基于改进遗传算法的STSK系统色散矩阵和3D星座的联合优化

空时移位键控（space-time shift keying,STSK）是一种用于多输入多输出（multi-input multi-output, MIMO）通信系统的调制方案,通过预先设计的色散矩阵集（dispersion matrix set,DMS）以实现系统在复用与分集之间的灵活设计。提出了改进的遗传算法（genetic algorithm,GA）辅助DMS和3D星座的联合优化,即将DMS和3D星座联合编码作为GA的染色体,并采用秩与行列式准则对应的编码增益作为总体适应度值。通过高效的选择以及改进的变异和交叉策略,可以获得低误码率（bit error rate,BER）的DMS和3D星座,同时利用3D星座的对称性,大幅度降低了编码增益对应的适应度值的计算复杂度。推导了3D STSK方案的理论平均成对差错概率（ABEP）。仿真结果表明,与传统的 GA 和随机搜索方案相比,本文提出的改进 GA在保证BER性能前提下可以显著降低系统实现的复杂度。     

多误码率固定速率OFDM自适应调制比特功率分配方法

一些应用中需要多误码率的固定速率OFDM自适应调制.本文首先对该多载波自适应调制需求中的比特功率优化分配问题建模,并进一步限定各OFDM子信道采用正交幅度调制(QAM),基于子信道调制比特的任意正实数和无限粒度假设详细分析了子信道集合划分和比特功率分配问题,由此得到一种实用的次最优子信道集合划分和比特功率分配方法.分析和仿真表明由于可得到比特分配的闭式表达,因此该方法计算复杂度低,且性能良好,是一种实用的多误码率固定速率OFDM自适应调制比特功率优化分配方法.     

有信道估计误差MIMO MRC系统性能研究

利用维希特随机矩阵理论和矩生成函数方法,推导了有信道估计误差的独立瑞利衰落信道上采用多输入多输出（MIMO）最大比合并（MRC）天线分集方案的矩形M进制正交幅度调制（MQAM）的平均误符号率（SER）解析表达式。数值计算结果阐明了信道估计误差和收发天线数对矩形MQAM调制MIMO MRC系统误码性能的影响。     

离散多载波调制中信道的非均匀分割

离散多载波调制中需要整个信道分成若干子信道。根据信道幅频特性的变化在不同频段有很大差异，提出了一种非均匀信道分割的方法，能使子信道数减少，子信道间能量的泄漏减少，子信道间的干扰相应减少，同时降低了调制时的计算复杂度。实验证明即使在比较少的子信道情况下，不影响系统的传输速率。     

基于非理想信道状态信息对DCO-OFDM-IM系统建模与分析

针对传统DCO-OFDM-IM系统进行信道估计时没有考虑信道误差影响,导致获得非理想信道状态信息（CSI）而造成信道估计有效性降低等问题,利用最小均方误差信道估计（MMSE）算法并基于非理想信道状态信息建立了DCO-OFDM-IM系统模型,推导了其理论误码率闭合表达式。同时考虑到DCO-OFDM-IM系统中采用最大似然检测算法（ML）当调制阶数增大时存在检测复杂度高,系统传输速率低等问题,而利用对数似然检测算法（LLR）降低检测复杂度,最后采用蒙特卡罗方法进行系统仿真性能的仿真对比。结果表明：当调制阶数和索引组合相同,误码率为10^-3时,LLR检测算法比ML所需信噪比平均改善约2 dB;在能量比为1∶8时,采用MMSE估计比LS信道估计算法系统性能改善约4 dB;当系统信道估计误差越小时,越可能获得理想CSI,提升了信道估计有效性。     

面向大规模MIMO的分块空间调制技术研究

广义空间调制（Generalized Spatial Modulation,GSM）是一种基于多输入多输出（Multiple-Input Multiple-Output, MIMO）系统的高效的数字调制技术,在传统调制符号之外同时通过激活天线模式组合发送信息比特。针对大规模MIMO系统中发送天线数量过多导致的激活天线模式组合的数量过多的问题,提出了一种分块空间调制算法,将每个发送天线子块的激活模式组合构成完整的激活天线模式组合,可有效降低大规模MIMO系统中发送端的调制复杂度。在接收端使用球形译码方法实现逐个子块的天线激活模式组合的解调,可以大幅降低接收端的计算复杂度。仿真分析表明,提出的球形译码算法可以在大幅降低计算复杂度的情况下,实现接近最大似然（Maximum Likelihood,ML）接收机算法的误比特率（Bit Error Rate,BER）性能,且可实现接收端检测性能和计算复杂度之间的最佳折衷。     

基于五维超混沌的太赫兹调制跳变性能分析

针对太赫兹通信信号容易被截获的问题,提出了一种基于五维超混沌系统的太赫兹调制跳变方法。根据信道的实时变化,采用改进的信噪比估计算法选择调制类,再通过五维超混沌系统产生的跳变图案随机地选择一种具体的调制方式。仿真结果表明,所提算法对信道的实时预测能力更为准确,在一定的计算复杂度下,相比于传统的混沌序列,其系统性能更好,具有更优的抗截获性能。     

