基于深度神经网络的正交频分复用波形外辐射源雷达参考信号重构

针对正交频分复用(OFDM)波形外辐射源雷达的参考信号获取问题,基于“解调-再调制”的重构方法结合了波形优势,能获得更为纯净的参考信号。该文在此基础上提出一种联合OFDM解调、信道估计、信道均衡和星座点逆映射的深度神经网络(DNN)重构方法,建立了基于DNN的参考信号重构方案,通过网络学习自适应深度挖掘从时域接收符号到传输码元之间的映射关系、隐式地估计信道响应,从而提高解调精度和重构性能。该文首先研究了仿真数据集的获取问题、DNN的搭建和训练问题,接着对基于DNN方法在导频数目减少、循环前缀的移除、存在符号定时偏差、存在载波频偏、对高峰值平均功率比信号进行时域加窗滤波等情况下的参考信号重构性能进行了仿真分析,仿真结果表明该方法对参考信号重构的有效性。     

基于深度神经网络的正交频分复用波形外辐射源雷达参考信号重构

针对正交频分复用(OFDM)波形外辐射源雷达的参考信号获取问题,基于"解调-再调制"的重构方法结合了波形优势,能获得更为纯净的参考信号.该文在此基础上提出一种联合OFDM解调、信道估计、信道均衡和星座点逆映射的深度神经网络(DNN)重构方法,建立了基于DNN的参考信号重构方案,通过网络学习自适应深度挖掘从时域接收符号到传输码元之间的映射关系、隐式地估计信道响应,从而提高解调精度和重构性能.该文首先研究了仿真数据集的获取问题、DNN的搭建和训练问题,接着对基于DNN方法在导频数目减少、循环前缀的移除、存在符号定时偏差、存在载波频偏、对高峰值平均功率比信号进行时域加窗滤波等情况下的参考信号重构性能进行了仿真分析,仿真结果表明该方法对参考信号重构的有效性.     

基于顺序统计量的索引调制信号检测方法

针对载波索引调制信号检测算法复杂度高的不足,结合索引调制符号概率密度分布特点,引入顺序统计量,提出了一种基于顺序统计量的索引调制信号检测方法。该方法直接依据不同支路统计检测量幅值对不同支路进行排序,将幅值较小支路判断为未激活,幅值较大支路判断为激活,并对调制符号进行解调。理论与仿真分析表明,该方法以适当牺牲系统误码性能为代价,大幅降低索引调制信号检测算法复杂度,相对于ML检测,当载波路数为4、BER为10~(-3)时,以0.25 dB系统误码性能为代价,算法复杂度降低50%,且随着载波数目增加,系统误码性能代价不断降低,复杂度降低程度不断增加。     

高阶调制的GLST系统中的检测算法

多天线无线通信系统的下行链路常采用GLST (Group Layered Space-Time architecture) 来增加接收分集度。该文基于GLST系统的等效多输入多输出模型,分析了该系统在符号传输上的正交性,并针对该系统在使用高阶调制时,传统的符号检测算法因计算复杂度太高而不可用的问题,提出了两种基于球形译码的低复杂度检测算法。仿真结果表明,两种算法均能获得逼近最大似然检测器的性能。     

声表面波扩频多符号检测器算法研究

基于多符号检测的DMPSK信号处理算法在改善系统检测性能方面是一条行之有效的方法．对于直接序列扩频系统，当采用DMPSK调制方式时，同样会存在检测性能恶化问题．为此，该文提出了基于SAW匹配滤波器的DMPSK直接序列扩频多符号检测器算法，并采用13位巴克码SAW抽头延迟线和固定延迟线组合设计对符号数N=3的DBPSK扩频多符号检测算法进行了实验系统验证，给出了相应结果。     

基于符号频域均衡的CPM迭代检测算法

该文研究了部分响应连续相位调制(CPM)信号的频域均衡技术,设计了一种新的发射信号帧结构,并在此基础上提出了一种低复杂度的CPM迭代检测算法,分析了该迭代检测算法的复杂度和误码性能。理论分析和仿真结果表明,在多径衰落信道下,该算法比已有的CPM双迭代频域均衡算法具有更低的复杂度和更好的性能。     

