基于连续干扰消除的毫米波MIMO系统混合预编码算法

该文研究多用户毫米波MIMO系统的混合模数预编码器和合并器设计。针对因信号传播漫散射造成的多用户间信号干扰问题,提出一种基于连续干扰消除(SIC)的鲁棒混合预编码算法。首先对信道矩阵进行正交分解,以消除来自已知用户信号的干扰,从而将含有非凸约束的多用户链路优化问题分解为多个单用户链路优化问题。然后采用相位提取算法逐个求解每个用户的最优传输链路,并结合最小均方误差(MMSE)准则求得多用户混合预编码矩阵。仿真结果表明,与现有的混合预编码算法相比,所提算法在强干扰环境下具有显著的性能优势。     

MIMO系统中一种改进的迭代串行干扰消除算法

多输入多输出(MIMO)系统中,串行干扰消除(SIC)算法复杂度相对较低,但干扰消除时存在差错传播问题。针对独立编码的垂直分层空时(V-BLAST)系统,提出一种联合译码的SIC算法,将译码完成的码字反馈给解调器,从接收信号中消除。引入一种码字排序准则,利用软信息对码字排序,优先对高可靠性码字译码。通过码字排序和联合译码提高检测符号的准确性,减少差错传播。仿真结果表明,与已存在的接收方法相比,该算法能有效地抑制差错传播,大幅提升误码率性能。      

大规模MIMO系统中的一种自适应SIC检测算法

与传统系统相比,大规模多入多出（MIMO）系统能更加有效地提高频谱效率。利用传统的最小均方误差（MMSE）信号检测算法求解大规模MIMO系统,虽然检测结果接近最优,但是矩阵的求逆运算导致计算的复杂度非常高。提出了一种自适应排序干扰消除（SIC）检测算法,在逐次超松弛（SOR）迭代运算的基础上,通过干扰消除降低待检测矩阵的维度。通过仿真分析,得出所提算法的复杂度低于Jacobi、SOR检测算法,且在迭代次数较少的情况下,算法的误码率（BER）性能明显优于SOR检测算法。     

频域均衡联合基于能量排序的部分并行干扰删除检测算法

针对频率选择性信道下的DS-CDMA系统,该文提出了一种联合MMSE(最小均方误差)频域均衡与基于能量排序的部分并行干扰删除(MMSE-EOPPIC)的多用户检测算法。该算法首先采用基于重叠剪切法的MMSE频域均衡代替Rake接收机对各用户的数据信息进行初始估计,然后在干扰删除的每个迭代级根据用户能量由强到弱的顺序依次对用户进行部分干扰删除;为了提高每个迭代级检测的可靠性,该文将当前级已经检测出的能量较强用户的硬判决值用于重构当前级能量较弱用户的多址干扰(MAI)。仿真结果表明MMSE-EOPPIC检测算法可以有效提高系统的比特误码率性能。     

异步DS-CDMA系统的等效同步多用户检测算法

第三代移动通信系统中，多用户检测技术是克服多址干扰(MAI)、增加系统容量的有效方法。本文针对多径衰落信道下的直扩码分多址(DS—CDMA)系统的上行链路，基于等效扩频码组Gram—Schmitz正交化与串行干扰消除(SIC)，提出了等效同步多用户检测(ESMUD)算法。分析最大比合并瑞克(RAKE)接收算法、传统的SIC算法以及本文算法的复杂度，并对三种算法的性能进行数值仿真。结果表明，本文提出的算法能有效抑制MAI，且计算复杂度与传统SIC算法在同一量级上。     

一种NOMA系统中的反馈深度神经网络检测算法

在上行非正交多址(Non-orthogonal Multiple Access, NOMA)系统中,针对传统基于串行干扰消除(Successive Interference Cancellation, SIC)检测存在同个时频块内用户间干扰的问题,提出了一种新型的NOMA检测算法。通过将SIC检测的反馈消除结构和深度神经网络结合起来,设计出了一种新型的反馈深度神经网络(Feedback Deep Neural Network, FDNN)结构。FDNN模型分为两个模块,检测模块通过深度神经网络实现非线性检测,反馈模块通过权重矩阵重构信号并消除用户干扰。通过重复检测和反馈过程,FDNN依次检测出各个用户的符号,并达到了良好的性能。仿真结果表明FDNN检测算法相较于SIC检测具有更低的误符号率和误比特率,并验证了其具有更良好的抗用户间干扰的性能。     

