Power Allocation and Antenna Selection for Heterogeneous Cellular Networks

In this paper,we investigate the system performance of a heterogeneous cellular network consisting of a macro cell and a small cell,where each cell has one user and one base station with multiple antennas.The macro base station(MBS)and the small base station(SBS)transmit their confidential messages to the macro user(MU)and the small user(SU)over their shared spectrum respectively.To enhance the system sum rate(SSR)of MBS-MU and SBS-SU transmission,we propose joint antenna selection combined with optimal power allocation(JAS-OPA)scheme and independent antenna selection combined with optimal power allocation(IAS-OPA)scheme.The JAS-OPA scheme requires to know the channel state information(CSI)of transmission channels and interference channels,while the IAS-OPA scheme only needs to know the CSI of transmission channels.In addition,we carry out the analysis for conventional round-robin antenna selection combined with optimal power allocation(RR-OPA)as a benchmark scheme.We formulate the SSR maximization problem through the power allocation between MBS and SBS and propose iterative OPA algorithms for JAS-OPA,IAS-OPA and RR-OPA schemes,respectively.The results show that the OPA schemes outperform the equal power allocation in terms of SSR.Moreover,we provide the closed-form expression of the system outage probability(SOP)for IAS scheme and RR scheme,it shows the SOP performance can be significantly improved by our proposed IAS scheme compared with RR scheme.

Antenna Selection and Power Allocation Design for 5G Massive MIMO Uplink Networks

Massive MIMO is one of the key technologies in future 5G communications which can satisfy the requirement of high speed and large capacity. This paper considers antenna selection and power allocation design to promote energy conservation then provide good quality of service(QoS) for the whole massive MIMO uplink network. Unlike previous related works, hardware impairment, transmission efficiency, and energy consumption at the circuit and antennas are involved in massive MIMO networks. In order to ensure the QoS, we consider the minimum rate constraint for each user and the system, which increases the complexity of power allocation problem for maximizing energy and spectral efficiency in massive MIMO system. To this end, a quantum-inspired social emotional optimization(QSEO) algorithm is proposed to obtain the optimal power control strategy in massive MIMO uplink networks. Simulation results assess the great advantages of QSEO which previous strategies do not have.

Adaptive Energy-Efficient Power Allocation for DAS with Imperfect Channel State Information and Antenna Selection

Considering that perfect channel state information(CSI) is difficult to obtain in practice,energy efficiency(EE) for distributed antenna systems(DAS) based on imperfect CSI and antennas selection is investigated in Rayleigh fading channel.A novel EE that is defined as the average transmission rate divided by the total consumed power is introduced.In accordance with this definition,an adaptive power allocation(PA) scheme for DAS is proposed to maximize the EE under the maximum transmit power constraint.The solution of PA in the constrained EE optimization does exist and is unique.A practical iterative algorithm with Newton method is presented to obtain the solution of PA.The proposed scheme includes the one under perfect CSI as a special case,and it only needs large scale and statistical information.As a result,the scheme has low overhead and good robustness.The theoretical EE is also derived for performance evaluation,and simulation result shows the validity of the theoretical analysis.Moreover,EE can be enhanced by decreasing the estimation error and/or path loss exponents.

A LOW-COMPLEXITY PORT SELECTION AND POWER ALLOCATION SCHEME IN DISTRIBUTION MIMO SYSTEMS

Information theoretical results have shown that Distributed Antenna Systems (DAS) can obtain higher capacity than Co-located Antenna Systems (CAS). In this paper,we investigate a downlink port selection and power allocation scheme in Distributed Multiple-Input Multiple-Output (D-MIMO) systems,where Distributed Antenna (DA) ports randomly locate in the cell. The contri-bution of this paper can be summarized as two parts. Firstly,we analyze how antenna correlation af-fects power allocation in D-MIMO systems. Secondly,based on large scale fading and antenna corre-lation,a low-complexity port selection and power allocation scheme is proposed. In the proposed scheme,we take both large scale fading and antenna correlation into consideration. Moreover,User Equipment (UE) only needs to feedback the rank of transmit antenna correlation matrix,which will not increase system complexity too much. Simulation results verify the capacity improvement based on the proposed power allocation scheme.