MIMO-OFDM系统中自适应技术研究

针对MIMO—OFDM系统的自适应调制技术进行分析,利用奇异值分解把MIMO—OFDM信道转化成一系列并行的子信道,然后将这些子信道按照信道增益从大到小的顺序排列。通过研究排序后信道增益的统计特性,提出了一种低复杂度的自适应传输方法来优化系统的发射功率。该方法通过固定排序后的子信道的功率和比特分配方案大大降低了复杂度。     

基于协方差反馈的多天线系统优化发送研究

为了克服多天线信道相关性的影响，提出一种新的自适应发送方案。应用空时分组码特征波束成型技术和格形编码调制（TCM）来获得分集增益和编码增益。针对采用和不采用交织器两种情况，基于成对差错概率（PEP）准则。分析了系统的统计性能，分别得到了使系统编码增益和分集增益最大化的TCM设计准则。根据注水法则和Lagrange乘子法求得波束间功率分配算法最优解。此外，码距作为优化功率加载算法中的权重因子，有效降低了获取波束成形分集的信噪比门限。分析和实验结果表明此方案复杂度低。能有效克服相关衰落。     

广义空间调制系统的正则化OMP检测算法

在广义空间调制（GSM）系统中,最大似然（ML）检测可以取得最优的检测性能,然而其计算复杂度随激活天线数的增加急剧增长。针对这一问题,提出了一种基于稀疏重构理论的低复杂度检测算法——正则化正交匹配追踪（ROMP）算法。该算法首先根据信道矩阵和当前残差的内积选取多个候选激活天线索引,接着对候选天线索引按正则化标准进行可靠性验证,剔除错误索引,缩小信号的搜索空间,最后通过求解最小二乘问题估计信号。仿真结果表明,与经典的正交匹配追踪（OMP）算法相比,所提算法以少许复杂度的增加为代价极大提升了检测性能,能够在检测性能与复杂度之间取得更好的折中。     

双模椭圆球面波多载波索引调制解调方法

该文在基于信号分组优化的椭圆球面波函数(PSWFs)多载波调制的基础上,引入双模索引调制思想,提出了双模PSWFs多载波索引调制解调方法(DM-MCM-PSWFs)。该方法利用未被激活的剩余部分子载波加载第2星座图产生的调制符号,以传输额外的信息比特,实现了基于信号分组优化的PSWFs多载波调制中频谱资源的进一步利用,有效提高了系统频带利用率以及误码性能。理论和仿真分析表明,相较于基于信号分组优化的PSWFs多载波调制,所提方法以适当牺牲系统复杂度为代价,具有更高的系统频带利用率和更优的系统误码性能,当误比特率为1×10^–5, n=7, k=3时,所提方法系统频带利用率、误码性能可分别提升9.15%, 2.4 dB。     

正交空间调制的低复杂度检测算法

针对正交空间调制(QSM)系统中激活天线数的不确定性、最大似然(ML)检测算法复杂度极高的缺点,提出了一种低复杂度检测算法.首先,该算法基于压缩感知(CS)信号重构理论,对系统模型进行重构,使固定激活天线系统中的低复杂度算法可以在新的系统模型中使用;然后,借鉴正交匹配追踪(OMP)算法的思想,选出一个激活天线备选集;最后,通过ML算法搜索备选集,选出激活天线和调制符号.仿真结果显示,相比ML检测算法,所提算法在性能丢失较小的情况下,降低了约90％的复杂度.     

广义空间调制系统的低复杂度检测算法

针对广义空间调制( GSM)系统接收端最大似然( ML)检测算法计算复杂度极高的缺点,提出了一种基于压缩感知( CS)信号重构理论的低复杂度信号检测算法。首先,在多输入多输出( MI-MO)信道模型下,通过改进正交匹配追踪( OMP)算法,得到一个激活天线索引备选集;然后,利用ML算法在该备选集中进行遍历搜索,检测出激活天线索引和星座调制符号。仿真结果表明所提算法的检测性能接近于ML算法,且复杂度约为ML算法的2%。因此,所提算法在保证检测性能的同时也大大降低了计算复杂度,实现了检测性能与复杂度之间的平衡。     

A simple low-complexity algorithm for generalized spatial modulation

Generalized spatial modulation (GSM) is an extension of spatial modulation which is significant for the next generation communication systems. Optimal detection process for the GSM is the maximum-likelihood (ML) detection which jointly detects the antenna combinations and transmitted symbols. However, the receiver is much more complicated than SM due to inter-antenna interference and/or increased number of combinations. Therefore, the computational complexity of the ML detection grows with the number of transmit antennas and the signal constellation size. In this letter, we introduce a novel and simple detection algorithm which uses sub-optimal method based on the least squares solution to detect likely antenna combinations. Once the antenna indices are detected, ML detection is utilized to identify the transmitted symbols. For obtaining near-ML performance while keeping lower complexity than ML detection, sphere decoding is applied. Our proposed algorithm reduces the search complexity while achieving a near optimum solution. Computer simulation results show that the proposed algorithm performs close to the optimal (ML) detection resulting in a significant reduction of computational complexity.     

