Joint Beamforming Optimization for Reconfigurable Intelligent Surface-Enabled MISO-OFDM Systems

In this paper,we investigate the reconfigurable intelligent surface(RIS)-enabled multiple-input-single-output orthogonal frequency division multiplexing(MISO-OFDM)system under frequency-selective channels,and propose a low-complexity alternating optimization(AO)based joint beamforming and RIS phase shifts optimization algorithm to maximize the achievable rate.First,with fixed RIS phase shifts,we devise the optimal closedform transmit beamforming vectors corresponding to different subcarriers.Then,with given active beamforming vectors,near-optimal RIS reflection coefficients can be determined efficiently leveraging fractional programming(FP)combined with manifold optimization(MO)or majorization-minimization(MM)framework.Additionally,we also propose a heuristic RIS phase shifts design approach based on the sum of subcarrier gain maximization(SSGM)criterion requiring lower complexity.Numerical results indicate that the proposed MO/MM algorithm can achieve almost the same rate as the upper bound achieved by the semidefinite relaxation(SDR)algorithm,and the proposed SSGM based scheme is only slightly inferior to the upper bound while has much lower complexity.These results demonstrate the effectiveness of the proposed algorithms.

Specific Order and Bisection Search Aided Joint 3D Beamforming and RIS Reflecting Optimization

In this paper,a three-node transmission model is conceived,where the base station(BS)node leverages 3D beamforming,the reconfigurable intelligent surface(RIS)node can constructively reconfigure the wireless channel,the user node only has a single antenna due to a limited price.Maximization of its downlink spectral efficiency is a joint optimization problem of three variables,namely phase-shift matrixΦof RIS,tilt angleθand beamforming vector w used in BS 3D beamforming.We solve this problem by employing the alternating optimization(AO)algorithm.But,in each iteration,a specific optimization order of firstlyΦ,secondlyθand finally w is proposed,which facilitates the search of optimalθin the way of narrowing its trust region and enabling unimodal property over the narrowed trust region.It finally results in a better combination of{Φ,θ,w}.

Joint Active and Passive Beamforming Design for Hybrid RIS-Aided Integrated Sensing and Communication

Integrated sensing and communication(ISAC) is considered an effective technique to solve spectrum congestion in the future. In this paper, we consider a hybrid reconfigurable intelligent surface(RIS)-assisted downlink ISAC system that simultaneously serves multiple single-antenna communication users and senses multiple targets. Hybrid RIS differs from fully passive RIS in that it is composed of both active and passive elements, with the active elements having the effect of amplifying the signal in addition to phase-shifting. We maximize the achievable sum rate of communication users by collaboratively improving the beamforming matrix at the dual function base station(DFBS) and the phase-shifting matrix of the hybrid RIS, subject to the transmit power constraint at the DFBS, the signal-to-interference-plus-noise-ratio(SINR) constraint of the radar echo signal and the RIS constraint are satisfied at the same time. The builtin RIS-assisted ISAC design problem model is significantly non-convex due to the fractional objective function of this optimization problem and the coupling of the optimization variables in the objective function and constraints. As a result, we provide an effective alternating optimization approach based on fractional programming(FP) with block coordinate descent(BCD)to solve the optimization variables. Results from simulations show that the hybrid RIS-assisted ISAC system outperforms the other benchmark solutions.

An adaptive beamforming algorithm based on direction vector rotation and joint iterative optimization

An adaptive beamforming algorithm named robust joint iterative optimizationdirection adaptive （RJIO-DA） is proposed for large-array scenarios. Based on the framework of minimum variance distortionless response （MVDR）, the proposed algorithm jointly updates a transforming matrix and a reduced-rank filter. Each column of the transforming matrix is treated as an independent direction vector and updates the weight values of each dimension within a subspace. In addition, the direction vector rotation improves the performance of the algorithm by reducing the uncertainties due to the direction error. Simulation results show that the RJIO-DA algorithm has lower complexity and faster convergence than other conventional reduced-rank algorithms.

