基于能量采集的大规模MIMO系统能效优化

研究基于能量采集的大规模多输入多输出(multiple-input multiple-output,MIMO)系统能效优化问题。保证用户服务质量、能量塔发射功率限制和能量采集时间约束下,为实现上行大规模MIMO系统能效最大化,对能量塔发射功率、能量采集时间进行联合优化。该问题属于非凸优化问题,首先通过分式规划理论将原优化问题等价转换,然后采用块坐标下降（block coordinate descent,BCD）方法,对能量塔发射功率、能量采集时间、系统能效进行迭代求解,提出了一种基于能量采集的大规模MIMO系统的联合优化能效算法(energy-efficient power and time allocation algorithm,EPTA)。仿真结果表明,在保证用户服务质量的情况下,与均时最小QoS保证算法(time-averaged minimum QoS guaranteed algorithm,TA-QoSA)、吞吐量资源分配算法(throughput maximization based power and time algorithm,TPTA)相比,该算法提高了系统能效。     

基于功率控制的大规模MIMO系统能效优化算法

能效优化是5 G通信领域的一个研究热点.首先针对单小区多用户上行大规模MIMO通信系统,在满足用户QoS需求和系统可容忍的信道有效噪声条件下,建立关于发射功率、导频序列长度、基站天线数的能效优化模型;其次,不同于传统利用迭代算法求解使系统能效最佳的基站天线数,提出了采用Lambert W函数分析得到最佳基站天线数的闭式表达式;最后根据分式规划理论,采用迭代优化算法联合优化系统导频序列长度、发射功率、基站天线数.仿真结果表明,该算法较现有算法能效提高了11.2％,说明该算法能有效提高系统性能.     

Compressive sampling optimization for user signal parameter estimation in massive MIMO systems

As the most promising technology in wireless communications, massive multiple-input multiple-output (MIMO) faces a significant challenge in practical implementation because of the high complexity and cost involved in deploying a separate front-end circuit for each antenna. In this paper, we apply the compressive sampling technique to reduce the number of required front-end circuits in the analog domain and the computational complexity in the digital domain. Unlike the commonly adopted random projections, we exploit the a priori probability distribution of the user positions to optimize the compressive sampling strategy, so as to maximize the mutual information between the compressed measurements and the direction-of-arrival (DOA) of user signals. With the optimized compressive sampling strategy, we further propose a compressive sampling Capon spatial spectrum estimator for DOA estimation. In addition, the user signal power is estimated by solving a compressed measurement covariance matrix fitting problem. Furthermore, the user signal waveforms are estimated from a robust adaptive beamformer through the reconstruction of an interference-plus-noise compressed covariance matrix. Simulation results clearly demonstrate the performance advantages of the proposed techniques for user signal parameter estimation as compared to existing techniques.     

Optimized and low-complexity power allocation and beamforming with full duplex in massive MIMO and small-cell networks

The Journal of Supercomputing - By combining massive multiple-input and multiple-output (Ma-MIMO) and small-cell approaches, it is possible to improve the capacity of the network, with the features...     

多用户MIMO系统中机会波束截断的信道反演

针对下行链路多用户MIMO系统,提出了一种简单的基于机会波束截断的信道反演方法。机会波束形成可以用最小的反馈获得MIMO系统慢衰落信道中下行链路的多用户分集增益和复用增益。在机会波束形成进行自适应信道截断的基础上,使各通信用户的信干噪比（SINR）相同,改善了系统的误比特率性能。仿真结果表明,总的用户数为30时,系统误比特率性能提高3~4 dB。信道反演比注水功率分配简单,降低了系统复杂度。     

大规模MIMO系统中低复杂度预编码技术研究

基于大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)系统,提出两类低复杂度预编码算法。首先,通过大规模MIMO系统的渐近正交信道特性近似求逆矩阵,提出了逐次超松弛(Successive Over Relaxation,SOR)预编码算法,能够降低矩阵计算复杂度。其次,在SOR基础上,进一步提出了共轭梯度(Conjugate Gradient,CG)算法,通过引入适当的预处理矩阵对矩阵进行预处理,使其特征值分布更为集中,降低了条件数,加快了算法的收敛速度,从而显著降低了计算复杂度。仿真表明,提出的SOR方法误码率性能优于传统的正则化迫零(Regularization Zero-Forcing,RZF)预编码,而CG算法能够在保证SOR误码率性能的情况下进一步降低计算时间复杂度。     

Asymptotic SEP of MIMO beamforming in two-hop AF relaying systems

This paper derives an asymptotic average symbol error probability(SEP)expression for multiple-input multiple-output(MIMO)two-hop amplify-and-forward(AF)relay systems with optimal beamforming.The analysis at high signal-to-noise ratio(SNR)quantifies the diversity order and array gain,which are valid for any antenna numbers of the terminals and arbitrary channel fading models.It is also proved that the system performance is dominated by the relatively degraded one of the two-hop channels.Our results based on the simple expressions provide valuable insights into the performance and practical design considerations of MIMO AF networks.     

