基于水声信道传播时延排序的分层空时信号检测

基于分层空时编码的多输入多输出技术是一种极具潜力的高速水声通信技术, 但要实现这种潜力需要复杂的空时信号处理方法, 以抵消来自水声信道的多径干扰和异步到达干扰, 以及叠加在接收端的各层信号之间的干扰. 对低复杂度的空时信号处理方案进行了研究, 提出了一种基于子信道传播时延排序的有序连续干扰抵消信号检测算法, 利用子信道间的传播时延差, 实现可使差错概率最小的最佳检测排序; 给出了利用信道估计, 以极低的计算量确定排序的方法, 从而可以大幅降低信号检测的计算复杂度. 采用低复杂度的单载波频域均衡来抵消水声信道中的码间干扰和异步到达干扰. 仿真结果表明, 基于时延排序的信号处理算法可以获得检测性能的改善, 而且性能增益在高数据率时更加显著. 研究结果表明, 采用有效的信号处理方法可使水声信道中造成信号检测干扰的传播时延成为改善系统性能的有利因素.     

Sparse asymptotic minimum variance based bearing estimation algorithm for a single vector hydrophone

For underwater target detection using a single vector hydrophone, sparse asymptotic minimum variance(SAMV) method is used to estimate the target bearing. The SAMV discretizes the entire scanning space and the target bearing is located at the position of the discrete direction. The SAMV algorithm utilizes the sparsity of the spatial signal to improve the estimation performance of the target bearing. Background noise level(BNL) of the bearing estimation of SAMV algorithm is lower than those of the conventional beam forming(CBF)method and minimum variance distortionless response(MVDR) method for different signal noise ratios(SNRs). When the SNR is higher than 0 d B, the direction-finding error of this algorithm is less than 2°. Moreover, the SAMV algorithm has a better dimensional orientation resolution capability. The experimental results show that the SAMV algorithm gives a bearing and time recording map with a lower BNL, which effectively verifies the effectiveness of SAMV algorithm in terms of underwater target detection.     

Experimental demonstration of optical MIMO transmission for SCFDM-PON based on polarization interleaving and direct detection

The OFDM-PON and SCFDM-PON based on powerful digital signal processing (DSP) are promising candidates for next-generation optical access networks. Recently polarization-division-multiplexing (PDM) transmission with direct detection has been proposed for OFDM-PON to effectively reduce the bandwidth requirement of optical and electrical components. However, the PDM scheme has high algorithm complexity. In this paper, we propose a polarization interleaving (PI) approach, which can significantly reduce the bandwidth requirement for components while achieving a similar 2×2 MIMO algorithm with coherently-detected PDM-OFDM scheme. Downstream PI-SCFDM transmission is experimentally demonstrated. The scheme can be easily extended to OFDM-PON.     

A Generalized Spatial Modulation System Using Massive MIMO Space Time Coding Antenna Grouping

Massive multiple input multiple output (MIMO), also known as a very large-scale MIMO, is an emerging technology in wireless communications that increases capacity compared to MIMO systems. The massive MIMO communication technique is currently forming a major part of ongoing research. The main issue for massive MIMO improvements depends on the number of transmitting antennas to increase the data rate and minimize bit error rate (BER). To enhance the data rate and BER, new coding and modulation techniques are required. In this paper, a generalized spatial modulation (GSM) with antenna grouping space time coding technique (STC) is proposed. The proposed GSM-STC technique is based on space time coding of two successive GSM-modulated data symbols on two subgroups of antennas to improve data rate and to minimize BER. Moreover, the proposed GSM-STC system can offer spatial diversity gains and can also increase the reliability of the wireless channel by providing replicas of the received signal. The simulation results show that GSM-STC achieves better performance compared to conventional GSM techniques in terms of data rate and BER, leading to good potential for massive MIMO by using subgroups of antennas.     

