Sensitivity Analysis Applied on the Electrical Power Systems Operation Planning

This paper proposes a study of electrical power system operation planning by Sensitivity Analysis (SA). The SA implemented was oriented by Optimal Power Flow (OPF). An optimal operation point was obtained by OPF. If some perturbation occurs in the power system, a new operation point will have to be evaluated to keep the feasibility of the power grid. This new operation point will be evaluated by SA. The SA was methodologically based in the theorem proposed by Fiacco. The set of non-linear equations generated by SA was solved by Newton´s method. Sparsity techniques were used. The principals contributions of this paper are the inclusion of transmission line constrained in the SA model and the complete use of the Fiacco´s theorem. Studies of cases were carried out in the IEEE 14, 118 and 300 buses, where the efficiency of the SA was verified.     

具有时延QoS保证的OFDM中继系统子载波配对与功率分配算法

 本文研究具有直接通信链路的OFDM解码转发(Decode-and-Forward,DF)中继系统的子载波配对与功率分配算法,目标是在满足业务时延QoS要求的前提下最大化系统容量.利用有效容量模型,首先把OFDM DF中继系统的子载波配对与功率分配问题形成为混合整数规划问题,然后把其转化为连续松弛凸规划问题,利用凸优化方法得到原问题的最优解,从而提出了一种联合最优的子载波配对与功率分配迭代算法.理论推导结果和仿真结果表明,最优子载波配对与功率分配不仅取决于子载波的信道增益,还取决于业务的时延QoS要求.与已有算法相比,本文算法获得的有效容量最大.     

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.     

MIMO中继下行通信系统中的多目标优化设计

针对通信系统中系统功率消耗和接收信号均方误差(MSE)存在冲突关系,无法同时达到性能最优的情况,采用多目标优化(MOO)框架联合优化系统功率消耗和接收信号MSE.文中考虑两跳的多入多出(MIMO)放大转发(AF)中继下行通信系统.联合优化形成的多目标优化问题是非凸且难以直接求解的,为此,提出一种基于帕累托最优策略的资源分配方法,该方法采用加权切比雪夫法,并引入中继预编码矩阵的一般结构和Schur补引理将优化问题转化为SDP问题.仿真结果验证了所提出方法的有效性,表明其具有更好的性能,同时也给出了系统功耗和MSE性能之间的帕累托最优边界.     

Optimal power allocation with AF and SDF strategies in dual-hop cooperative MIMO networks

Dual-hop cooperative Multiple-Input Multiple-Output (MIMO) network with multi-relay cooperative communication is introduced. Power allocation problem with Amplify-and-Forward (AF) and Selective Decode-and-Forward (SDF) strategies in multi-node scenario are formulated and solved respectively. Optimal power allocation schemes that maximize system capacity with AF strategy are presented. In addition, optimal power allocation methods that minimize asymptotic Symbol Error Rate (SER) with SDF cooperative protocol in multi-node scenario are also proposed. Furthermore, performance comparisons are provided in terms of system capacity and approximate SER. Numerical and simulation results confirm our theoretical analysis. It is revealed that, maximum system capacity could be obtained when powers are allocated optimally with AF protocol, while minimization of system’s SER could also be achieved with optimum power allocation in SDF strategy. In multi-node scenario, those optimal power allocation algorithms are superior to conventional equal power allocation schemes.     

Security analysis and optimization

An operationally "secure" power system is one with low probability of blackout or equipment damage. The power system control processes needed to maintain a designated security level at minimum operating cost are extremely complicated. They increasingly depend upon on-line computer security analysis and optimization. This on-line technology is still relatively new, with enormous further potential. Since security and optimality are normally conflicting requirements of power system control, it is inappropriate to treat them separately. Therefore, they are slowly becoming coalesced into a unified hierarchical mathematical problem formulation: one that is, however, far too complex to afford anything but an approximate, near-optimal solution. The practical validity of this unifying trend relies on being able to incorporate all significant security constraints within the process. The main two current computational tools in this field are contingency analysis and special operations-oriented versions of optimal power flow (OPF). Contingency analysis identifies potential emergencies through extensive "what if?." simulations on the power system network. OPF is a major extension to the conventional dispatch calculation. It can respect system static security limits, and can schedule reactive as well as active power. Moreover, the advanced versions of OPF include or interface with contingency analysis. This paper reviews present formulations and methods, and tries to point out areas of difficulty that constitute the main challenges for successful practical on-line implementations over the coming years.     