Zero forcing based sphere decoder for generalized spatial modulation systems

To reduce the number of radio frequency (RF) chains in multiple input multiple output (MIMO) systems, generalized spatial modulation (GSM) techniques have been proposed in the literature. In this paper, we propose a zero‐forcing (ZF)‐based detector, which performs an initial pruning of the search tree that will be considered as the initial condition in a sphere decoding (SD) algorithm. The proposed method significantly reduces the computational complexity of GSM systems while achieving a near maximum likelihood (ML) performance. We analyze the performance of the proposed method and provide an analytic performance difference between the proposed method and the ML detector. Simulation results show that the performance of the proposed method is very close to that of the ML detector, while achieving a significant computational complexity reduction in comparison with the conventional SD method, in terms of the number of visited nodes. We also present some simulations to assess the accuracy of our theoretical results.     

Adaptive subcarrier and bit allocation based on ant colony optimization

The problem of resource allocation in multiuser orthogonal frequency division multiplexing (OFDM) system is a combinatorial optimization problem, difficult to obtain optimal solutions in polynomial time. For the sake of reducing complexity, it can be solved either by relaxing constraints and making use of linear algorithms or by metaheuristic methods. In this paper, an algorithm based on ant colony optimization (ACO), which is a typical algorithm of metaheuristic methods, is proposed for the problem, utilizing excellent search performance of ACO to obtain good solutions. In addition, a parameter is applied to balance the efficiency and fairness of resource allocation. Performance analysis between algorithms based on ACO and genetic algorithm (GA) is carried out, indicating that the proposed algorithm based on ACO outperforms traditional linear algorithms as well as GA in the system throughput with assurance of fairness simultaneously, being as a promising technology for OFDM resource allocation.     

Improved generalized spatial modulation via antenna selection

When a high spectral efficiency is needed, the cost of Euclidean distance‐based antenna selection for spatial modulation (EDAS‐SM) in terms of hardware, size, and computational complexity is significantly increased because of the large transmit antenna array required. In comparison, generalized spatial modulation (GSM) can match the spectral efficiency of EDAS‐SM, while using significantly fewer transmit antenna elements. However, the error performance of GSM is naturally limited because of the use of a predetermined and fixed set of transmit antenna combinations. By exploiting knowledge of the channel, the optimal set of transmit antenna combinations can be selected by maximizing the minimum Euclidean distance between transmit vectors. In this paper, an adaptive scheme for selection of the optimal set of transmit antenna combinations is proposed to improve the reliability of GSM. The computational overhead of the said scheme is relatively high; hence, a low‐complexity suboptimal scheme for selection of the set of transmit antenna combinations is further proposed. The improved GSM schemes address the spectral efficiency limitation of EDAS‐SM, while demonstrating superior error performance.     

基于互满正交设计的差分空时分组码

本文针对多天线系统提出了基于互满正交设计的差分空时分组码(Amicable- orthogonal-design-based Differential Space-Time Block Code,ADSTBC).与已有的差分空时调制方法相比,ADSTBC对信号星图无任何限制,因而可采用高效的调制技术(如QAM、APSK等)提高频谱效率.基于平坦Rayleigh衰落信道,给出了具有线性复杂度的最大似然差分译码器(Maximum-Likelihood Differential Decoder,MLDD).若在ADSTBC中采用QAM星图,MLDD可进一步简化成独立地检测每一数据符号的实部和虚部,降低了实现代价;并且,随着QAM星图阶数的增加,MLDD用于检测单个数据符号的计算量将保持不变.     

Constellation design and performance analysis of the parallel spatial modulation

Spatial modulation (SM) is a relatively recent multiple‐input multiple‐output (MIMO) system in which information is carried by the index of the antenna used for transmission as well as by the conventional signal symbols. Several systems that build upon SM have since been proposed including the generalized SM (GSM), a variant of GSM with multiple active antennas (MA‐SM), quadrature SM (QSM), and parallel SM (PSM), among others. The PSM system can increase the spectral efficiency by splitting the antenna set into groups and applying SM independently in each group using the same signal symbol. In this paper, we first derive the upper bound on the error probability of the PSM. The search of the optimal constellation set is then formulated as a multi‐objective optimization problem, where the obtained constellation minimizes the asymptotic error probability. We conclude that as the number of antenna groups increases, the proposed constellation converges to the conventional phase‐shift keying at relatively low number of transmit antennas. The simulation results show that the proposed constellation outperforms conventional constellations by as much as 5 dB, for high‐modulation orders. Since the multi‐objective optimization is independent of the channel matrix, it can be easily done off‐line. This implies that these gains come at no complexity or delay cost.