Two-level-enforced security with hybrid precoding for multicast massive MIMO wiretap systems

Multicast hybrid precoding reaches a compromise among hardware complexity, transmission performance and wireless resource efficiency in massive MIMO systems. However, system security is extremely challenging with the appearance of eavesdroppers. Physical layer security (PLS) is a relatively effective approach to improve transmission and security performance for multicast massive MIMO wiretap systems. In this paper, we consider a transmitter with massive antennas transmits the secret signal to many legitimate users with multiple-antenna, while eavesdroppers attempt to eavesdrop the information. A fractional problem aims at maximizing sum secrecy rate is proposed to optimize secure hybrid precoding in multicast massive MIMO wiretap system. Because the proposed optimized model is an intractable non-convex problem, we equivalently transform the original problem into two suboptimal problems to separately optimize the secure analog precoding and secure digital precoding. Secure analog precoding is achieved by applying singular value decomposition (SVD) of secure channel. Then, employing semidefinite program (SDP), secure digital precoding with fixed secure analog precoding is obtained to ensure quality of service (QoS) of legitimate users and limit QoS of eavesdroppers. Complexity of the proposed SVD-SDP algorithm related to the number of transmitting antennas squared is lower compared with that of constant modulus precoding algorithm (CMPA) which is in connection with that number cubed. Simulation results illustrate that SVD-SDP algorithm brings higher sum secrecy rate than those of CMPA and SVD-SVD algorithm.     

低复杂度的MIMO声呐协方差矩阵重构方法*

多输入多输出声纳在对目标进行测向时会产生复杂的运算量,从而降低算法的测向效率。针对这一问题,提出了一种基于降维变换方法的低复杂度协方差矩阵重构方法。该方法能够抑制噪声,提高目标侧向性能。首先利用降维变换方法对接收信号进行波束形成,获得低维度的协方差矩阵,再对矩阵进行Toeplitz处理,抑制矩阵的相干性。所得到的新的协方差矩阵,通过特征分解获得噪声子空间和信号子空间,利用MUSIC方法进行测向。为了进一步降低运算复杂度,利用阵型所满足的旋转不变性,可以采用ESPRIT算法对目标进行波达方向估计。理论分析和实验结果表明,该方法有效降低了运算复杂度,提高了算法的测向性能。在有限快拍数的情况下,与传统测向方法相比,具有运算速度快,目标分辨力强的特点。     

An effective PSO-based power allocation for target tracking in MIMO radar with widely separated antennas

In MIMO radar with widely separated antennas, the antennas are spaced far from each other and the target is seen from different angles. In this type of radars, each receiver collects all transmit signals and transmits them to the central processor unit. Power allocation is an important part of military operations. Therefore, it is a primary factor that requires to be taken into account in the designing of target tracking problems in MIMO radar. In fact, the power allocation finds an optimum strategy to allot power to transmit antennas with the goal of minimizing the target tracking errors under specified transmit power constraints. In this paper, the performance of power allocation for target tracking in MIMO radar with widely separated antennas is investigated. For this purpose, first, a MIMO radar with distributed antennas is configured and a target motion model using the constant velocity (CV) method is modeled. Then Joint Cramer Rao bound (CRB) for target parameters (joint target position and velocity) estimation error is computed. This is applied as a power allocation problem objective function. Because a complex Gaussian model is considered for target radar cross-section (RCS), this function becomes complicated. Due to the nonlinearity of this objective function, the proposed power allocation problem is nonconvex. Therefore, a particle swarm optimization (PSO) -based power allocation algorithm is proposed to solve it. In simulation experiments, the performance of the proposed algorithm in different conditions such as a different number of antennas and antenna geometry configurations is evaluated. Results prove the validity of the proposed algorithm.     

Characterization of distributed mode crosstalk in few-mode fiber links with low MIMO complexity

We theoretically model and numerically analyze the linear behavior of distributed mode crosstalk in a step-index weakly-coupled 7-mode fiber. This fiber is assumed to be used for one-polarization uncoupled mode-division multiplexing (MDM) systems with: (1) sparse MIMO equalizers which are performed over only degenerate LP modes, or (2) one single differential mode delay-independent MIMO equalizer which is performed over all non-degenerate LP modes. For the above two low-complexity MIMO configuration schemes, the impacts of distributed mode coupling on the multi-path interference-dependent achievable distance and system quality are empirically evaluated, through the numerical simulations for uncoupled MDM transmissions of a single-channel 28 GBaud QPSK signal over the fiber.     