联合天线选择与用户调度的大规模MIMO系统能效优化算法

在大规模多输入多输出(multiple-input multiple-output,MIMO)系统中,合理的天线选择、用户调度以及用户功率分配方案,对提升系统能效、节省资源成本有着重要的作用.针对大规模MIMO下行链路通信场景,基于能效最大化准则,提出了一种联合天线选择、用户调度以及功率分配的低复杂度优化算法.首先,针对天线选择和用户调度问题,结合递增递减的选择思想,以最大化系统能效为目标,对天线和用户进行双向交替搜索;其次,对于搜索过程中的用户功率分配问题,采用分式规划理论和拉格朗日对偶算法得到最优能效功率的闭式解,三个参数进行迭代优化,从而得到系统最优能效.仿真结果表明,本文所提算法不仅具有低复杂度而且具有较好性能,能够有效降低大规模MIMO系统的能耗.

Resource allocation in downlink OFDMA distributed multipoint systems with incoherent and coherent transmitters

Distributed multipoint systems （DMS） are important and timely for the move to future broadband wireless communication systems. Traditional studies on DMS have mainly focused on the issues with the spatial division multiple access such as precoding techniques, which only consider a narrowband case. This paper addresses the downlink radio resource management of the orthogonal frequency division multiple access DMS （OFDMA-DMS）, including power allocation between users or subcarriers, and distributed antenna selection. Signal models with incoherent and coherent transmitters are built. To maximize the system throughput, for the incoherent transmitter case, a strategy based on the iterative water-filling power allocation is proposed to approach the optimality. As for the coherent case, where coherent additions of the signal could occur at the users, the problem is transformed into an integer programming which is solvable. Numerical results show that the gain from the coherent transmitter is promising. And to achieve a near-optimal solution, only part of the DA ports will be used, which have better channel conditions.

增强物理层安全的联合发射天线选择和人工噪声技术

针对未来多天线通信系统的安全问题,提出了一种增强无线物理层安全的传输策略。该策略基于发射天线的选择以及人工噪声,并采用Alamouti编码。它可以使发射机和接收机以较低的复杂度代价提高合法信道的分集增益,同时有效干扰窃听信道,使通信系统获得更高的安全容量。对系统安全容量、天线数量、以及信息信号与人工噪声之间的功率分配等问题进行了研究,仿真结果表明,该传输策略能够有效地增强物理层安全。

Turbo-BLAST系统中天线选择和功率分配算法

提出一种适用于Turbo-BLAST系统的自适应天线选择和功率分配算法,所提算法以误比特率为优化目标,并且考虑了信道估计误差的影响。在总功率约束条件下,采用所提算法进行天线选择和自适应功率分配,并利用软干扰抵消算法对接收信号进行迭代检测,以进一步改善系统性能。仿真结果表明:采用所提算法可以显著改善系统的误比特率性能。

目标定位的分布式MIMO雷达资源分配算法

为了提高分布式多输入多输出(multiple input multiple output,MIMO)雷达的目标定位精度,分析了阵元、发射功率和信号带宽对定位精度的影响,进而提出一种将三者联合优化的资源分配算法。首先,以最小化目标的平方位置误差下限(squared position error bound,SPEB)为目标函数,建立了包含阵元、功率和带宽3个变量的优化模型;然后,通过推导最优功率分配矢量和带宽分配矢量的关系简化模型;最后利用凸松弛和循环最小化算法将优化问题转化成二阶锥形式,求解得到最优的资源分配策略。仿真结果表明,利用所提出的算法进行资源分配能明显提高目标的定位精度;信号有效带宽对目标定位精度的影响大于发射功率。