Cross-layer design for cooperative MIMO systems with relay selection and imperfect CSI

Based on imperfect channel state information (CSI) and relay selection, a cross-layer optimization scheme is developed for cooperative MIMO system, and the corresponding system performance is investigated in Rayleigh fading channel. By the theoretical analysis and mathematical manipulation, the average spectral efficiency (SE), packet error rate (PER) and packet loss rate (PLR) are derived. As a result, closed-form expressions of the average SE, PER and PLR are obtained, respectively. According to these, a cross-layer design (CLD) scheme subject to the average PER constraints is proposed to avoid the SE performance loss caused by the conventional instantaneous PER constraints. The scheme optimizes the adaptive thresholds by maximizing the average SE of the system, and an iteration method joint the Lagrange multiplier and Newton methods is presented to solve the above optimization problem. Simulation results verify the validity of the theoretical analysis. The results show that the proposed CLD scheme can improve the SE while target PLR is maintained. The CLD scheme under average PER constraints has higher SE than the conventional CLD scheme under instantaneous PER constraints.     

基于天线分组的高铁大规模多输入多输出自适应波束赋形方案

针对高铁大规模多输入多输出（MIMO）系统的吞吐量未被充分提升的问题,提出一种基于天线分组的自适应波束传输方案。首先利用基站（BS）预知的列车位置信息,并将波束赋形技术引入高速场景,建立高铁大规模MIMO的三维模型;其次验证BS天线分组情况下,子波束的吞吐量与其对应的发射天线数满足非线性关系,且子波束天线数变化并未对其他波束的吞吐量产生影响。基于此,以天线分组的自适应波束赋形方案对列车运行至不同位置的波束数和子波束所需的发射天线数进行调整,保证不同位置的最优系统吞吐量。计算机仿真表明,该方案与传统的单波束、双波束、八波束相比,在列车距基站125 m范围内分别实现了系统吞吐量87.9%、62.3%、50.6%的提升,在125 m之外与单波束赋形的系统吞吐量相近。实验结果表明,所提方案无论列车距BS较近或较远时,系统吞吐量均处于最佳水平,更好地适应高速铁路环境。     

An iterative BiGAMP-based receiver for coded massive MIMO systems with low-resolution ADCs

Dear editor,The hundreds of antennas deployed in massive multiple-input-multiple-output (MIMO) systems lead to an insup-portable burden in terms of hardware co...     

Maximum-likelihood detection based on branch and bound algorithm for MIMO systems

Maximum likelihood detection for MIMO systems can be formulated as an integer quadratic programming problem. In this paper, we introduce depth-first branch and bound algorithm with variable dichotomy into MIMO detection. More nodes may be pruned with this structure. At each stage of the branch and bound algorithm, active set algorithm is adopted to solve the dual subproblem. In order to reduce the complexity further, the Cholesky factorization update is presented to solve the linear system at each iteration of active set algorithm efficiently. By relaxing the pruning conditions, we also present the quasi branch and bound algorithm which implements a good tradeoff between performance and complexity. Numerical results show that the complexity of MIMO detection based on branch and bound algorithm is very low, especially in low SNR and large constellations.     

Performance analysis of MIMO systems with arbitrary number transmit antenna selection and OSTBC in the presence of imperfect CSI

In this paper, the performance analysis of MIMO systems with arbitrary number transmit antenna selection (TAS) and orthogonal space-time block coding (STBC) in Rayleigh fading channels for imperfect channel state information (CSI) is presented. For the performance analysis, the moment generating function of the system effective SNR as well as its upper and lower bounds are derived. Then, accurate and approximate expressions of bit error rate (BER) of MIMO-TAS-STBC with MPSK and MQAM are further derived. Using the approximate BER formula and imperfect CSI, an adaptive antenna selection scheme is developed for minimizing the BER. The diversity gain and coding gain are analyzed at high SNR. The results indicate that the MIMO-TAS-STBC for imperfect CSI can only achieve partial diversity order KN and the coding gain is affected by K, N, code rate, modulation pattern, and channel correlation coefficients, when K transmit antennas are selected and N receive antennas are used. Simulation results show that the theoretical analysis matches the simulation result well, and the approximate expressions are close to the accurate ones but have a lower complexity.     

Transformer-based downlink precoding design in massive MIMO systems for 5G-advanced and 6G

<正>Massive multiple-input multiple-output(MIMO) with a larger number of base station(BS) antennas has been regarded as a key technique to support digital twins and the metaverse in future 5G-Advanced and 6G [1]. To realize the expected high-speed transmission, the channel information state(CSI) is required to design the precoding in BS.     