基于深度学习的ACO-OFDM自由空间光通信中信号检测

针对自由空间光通信(free-space optical communication, FSO)系统中大气湍流引起的光强起伏闪烁效应对正交幅度调制(quadrature amplitude modulation, QAM)信号影响很大,缺少实时信道信息时的最大似然(maximum likelihood, ML)检测器性能较差问题,本文提出了一种基于深度学习(deep learning, DL)的信号检测器。其网络框架采用了一个具有全连接层的深度学习神经网络(deep-learning neural network, DNN),实现了无导频的ACO-OFDM空间通信系统中信道盲估计、信道均衡和信号解调。仿真结果表明：在中强湍流大气信道下训练的DNN检测器,8QAM、16QAM和64QAM等调制信号解调的误码率分别可以下降到在2×10^-5、5×10^-5和5×10^-4左右,具有优越性能和鲁棒性,能较好抑制大气湍流引起的信道衰落。     

基于SVM的广义空移键控可见光通信系统信号检测算法

针对室内广义空移键控(GSSK)调制的可见光通信(VLC)系统,该文提出一种基于支持向量机(SVM)的机器学习信号检测算法。在一般的VLC系统中,极大似然(ML)检测是最优检测算法,但是ML检测算法具有很高的计算复杂度。为了解决此问题,该文利用机器学习中的SVM分类思想实现对系统接收端的信号检测,以在保证信号检测正确率的情况下,降低计算复杂度,提高GSSK-VLC系统的信号检测效率。仿真结果表明,该文提出的针对室内GSSK-VLC系统的SVM信号检测算法与ML检测算法相比,在具有接近ML的误比特率(BER)性能的同时,计算复杂度明显降低,有效提升了系统的检测性能。     

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。     

基于SVM的广义空移键控可见光通信系统信号检测算法

针对室内广义空移键控(GSSK)调制的可见光通信(VLC)系统,该文提出一种基于支持向量机(SVM)的机器学习信号检测算法.在一般的VLC系统中,极大似然(ML)检测是最优检测算法,但是ML检测算法具有很高的计算复杂度.为了解决此问题,该文利用机器学习中的SVM分类思想实现对系统接收端的信号检测,以在保证信号检测正确率的情况下,降低计算复杂度,提高GSSK-VLC系统的信号检测效率.仿真结果表明,该文提出的针对室内GSSK-VLC系统的SVM信号检测算法与ML检测算法相比,在具有接近ML的误比特率(BER)性能的同时,计算复杂度明显降低,有效提升了系统的检测性能.     

Low-Complexity Symbol Detector for MIMO-OFDM-Based Wireless LANs

In this brief, a low-complexity hardware architecture for multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) symbol detectors with two transmit and two receive antennas is proposed. The detectors support two MIMO-OFDM schemes of space-frequency block coded OFDM and space-division multiplexing OFDM in order to achieve higher performance and throughput. However, symbol detection processes for these two schemes have high computational complexity, which is a burden to hardware implementation of MIMO-OFDM symbol detectors. In order to reduce complexity, the proposed symbol detector is designed with shared architecture, where similar functional blocks are merged and share the hardware resources, and results in the reduction of logic gates by 34% over a conventional architecture employing two individual detectors     

Genetic algorithm assisted joint multiuser symbol detection andfading channel estimation for synchronous CDMA systems

A novel multiuser code division multiple access (CDMA) receiver based on genetic algorithms is considered, which jointly estimates the transmitted symbols and fading channel coefficients of all the users. Using exhaustive search, the maximum likelihood (ML) receiver in synchronous CDMA systems has a computational complexity that is exponentially increasing with the number of users and, hence, is not a viable detection solution. Genetic algorithms (GAs) are well known for their robustness in solving complex optimization problems. Based on the ML rule, GAs are developed in order to jointly estimate the users' channel impulse response coefficients as well as the differentially encoded transmitted bit sequences on the basis of the statistics provided by a bank of matched filters at the receiver. Using computer simulations, we showed that the proposed receiver can achieve a near-optimum bit-error-rate (BER) performance upon assuming perfect channel estimation at a significantly lower computational complexity than that required by the ML optimum multiuser detector. Furthermore, channel estimation can be performed jointly with symbol detection without incurring any additional computational complexity and without requiring training symbols. Hence, our proposed joint channel estimator and symbol detector is capable of offering a higher throughput and a shorter detection delay than that of explicitly trained CDMA multiuser detectors     

