Performance optimization of cell-free massive MIMO system with power control approach

The cell-free massive MIMO (multiple-input multiple-output) system involves a large number of access points serving a smaller number of mobile users (MUs) over identical time/ frequency resource. By providing large number of service antennas closer to the MUs, the cell-free massive MIMO can offer great spectral efficiency, better macro-diversity and minimal path loss. Despite several advantages, the cell-free massive MIMO suffers from energy overloading caused by uncontrolled backhaul power consumption for large number of distributed access points (APs) and pilot contamination during channel estimation. In this paper, we have taken into consideration a cell-free massive MIMO system with APs equipped with multiple antennas performing time-division-duplex (TDD) operation. Here, all the APs coordinate through a constrained backhaul network for joint transmission of signals to all the users simultaneously by multiplying the received signal with the normalized conjugate of the estimated channel state information (CSI) and send back a rounded off version of the weighted pattern to the central processing unit (CPU). Finally, an effective user defined algorithm is presented involving selection and grouping of various APs based on their individual contributions for a particular MU to improve the overall performance of the system.     

Power and energy optimization with reduced complexity in different deployment scenarios of massive MIMO network

With the increase in the number of users, the role of massive MIMO has become more significant. But there is a significant increase in the power and energy consumption in the massive MIMO network for transmission, processing, and reception. Hence, the prior role is to reduce the power consumption and increase the energy efficiency of the network. In this paper, the work is done to reduce the power consumption, while maintaining the reduced complexity in the massive MIMO and small‐cell scenario, and to increase the energy efficiency, by optimizing the number of users and number of transmission antennas in the massive MIMO scenario. This paper has also found out the optimal values of the energy efficiency, number of transmission antennas, and number of users for a massive MIMO network in different deployment scenarios like indoor hotspot, ultradense, dense urban, urban, suburban, and rural areas in both single‐cell scenario and multicell scenario at the base station and user equipment side according to the ITU‐R M.2135 standard.     

Secure transmission in one-bit cell-free massive MIMO system with multiple non-colluding eavesdroppers

Wireless Networks - This paper investigates the secrecy performance of the one-bit cell-free massive multiple-input multiple-output system in the presence of multiple non-colluding eavesdroppers...     

Low complexity codebook search method in massive MIMO system

In Massive MIMO systems for 5G networks,precoding technology is one of the key technologies.Aiming at user side codebook search method of the discrete Fourier transform (DFT) rotation codebook,a low complexity search algorithm was proposed.In this algorithm,all horizontal and vertical codebooks were grouped separately according to the characteristics that the precoding vectors with the same column of DFT rotation codebooks had the smallest chordal distance and the smaller chordal distance have the stronger correlation,and then the optimal horizontal and vertical codewords with maximum channel gain were obtained to form 3D precoding code-books.The simulation results indicate that the searching complexity of the proposed method is significantly reduced under conditions of insuring the system performance,moreover,this advantage becomes greater with the number of antennas increasing.     

毫米波大规模MIMO系统中低复杂度混合预编码方法

针对毫米波大规模多输入多输出(MIMO)系统混合预编码方案设计的难点,提出了一种低复杂度混合预编码方法。首先基于奇异值分解,构造初始射频(RF)预编码矩阵,然后构造数字预编码矩阵。进而将残差矩阵最大左奇异矢量构造的矢量添加到RF矩阵的最后一列,以更新初始RF矩阵。经过多次迭代,从而形成最终RF预编码矩阵。最后基于最小二乘准则设计数字预编码矩阵。理论分析和仿真结果表明,相比于基于正交匹配追踪(OMP)算法的混合预编码设计方法,该方法在计算复杂度大幅下降的同时,其性能远远优于基于OMP算法的混合预编码方法,同时在数据流数相对较小时,其性能接近最优的全数字预编码设计方法。     

Adaptive joint precoding and pre‐equalization with reduced complexity in massive MIMO systems

In this paper, a massive multiple input multiple output downlink scenario is considered where the number of users varies in a large dynamic range. An adaptive joint precoding and pre‐equalization with reduced complexity is proposed. Specifically, the successive over‐relaxation method is employed in the pre‐equalization process to avoid the high‐dimensional channel matrix inversion, and a reduced‐length feedback filter is proposed to reduce the computational complexity of the precoding. Moreover, an adaptive transceiver structure is proposed to switch on/off the precoding process so that multiple users can be accommodated with the least cost of the computational complexity. Simulation results show that, compared with the traditional scheme, the proposed adaptive joint precoding and pre‐equalization can save about 90% of the computational complexity. Copyright © 2016 John Wiley & Sons, Ltd.     

