Alternative threshold-based channel estimation and message-passing-based symbol detection in MIMO-OTFS systems using superimposed pilots

In this work, we investigate the challenging problem of channel estimation in high-mobility environments for advanced mobile communication systems (5G and beyond). First, we propose an iterative algorithm for channel estimation and symbol detection in the delay-Doppler domain for multiple-input multiple-output orthogonal time–frequency space (OTFS) system. The proposed algorithm is based on a superimposed pilot pattern to improve the spectral efficiency of the system. It iterates between data-aided channel estimation and message-passing-aided data detection. The channel estimation step is based on a threshold method. This step considers interference-plus-noise caused by the data symbols and the additive noise to adapt the threshold at each iteration. The data detection step is based on an adapted version of the message-passing algorithm proposed in the literature for uncoded OTFS. Then, to improve the channel estimation efficiency, we suggest an interference cancellation scheme executed at each iteration of the proposed algorithm. Finally, we compare the computational complexity and the achieved performance in terms of normalized mean square error of channel estimation, bit error rate, and spectral efficiency against five state-of the-art methods.     

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.     

Frequency-domain channel estimation and equalization for shallow-water acoustic communications

A new frequency-domain channel estimation and equalization (FDE) scheme, combined with a new group-wise phase correction scheme, is proposed for single-carrier (SC) underwater acoustic communications systems employing single transducer and multiple hydrophones. The proposed SC-FDE scheme employs a 2N-point Fast Fourier Transform (FFT) to estimate and equalize the channel in frequency domain, where N is the number of symbols in a data block. Both the frequency-domain channel estimation and equalization are designed by the linear minimum mean square error criterion. Initial channel estimation is performed by a pilot signal block and later updates are achieved using the detected data blocks. The proposed phase correction scheme utilizes a few pilot symbols in each data block to estimate the initial phase shift and then correct it for the block to combat the large phase rotation due to the instantaneous Doppler drifts in the acoustic channels. Time-varying instantaneous phase drifts are re-estimated and compensated adaptively by averaging the phase variation across a group of symbols. The proposed SC-FDE and phase correction method is applied to the AUVFest’07 experimental data measured off the coast of Panama City, Florida, USA, June 2007. With the Quadrature Phase Shift Keying (QPSK) modulation and a symbol rate of 4 ksps, the proposed scheme achieves an average uncoded bit error rate on the order of 1×10^?4 for fixed-to-fixed channels with the source–receiver range of 5.06 km. For the moving-to-fixed source–receiver channels where the source–receiver range is 1–3 km, the multipath delay spread is 5 ms, the average Doppler shifts are ±20 Hz, and the maximum instantaneous Doppler drifts from the mean is ±4 Hz, the proposed scheme achieves an average uncoded bit error rate on the order of 1×10^?3.     

近程高速水声毫米波通信仿真与试验验证*

水声信道的严重双扩特性极大地限制了水声高速通信的稳健性。针对近程高速水声通信技术的需求,该文基于水声毫米波频段,提出了一种基于超奈奎斯特发射技术及高阶调制技术的单载波的水声毫米波通信技术。而超奈奎斯特发射技术以及高阶调制给接收机带来了极大的挑战:一是超奈奎斯特发射技术引入了严重的符号间干扰,二是高阶调制系统的符号检测对信道估计的精度要求很高。针对以上问题,该文提出了一种基于迭代信道估计技术的迭代软反馈DFE接收机技术。仿真实验表明:采用128QAM高阶调制时,在较为严重的多途衰落信道条件下,采用所提出的接收机可以在15 dB时实现无误码传输。信道水池试验证明:当通信带宽为300 kHz,通信符号率为300k符号/秒且采用64QAM调制时,在发射平台慢速运动的条件下可以实现900 kbps的净数据率,其相应的频谱利用率高达6 Bits/s/Hz。     

Delay-Doppler domain decision feedback turbo equalization for OTFS modulation

Emerging evidence indicates that orthogonal time–frequency space (OTFS) modulation is a potential candidate modulation scheme for high mobility wireless communications. However, OTFS may experience significant inter-symbol interference (ISI) and inter-Doppler interference (IDI) in the receiver. In this paper, we propose a soft decision feedback turbo equalization for OTFS transmission over delay-Doppler channels to jointly combat both interferences. A novel block decision feedback equalization (BDFE) algorithm is constructed using the band feature of the channels in the delay-Doppler domain. The feedforward and feedback filters are designed by the delay-time channels coefficients. According to the designed filter, an equivalent system model is employed to allow turbo equalization. The posterior probability is established using the soft prior information and feedback filter, and then fed back to the channel decoder as external information. Both theoretical analysis and simulation results demonstrate the effectiveness of the proposed scheme in improving the bit error rate (BER) performance and combat various interference. Numerical simulations are finally provided to justify the validity of the proposed scheme in improving the bit error rate (BER) performance and combating various interference.     