一种改进的大规模MIMO发射天线选择算法

为了降低天线选择算法在大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)系统下的误码率和复杂度,以用户端接收的总功率为优化目标,提出一种最大化所有用户接收总功率的天线选择算法。该算法将优化目标函数转化为凸函数,并利用凸优化方法求得其有效解。仿真结果表明,所提天线选择算法与传统的最大和容量算法相比,具有较好的系统误码率性能,且运算复杂度低,但系统容量有所降低。

Turbo-BLAST系统中基于容量的天线选择和功率分配算法

在不完全信道状态信息条件下,提出了一种适用于Turbo-BLAST系统的天线选择和功率分配算法。所提算法以信道容量最大化为准则,从所有天线中选取一组天线子集用于发射,并对选择的天线子集进行注水功率分配,以充分利用Turbo-BLAST系统的空间复用增益并提高信道容量。在接收端,采用Turbo原理对接收信号进行迭代检测以改善系统的误比特率性能。仿真结果表明采用所提算法不仅可以显著提高系统的信道容量,而且误比特率性能也得到明显的改善。

DWCS中的天线动态选择算法

针对分布式无线通信系统上行链路,在考虑CDMA多址方式和多用户检测接收机的条件下,提出了一种天线动态选择算法,即在保证虚拟小区接收到的比特信干噪比达到门限的前提下,通过对比特信干噪比的增量和接收机复杂度的增量进行折中,动态地选择尽可能多的接收天线,使移动终端平均发射功率最小化。仿真结果表明,与天线静态选择方法相比,该算法可获得与其几乎相同的中断容量,移动终端平均发射功率减小量与接收机平均复杂度的比值高于天线静态选择方法。

大规模MIMO系统的智能天线选择与功率分配方法研究

为了满足大规模多输入多输出(multiple input multiple output,MIMO)系统的数据传输需求并降低系统能耗,提出一种基于量子化学反应优化的智能天线选择与功率分配方法。根据大规模MIMO系统不同时段的用户传输需求建立智能天线选择与功率分配模型,推导出其最大能效方程。为有效求解该非线性、多约束的混合优化难题,结合量子计算和化学反应优化机制的优势设计了量子化学反应优化算法,可得到最佳的天线选择与功率分配方案。仿真结果表明,所提的智能天线选择与功率分配方法能实时满足用户的信息传输需求,显著提高系统能效。针对不同的仿真场景,所提方法与现有的智能算法与分配策略相比均可得到最高的系统能效。

基于天线选择和功率分配的联合迭代优化算法

当使用所有天线进行无线数据传输时,大规模多输入多输出(Multiple-Input multiple-Output,MIMO)系统中的基站需要使用与天线数相同的射频链路,导致系统的实现复杂度增加,降低了系统的能效。针对能效降低的问题,提出了一种天线选择和功率分配的联合迭代优化算法。该算法在给定初始发送功率的条件下,随机生成一个天线集合作为内循环的初始值,内循环每次从余下的天线集合中选择一根具有最大能效的天线进行替换,得出最优天线集合,求出相应的最优发送功率,并以此作为下次外循环发送功率的初始值。仿真结果表明,所提算法在降低计算复杂度的前提下,几乎可以达到近似于最优穷举搜索算法的能效性能。

采用最大比率发射技术的天线选择系统性能研究

Alamouti方案(ALS)是仅有满速率的正交空时分组码(STBC),通过天线选择技术和优化发射功率提高其性能。若发射端知道全部的系统信道状态信息(CSI),文章提出在发射端混合最大比率发射(MRT)分集技术和发射天线选择(TAS)技术。基于信道增益系数大小的Frobenius范数最大的天线选择准则,系统选择瞬时输出信噪比(SNR)最大的天线子集{NT,2;mr}工作。采用MRT对Alamouti编码的双天线发射功率进行优化分配。仿真结果表明:采用混合MRT和TAS两技术后,基于ALS的多天线系统的比特误码率(BER)性能和信道容量均得到了明显改善。这一结论对有充足CSI的通信系统用于克服信道影响和提高系统传输速率具有借鉴意义。