A Novel Multiuser Detection Algorithm for CDMA-Based MIMO-OFDM System

1Introduction Recently,MultipleInputMultipleOutput(MIMO)wirelesstechnologieshavecapturedalotofresearchin terest,giventhatthecapacityincreaseturnsouttobe achievable[1-2].OrthogonalFrequencyDivisionMulti plexing(OFDM)techniquehastheremarkablecharac teristic…     

Optimal Lattice-Reduction Aided Successive Interference Cancellation for MIMO Systems

In this letter, we investigated the optimal minimum-mean-squared-error (MMSE) based successive interference cancellation (SIC) strategy designed for lattice-reduction aided multiple-input multiple-output (MIMO) detectors. For the sake of generating the MMSE-based MIMO symbol estimate at each SIC detection stage, we model the so-called effective symbols generated with the aid of lattice-reduction as joint Gaussian distributed random variables. However, after lattice-reduction, the effective symbols become correlated and exhibit a non-zero mean. Hence, we derive the optimal MMSE SIC detector, which updates the mean and variance of the effective symbols at each SIC detection stage. As a result, the proposed detector achieves a better performance compared to its counterpart dispensing with updating the mean and variance, and performs close to the maximum likelihood detector.     

Group detection for synchronous Gaussian code-divisionmultiple-access channels

The concept of group detection Is introduced to address the design of suboptimum multiuser detectors for code-division multiple-access (CDMA) channels. A group detection scheme consists of a bank of P group detectors, one each for detecting the information symbols of users in each group of a P group partition of the K simultaneously transmitting users. In a parallel group detection scheme, these group detectors operate independently, whereas in a sequential scheme, each group detector. Uses the decisions of the previous group detectors to successively cancel the interference from those users. Group detectors based on the generalized likelihood ratio test (GLRT) are obtained for the synchronous Gaussian CDMA channel. The complexity of these detectors is exponential in the group size, whereas that of the optimum detector is exponential in K. Since the partition of users is a design parameter, group sizes can be chosen to satisfy a wide range of complexity constraints. A key performance result is that the GLRT group detectors are optimally group near-far resistant. Furthermore, upper and lower bounds on the asymptotic efficiency of the sequential group detectors are derived. These bounds reveal that the sequential group detectors can, under certain conditions, perform as well as GLRT group detectors of much larger group sizes. Group detection provides a unifying approach to multiuser detection. When the users are partitioned into K single-user groups, the GLRT, a modified form of GLRT, and the sequential group detectors reduce to previously proposed suboptimal detectors; namely, the decorrelator, the two-stage detector, and the decorrelating decision-feedback detector, respectively. For the other nontrivial partitions, the group detectors are new and have a performance that is commensurate with their complexity     

Symbol detection in spatial multiplexing system using particle swarm optimization meta-heuristics

Symbol detection in multi-input multi-output (MIMO) communication systems using different particle swarm optimization (PSO) algorithms is presented. This approach is particularly attractive as particle swarm intelligence is well suited for real-time applications, where low complexity and fast convergence is of absolute importance. While an optimal maximum likelihood (ML) detection using an exhaustive search method is prohibitively complex, PSO-assisted MIMO detection algorithms give near-optimal bit error rate (BER) performance with a significant reduction in ML complexity. The simulation results show that the proposed detectors give an acceptable BER performance and computational complexity trade-off in comparison with ML detection. These detection techniques show promising results for MIMO systems using high-order modulation schemes and more transmitting antennas where conventional ML detector becomes computationally non-practical to use. Hence, the proposed detectors are best suited for high-speed multi-antenna wireless communication systems. Copyright © 2008 John Wiley & Sons, Ltd.     