Pilot design and doubly-selective channel estimation for faster-than-Nyquist signaling

Being capable of enhancing the spectral efficiency (SE), faster-than-Nyquist (FTN) signaling is a promising approach for wireless communication systems. This paper investigates the doubly-selective (i.e., time- and frequency-selective) channel estimation and data detection of FTN signaling. We consider the intersymbol interference (ISI) resulting from both the FTN signaling and the frequency-selective channel and adopt an efficient frame structure with reduced overhead. We propose a novel channel estimation technique of FTN signaling based on the least sum of squared errors (LSSE) approach to estimate the complex channel coefficients at the pilot locations within the frame. In particular, we find the optimal pilot sequence that minimizes the mean square error (MSE) of the channel estimation. To address the time-selective nature of the channel, we use a low-complexity linear interpolation to track the complex channel coefficients at the data symbols locations within the frame. To detect the data symbols of FTN signaling, we adopt a turbo equalization technique based on a linear soft-input soft-output (SISO) minimum mean square error (MMSE) equalizer. Simulation results show that the MSE of the proposed FTN signaling channel estimation employing the designed optimal pilot sequence is lower than its counterpart designed for conventional Nyquist transmission. The bit error rate (BER) of the FTN signaling employing the proposed optimal pilot sequence shows improvement compared to the FTN signaling employing the conventional Nyquist pilot sequence. Additionally, for the same SE, the proposed FTN signaling channel estimation employing the designed optimal pilot sequence shows better performance when compared to competing techniques from the literature.     

Distributed spatial media-based modulation for relay networks with RF mirrors

A novel distributed spatial media-based modulation scheme is proposed in this paper by cleverly utilizing distributed spatial modulation (DSM) and media-based modulation (MBM) principles. This proposed scheme is referred to as distributed channel modulation (DCM) for relay networks. In this scheme, decode-and-forward relaying protocol is adopted, and the channel states are exploited for transmitting extra information bits by using a number of radio frequency (RF) mirrors that are placed near each relay. To provide a fair comparison with the conventional state-of-the-art schemes, the symbol error rate (SER) performance of DSM scheme is evaluated. Besides, a low complexity detection technique known as iterative maximum ratio combining (i-MRC) is used in order to reduce the receiver complexity of the proposed scheme. Simulation results demonstrate that the proposed DCM scheme significantly outperforms DSM scheme for the same average rate. It is also shown that there is a negligible degradation in the SER performance of the proposed DCM scheme when i-MRC detection is used as compared to the performance with maximum likelihood (ML) detection. Furthermore, a significant reduction in the receiver complexity is achieved by using i-MRC detection technique in contrast to the results with ML detector. It has been also revealed that the proposed DCM scheme shows a performance drop of about 3 dB when the availability of an imperfect channel state information (CSI) is assumed with the presence of channel estimation errors (CEEs). Finally, simulation results have confirmed the analytical findings.     

Optimally balancing data and pilot power for adaptive MIMO-OFDM systems

While pilot symbols facilitate channel estimation, they reduce the transmit energy for data symbols per OFDM symbol under a fixed total transmit power constraint. In this paper, we investigate the effect of the pilot-to-data power ratio (PDPR) on multilevel quadrature amplitude modulation (M-QAM) multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems with adaptive modulation in order to provide a basic framework for finding the optimal PDPR in current and emerging standards using MIMO-OFDM. In particular, we derive the optimal PDPR in terms of average symbol error rate (SER) and spectral efficiency according to different receiver types such as zero-forcing (ZF) and minimum mean square error (MMSE). Employing the optimal PDPR results in higher spectral efficiency and lower SER without using any additional resource.     

