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.

CoMP-transmission-based energy-efficient scheme selection algorithm for LTE-A systems

In order to achieve higher system energy efficiency （EE）,a new coordinated multipoint （CoMP）-transmission-based scheme selection energy saving （CTSES）algorithm is proposed for downlink homogeneous cellular networks.The problem is formulated as an optimization of maximizing system EE,under the constraints of the data rate requirement and the maximum transmit power.The problem is decomposed into power allocation and alternative scheme selection problems.Optimal power allocation is calculated for CoMP-JT （joint transmission）and CoMP-CS （coordinated scheduling） transmissions,and the scheme with higher EE is chosen. Since the optimal problem is a nonlinear fractional optimization problem for both CoMP transmission schemes, the problem is transformed into an equivalent problem using the parametric method. The optimal transmit power and optimal EE are obtained by an iteration algorithm in CoMP-JT and CoMP-CS schemes.Simulation results show that the proposed algorithm offers obvious energy-saving potential and outperforms the fixed CoMP transmission scheme.Under the condition of the same maximum transmit power limit,the empirical regularity of user distribution for scheme choice is presented, and using this regularity, the computational complexity can be reduced.

Proportional Fairness-Based Energy-Efficient Power Allocation in Downlink MIMO-NOMA Systems with Statistical CSI

In this paper, proportional fairness(PF)-based energy-efficient power allocation is studied for multiple-input multiple-output(MIMO) non-orthogonal multiple access(NOMA) systems. In our schemes, statistical channel state information(CSI) is utilized for perfect CSI is impossible to achieve in practice. PF is used to balance the transmission efficiency and user fairness. Energy efficiency(EE) is formulated under basic data rate requirements and maximum transmitting power constraints. Due to the non-convex nature of EE, a two-step algorithm is proposed to obtain sub-optimal solution with a low complexity. Firstly, power allocation is determined by golden section search for fixed power. Secondly total transmitting power is determined by fractional programming method in the feasible regions. Compared to the performance of MIMO-NOMA without PF constraint, fairness is obtained at expense of decreasing of EE.

Energy efficient resource allocation for D2D multicast communications

The resource allocation for device-to-device(D2D)multicast communications is investigated.To achieve fair energy efficiency(EE)among different multicast groups,the max-min fairness criterion is used as the optimization criterion and the EE of D2D multicast groups are taken as the optimization objective function.The aim is to maximize the minimum EE for different D2D multicast groups under the constraints of the maximum transmit power and minimum transmit rate,which is modeled as a non-convex and mixed-integer fractional programming problem.Here,suboptimal resource allocation algorithms are proposed to solve this problem.First,channel assignment scheme is performed to assign channel to D2D multicast groups.Second,for a given channel assignment,iterative power allocation schemes with and without loss of cellular users’rate are completed,respectively.Simulation results corroborate the convergence performance of the proposed algorithms.In addition,compared with the traditional throughput maximization algorithm,the proposed algorithms can improve the energy efficiency of the system and the fairness achieved among different multicast groups.

Energy-Efficient Target Channel Sequence Design for Spectrum Handoffs in Cognitive Radio Networks

Cognitive radio is considered as an efficient way to improve the spectrum efficiency. As one of its key technologies,spectrum handoff can guarantee the transmission continuity of secondary users(SUs). In this paper,we address a new and more generalized spectrum handoff problem in cognitive radio networks(CRNs),by considering simultaneously energy efficiency,multiple spectrum handoffs and multiple channels. Furthermore,effects of the primary users'(PUs')arrival and service rate on the target channel sequence selection are also considered. In order to obtain the energy-efficient target channel sequence,we firstly analyze the energy consumption and the number of delivered bits per hertz in the spectrum handoff process,and formulate a ratio-type energy efficiency optimization problem,which can be transformed into a parametric problem by utilizing fractional programming. Then,we propose an algorithm combining dynamic programming with bisection(DPB)algorithm to solve the energy efficiency optimization problem. Our simulation results verify that the designed target channel sequence has better performance than the existing algorithms in terms of energy efficiency.

