能够有效克服被动RIS引起的“乘性衰落”效应。鉴于此,该文提出一种混合RIS辅助通信感知一体化(ISAC)的下行链路传输系统。为探究数据传输速率与能耗之间的内在关联,该文以RIS辅助ISAC网络能量效率最大化为目标,在满足基站(BS)发射功率、波束图增益以及混合RIS功率和幅值约束的条件下,联合优化基站端的波束赋形和混合RIS的相移。为解决该复杂的分数规划问题,提出基于交替优化(AO)的算法来求解。为克服AO算法中引入辅助变量造成算法复杂度高的难题,利用耦合优化变量的关联,提出一种基于级联深度学习网络的求解算法。仿真结果表明,提出的混合RIS辅助ISAC方案在和速率、能效方面皆优于现有方案,且算法收敛速度快。

基于射频链选择和混合预编码设计的双功能雷达通信能效最大化研究

针对毫米波大规模多天线双功能雷达通信系统能量效率低、硬件成本过高等问题,建立双功能雷达通信新模型,提出并求解系统能效最大化问题。首先,考虑硬件损耗干扰和多用户干扰等因素,提出新的通信模型与雷达模型。其次,构建具有感知约束和功率约束的能效最大化问题,并将该非凸问题分解为两个子问题。最后,利用基于近似优化方法的分数规划和加权正交匹配追踪算法,分别实现射频链选择子问题和混合预编码设计子问题的优化求解。仿真结果表明,所提机制方案不仅实现了系统能效最大化,同时实现了通信性能与感知性能之间的折中优化。

基于深度强化学习的IRS辅助NOMA-MEC通信资源分配优化

为了解决无法与边缘服务器建立直连通信链路的盲区边缘用户卸载任务的问题,设计了一个基于深度强化学习(deep reinforcement learning, DRL)的智能反射面(intelligent reflecting surface, IRS)辅助非正交多址(non-orthogonal multiple access, NOMA)通信的资源分配优化算法,以获得由系统和速率和能源效率(energy efficiency, EE)加权的最大系统收益,从而实现绿色高效通信。通过深度确定性策略梯度(deep deterministic policy gradient, DDPG)算法联合优化传输功率分配和IRS的反射相移矩阵。仿真结果表明,使用DDPG算法处理移动边缘计算(mobile edge computing, MEC)的通信资源分配优于其他几种对比实验算法。

无线网络中高效能量感知的路由分配算法

在无线网络中,由于传输过程会产生能耗,当部分节点能量耗尽时,网络的有效生命周期可能受到显著影响。鉴于此,文章研究了一种高效能量感知的路由分配算法,构建了一个无线网络传感器节点的能耗模型,该模型综合考虑了计算能耗、感知能耗、发射数据能耗和接收数据能耗4个关键方面。在进行无线网络路由分配时,充分权衡了有限的传输资源与延长网络有效生命周期的迫切需求。并基于上述能耗模型,设计了一种能效最大化的路由分配算法。测试结果表明,采用该算法的网络节点在运行至第400个周期时,平均剩余能量才降至零,这意味着设计的路由算法能够显著延长网络生存时间,并展现出卓越的传输性能。