Flywheel replacement of IM with convenient power electronic circuitry: SSA-ANFIS

ABSTRACT

Purpose

This paper proposes a novel approach for flywheel replacement of Induction Motor (IM) with convenient power electronic circuitry. Methology: The proposed technique is the joined execution of the Salp Swarm Optimization Algorithm (SSA) plus Adaptive Neuro-Fuzzy Interference System (ANFIS) based duty ratio controller. Findings: In the proposed technique, the ANFIS procedure is enhanced by using the SSA algorithm with regards to the minimum error objective function. Here, the inverter switching states are updated by the proposed approach by constraining the error between the setpoint torque and the demand torque to the extended target work. With this proposed technique, unbalance between the demand torque and the generated torque is found with high precision and snappier execution. It is used to haul pull out the torsional pulsation in touchy load-connected transmission systems. Result: The execution of the proposed technique is actualized in the MATLAB/Simulink platform and compared with the existing methods.     

组播OFDM系统中基于子载波成对调整的功率分配算法

 研究了在总功率的约束下,组播OFDM系统中最大化组播速率的功率分配问题. 首先引入贪婪算法求解该问题以降低复杂度,但贪婪算法只执行预设的固定次数分配后即停止,其截止条件与功率分配过程中的状态无关,无法有效地提高系统性能;进而提出了两种基于子载波成对调整的功率分配算法,在初始功率分配的基础上选择子载波对交换功率,将功率重新分配给能够使组播速率提高得最多的子载波,并只在无法进一步提高组播速率时才停止调整. 研究和仿真结果表明,所提出的两种调整算法能够有效地提高组播速率,性能接近根据凸函数优化方法求得的最优解,且显著降低了算法的复杂度.     

Impact of antenna and beam-selection-based sectored relay planning for performance evaluation of 4G LTE-A tri-sectored cell

The deployment of Relay Nodes (RNs) in 4G LTE-A networks, mainly originating from the wireless backhaul link, provides an excellent network planning tool to enhance system performance. Better coordination between the base station and relays to mitigate inter-cell interference becomes an important aspect of achieving the required system performance, not only in the single-cell scenario, but also in multi-cell scenarios. In this paper, we model and analyze two basic approaches for designing a 4G LTE-A tri-sectored cellular system. The approaches are based on Antenna Selection Sectored Relaying (ASSR) and Beam Selection Sectored Relaying (BSSR). The main purpose of the proposed schemes is to enhance system performance by improving the quality of the wireless relay backhaul link. In this technique, antenna selection takes into consideration Non-Line-Of-Sight (NLOS) communication, whereas BSSR considers the case of Line-Of-Sight (LOS) communication using heuristic beam forming approach. The resource allocation problem has also been investigated for relay based cooperative LTE-A tri-sectored cell in the downlink. The best possible location for relay node in the sector, power allocation and MIMO channel modeling is formulated as an optimization problem with the aim of maximizing the end to end link rate and the Signal to Interference plus Noise Ratio (SINR) of 4G LTE-A systems. Power allocation/optimization has been solved by means of the duality equation of the stationary Karush-Kuhn-Tucker (KKT) condition and is used to derive optimal values for the beam forming vector on both the relay as well as the access link. The performance of the proposed scheme is verified through simulations carried out using MATLAB software. The simulation results show a significant improvement in the SINR, throughput capacity, and coverage area of the 4G LTE-A cell, while guaranteeing better quality of service.     