Energy-Efficient Sensing and Transmission for Multi-Hop Relay Cognitive Radio Sensor Networks

How to achieve transmissions in an energy-efficient way in multi-hop decode and forward(DF) relay cognitive radio sensor networks(CRSNs) is important since sensor nodes in CRSNs are usually battery powered. This paper aims to maximize energy efficiency(EE) by joint optimizing sensing time and power allocation in multi-channels & multihops DF relay CRSNs under constraints on outage probability and sensing performance. First, we design a channel selection scheme for sensing according to the available probabilities of multi channels. Second, we analyze the expected throughput and energy consumption and formulate the EE problem as a concave/concave fractional program. Third, coordinate ascent and Charnes-Cooper Transformation(CCT) methods are used to transform the nonlinear fractional problem into an equivalent concave problem. Subsequently, the closed form of outage probability is derived and the convergence rate of the iterative algorithm is analyzed. Finally, simulation results show that the proposed scheme can achieve effective EE.

Energy Efficient Resource Allocation in Timesharing Multiuser Systems with Hybrid Energy Harvesting Transmitter

An energy effi cient resource allocation scheme in timesharing multiuser system with a hybrid energy harvesting transmitter is studied in this paper. Specially, the operation energy of system is supplied by constant energy and energy harvesting, which harvests energy from external environment. Our goal is to maximize the energy effi ciency of timesharing multiuser systems by considering jointly allocation of transmission time and power control in an off-line manner. The original nonconvex objective function is transformed into convex optimization problem via the fractional programming approach. Then, we solve the convex problem by Lagrange dual decomposition method. Simulation results show that the proposed energy efficient resource allocation scheme has a better performance than the scheme which decomposes optimization problem into two parts(power allocation, time allocation) to solve iteratively.

Energy Efficient Modelling of a Network

Most of the networks are generally less energy efficient and most of the time resources are underutilized. Even resources of busy networks are also underutilized and thus networks show energy inefficient management system. This paper focuses on how to obtain minimum resources for the current situation of the network to maintain connectivity, power saving and quality of service. Four different models are proposed in this perspective with different purposes and functions. These models determine the minimum resources under certain constrains. Two types of services namely, "minimum bandwidth" and "trivial file transfer" are considered. For "minimum bandwidth" service, minimum edge, minimum delay and minimum change models are proposed. Here data rate switch and enable/disable of edges are placed in these models for power saving strategy. Another model, multi flow is proposed for "trivial file transfer" service. It is proposed for transferring files through multiple flows in multiple paths from source to destination. All models except multi flow model are mixed integer programming optimization problem.

基于Dinkelbach-Quadratic算法的高速铁路通信能效优化功率分配研究

为了提高高速铁路通信能量效率,提出一种基于Dinkelbach-Quadratic变换的迭代算法来解决高速铁路通信下行链路的功率分配问题。通过在小区内部署远程天线单元构建高速铁路通信分布式天线系统模型,基于完美信道状态信息(CSI)和列车内接入点反馈的最小速率要求建立能效优化模型;基于所建立的非凸优化模型,采用Dinkelbach变换将分数形式的非线性规划问题转换为求差形式;利用Quadratic变换特性对目标函数进一步展开;采用交替迭代的方法完成能效最优的功率分配。仿真结果表明:所提算法在满足最小传输速率和最大发射功率的同时,可以兼顾系统能量效率和频谱效率;随着发射天线数量和接收中继数量的增加,系统性能也有明显提升,相比传统算法系统的能效和频谱效率至少提升5%。

IRS辅助的MISO系统安全节能通信方案

智能反射面(Intelligent Reflecting Surface,IRS)是提高未来B5G/6G网络安全节能通信的一项革命性技术。本文针对IRS辅助下行多输入单输出(Multiple Input Single Output,MISO)系统的高安全风险与高能耗问题,分析了发送端发射波束成形和IRS相移矩阵对系统安全节能通信的影响,构建合法用户保密率约束下的系统发射功率最小化问题。针对该非凸多变量耦合问题,提出一种基于逐次凸逼近(successive convex approximation,SCA)的二阶锥规划算法迭代求解问题。与传统迭代优化算法不同,该算法在每次迭代中同时更新所有优化变量,并最终获得原问题的次优解。仿真结果表明,相较于无IRS方案、随机相移方案与SDR迭代优化方案三种基准方案,所提算法均在保证合法用户保密质量的同时实现了更低的发射功率,证明了本文算法的有效性与IRS在未来安全节能通信系统中的巨大潜力。