Low-complexity soft-output signal detector based on AI-SSOR preconditioned conjugate gradient method over massive MIMO correlated channel

Massive multiple-input multiple-output (MIMO) techniques with a large number of antenna elements at base station (BS) have been proved as an alternative to provide potential opportunity to increase the spectrum and energy efficiency. However, in the system, there generally exists a spatial correlation effect due to insufficient antenna elements spacing and/or the lack of rich scattering at BS. The minimum mean square error (MMSE) method performs signal detection at the expense of large-scale matrix inversion operation. Thus, the conjugate gradient (CG) method has received a lot of attentions to realize the MMSE detection efficiently. Unfortunately, this efficiency can be compromised due to the ill-conditioned equalization matrix of MMSE method over the correlated channel environments. Moreover, the hard output signal detection exhibits a sharply degradation in performance for higher-order quadrature amplitude modulation (QAM). Therefore, the modern communication systems use the soft-output information, i.e., log-likelihood ratio (LLR) along with the forward error-correcting code (FEC) to achieve satisfactory performance. The LLR computation along with a higher-order QAM remains challenging due to the exhaustive search of symbol in the modulation constellation. In this paper, a low-complexity soft-output signal detector based on approximate inverse symmetric successive over-relaxation preconditioned conjugate gradient (AI-SSOR-CG-SOD) method is proposed to realize MMSE method detection for uplink multiuser massive MIMO correlated channel. In the proposed method, a new preconditioner, an AI-SSOR, which is based on the Neumann series approximation of the inverse of the conventional SSOR preconditioner is firstly developed to handle ill-conditioned matrix, and then incorporated with CG method to improve the convergence rate and performance. According to the characteristic of the Gray-coding that adjacent symbols in the constellation set have only one different bit, the constellation set is divided multiple times based on the bits of the inphase and the quadrature components of the symbol, which reduces the complexity of the LLR computation of the transmitted bits by avoiding the exhaustive search process. Simulation results show that the AI-SSOR preconditioner is robust against spatial correlation effect, and the proposed detector converges at 3 iterations. Simulation results also show that the proposed detector achieves a better trade-off between the complexity and the performance compared to other existing detectors.     

Near optimal performance joint semi-blind channel estimation and data detection techniques for Alamouti coded single-carrier (SC) MIMO communication systems

In this paper, two novel joint semi-blind channel estimation and data detection techniques are proposed and investigated for Alamouti coded single-carrier (SC) multiple-input multiple-output (MIMO) communication system using Rayleigh flat fading channel model. In the first novel semi-blind technique, blind channel estimation can be performed by using singular value decomposition (SVD) of received output autocorrelation matrix and training based channel estimation for orthogonal training symbols can be performed by using orthogonal pilot maximum likelihood (OPML) algorithm. Further using, that semi-blind channel estimate and received output, data detection is performed by using Maximum likelihood (ML) detection. Finally we derived new training symbols from error covariance matrix of estimated data and known orthogonal training symbols, which further applied to OPML algorithm for final channel estimate. In the second novel semi-blind technique, blind channel estimation can be performed by using matrix triangularization based on householder QR decomposition (H-QRD) of received output autocorrelation matrix instead of SVD decomposition. Other steps are same as the first novel technique to calculate data detection and final channel estimation. Simulation results are presented under 2-PSK, 4-PSK, 8-PSK and 16-QAM data modulation schemes using 2 transmitters and different combinations of receiver antennas to investigate the performances of novel techniques compare to conventional whitening rotation (WR) and rotation optimization maximum likelihood (ROML) based semi-blind channel estimation techniques. Result demonstrates that novel techniques outperform others by achieving near optimal performance.     

Joint precoding and equalization design for reduced-CP OTFS based on the compact input–output model

This work studies the system design for the reduced cycle-prefix (CP) orthogonal time frequency space (OTFS) modulation in which only one CP is inserted in each frame. First, a compact input–output model is established which does not need to assume that the delay and Doppler shifts are on the delay-Doppler sampling grids. Second, based on the proposed compact model, a joint precoding and equalization method is proposed for the reduced-CP OTFS modulation, which can decomposes the whole reduced-CP OTFS communication system into parallel multiple-input multiple-output (MIMO) subsystems. Hence, the computational complexity can be greatly reduced. Simulation results of the bit error rate (BER) performance of the proposed method are reported and compared with the standard method under different system parameters.     

Bandwidth-efficient frequency-domain equalization for single carrier multiple-input multiple-output underwater acoustic communications

This paper proposes a single carrier (SC) receiver scheme with bandwidth-efficient frequency-domain equalization (FDE) for underwater acoustic (UWA) communications employing multiple transducers and multiple hydrophones. Different from the FDE methods that perform FDE on a whole data block, the proposed algorithm implements an overlapped-window FDE by partitioning a large block into small subblocks. A decision-directed channel estimation scheme is incorporated with the overlapped-window FDE to track channel variations and improve the error performance. The proposed algorithm significantly increases the length of each block and keeps the same number of training symbols per block, hence achieving better data efficiency without performance degradation. The proposed scheme is tested by the undersea data collected in the Rescheduled Acoustic Communications Experiment (RACE) in March 2008. Without coding, the 2-by-12 MIMO overlapped-window FDE reduces the average bit error rate (BER) over traditional SC-FDE schemes by 74.4% and 84.6% for the 400 m and 1000 m range systems, respectively, at the same data efficiency. If the same BER performance is required, the proposed algorithm has only 8.4% transmission overhead, comparing to over 20% overhead in other existing UWA OFDM and SC-FDE systems. The improved data efficiency and/or error performance of the proposed FDE scheme is achieved by slightly increased computational complexity over traditional SC-FDE schemes.     