Joint resource allocation for hybrid NOMA-assisted MEC in 6G networks

Multi-access Edge Computing (MEC) is an essential technology for expanding computing power of mobile devices, which can combine the Non-Orthogonal Multiple Access (NOMA) in the power domain to multiplex signals to improve spectral efficiency. We study the integration of the MEC with the NOMA to improve the computation service for the Beyond Fifth-Generation (B5G) and the Sixth-Generation (6G) wireless networks. This paper aims to minimize the energy consumption of a hybrid NOMA-assisted MEC system. In a hybrid NOMA system, a user can offload its task during a time slot shared with another user by the NOMA, and then upload the remaining data during an exclusive time duration served by Orthogonal Multiple Access (OMA). The original energy minimization problem is non-convex. To efficiently solve it, we first assume that the user grouping is given, and focuses on the one group case. Then, a multilevel programming method is proposed to solve the non-convex problem by decomposing it into three subproblems, i.e., power allocation, time slot scheduling, and offloading task assignment, which are solved optimally by carefully studying their convexity and monotonicity. The derived solution is optimal to the original problem by substituting the closed expressions obtained from those decomposed subproblems. Furthermore, we investigate the multi-user case, in which a close-to-optimal algorithm with low-complexity is proposed to form users into different groups with unique time slots. The simulation results verify the superior performance of the proposed scheme compared with some benchmarks, such as OMA and pure NOMA.     

一种基于SIC-CDM的低复杂度混合波束赋形方案

为了平衡毫米波大规模多输入多输出系统的性能和硬件开销,降低系统功耗,以频谱效率为优化目标,在部分连接结构下提出了一种收发端联合设计的低复杂度混合波束赋形方案。首先,基于连续干扰消除将原始优化问题转化为多个子阵的速率优化问题;然后,利用坐标下降法完成模拟波束赋形矩阵设计;最后,引入等效信道矩阵大幅降低矩阵维度,再对其进行奇异值分解获得数字波束赋形矩阵。仿真结果表明,与其他算法相比,所提算法在系统功耗降低的同时保持了较优的性能,且性能逼近部分连接结构的最优方案。     

基于速率约束的OFDM中继链路能效最优资源分配策略

该文研究解码转发(DF)模式的OFDM中继链路的能效最大化资源分配问题。与现有典型的固定速率最小化发射功率或无约束最大化能效算法不同,该文考虑电路功率消耗的前提下,将问题建模为以最大化系统能效为目标,同时考虑用户最小速率需求、源节点S和中继节点R各自总发射功率约束下的联合子载波配对和最优功率分配问题。证明了速率和功率联合约束条件下中继链路全局能效最优解的唯一性,在此基础上提出一种低复杂度联合最优资源分配策略。仿真结果表明,该文所提方案能够在最小速率和S/R节点最大发射功率约束下自适应分配功率资源,实现系统能效最优,并能够降低链路的中断概率。     

基于MADDPG的无人机辅助通信功率分配算法

针对多无人机(unmanned aerial vehicle, UAV)作为空中基站辅助通信的吞吐量和公平性问题,提出了一种基于多智能体深度确定性策略梯度算法(multi-agent deep deterministic policy gradient algorithms, MADDPG)的功率分配算法,该算法通过联合优化UAV基站的功率分配和用户接入以提高系统吞吐量和公平性。本文首先构建了UAV基站为地面建立通信服务的三维场景,然后通过联合功率、用户关联和UAV位置约束,构建了吞吐量和公平性最大化的问题模型。考虑到该问题的复杂性,本文将所构建的优化问题建模为马尔科夫决策过程(Markov decision process, MDP),通过引入深度确定性策略梯度算法(deep deterministic policy gradient algorithm, DDPG)解决该问题。仿真结果表明,本文提出的基于MADDPG的UAV基站功率分配算法与其他算法相比,可以有效地提升系统的吞吐量和用户的公平性,提高通信的服务质量。     

结合电磁带隙结构和磁性材料的PCB电源接地层设计

在印制电路板的设计中,供电系阻抗谐振所引起的噪声和电磁干扰问题一直是设计人员关注的焦点。已有的研究显示,适当的电磁带隙（EBG）结构可以有效地降低供电系的电磁干扰。本文通过运用基于快速算法和分解元法的计算机仿真,研究供电系EBG结构中采用磁性材料后的阻抗特性。研究表明,在供电系内侧增加磁性材料涂层,能在原有基础上进一步抑制电磁干扰。     

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.     