Joint Beamforming for Delay Optimal Transmission in Cache-Enabled Wireless Backhaul Networks

This paper considers the problem of joint beamformer design for a two-tier wireless network,whereby a set of cache-enabled access points(APs)are connected to the base station(BS)via wireless backhaul links.The APs can prefetch and store the files requested by users,to serve users directly in the access links.Thus low-latency transmissions are enabled as the transmission in the backhaul links is saved.However,due to the limited cache capacity,not all requested files can be stored in the APs,some of the non-cached APs then should be utilized as long as their transmission delays in the access and backhaul links can be well addressed.Two delay optimal beamformer design(DOBD)problems are formulated to minimize the overall delay incurred in the backhaul and access link transmissions via a joint optimization of the beamformer at the BS and APs.We consider the DOBD problem under non-fragment and fragment caching policies,both involving nonconvex link rate constraints.The semi-definite relaxation(SDR)and sequential convex approximation(SCA)schemes are adopted to approximate the nonconvex problems into convex ones,which are then iteratively solved.Numerical results demonstrate the convergence and improved transmission delay performance of the proposed scheme under various network settings.

智能反射面辅助的MU-MISO车联网毫米波通信联合波束赋形

近年来,车联网通信系统提出了大规模数据传输,通信连接稳定等需求。高频段通信凭借超高传输速率,低时延的特点被引入了该系统,但是,高频段通信仍面临着阻塞问题的挑战。因此,智能反射面作为一种可解决高频段通信阻塞问题的技术,成为车联网高频通信的热点议题。智能反射面由超材料构成,其表面的多个无源反射单元均匀排布形成阵列,所有反射单元的反射系数均可通过智能控制器实时调节,控制反射波束的方向和形状,来实现对无线传输环境的智能调控,提供增强视距通信、扩展通信覆盖范围等服务。针对MU-MISO车联网毫米波通信场景下,基站与车辆用户视线通信链路因遮蔽,车辆高速移动等因素,面临随机中断的问题,将智能反射面引入该系统,并结合该系统提出了基于半定松弛问题的交替迭代优化算法,以提高通信稳定性。该方法将系统中基站波束赋形矩阵优化和智能反射面相移矩阵优化分解为两个子问题,通过定义半定矩阵变量并适当放松特定的约束条件将两个子问题近似成为半定松弛问题,随后,使用交替优化技术对其进行迭代求解以实现联合优化,通过最大化同时刻通信车辆中的最小信干噪比来确保通信稳定性。仿真结果表明,该方法显著提高了车辆用户的信干噪比和基站的总频带利用率,确保了动态场景中车辆与基站的通信稳定性。

面向多任务动态场景的雷达与干扰空时协同波束联合优化方法

现代雷达对抗形势复杂多变,体系与体系的作战已成为基本特点,而体系整体性能关乎着战场的主动权乃至最终的胜负。通过优化体系中雷达与干扰波束资源可以提升整体性能,获得在空间、时间域优效的低截获探测性能,然而空时域协同波束联合优化是一个复杂多参数耦合的非凸问题。该文针对空时域多任务动态场景,建立了以雷达探测性能为优化目标,以干扰性能以及能量限制为约束条件的优化模型。为求解该模型,该文提出了基于迭代优化的空时协同波束联合设计方法,即以雷达发射、接收、多干扰机发射波束交替迭代优化。其中,针对多干扰机协同优化的不定矩阵二次约束二次规划(QCQP)问题,该文基于可行点追踪-连续凸逼近(FPP-SCA)算法,在SCA算法的基础上,通过引入松弛变量与惩罚项,保证算法在合理松弛度下的可行性,解决了矩阵不定情况下难以获得可行解的问题。仿真表明,在一定的干扰机能量约束下,该文所提方法在保证雷达高性能探测目标且不受干扰情况下,同时实现了多干扰机在空时域干扰对方每个平台以掩护我方雷达探测的效果;相比传统算法,在动态场景中基于FPP-SCA算法的协同干扰具有更优效果。

基于多天线波束赋形的CRN分布式上行功率控制算法

针对工作于underlay模式的认知无线网络(CRN,Cognitive Radio Network)上行功率控制问题,本文提出一种基于多天线波束赋形,由认知基站和认知用户联合优化的分布式上行功率控制算法.联合优化的具体步骤为认知基站通过求解最大广义特征值问题完成多天线波束赋形优化;认知用户先将非线性功率优化问题转换为几何规划凸优化问题,再使用梯度法完成分布式发送功率优化;认知基站和认知用户交替优化,实现网络效用最大化.数值仿真显示,同只优化认知用户功率的上行功率控制算法相比,认知基站和认知用户联合优化的上行功率控制算法不仅能得到更大的网络效用值,而且对主用户的干扰具有鲁棒性.