Symbol detection based on Voronoi surfaces with emphasis on superposition modulation

A challenging task when applying high-order digital modulation schemes is the complexity of the detector. Particularly, the complexity of the optimal a posteriori probability (APP) detector increases exponentially with respect to the number of bits per data symbol. This statement is also true for the Max-Log-APP detector, which is a common simplification of the APP detector. Thus it is important to design new detection algorithms which combine a sufficient performance with low complexity. In this contribution, a detection algorithm for two-dimensional digital modulation schemes which cannot be split-up into real and imaginary parts (like phase shift keying and phase-shifted superposition modulation (PSM)) is proposed with emphasis on PSM with equal power allocation. This algorithm exploits the relationship between Max-Log-APP detection and a Voronoi diagram to determine planar surfaces of the soft outputs over the entire range of detector input values. As opposed to state-of-the-art detectors based on Voronoi surfaces, a priori information is taken into account, enabling iterative processing. Since the algorithm achieves Max-Log-APP performance, even in the presence of a priori information, this implies a great potential for complexity reduction compared to the classical APP detection.     

An asynchronous multiuser CDMA detector based on the Kalman filter

We introduce a multiuser receiver based on the Kalman filter, which can be used for joint symbol detection and channel estimation. The proposed algorithm has the advantage of working even when the spreading codes used have a period larger than one symbol interval (“long codes”), unlike adaptive equalizer-type detectors. Simulation results which demonstrate the performance advantage of the proposed receiver over the conventional detector, the minimum mean squared error (MMSE) detector and a recursive least squares (RLS) multiuser detector are presented. A thorough comparison of the MMSE detector and the proposed detector is attempted because the Kalman filter also solves the MMSE parameter estimation problem, and it is concluded that, because the state space model assumed by the Kalman filter fits the code division multiple access (CDMA) system exactly, a multiuser detector based on the Kalman filter must necessarily perform better than a nonrecursive, finite-length MMSE detector. The computational complexity of the detector and its use in channel estimation are also studied     

A new approximation to the symbol error probability for codedmodulation schemes with maximum likelihood sequence detection

The power efficiency of coded modulation schemes in additive white Gaussian noise depends on the signal space distribution of their most common error events. Symbol error probability calculation allowing for the pairwise interaction of these error events is discussed. Two optimality criteria are considered for detectors. The first minimizes the probability of symbol error for each symbol decision. This is called the symbol-to-symbol detector. The second (which is superior) is the maximum likelihood sequence detector (MLSD). A lower bound for the symbol-to-symbol detector and an approximation to the MLSD symbol error probability are described. The theoretical performance difference between these two detectors is given. The results are more accurate than minimum squared Euclidean distance predictions, especially at low and intermediate signal-to-noise ratios. The MLSD symbol error probability approximation is obtained for considerably less cost than computer simulation and gives more insight into the signal space structure of the scheme being analyzed. Numerical results are presented for a continuous phase modulation (CPM) example     

Joint synchronization in Eureka 147 DAB system based on abruptphase change detection

We present a joint symbol, frame, and carrier synchronization method for the Eureka 147 DAB signal. Symbol timing is determined first by detecting an abrupt change in the phase angle of the complex product between the last quarter of a useful symbol and its cyclic extension in the guard interval. The detection of this abrupt change is based an the maximal likelihood (hit) principle. Frequency offset of fractional carrier spacing is estimated from the phase angle of the autocorrelation after symbol timing is estimated. Coarse frame synchronization and null symbol detection can also be achieved through this correlation information. Frequency offset of integral carrier spacing is determined from the convolution outputs between a received phase reference symbol and several locally generated but frequency-shifted phase reference symbols. We found the length of a guard interval is the most important parameter for the synchronization algorithm to work. Simulation results show that the performance of this synchronization method approaches to the ideal synchronization case in both an additive white Gaussian noise (AWGN) channel and a two-path Rayleigh fading channel