Design of pre-processing algorithms for efficient MIMO–OFDM receiver architectures

Modern wireless communication applications are characterized by the need for advanced signal processing techniques such as Multiple-Input Multiple-Output (MIMO) technology for achieving high throughput and diversity and Orthogonal Frequency Division Multiplexing (OFDM) for achieving robustness to multipath fading. The implementation of such techniques at the transceiver level typically involves the design of algorithms with high processing complexity.This paper considers the efficient design of MIMO–OFDM receivers in preamble-based systems and addresses the problem of large processing delays associated with pre-computations and symbol detection. The existence of large processing delays has a huge impact on the performance and resource requirements (vector processing, increased clock rates and increased power consumption) of modern receivers. More specifically, we address the performance and complexity bottleneck introduced by the pre-computations involved for MIMO–OFDM channel decomposition. We propose a redesign of channel decomposition algorithms which achieves a better matching of the processing rate of MIMO–OFDM receivers to the real-time processing deadlines imposed by the structure of the incoming data packets. It is demonstrated that for a specific MIMO-OFDM channel training frame structure (alternating antenna preamble), simple modifications to typical channel decomposition algorithms can achieve significant processing performance and complexity gains compared to typical receiver designs.     

MIMO声呐的双尺度旋转不变子空间波达方向估计

为了提高单基地多输入多输出(MIMO)声呐阵列的波达方向估计性能,提出了双尺度旋转不变子空间(DR-ESPRIT)算法.结合MIMO阵列虚拟阵列的结构特征,首先利用ESPRIT算法通过各条虚拟线阵内、基线间距不大于半波长的子阵间的旋转不变关系得到无模糊的粗估计结果,之后利用虚拟线阵间、基线较长的子阵间的旋转不变关系得到...     

认知无线电网络中利用FEC和DE的多节点频谱感知算法

针对认知无线电网络(CRN)中空闲频谱感知困难的问题,本文提出了基于前向纠错和差分进化算法的多节点频谱感知算法。首先,利用基于差分进化算法的协同检测完成信号感知;然后,研究了信道噪声对频谱感知性能的影响;最后,分析了前向纠错技术在信道存在噪声时对频谱感知性能的影响。仿真实验将纠错和无纠错控制信道的不同信噪比作为依据,采用三种不同的检测方法评估了本文算法。仿真实验结果表明,在存在噪声的认知无线电网络中,本文算法提高了系统的性能和检测概率,且协同感知算法的性能随着节点数目的增加而提高,该算法适合应用于实时性要求较高的应用程序。     

Direction-of-arrival estimation for co-located multiple-input multiple-output radar using structural sparsity Bayesian learning

This paper addresses the direction of arrival(DOA) estimation problem for the co-located multiple-input multipleoutput(MIMO) radar with random arrays. The spatially distributed sparsity of the targets in the background makes compressive sensing(CS) desirable for DOA estimation. A spatial CS framework is presented, which links the DOA estimation problem to support recovery from a known over-complete dictionary. A modified statistical model is developed to accurately represent the intra-block correlation of the received signal. A structural sparsity Bayesian learning algorithm is proposed for the sparse recovery problem. The proposed algorithm, which exploits intra-signal correlation, is capable being applied to limited data support and low signal-to-noise ratio(SNR) scene. Furthermore, the proposed algorithm has less computation load compared to the classical Bayesian algorithm. Simulation results show that the proposed algorithm has a more accurate DOA estimation than the traditional multiple signal classification(MUSIC) algorithm and other CS recovery algorithms.     