学习驱动的分布式异构混合流水车间批量流能效调度优化

本文研究了分布式异构混合流水车间批量流能效调度问题,其中每个工厂的加工效率不同,工件可以分割成若干子批进入加工系统.以最大完成时间和总能耗为优化目标,建立了混合整数规划模型.本文提出了一种学习驱动的多目标进化算法,包括学习驱动的全局搜索和局部搜索.引入Q学习作为学习引擎,以种群和非支配解集的评价作为环境反馈信号,通过不断的学习来动态指导搜索操作的选择;基于问题特征,设计了算法的状态集、动作集和奖励机制.Q学习的引入能够及时感知当前搜索的状态,减少搜索操作的盲目性,提高搜索的效率.通过对仿真数据集的测试,表明所提出算法能够有效地求解分布式异构混合流水车间批量流能效调度问题.

A hierarchical multi-purpose roller shade controller to enhance indoor comfort and energy efficiency

Building envelope is the sole interface with outdoors that can lie in the level of integrating control strategies to enhance occupant's comfort and building performance. However, in the literature, there is still a lack of multi-purpose control strategies which can balance diverse and antagonistic aspects of occupant comfort while keeping the building energy consumption under control. Therefore, this research proposed an integrated simulation-based workflow using EnergyPlus and Python-based package, OpyPlus, to adjust a roller shade for single occupied office space in a hot and arid climate. The developed system aims to improve both occupant's visual and thermal comfort considering daylight level, potential glare at occupant's field of view, view to outdoor satisfaction, and Fanger PMV method, and then, managing the HVAC system in favor of enhancing building energy efficiency. Furthermore, the controller's performance was compared to multiple fixed controls and another multi-agent control scenario from visual and thermal comfort and energy performance perspectives. The developed control system could perform effectively to provide a glare-free view and higher thermally comfortable indoor environment by 99% for the occupant across the year, while improving the heating and cooling loads. The research provides novel insights and potential future integrations for facade designers and building operators.

Low Complexity Energy Optimization Algorithm for Massive MIMO systems

We consider the problem of nearly optimal energy efficiency in massive(Multiinput Multi-output) MIMO systems. Considering the correlated channel in practice, we derive the ergodic expression with zero-forcing precoding and analyze the simplified antennas selection method. Aiming at optimizing the energy efficiency, the closed form expressions of the nearly optimal number of transmit antennas and transmit power are given under the circuit consumption model. The joint solution of the number of transmit antennas and transmit power was replaced to only solve transmit power. Based on the expression only related with transmit power, we give an energy efficiency optimization algorithm. The simulation results show that the proposed algorithm can achieve nearly optimal energy efficiency with fast convergence speed.

基于动态规划法的原油管道外输系统运行能耗优化

随着油田的不断开发以及输油设备的长期运行,输油站内机泵、加热炉等耗能设备与输量产生不匹配的现象,致使外输系统整体耗能呈现逐年增涨的趋势。针对上述问题,建立以外输系统总能耗费用为目标函数的原油管道外输系统优化模型,通过外输系统热力水力计算以及约束条件确定外输系统内各站进站参数允许区间进而反算得到外输运行参数方案合集,采用动态规划法通过输油站设备能效计算对输油泵和加热炉的设备组合方案进行优化,选取当前输量下总能耗费用最低的方案作为优化的最优结果。通过此方法对某外输系统进行优化,总能耗费用降低约16%。

能效最优准则下无人机空中高度的优化算法

利用无人机(unmanned aerial vehicle,UAV)组建空中移动基站,可为地面终端用户提供更灵活、高效的接入服务。受UAV覆盖范围和有限能量的约束,研究如何建立能效最优的UAV部署算法至关重要。为此,提出能效最优准则下无人机空中高度的优化算法(optimal energy efficient based aerial altitude optimization,EEAO)。先在满足用户最小速率要求和UAV高度的约束条件下,构建能效最优的高度优化的目标函数。同时,定义能效时不仅考虑UAV的通信能耗,还考虑UAV在垂直爬升和悬停阶段的能耗。再分别利用和序列凸规划法(sequential convex programming,SCP)和单调分式规划法(monotonic fractional programming,MFP)求解目标函数,获取能效最优的UAV高度值。性能分析表明,基于SCP法求解的最优高度值与基于MFP法求解的最优高度值相近。同时,相比于不考虑悬停阶段UAV的能耗,考虑悬停阶段UAV的能耗可提升网络能效。