MIMO systems with full and reduced hardware complexity

MIMO (multiple-input-multiple-output) systems propose enormous gains in the capacity of wireless systems without requiring more spectral resources. This paper first gives an overview of the use of MIMO for diversity and spatial multiplexing, and the use of channel state information in MIMO systems. It then explores the use of antenna selection as a means for the reduction of the hardware complexity. It is shown that the performance in a spatial-multiplexing application is almost as good as that of full-complexity systems as long as the number of RF chains is at least as large as the number of data streams.     

大规模MIMO系统低复杂度混合迭代信号检测

在大规模MIMO系统上行链路信号检测算法中,最小均方误差(MMSE)算法能获得接近最优的线性检测性能.但是,传统的MMSE检测算法涉及高维矩阵求逆运算,由于复杂度过高而使其在实际应用中难以快速有效地实现.基于最速下降(steepest descent,SD)算法和高斯一赛德尔(Gauss-Seidel,GS)迭代的方法提出了一种低复杂度的混合迭代算法,利用SD算法为复杂度相对较低的GS迭代算法提供有效的搜索方向,以加快算法收敛的速度.同时,给出了一种用于信道译码的比特似然比(LLR)近似计算方法.仿真结果表明,通过几次迭代,给出的算法能够快速收敛并接近MMSE检测性能,并将算法复杂度降低一个数量级,保持在O(K2).     

A survey on user-centric cell-free massive MIMO systems

The mobile data traffic has been exponentially growing during the last several decades. This was enabled by the densification of the network infrastructure in terms of increased cell density (i.e., Ultra-Dense Network (UDN)) and/or the increased number of active antennas per Access Point (AP) (i.e., massive Multiple-Input Multiple-Output (mMIMO)). However, neither UDN nor mMIMO will meet the increasing demand for the data rate of the Sixth Generation (6G) wireless communications due to the inter-cell interference and large quality-of-service variations. Cell-Free (CF) mMIMO, which combines the best aspects of UDN and mMIMO, is viewed as a key solution to this issue. In such systems, each User Equipment (UE) is served by a preferred set of surrounding APs cooperatively. In this paper, we provide a survey of the state-of-the-art literature on CF mMIMO. As a starting point, the significance and the basic properties of CF mMIMO are highlighted. We then present the canonical framework to discuss the essential details (i.e., transmission procedure and mathematical system model). Next, we provide a deep look at the resource allocation and signal processing problems related to CF mMIMO and survey the up-to-date schemes and algorithms. After that, we discuss the practical issues in implementing CF mMIMO and point out the potential future directions. Finally, we conclude this paper with a summary of the key lessons learned in this field.     

无人机辅助无蜂窝大规模MIMO中的空地协同调度

无蜂窝大规模多输入多输出(multiple-input multiple-output,MIMO)技术采用大量接入点(access point,AP)为地面用户提供高效的通信服务,但在有高速移动用户的场景中,会加剧对信道状态信息的依赖。为了减少导频资源的消耗,提出了一种综合无人机(unmanned aerial vehicle,UAV)辅助通信和无蜂窝大规模MIMO通信的双系统架构,该架构能够预测高速移动用户的轨迹,利用无人机为其提供可靠通信;进一步提出了基于深度强化学习(deep reinforcement learning,DRL)的无人机轨迹设计和地面用户调度方案,在满足各类约束的前提下实现系统总和速率最大化。仿真结果表明,与现有方案相比,所提方案能够有效提升系统容量。     