A dual phase genetic algorithm for improved initial access in 5G millimeter wave communication

Initial access (IA) in 5G millimeter wave (mmWave) communication is the problem of establishing a directional link between the base station (BS) and the user equipment (UE). For a multiple-input multiple-output (MIMO) system, where both the BS and UE have many antennas, finding the optimal beams can be prohibitively expensive in terms of delay and computation. In this work, we propose a meta-heuristic approach which is a modified dual-phase genetic algorithm. Since it is a meta-heuristic approach, it is generic and hence does not require extensive modifications to apply to different scenarios, it also does not require context information such as prior knowledge of channel state or statistics of user behavior. The proposed method is using iterative search for the optimal beams, but switch to a different fine-grained search phase on later iterations in order to quickly converge to the local optimum. The effect of this approach is analyzed in terms of capacity achieved vs number of transmit and receive antennas at BS and UE, codebook size, outage probability, total transmitted power, and other parameters specific to this particular dual-phase method. The proposed work has shown improved performance when compared to the existing similar work done in Souto et al. (2019) in terms of capacity achieved (2.12%), reduced power consumption (8.57%), and reduced IA delay (35% to 50%).     

Performance analysis and precoder design for IRS-assisted MIMO system with non-linear power amplifiers

In this work, a point-to-point intelligent-reflecting-surface (IRS) assisted single input single output (SISO) model is first developed and later modified to the multiple-input multiple-output (MIMO) case. For the SISO case, the transmitter or the access point (AP) is assumed to be equipped with a non-linear memory-less power amplifier (PA). A PA is assumed to be present for each data stream at the AP or base station (BS) side for the MIMO case. For both cases, three different types of PAs (A) Soft envelope limiter (SEL), (B) Solid-state power amplifier (SSPA), and (C) Travelling wave tube amplifier (TWTA) are considered. A Bussgang decomposition of the transmitted signal is then utilized for further analysis. A modified channel model is conceived for the AP-IRS-User equipment (UE) link based on the reflecting IRS elements’ radar cross-section (RCS). For the SISO system, both perfect and imperfect geometries of IRS elements with uniformly distributed element size errors are considered. An approximate closed-form expression for the upper bound on the above system’s spectral efficiency (SE) with ideal IRS elements is derived. The expression of the SE for two special cases of (A) Infinitely large IRS and (B) Ideal PA is deduced. Approximate closed-form expressions of various quality parameters for the IRS-assisted MIMO system with ideal and non-ideal PAs are also derived and verified numerically. Maximum ratio transmission (MRT) based precoder for this system is designed assuming that the information about the BS-UE link, BS-IRS-UE link, and the non-linear PA statistics is available at the BS. A closed-form expression for the SE of this system with this precoder is then derived and verified for various parametric situations.     

Multi-input multi-output underwater communications over sparse and frequency modulated acoustic channels

This paper addresses multi-input multi-output (MIMO) communications over sparse acoustic channels suffering from frequency modulations. An extension of the recently introduced SLIM algorithm, which stands for sparse learning via iterative minimization, is presented to estimate the sparse and frequency modulated acoustic channels. The extended algorithm is referred to as generalization of SLIM (GoSLIM). The sparseness is exploited through a hierarchical Bayesian model, and because GoSLIM is user parameter free, it is easy to use in practical applications. Moreover this paper considers channel equalization and symbol detection for various MIMO transmission schemes, including both space-time block coding and spatial multiplexing, under the challenging channel conditions. The effectiveness of the proposed approaches is demonstrated using in-water experimental measurements recently acquired during WHOI09 and ACOMM10 experiments.     

Data detection based on matrix decomposition for massive MIMO systems in realistic channel scenarios

Massive multiple-input multiple-output (MIMO) is a key technology for modern wireless communication systems. In massive MIMO receivers, data detection is a computationally expensive task. In this paper, we explore the performance and the computational complexity of matrix decomposition based detectors in realistic channel scenarios for different massive MIMO configurations. In addition, data detectors based on decomposition algorithms are compared to the approximate-inversion detection (AID) methods. It is shown that the alternating-direction-method-of-multipliers-based-Infinity-Norm (ADMIN) detection is promising in realistic channel environment and the performance is stable even when the ratio of the base-station (BS) antenna elements to the number of users is small. In addition, this paper studies the performance of several detectors in imperfect channel state information (CSI) and correlated channels. Our work provides valuable insights for massive MIMO systems and very large-scale integration (VLSI) designers to select the appropriate massive MIMO detector based on their specifications.     