MIMO two-way relay channel with superposition coding and imperfect channel estimation

This paper deals with the superposition coding (SPC) scheme in multiple-input multiple-output two-way relay channels subject to imperfect channel estimation. In this scenario, two multiple antenna terminals, which are unable to communicate directly, exchange information with each other via a multiple antenna relay. We determine the impact of the channel estimation error degradation on the achievable rate region for two main SPC techniques: (a) SPC without channel state information (CSI) at the users, (b) SPC with an imperfect CSI at the users where a waterfilling power allocation is employed. We demonstrate that imperfect CSI significantly improves the achievable rate at low signal-to-noise ratios (SNRs) while it becomes less critical at high SNRs. In addition, a SPC power allocation technique that incorporates the average channel statistics and does not require any instantaneous CSI is also investigated. We show how the available power is split between the two bi-directional (superimposed) data flows in order to maximize the system performance and to support fairness as well as to maximize the achievable sum-rate.     

混合IRS辅助大规模MISO中基于显性信道估计的联合波束成形设计

针对智能反射面辅助大规模MISO系统的波束成形设计,当前大多数研究都是基于信道状态信息完全已知,且未考虑基站端主动波束成形矩阵与智能反射面反射系数矩阵存在的耦合关系。智能反射面辅助的信道为级联信道,难以进行估计,且导频开销较大。鉴于此,采用了一种包含有源和无源元件的混合智能反射面架构,首先基于毫米波信道的稀疏特性,利用压缩感知算法估计信道,并设计一种融合注意力机制的两级卷积网络框架,以最大化和速率为目标,联合优化设计智能面反射系数矩阵和波束成形矩阵。实验结果表明,相比已有方法,所提方法可以使用更少的导频获得更优的和速率性能,有效减少了导频损耗,降低了计算时间复杂度。且当通信环境发生变化时,网络亦具有良好的鲁棒性。     

Joint DOA and channel estimation with data detection based on 2D unitary ESPRIT in massive MIMO systems

We propose a novel method for joint two-dimensional (2D) direction-of-arrival (DOA) and channel estimation with data detection for uniform rectangular arrays (URAs) for the massive multiple-input multiple-output (MIMO) systems. The conventional DOA estimation algorithms usually assume that the channel impulse responses are known exactly. However, the large number of antennas in a massive MIMO system can lead to a challenge in estimating accurate corresponding channel impulse responses. In contrast, a joint DOA and channel estimation scheme is proposed, which first estimates the channel impulse responses for the links between the transmitters and antenna elements using training sequences. After that, the DOAs of the waves are estimated based on a unitary ESPRIT algorithm using previous channel impulse response estimates instead of accurate channel impulse responses and then, the enhanced channel impulse response estimates can be obtained. The proposed estimator enjoys closed-form expressions, and thus it bypasses the search and pairing processes. In addition, a low-complexity approach toward data detection is presented by reducing the dimension of the inversion matrix in massive MIMO systems. Different cases for the proposed method are analyzed by changing the number of antennas. Experimental results demonstrate the validity of the proposed method.     

基于改进粒子群算法的毫米波大规模MIMO混合预编码方案

针对毫米波大规模多输入多输出（MIMO）系统中基于传统粒子群优化（PSO）算法的混合预编码方案,在迭代后期收敛速度较慢以及容易陷入局部最优值的问题,提出了一种基于改进PSO算法的混合预编码方案。首先,随机初始化粒子的位置矢量和速度矢量,并以最大化系统和速率为目标求解初始群体最优位置矢量;其次,更新位置矢量和速度矢量,并随机地选择更新后的两个粒子的个体历史最优位置矢量进行加权求和作为新的个体历史最优位置矢量,从中挑选出若干个使系统和速率最大的粒子,将其个体历史最优位置矢量的加权平均值作为新的群体最优位置矢量,并与之前的群体最优位置矢量比较,经过多次迭代形成最终的群体最优位置矢量即为所求的最佳混合预编码矢量,并对其进行归一化;最后,根据归一化后的混合预编码矢量设计最终的模拟预编码矩阵和数字预编码矩阵。仿真结果表明,与基于传统PSO算法的混合预编码方案相比,所提改进方案在收敛速度与和速率上都得到优化;其收敛速度提高约100%,且性能可以达到全数字预编码方案的90%,因此,该改进方案能够有效提升系统性能且加快收敛。     

Precoder and combiner design for dynamically sub-connected hybrid architecture with low-resolution DACs/ADCs in mmWave massive MIMO systems

Dear editor, The integration of mmWave and massive multiple-input multiple-output (mMIMO) becomes inevitable in future 5G wireless communication systems[1],in w...