导向矢量和协方差矩阵联合迭代估计的稳健波束形成算法

针对自适应波束形成器在目标导向矢量存在约束偏差时性能急剧下降的问题,该文提出一种目标导向矢量和干扰噪声协方差矩阵联合迭代估计的稳健波束形成算法。该算法首先采用稀疏重构的方法得到目标导向矢量的初始值,并通过从采样协方差矩阵中剔除目标信号估计值完成干扰加噪声协方差矩阵的初始化;然后在建立导向矢量误差优化模型的基础上,采用凸优化方法对目标导向矢量和干扰加噪声协方差矩阵联合迭代求解。最后利用目标导向矢量和干扰加噪声协方差矩阵的稳态估计值获得自适应权矢量。仿真结果表明该算法提高了波束形成器在目标导向矢量约束偏差时的输出信干噪比。

通感一体去蜂窝超大规模MIMO与高频段无线接入技术

随着全息类通信、数字孪生、虚实空间融合互动等新兴应用的快速涌现,未来移动通信系统需要能够支持超大容量无线传输和极高精度定位。基于去蜂窝超大规模MIMO系统的分布式天线阵列和以用户为中心的可伸缩服务架构,进行通信与感知一体化设计,实现高能/谱效传输与带内高精度感知定位,是一种有效的解决思路。首先介绍了未来移动通信系统的潜在技术和应用需求;接着从波束成形与系统性能分析、导频分配与信道估计、回程/前向链路受限下的性能优化等方面介绍了去蜂窝超大规模MIMO系统的研究现状;之后从基本信息理论分析、通信感知一体化接入方法、联合信道估计和位置更新方法、位置信息辅助的分布式波束成形、高能/谱效的分布式资源优化、高频段近场无线接入等方面分析了通感一体去蜂窝超大规模MIMO技术的未来研究方向;最后总结了通感一体去蜂窝超大规模MIMO系统的主要优势和未来仍需关注的问题。

5G超密异构网络中联合虚拟波束形成的小区选择算法

随着移动通信技术的发展,移动用户设备呈爆炸性增长趋势,使异构网中小区密集,造成小区间严重干扰,严重影响系统吞吐量。基于5G超密异构网络结构,研究了以用户为中心,基于动态小区选择与联合优化,提出了联合虚拟波束形成的小区选择算法。仿真结果表明:该算法协调了小区间干扰和频谱效率的矛盾,提高了边缘用户的性能和小区的平均吞吐量。

阵元位置激励联合优化的低旁瓣自适应波束形成

传统波束形成仅通过优化阵元激励权值达到设计目标,忽略了阵元位置对波束形成性能的影响。为进一步提高波束形成性能,文中综合考虑阵元位置和激励对波束目标的影响,提出了一种新的低旁瓣自适应波束形成设计方法。该方法分为两步:第一步,在一定阵元孔径和阵元个数下,以低旁瓣波束图的设计目标,采用正交匹配追踪的方法求解阵元位置及激励;第二步,在保留低旁瓣波束图特征的基础上将静态波束激励权值转化为自适应波束激励权值,抑制了强干扰,提高了输出信干噪比。仿真实验证明:在相同的设计目标下,该方法所设计的阵列相比均匀线阵需要更少的阵元个数,节约了硬件开销,同时文中算法综合了低旁瓣静态波束形成与自适应波束形成的优点,提高了阵列增益,增强了算法稳健性。