TDD模式下无小区大规模MIMO系统的导频干扰抑制

为了缓解无小区大规模MIMO系统的导频干扰、提高系统性能,提出了一种综合导频复用、AP选择和功率控制的导频干扰抑制策略。首先,提出了基于用户距离排序的导频分配,合理地避免了两个距离较近的用户复用导频,以减少导频干扰的影响。然后,通过分析系统吞吐量表达式中的影响因子确定AP选择的标准,只选择对用户的宏分集增益有主要影响的AP作为用户的服务AP。最后,提出了一种改进的最大最小功率控制算法,提高系统平均吞吐量。仿真结果显示,提出的导频干扰抑制策略有效地减轻了导频干扰带来的性能损失。     

大规模MIMO系统中基于权重二对角迭代的低复杂度预编码

大规模MIMO系统中由于系统下行链路的迫零（zero forcing,ZF）预编码中存在大矩阵求逆运算,随着用户数与天线数的增加,其复杂度随之增加。为了降低复杂度,提出了一种基于雅克比（Jacobi）迭代算法的改进预编码算法,用下二对角矩阵作为迭代矩阵,并且将迭代结果与上一步迭代结果进行权重相加来加速迭代。根据大规模MIMO系统信道矩阵的对角占优特性,将矩阵求逆的诺依曼近似的第一项作为迭代的初始值进一步加速迭代。相比于传统迫零预编码方案,提出的方案可以降低一个量级的算法复杂度,并且保证了预编码方案的性能。     

Low complexity detection algorithm based on optimized Neumann series for massive MIMO system

An optimized Neumann series ( NS ) approximation isdescribed based on Frobenius matrix decomposition, this method aims to reduce the high complexity, which caused by the large matrix inversion of detection algorithm in the massive multiple input multiple output (MIMO) system. The large matrix in the inversion is decomposed into the sum of the hollow matrix and a Frobenius matrix, and the Frobenius matrix has the diagonal elements and the first column of the large matrix. In order to ensure the detection performance approach to minimum mean square error (MMSE) algorithm, the first three terms of the series approximation are needed, which results in high complexity as O(K³), where K is the number of users. This paper further optimize the third term of the series approximation to reduce the computational complexity from O(K³) to O(K²). The computational complexity analysis and simulation results show that the performance of proposed algorithm can approach to MMSE algorithm with low complexity O(K²).     

基于V-BLAST的特征波束形成技术在大规模MIMO中的运用

为了获得较高的频谱效率及较低的误码率,提出了大规模MIMO系统中基于V-BLAST的特征波束形成技术(the Eigen-Beamforming combined with V-BLAST,V-BLAST&E-BF),利用大规模天线形成多个特征波束,在这些特征波束上传输多个码流,既可以获得阵列增益又可以获得复用增益.仿真结果表明:提出的方案较MF预编码和传统特征波束形成技术具有较好的性能,并且在接收端无须进行传统V-BLAST的检测算法(如MMSE检测、ZF检测)亦可分离出信号.     

Training sequence optimization in MIMO systems with colored interference

We address the problems of channel estimation and optimal training sequence design for multiple-input multiple-output systems over flat fading channels in the presence of colored interference. In practice, knowledge of the unknown channel is often obtained by sending known training symbols to the receiver. During the training period, we obtain the best linear unbiased estimates of the channel parameters based on the received training block. We determine the optimal training sequence set that minimizes the mean square error of the channel estimator under a total transmit power constraint. In order to obtain the advantage of the optimal training sequence design, long-term statistics of the interference correlation are needed at the transmitter. Hence, this information needs to be estimated at the receiver and fed back to the transmitter. Obviously it is desirable that only a minimal amount of information needs to be fed back from the receiver to gain the advantage in reducing the estimation error of the short-term channel fading parameters. We develop such a feedback strategy in this paper.     

低复杂度的双基地MIMO雷达阵列优化方法

针对双基地MIMO雷达的稀疏发射和接收阵列联合优化时由于二维角度搜索导致其计算量较大的问题,提出一种低复杂度的双基地MIMO雷达稀疏阵列优化方法。该方法利用MIMO雷达方向图乘积定理将二维角度的优化目标函数分解成两个一维角度的目标函数,然后可以利用遗传算法分别对发射阵列和接收阵列进行优化。该方法不需要二维角度搜索优化,在保证阵列方向图旁瓣抑制性能的基础上,降低了阵列优化的复杂度。仿真结果验证了所提方法的有效性。     