Security enhancement with joint transmit antenna selection and transmit beamforming under energy harvesting constraints

This paper considers a multiuser multiple‐input single‐output (MISO) broadcasting system with simultaneous wireless information and power transfer (SWIPT), which consists of one information receiver (IR) and several energy harvesting receivers (ERs) which are capable of eavesdropping the legitimate signals. For reducing cost and hardware complexity, transmit antenna selection (TAS) is applied in the transmitter. We aim to maximize the achievable secrecy rate under the individual energy harvesting constraint at the ERs and the transmit power constraint at the transmitter by jointly optimizing TAS, transmit beamforming, and artificial noise (AN). The joint optimization problem is a non‐convex mixed integer programming problem. We apply variable replacements to decouple the variable couplings and relax and approach the binary constraint by the difference of two convex constraints. Afterwards, penalty method and constrained concave convex procedure (CCCP) are applied to transform the relaxed problem into a sequence of semi‐definiteness programming (SDP) problems. Simulation results shows that our proposed joint optimization scheme is superior over existing non‐joint optimization schemes. This paper studies the joint transmit antenna selection (TAS), transmit beamforming, and artificial noise (AN) optimization in a multiple‐input multiple‐output (MISO) wiretap system with simultaneous wireless information and power transfer (SWIPT). The joint optimization problem is nonconvex and we propose a penalty method based scheme to solve it. The simulation results show that our joint optimization scheme is superior to other non‐joint optimization schemes.     

Learning-based joint UAV trajectory and power allocation optimization for secure IoT networks

Non-Orthogonal Multiplex Access (NOMA) can be deployed in Unmanned Aerial Vehicle (UAV) networks to improve spectrum efficiency. Due to the broadcasting feature of NOMA-UAV networks, it is essential to focus on the security of the wireless system. This paper focuses on maximizing the secrecy sum rate under the constraint of the achievable rate of the legitimate channels. To tackle the non-convexity optimization problem, a reinforcement learning-based alternative optimization algorithm is proposed. Firstly, with the help of successive convex approximations, the optimal power allocation scheme with a given UAV trajectory is obtained by using convex optimization tools. Afterwards, through plenty of explorations of the wireless environment, the Q-learning networks approach the optimal location transition strategy of the UAV, even without the wireless channel state information.     

Simultaneously optimizing crosstalk and power for instruction bus coupling capacitance using wire pairing

The use of deep-submicrometer (DSM) technology increases the capacitive coupling between adjacent wires leading to severe crosstalk noise, which causes power dissipation and may also lead to malfunction of a chip. In this paper, we present a technique that reduces crosstalk noise on instruction buses. While previous research focuses primarily on address buses, little work can be applied efficiently to instruction buses. This is due to the complex transition behavior of instruction streams. Based on instruction sequence profiling, we exploit an architecture that encodes pairs of bus wires and permute them in order to optimize power and noise. A close to optimal architecture configuration is obtained using a genetic algorithm. Unlike previous bus encoding approaches, crosstalk reduction can be balanced with delay and area overhead. Moreover, if delay (or area) is most critical, our architecture can be tailored to add nearly no overhead to the design. For our experiments, we used instruction bus traces obtained from 12 SPEC2000 benchmark programs. The results show that our approach can reduce crosstalk up to 50.79% and power consumption up to 55% on instruction buses.     

基于功耗最小的光电多芯片组件优化划分算法

通过将光电MCM的功耗约束转化为光互连数最少的问题,建立了芯片级划分模型,解决了用遗传算法处理结构化设计的芯片级划分的问题,提出了基于功耗最小的光电MCM划分优化算法.实际的设计结果表明,该算法具有更强的寻优能力,比以往的算法更适合光电MCM的划分,可使系统功耗降低50%以上.     

RIS辅助共生无线电通信的速率分拆多址资源分配方案

随着通信场景和网络架构的日益复杂,无线电网络频谱资源稀缺与能耗问题是无线通信的关键挑战,为此,提出了一种新的基于速率分拆多址接入技术（Rate Splitting Multiple Access,RSMA）广播信号的可重构智能表面（Reconfigurable Intelligent Surface,RIS）辅助共生无线电（Symbiotic Radio,SR）系统方案。在该方案中主发射机采用RSMA的方式广播信号,并将传统高功耗次发射机用低功耗RIS替代协作信号的后向散射传输。在主发射机发射波束形成,RIS相移系数和公有信息速率分配约束下,构建了最大化主接收最小速率的问题。由于问题的非凸性和变量的耦合性,提出了一种基于逐次凸逼近,凸差函数,罚函数的交替优化方法以求得次优解。仿真结果表明,与空分多址接入和非正交多址接入方案相比,所提RSMA方案能显著提升用户速率。