低复杂度的智能天线目标信号接收设计仿真

智能天线利用波束成形可有效接收目标信号并抑制干扰。当天线阵元数过多时,传统波束成形算法直接处理所有天线阵元的接收数据,导致计算复杂度大和收敛速度慢,无法实时有效地接收目标信号。为降低计算复杂度,提高收敛速度,提出一种降秩自适应波束成形算法,用于低复杂度的智能天线目标信号接收。所提算法基于随机梯度法,运用联合迭代优化对信号降秩,并使权重向量和投影矩阵相互迭代达到收敛。同时集员方法实现数据选择性更新,改进步长,提高收敛速度。并进行计算复杂度和收敛性能分析。仿真结果表明,相对其它随机梯度法,所提算法的输出性能更好,保持较低的更新比率。所提算法加快收敛速度,降低计算复杂度,保证接收目标信号精度。

基于自适应猫群算法的多目标波束赋形

针对大规模阵列天线波束赋形中智能优化算法精度不高、收敛速度不快等问题,提出一种自适应猫群算法。在猫群算法的基础上,使其基本参数根据迭代次数及适应度函数值自适应改变,并在搜寻模式中加入赌轮盘和精英选择相结合的策略,以增加种群多样性。多种智能优化算法的性能仿真对比分析表明,自适应猫群算法具有快速收敛、全局寻优的能力。将该算法应用到2×8阵列天线多目标波束赋形中仿真,结果表明自适应猫群算法能够有效提高阵列天线多目标波束赋形的效率和精度。

多用户间干扰下IRS辅助毫米波波束赋形研究

为解决IRS辅助毫米波通信系统中的干扰问题,提出了一种基于最大化系统总传输速率的联合波束赋形方案。该方案采用分数规划和二次型变换等技术,通过交替优化算法进行连续相移的联合波束赋形设计。针对实际硬件实现中的反射相移离散问题,进一步提出了一种离散相移的交替优化算法。仿真结果表明,该算法能够有效提高系统总传输速率,并且离散相移方案的总传输速率与连续相移方案趋近。

多用户MISO场景的3D波束赋形和IRS反射优化设计

针对无线信号易受信道影响的难题,将智能反射面(Intelligent Reflecting Surface,IRS)被动波束赋形和基站(Base Station,BS)3D波束赋形进行联合设计。为最大化加权和速率,首先基于投影梯度下降法和加权均方误差等效原理,提出了一种多用户交替优化算法(Multi-User Alternative Optimization,MU-AO),其具有普适性。其次,基于高信噪比(Signal-to-Noise Ratio,SNR)近似原理,提出了一种多用户三级优化算法(Multi-User Three Stage Optimization,MUTSO),通过计算次优组合解来降低计算复杂度。最后,基于BS天线下倾角预定义策略,提出了一种多用户基于直射链路优化算法(Multi-User Direct-Link Based Optimization,MU-DLBO),能够实现复杂度与性能的均衡。实验表明,所提算法均能有效提升系统加权和速率,体现了联合波束赋形的有效性。

基于阵元位置和激励联合优化的波束成形算法

传统波束成形通过优化激励权值以达到设计目标,忽略了阵元位置对波束成形性能的影响。从阵元位置和激励两方面进行优化,首先以静态波束图为目标,利用矩阵束算法求解阵元位置和阵元激励权值,在保留静态波束图特征的基础上将静态波束激励权值转化为自适应波束激励权值,进一步提高波束成形输出信干噪比。仿真实验证明,在相同的设计目标下,该方法所设计的阵列相比均匀线阵需要更少的阵元个数,节约了硬件开销。

面向高空平台通信的模拟波束赋形与用户分组联合优化

在高空平台多波束多用户通信场景下,为提高系统传输速率和多用户通信的公平性,提出了一种基于最大-最小(Max-Min)准则的模拟波束赋形与用户分组联合优化算法。在初始阶段对地面多用户随机分组,基于Max-Min准则求解当前分组下各波束的模拟权值向量,进而基于最大信噪比准则对用户重新分组,并通过算法迭代最终获得最优的用户分组和波束权值。仿真结果表明,该算法相比传统模拟波束赋形与用户分组的结合算法具有更好的信噪比性能,提高了高空平台通信系统的传输速率,且保证了多用户通信的公平性。