移动多用户通信系统的功率分配智能优化

随着第五代移动通信技术的发展,移动用户的数量呈爆炸性增长.但是由于复杂多变的移动通信环境,对移动多用户通信系统的性能研究非常复杂,也是一个亟待解决的问题.本文在N-Nakagami信道下,建立了移动多用户通信系统模型,设计了发射天线选择(Transmit Antenna Selection,TAS)方案,研究了移动多用户通信系统的中断概率(Outage Probability,OP)性能.针对TAS方案,推导了系统中断概率的闭合表达式.然后建立功率优化目标函数,提出了基于增强灰狼算法的功率分配优化机制.和差分进化算法(Differential Evolution,DE),粒子群优化算法(Particle Swarm Optimization,PSO),布谷鸟搜索算法(Cuckoo Search,CS),萤火虫算法(Firefly Algorithm,FA)等进行了比较,仿真结果表明:本文所提出的算法优化性能更好,理论分析的正确性得到了验证。

不完全信道状态信息条件下Turbo-BLAST系统中注水天线选择和功率分配算法

该文针对信道估计有误差情况,在不完全信道状态信息条件下提出了一种适用于Tur-bo-BLAST系统的天线选择和功率分配算法,发送端在总功率约束条件下,依据信道状态信息进行发送天线选择,并对选择的天线进行注水功率分配,以获得最大化信道容量。在接收端,采用Turbo原理对接收信号进行迭代检测,以改善系统的误比特率性能。仿真结果表明,采用所提算法不仅可以显著提高系统的信道容量,误比特率性能也有所改善。

大规模MIMO系统能效优化算法研究综述

随着移动用户数与高速数据业务应用的快速增长,满足下一代网络更高频效和能效需求的大规模多输入多输出(multiple-input multiple-output,MIMO)技术成为5G的研究热点.天线数目的增加,导致大规模MIMO系统的能耗也将急剧增加.优化大规模MIMO系统能效以实现绿色通信成为5G资源管理的一项重要研究课题.针对该问题,对大规模MIMO能效优化算法进行了综述与展望.介绍了大规模MIMO系统能效模型,综述了大规模MI-MO系统能效优化算法研究现状,包括基于天线选择的能效优化、基于功率分配的能效优化、频效和能效的折中和多参数联合优化能效;探讨了学术界在大规模MIMO系统能效优化算法上的重要研究成果;总结了当前研究中存在的主要问题,展望了大规模MIMO系统能效优化未来的研究趋势.

基于空间调制的天线选择和能效优化算法

空间调制是一种新型的具有低复杂度的多输入多输出通信技术,并成为近年来的研究热点。为了进一步提升空间调制的误比特率性能,该文研究了空间调制中的发射天线选择问题,提出了一种基于信道列向量夹角和范数的低复杂度的发射天线选择算法。该算法能够有效减少矩阵运算,并且不需要考虑差异向量,因此具有极低的计算复杂度,且适用于发射天线数目明显高于接收天线数目的情况。同时,加入了功率修正因子,有效地控制发射信号功率,极大程度地提升了能量效率。仿真结果表明,该算法能够在限制发射功率的前提下,以极低的计算复杂度逼近最优算法的性能。

基于波形优化和天线位置选择的MIMO雷达波束扫描算法研究

为实现集中式多输入多输出(MIMO)雷达波束扫描,本文在峰值平均功率比(PAPR)、能量以及布尔(天线位置选择)约束下,基于min-max波束图匹配准则,首次提出MIMO雷达天线位置和多组探测波形(一组波形对应一个独立的波束图)的联合优方法。由于非凸PAPR约束、布尔约束以及min-max目标函数的非凸非光滑性导致了优化问题成为典型的大规模NP-难问题。为求解该NP-难优化问题,该文首先利用Lawson算法将min-max问题转化为迭代加权最小二乘(ILS)问题,然后根据上界函数最小化(MM)准则简化ILS优化问题,最后用交替方向乘子法(ADMM)求解简化后的上界优化问题。数值仿真结果检验了所提算法的有效性。