Target parameter estimation for OTFS radar using sparse signal processing

In this paper, we consider a monostatic radar receiver for a joint communication and radar (JCR) system that transmits orthogonal time frequency space (OTFS) frames for target detection and parameter estimation. The circular prolate pulse shape (CPPS) is employed over the OTFS signal as it has lower out-of-band (OoB) power radiation in comparison with the rectangular pulse shaped (RPS) OTFS. The PAPR of CPPS OTFS signal shows lowest value for larger frame duration and hence the signal can be considered to be a good candidate for JCR system. In the Delay-Doppler (DD) domain, the radar channel is sparse and therefore, we model the target detection problem as a sparse recovery problem to generate target profiles with higher peak-to-sidelobe ratio (PSLR). The target detection is carried out in the DD domain, the time–frequency (TF) domain, and in the time domain (TD). Sparse signal recovery algorithms like the orthogonal matching pursuit (OMP) algorithm, the subspace pursuit (SP) algorithm, and the sparse Bayesian learning (SBL) based algorithm are used in target parameter estimation. The performance of these algorithms are compared in terms of their computational complexity, the root mean squared error (RMSE) in the estimates of range and velocity and PSLR value in the target profiles. Simulation results validate that the proposed CPPS OTFS based radar system could detect the targets accurately in all the three domains and produce target profiles with almost zero side lobes.     

基于模糊聚类算法的工控网络控制故障检测方法研究

针对传统大型工控网络控制故障检测过程中,没有考虑故障延时特性,从而导致的故障信号检测准确率下降,检测效率降低。为此,提出一种基于模糊算法的大型工控网络控制故障检测方法。引入模糊算法,对大型工控网络控制中的故障信号延迟进行模糊化建模,通过随机时延切换设计故障观测参数和故障观测参数的残差对大型工控网络控制系统进行故障检测,克服信号延迟弊端。实验结果表明,利用本文方法进行大型工控网络控制系统故障检测,能够有效提高故障的准确率,效果令人满意。     

Acceleration target detection based on LFM radar

In radar systems, the echo signal caused by an accelerated target can be similarly considered as linear frequency modulation (LFM) signal. In high signal-to-noise ratio (SNR), discrete polynomial-phase transform (DPT) algorithm can be used to detect the echo signal, as it has low computation complexity and high real-time performance. However, in low SNR, the DPT algorithm has a large mean square error of the rate of frequency modulation and a low detection probability. In order to detect LFM signal in low SNR, this paper proposes a detection method, segment discrete polynomial-phase transform (SDPT), which means, at first, dividing the whole echo pulses into several segments with same duration in time domain, and then, using coherent accumulation method of DFT to segments, at last, processing this signal with DPT in intra-segment. In the case of a large number of segments, the SDPT can improve the output SNR. In addition, in a certain SNR, to the target signal with big sampling interval, large acceleration and less segments, this paper proposes an algorithm to detect the LFM signal generated from the combination of an improved DPT (IDPT) and fractional Fourier transform (FRFT). The output SNR of this algorithm is connected with the length of time delay. In the simulation, when the length of the time delay is 0.2 N, the output SNR is 2.5 dB more than that which results from directly using DPT. Finally, the detection performance and algorithm complexity of the proposed algorithm were analyzed, and the simulated and measured data verify the effectiveness of the algorithm.     

Principal curvature for infrared small target detection

Small target detection in infrared image with complex background and low signal–noise ratio is an important and difficult task in the infrared target tracking system. In this paper, a principal curvature-based method is proposed. The principal curvatures of target pixels are negative and their absolute values are larger than that of background pixels and noise pixels in a Gaussian-blurred infrared image. The proposed filter takes a composite function of the curvatures for detection. An approximate model is also built for optimizing the parameters. Experimental results show that the proposed algorithm is effective and adaptable for infrared small target detection in complex background. Compared with several popular methods, the proposed algorithm demonstrates significant improvement on detection performance in terms of the parameters of signal clutter ratio gain, background suppression factor and ROC.