低复杂度迭代干扰消除的OFDMA初始测距算法

针对现有正交频分多址(OFDMA)系统初始测距算法检测性能不佳以及复杂度过高的问题,该文提出一种低复杂度迭代干扰消除的多用户初始测距算法。该算法采用迭代并行干扰消除方法,在接收端按照最大功率准则并行检测活动测距用户的有效径,利用估计出的有效径参数信息来减小信道估计中的干扰,再通过重构和并行消除多用户测距信号。计算机仿真结果表明,与连续多用户检测(SMUD)干扰抵消算法相比,当信噪比为9 dB,且一个测距时隙内测距用户数为8时,所提算法的复杂度大约是SMUD的25%,检测性能大约提高了5%。     

基于神经网络和粒子群算法的MC-CDMA多用户检测

MC-CDMA系统是干扰受限系统,存在多址干扰。本文提出一种基于神经网络和粒子群算法的MC-CDMA多用户检测方法。在粒子位置更新中,随机选择部分粒子作为Hopfield神经网络的神经元组成的个体,进行神经网络的更新;其他粒子仍采用原粒子群算法的位置更新策略,从而能加快粒子群算法的收敛速度以及降低计算复杂度。仿真表明在算法参数相同时,该多用户检测方法在误码率、收敛速度、系统容量、抗远近能力等方面都优于基于粒子群算法的多用户检测和基于神经网络的多用户检测,更加逼近于最佳多用户检测的性能。     

互信息量最大化和低复杂度的全双工中继网络自干扰消除和干扰对齐算法

该文针对多用户全双工中继干扰信道,同时考虑全双工中继自干扰消除及用户端干扰消除问题。首先,提出了基于最大化全局互信息量的自干扰消除和干扰对齐算法,并具体给出了自干扰消除矩阵的具体求解算法和方案。进一步,给出了信号对齐和干扰抑制的可行性条件,并分析了系统的互信息量和所受干扰噪声功率,以及系统的自由度。理论分析和仿真结果显示,与现有的典型全双工中继方案相比,该文算法可以提高系统互信息量和自由度,并有较低误码率;另外,该文算法的中继只需进行简单的功率约束,不需做复杂的信号处理,降低了整个系统的信号处理复杂度。     

一种基于检测信息可靠度的部分软干扰消除迭代多用户检测算法

该文提出了一种基于检测信息可靠度的部分软干扰消除迭代多用户检测算法。如果由从信道译码器所获得的发送字符的先验信息大于某一预定阈值,则认为对其检测具有较高的可靠度,因而可以考虑将其对应的多址干扰成分从匹配滤波器输出向量中直接消除,相当于减小了干扰用户的数目,从而可以减小迭代多用户检测算法的复杂度。该算法的计算复杂度能够随着多址干扰的减小和信道信噪比的增大而降低。     

Reliability feedback–aided low‐complexity detection in uplink massive MIMO systems

Massive multiple‐input multiple‐output (MIMO) plays a crucial role in realizing the demand for higher data rates and improved quality of service for 5G and beyond communication systems. Reliable detection of transmitted information bits from all the users is one of the challenging tasks for practical implementation of massive‐MIMO systems. The conventional linear detectors such as zero forcing (ZF) and minimum mean square error (MMSE) achieve near‐optimal bit error rate (BER) performance. However, ZF and MMSE require large dimensional matrix inversion which induces high computational complexity for symbol detection in such systems. This motivates for devising alternate low‐complexity near‐optimal detection algorithms for uplink massive‐MIMO systems. In this work, we propose an ordered sequential detection algorithm that exploits the concept of reliability feedback for achieving near‐optimal performance in uplink massive‐MIMO systems. In the proposed algorithm, symbol corresponding to each user is detected in an ordered sequence by canceling the interference from all the other users, followed by reliability feedback‐based decision. Incorporation of the sequence ordering and the reliability feedback‐based decision enhances the interference cancellation, which reduces the error propagation in sequential detection, and thus, improves the BER performance. Simulation results show that the proposed algorithm significantly outperforms recently reported massive‐MIMO detection techniques in terms of BER performance. In addition, the computational complexity of the proposed algorithm is substantially lower than that of the existing algorithms for the same BER. This indicates that the proposed algorithm exhibits a desirable trade‐off between the complexity and the performance for massive‐MIMO systems.     