Effects and optimization of pilot sequence length on rate in multiuser massive MIMO FDD system

The effect of pilot sequence length on the asymptotic performance of the ergodic rate was investigated for the multiuser massive multiple-input multiple-output (MIMO) frequency division duplexing (FDD) downlink system.Firstly,the analytical expression of the ergodic rate was derived by using the principle of deterministic equivalence,based on which,it was discovered from the analytical results in two-fold that the normalized pilot sequence length (defined as the pilot sequence length divided by the number of BS antennas) tends to zero yet the rate was guaranteed to grow large without limit as long as the BS antenna number continues to increase,the rate saturates to a certain level if the BS antenna number becomes large with fixed pilot sequence length.Moreover,the pilot sequence length was optimized based on the sum-rate maximization within a finite channel coherence time,and a closed-form solution was deduced under a special correlated channel by means of Lambert W function.Simulation results validate the correctness of the theoretical analysis results and verify the effectiveness of the proposed closed-form solution of the optimal pilot sequence length.     

Average power allocation based sum-rate optimization in massive MIMO systems

This paper exploits variations in the average channel gains in multi-cell multi-user massive multiple input multiple output (MIMO) systems. An average transmit power-control-based sum-rate optimization scheme is presented for the uplink of the system. The matched filtering (MF) and the zero forcing (ZF) processors are considered with perfect and imperfect channel state information at receiver (CSIR) under frequency flat Rayleigh fading channel. An average power-control-based system model is constructed for analyzing the sum-rate and formulating an optimization problem. A discrete level combinatorial optimization is performed for MF and ZF sum-rate under perfect and imperfect CSIR. The numerical results show a significant improvement in the sum-rate and power consumption. A low complexity algorithm for numerical optimization of the sum-rate is proposed. The performance of algorithm is quantified with different scenarios including different number of users, macro cells, and micro cells with low and high inter-cell interference powers. The evaluation results show that the improvement in sum-rate and energy efficiency increases with inter-cell interference power and the number of MTs.     

大规模MIMO系统中的动态导频分配方案

针对多小区大规模多输入多输出(MIMO)系统中存在的导频污染问题,通过分析MMSE信道估计产生的误差,提出了一种动态导频分配方案.该方案将目标小区与相邻小区相邻的边缘区域内的用户进行动态导频分配,剩余区域内的用户进行随机导频分配来提升系统下行链路的平均可达和速率.仿真结果表明,该方法不仅能显著地提升信道估计的性能,而且还能有效地提高整个系统下行链路的可达和速率.     

Energy and spectral efficiency improvement using improved shark smell-coyote optimization for massive MIMO system

Massive multiple-input multiple-output (MIMO) can considerably enhance the “spectral efficiency and energy efficiency” since it is a major technique for future wireless networks. Thus, the performance needs a huge count of base station antennas to serve a smaller number of terminals in conventional MIMO methodology. Large-scale radio frequency (RF) chains represent the large-scale antennas. There is a need of implementing an effective massive MIMO system for maximizing the efficient performance of the system with high “spectral efficiency and energy efficiency” owing to the high cost of RF chains, and the higher power consumption. In this paper, a massive MIMO communication system is implemented to satisfy the requirements regarding “energy efficiency and spectral efficiency.” Here, the number of base station antennas, the transmit power, and beam forming vectors are optimized to maximize “energy efficiency and spectral efficiency” when the channel capacity is known to be higher than some threshold values. The novelty of this work is a new hybrid optimization adaptive shark smell-coyote optimization (ASS-CO) algorithm is developed for improving energy efficiency. The optimization is done with the help of the hybrid optimization ASS-CO Algorithm. The proposed ASS-CO algorithm-based massive MIMO communication system is evaluated by experimental analysis. From the result analysis, the maximum resource efficiency is observed by SS-WOA, which is 6.6%, 50%, 6.6%, 6.6%, and 6.6% maximized than rider optimization algorithm (ROA), spotted hyena optimization (SHO), lion algorithm (LA), Shark Smell Optimization (SSO), and Coyote Optimization Algorithm (COA) by taking the count of base stations as 4. The superior performance enhancement regarding “spectral efficiency and energy efficiency” is accomplished over the traditional systems.