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。     

采用相位判决的低复杂度广义空间调制检测算法

针对广义空间调制(GSM)系统中信号检测复杂度过高的问题,提出了一种基于相位判决的低复杂度检测算法.首先根据一种排序准则对天线组合进行排序,然后将排序后的天线组合中的符号向量依次通过基于相位判决的迫零(ZF)均衡器进行检测,最终得到星座调制符号和激活天线组合.分析和仿真结果表明,该检测算法可以有效缩小接收端的搜索范围,在提供与最大似然(ML)检测算法相近的误比特率(BER)性能的同时,计算复杂度降低了98％.     

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稀疏检测算法

空间调制技术作为一种新颖的多天线传输技术,这些年来受到业界的广泛关注,从单一激活天线的空间调制已扩展到多根激活天线的广义空间调制,进一步提高了频带效率。但是,采用多根激活天线发送也给系统的解调带来了困难。由于多激活天线的使用,最大似然（ML）检测算法的计算复杂度将随着激活天线数的增加呈指数增加,是一个NP-hard问题。针对上述问题,本文利用空间调制信号本身固有的稀疏特性,提出了一种次最优检测算法,新算法可以在保证检测性能的前提下,大大降低算法的计算量。不仅如此,由于稀疏性的引入,新算法还可以应用到发送天线数大于接收天线数的情况,计算机仿真试验验证了其有效性。      

BPSK通信系统的部分最优MIMO检测算法

在BPSK调制下,基于最大似然（Maximum Likelihood,ML）准则的MIMO检测器是一个二进制二次规划问题,其计算复杂度随着天线数的增多呈指数增加,当天线数较多时,其计算量太大,无法满足实时通信的要求。本文提出了一种新的MIMO检测算法。使用新算法,可以在很小的计算开销下,求解出ML检测器的部分全局最优解,然后,将优先检测出的部分最优解从原二进制二次规划问题中剔除得到一个相对小规模问题,最后使用传统的次最优检测算法对该小规模问题进行求解。这样,新算法不仅可以得到比传统的次最优检测器更低的误码率,计算量又远小于ML最优检测器。本文的仿真结果验证了新算法的有效性。      

低复杂度空间调制MPSK信号的最优检测

针对MQAM信号,已有低复杂度的最优检测算法,但是针对MPSK信号还没有类似的最优检测算法发表,因此从二维矢量量化的ML解调角度出发,利用MPSK星座图的特性,给出了与调制符号阶数无关的ML简化算法,避免了ML联合检测算法中对调制符号空间的搜索,极大地降低了算法复杂度。新算法不仅与ML最优检测算法具有完全相同的性能,而且具有较低的复杂度,有较好的理论和实际意义。该算法在天线技术和绿色通信技术中有较好的实际应用意义。     

空间调制系统低复杂度的天线选择算法

空间调制(Spatial Modulation,SM)是一种特殊的多天线传输技术,利用发送天线索引和发送的符号共同传递信息.为了获得发送分集增益,人们将天线选择技术应用到SM系统中,提高SM系统解调性能.在天线选择技术中,最大-最小欧式距离(Euclidean Distance Antenna Selection,EDAS)准则应用较为广泛,但是它的全搜索求解方法复杂度高,限制了其应用.为此,本文利用空间调制系统和调制符号本身的特性,从2维量化解调的视角出发,给出了两种低复杂度的最优天线选择算法,并通过计算机仿真和复杂度分析,表明了该算法的有效性和最优性.     

衰落信道CDMA系统联合检测的球译码算法

利用穷搜索的ML联合检测,其复杂度随输入长度指数增长,因而实际系统中很难实现。球译码算法以多项式的复杂度取得接近ML的性能引起广泛的研究。提出一种用于衰落信道LAS-CDMA系统联合检测的球译码算法,与原始算法相比,该算法具有更低的复杂度,对初始半径的选择敏感性更低。在初始半径的选择足够大之后,即使对于大的调制星座,也可以在保证低复杂度的同时取得ML解,对于LAS-CDMA系统,该算法在ML的意义上是最优的。     

连续衰落信道下适用于酉空时调制的最大似然多符号差分检测算法

为酉空时调制系统设计的多符号差分球形译码(MSDSD)能以较低复杂度获得最大似然(ML)检测性能。但是,该算法基于准静态信道假设,当将它用于快衰落信道时会出现严重的误码平层现象。本文基于连续衰落信道假设,推导了一种ML度量的递推形式,并将其嵌入自动球形译码算法中,得到了的多符号差分自动球形译码(MSDASD)算法。该算法适用于一般酉空时星座,克服了MSDSD的误码平层现象,可达到ML检测的性能,其平均复杂度在大多数情况下低于相同假设下的判决反馈检测算法。     

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。     

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.     

An efficient approximate maximum likelihood signal detection for MIMO systems

This paper proposes an efficient approximate Maximum Likelihood （ML） detection method for Multiple-Input Multiple-Output （MIMO） systems, which searches local area instead of exhaustive search and Selects valid search points in each transmit antenna signal constellation instead of all hyperplane. Both of the selection and search complexity can be reduced significantly. The method performs the tradeoff between computational complexity and system performance by adjusting the neighborhood size to select the valid search points. Simulation results show that the performance is comparable to that of the ML detection while the complexity is only as the small fraction of ML.     

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.     

Spatial Modulation

Spatial modulation (SM) is a recently developed transmission technique that uses multiple antennas. The basic idea is to map a block of information bits to two information carrying units: 1) a symbol that was chosen from a constellation diagram and 2) a unique transmit antenna number that was chosen from a set of transmit antennas. The use of the transmit antenna number as an information-bearing unit increases the overall spectral efficiency by the base-two logarithm of the number of transmit antennas. At the receiver, a maximum receive ratio combining algorithm is used to retrieve the transmitted block of information bits. Here, we apply SM to orthogonal frequency division multiplexing (OFDM) transmission. We develop an analytical approach for symbol error ratio (SER) analysis of the SM algorithm in independent identically distributed (i.i.d.) Rayleigh channels. The analytical and simulation results closely match. The performance and the receiver complexity of the SM-OFDM technique are compared to those of the vertical Bell Labs layered space-time (V-BLAST-OFDM) and Alamouti-OFDM algorithms. V-BLAST uses minimum mean square error (MMSE) detection with ordered successive interference cancellation. The combined effect of spatial correlation, mutual antenna coupling, and Rician fading on both coded and uncoded systems are presented. It is shown that, for the same spectral efficiency, SM results in a reduction of around 90% in receiver complexity as compared to V-BLAST and nearly the same receiver complexity as Alamouti. In addition, we show that SM achieves better performance in all studied channel conditions, as compared with other techniques. It is also shown to efficiently work for any configuration of transmit and receive antennas, even for the case of fewer receive antennas than transmit antennas.