User scheduling and power allocation for downlink multi-cell multi-carrier NOMA systems

In Non-Orthogonal Multiple Access(NOMA),the best way to fully exploit the benefits of the system is the efficient resource allocation.For the NOMA power domain,the allocation of power and spectrum require solving the mixed-integer nonlinear programming NP-hard problem.In this paper,we investigate user scheduling and power allocation in Multi-Cell Multi-Carrier NOMA(MCMC-NOMA)networks.To achieve that,we consider Weighted Sum Rate Maximization(WSRM)and Weighted Sum Energy Efficiency Maximization(WSEEM)problems.First,we tackle the problem of user scheduling for fixed power using Fractional Programming(FP),the Lagrange dual method,and the decomposition method.Then,we consider Successive Pseudo-Convex Approximation(SPCA)to deal with the WSRM problem.Finally,for the WSEEM problem,SPCA is utilized to convert the problem into separable scalar problems,which can be parallelly solved.Thus,the Dinkelbach algorithm and constraints relaxation are used to characterize the closed-form solution for power allocation.Extensive simulations have been implemented to show the efficiency of the proposed framework and its superiority over other existing schemes.

常导高速磁浮列车节能速度曲线鲁棒优化研究

列车速度曲线的优化是轨道交通节省运营成本的重要途径之一,鲁棒优化方法有助于克服速度曲线节能优化中的不确定因素。针对常导高速磁浮列车节能速度曲线优化,首先将区间划分为若干区段,进而将列车运行控制的停车点步进机制、列车悬浮运行的阻力特性、区间限速、运行时分与乘坐舒适性要求以及列车总重的不确定性纳入鲁棒优化模型,并结合三项引理将其重构为带二次项和区间数的混合整数规划(MIP)问题。联合Gurobi求解器以及基于可接受目标函数值波动范围的鲁棒优化方法讨论求解算法。借助仿真算例检验模型与算法的有效性,分析列车总重、运行时分以及区段划分对运行能耗的影响。研究结果显示:列车总重取最大值时所得悲观速度曲线与列车总重取最小时所得乐观速度曲线存在差异,优化速度曲线时有必要考虑列车总重的不确定性;列车总重取最小与最大时,鲁棒速度曲线分别较悲观速度曲线和乐观速度曲线节能2.222%和7.087%;在大多数情况下,鲁棒速度曲线能够克服悲观速度曲线的保守特性;放松对运行时分的要求有助于降低列车运行能耗;选择合适的区段划分间隔有助于获取更优的节能速度曲线。所提出方法可为常导高速磁浮列车节能速度曲线的获取提供有效手段,对其他制式轨道交通列车速度曲线的优化也有参考价值。

基于变质心高度策略仿人机器人变步长步态规划

针对仿人机器人步态切换越障需求,提出一种基于变质心高度策略的变步长步态规划方法。首先,分析质心(CoM)高度变化对机器人步态的影响,将1个步行周期分为3个阶段,包括质心下降、上升和定高度过程,规划质心高度轨迹。然后,规划零力矩点(ZMP)参考轨迹,基于变高度倒立摆模型,通过托马斯算法解算质心轨迹。为了增强鲁棒性,考虑到机器人越障时步行参数变化的步态切换需求,提出一个具有等式约束的二次规划问题,求解质心优化轨迹。最后,以NAO机器人为实验平台,进行变步长步行对比实验。结果表明:所提出的基于变质心高度策略的步态规划方法,能够实现机器人大步幅变步长稳定步行,实现步态平滑切换,相比于恒定质心高度方法,所提方法的步行平均能耗降低约10%。

Measurement of energy efficiency based on frontier approaches

The improvement of energy efficiency has played an important role in the global low-carbon economy and the sustainable development process.There are many methodologies for estimating energy efficiency in academia.This article introduces four frontier methods for energy efficiency evaluation:nonparametric data envelopment analysis(DEA),parametric linear programming,stochastic frontier analysis(SFA),and the meta-frontier approach.The advantages and disadvantages of these methods are discussed.Finally,suggestions for model selection in energy efficiency measurement are offered.