New detection algorithms based on the jointly Gaussian approach and successive interference cancelation for iterative MIMO systems

A new detection algorithm based on the jointly Gaussian approach (JGA) and successive interference cancelation (SIC), named the SIC‐JGA algorithm, is proposed for iterative MIMO systems. Both the a priori symbol estimates of the undetected layers and the a posteriori symbol estimates of the previously detected layers are used in the SIC process. To reduce the complexity of covariance matrix inversion in the proposed algorithm, simplified algorithms named S‐SIC‐JGA are investigated for both the single path and multipath block fading channels under M Phase Shift Keying modulations. A detailed complexity analysis is presented for both the SIC‐JGA and S‐SIC‐JGA algorithms. Numerical results exhibit that the system using the SIC‐JGA algorithm outperforms the JGA algorithm, with an additional complexity for determining the detection order of SIC. The S‐SIC‐JGA algorithm has a much lower complexity compared with the SIC‐JGA algorithm while experiencing an acceptable system performance degradation. Proper detection algorithms can be chosen in different iterations to balance the system performance and the detection complexity. Copyright © 2012 John Wiley & Sons, Ltd.     

高速移动环境下基于动态CSI的MIMO系统改进模代数预编码

针对高速移动场景下信道快时变特性给多输入多输出（Multiple Input Multiple Output,MIMO）系统预编码带来的技术挑战,提出基于动态信道状态信息（Channel State Information,CSI）的MIMO系统改进模代数预编码（Tomlinson-Harashima Precoding,THP）算法.该算法利用信道统计量信息以及相关性构建动态CSI模型,更准确实时地体现当前信道状况;进一步地,提出一种基于循环遍历搜索的多用户调度算法,以最大化信干噪比为目标进行用户调度,且将多用户调度算法应用到THP中,提高了预编码精度.仿真结果表明,所提算法相比传统预编码算法具有更优的误码率性能和信道容量,验证了所提算法能有效地适应高速移动场景.     

iPAS: A deep Monte Carlo Tree Search-based intelligent pilot-power allocation scheme for massive MIMO system

Massive Multiple-Input-Multiple-Output (MIMO) is a promising technology to meet the demand for the connection of massive devices and high data capacity for mobile networks in the next generation communication system. However, due to the massive connectivity of mobile devices, the pilot contamination problem will severely degrade the communication quality and spectrum efficiency of the massive MIMO system. We propose a deep Monte Carlo Tree Search (MCTS)-based intelligent Pilot-power Allocation Scheme (iPAS) to address this issue. The core of iPAS is a multi-task deep reinforcement learning algorithm that can automatically learn the radio environment and make decisions on the pilot sequence and power allocation to maximize the spectrum efficiency with self-play training. To accelerate the searching convergence, we introduce a Deep Neural Network (DNN) to predict the pilot sequence and power allocation actions. The DNN is trained in a self-supervised learning manner, where the training data is generated from the searching process of the MCTS algorithm. Numerical results show that our proposed iPAS achieves a better Cumulative Distribution Function (CDF) of the ergodic spectral efficiency compared with the previous suboptimal algorithms.     

A novel artificial bee colony detection algorithm for massive MIMO system

As a hot‐spot of 5G, the research on detection algorithms for massive multiple input multiple output (MIMO) system is significant but difficult. The traditional MIMO detection algorithms or their improvements are not appropriate for large scaled antennas. In this paper, we propose artificial bee colony (ABC) detection algorithm for massive MIMO system. As one advanced technology of swarm intelligence, ABC algorithm is most efficient for large scaled constrained numerical combinatorial optimization problem. Therefore, we employ it to search the optimum solution vector in the modulation alphabet with linear detection result as initial. Simulation and data analysis prove the correctness and efficiency. Versus the scale of massive MIMO systems from 64 × 64 to 1024 × 1024 with uncoded four‐quadrature‐amplitude‐modulation signals, the proposed ABC detection algorithm obtains bit error rate of 10 ^− 5 at low average received signal‐to‐noise‐ratio of 12 dB with rapid convergence rate, which approximates the optimum bit error rate performance of the maximum likelihood and achieves the theoretical optimum spectral efficiency with low required average received signal‐to‐noise‐ratio of 10 dB in similar increasing regularity, over finite time of low polynomial computational complexity of per symbol, where N_T denotes the transmitting antennas' number. The proposed ABC detection algorithm is efficient for massive MIMO system. Copyright © 2016 John Wiley & Sons, Ltd.     

基于块对角化的格基规约GMD矢量预编码

研究了对多用户多输入多输出下行链路进行块对角化后,使用格基规约算法的几何均值分解矢量预编码的实现方法。根据块对角化思想将多用户多天线信道分解为等价并行子信道,基于等价子信道给出了单个用户的几何均值分解矢量预编码的传输方案,通过使用格基规约算法分别结合连续干扰消除和垂直分层空时编码两种方法,求解矢量预编码中的扰动矢量。仿真表明,提出的方法误码率性能优于块对角化矢量预编码算法2 dB以上,而且能在不降低系统性能的前提下降低计算复杂度。     

基于切比雪夫-迹迭代的大规模MIMO系统软输出信号检测

在多用户大规模多输入多输出(MIMO)系统信号检测算法中,最小均方误差(MMSE)算法可取得近似最优性能,但MMSE算法中高维矩阵求逆的复杂度过高,导致在实际应用中难以快速有效地实现。同时,对于高阶正交幅度调制(HQAM),如果符号向比特的解映射采用硬判决,将会导致后续信道译码的性能明显下降。因此,该文针对采用格雷编码的HQAM的多用户大规模MIMO系统,提出一种基于切比雪夫-迹迭代(CTI)的低复杂度软输出信号检测算法。该算法不但有效地规避了信号检测所需的高维矩阵求逆,同时,利用格雷编码的调制信号的比特翻转特性和二叉树结构,给出了一种融合三叉链表搜索的比特对数似然比(LLR)简化计算方法。仿真结果表明,该文所提的软输出信号检测算法最多需要3次迭代就能收敛并可取得接近MMSE算法的性能,在复杂度和性能之间取得了很好的折中。     

基于非正交多址的室内可见光通信系统性能优化方法

室内可见光通信(Visible Light Communication, VLC)系统常用的非对称限幅光OFDM(Asymmetrically Clipped Optical Orthogonal Frequency Division Multiplexing, ACO-OFDM)与直流偏置光OFDM(DC-biased Optical OFDM, DCO-OFDM)采用加循环前缀、信道均衡和载波复用等方法解决信道干扰及多用户复用问题,但均以牺牲有效性为代价。非正交多址(Non-orthogonal Multiple Access, NOMA)通过功率域复用提高频谱利用率,利用串行干扰消除(Successive Interference Cancelling, SIC)进行多用户信号处理,有效兼顾可靠性与有效性。将NOMA应用于室内可见光系统,建立基于NOMA-DCO-OFDM的可见光信号传输及信道增益模型。通过功率域多用户信道差异计算信道增益,进行功率分配实现功率域复用,提高系统容量和通信速率;利用SIC按功率分配算法对用户逐一解调,减弱信道干扰,提高可靠性。通过理论分析和仿真实验验证表明:该系统的通信速率达到6.8×107 bit·s?1,且合速率受用户数量的影响不显著。2用户下,误码率(Bit Error Rate, BER)为10?4时用户1有5.2 dB左右的性能提升,用户2有2.3 dB左右的性能提升,通信可靠性也明显提高。