Chaotic secure communication based on strong tracking filtering

A scheme for implementing secure communication based on chaotic maps and strong tracking filter (STF) is presented, and a modified STF algorithm with message estimation is developed for the special requirement of chaotic secure communication. At the emitter, the message symbol is modulated by chaotic mapping and is output through a nonlinear function. At the receiver, the driving signal is received and the message symbol is recovered dynamically by the STF with estimation of message symbol. Simulation results of Holmes map demonstrate that when message symbols are binary codes, STF can effectively recover the codes of the message from the noisy chaotic signals. Compared with the extended Kalman filter (EKF), STF has a lower bit error rate.     

Doubly selective channel estimation and equalization based on ICI/ISI mitigation for OQAM-FBMC systems

Over a doubly selective channel, broadband transmission systems face challenges in channel estimation and equalization. High mobility causes inter-carrier interference (ICI), while multipath transmission induces inter-symbol interference (ISI). In this paper, we present a mitigation method of ICI/ISI for the offset quadrature amplitude-modulated filter bank multi-carrier (OQAM-FBMC) system. It features low inherent imaginary interference (IMI) sensitivity and high efficiency. Specifically, a pilot indices optimization algorithm and a sparse adaptive orthogonal subspace pursuit (SAOSP) algorithm are presented based on the 2-D channel modeling scheme. The guard pilots are first added to mitigate the effect of ICI. Then the index optimization and SAOSP algorithms are applied to achieve a high-accuracy estimation of sparse channel coefficients. In addition, a threshold judgment suboptimal minimum mean square error (MMSE) equalization method is presented based on the variability of the interference power. The method uses normalized interference power thresholds to estimate the ISI dimension and reduce the equalization data, thus mitigating the effect of ISI and achieving efficient equalization. To verify the above methods, single-input-single-output (SISO) and multiple-input-multiple-output (MIMO) models are built. Simulation results indicate a 3-5 dB improvement in channel estimation accuracy. The suboptimal MMSE equalization results are close to the optimal MMSE with about four orders of magnitude reduction in complexity.     

基于扩频码的单载波迭代频域均衡水声通信

单载波时域均衡在长时延扩展水声信道中计算量大,并对接收机参数的选择较为敏感,可靠性低,而正交频分复用信号峰均功率比高、对频率偏移敏感. 针对这些问题,提出基于扩频码的单载波块传输高速率水声通信方法和基于T/4分数间隔迭代频域均衡的接收机算法. 该接收机利用已知扩频码进行信道估计以及对由多普勒偏移引起的旋转相位进行估计,并通过一种低复杂度迭代频域均衡算法改善系统性能. 开展了湖上实验研究,结果表明在浅水1.8 km距离且复杂多径干扰条件下,利用BPSK/QPSK调制可实现10^-2–10^-4的误码率并达到1500–3000 bit/s的有效数据率. 关键词： 水声通信 单载波 频域均衡 迭代处理     

一种鲁棒性强的OFDM 水声通信系统*

为了在不同衰落水声信道下实现正交频分复用水声通信,该文提出了一种鲁棒性强的正交频分复用水声通信方案,方案包括编码调制、信道估计和多普勒估计等内容。为了使该系统更稳健,整个信道编码分为两个步骤。首先,循环冗余校验编码器和里德-所罗门编码器用于编码整个数据包,然后循环冗余校验编码器和Turbo 编码器用于编码每个数据帧,其中比特交织编码调制技术用来对抗信道的时变特性。为了得到水声信道估计,使用线性最小均方误差估计器来处理导频数据得到信道估计值。多普勒估计包括帧的多普勒估计和符号的多普勒估计。实验结果表明该系统在不同的衰落信道下都能实现正确的跟踪和译码,系统的鲁棒性能优越。此外,该系统算法计算简单,易于实现,具有良好的工程应用价值。     

Comparison between direct-sequence and multicarrier spread-spectrum acoustic communications in time-varying channels

Underwater communication experiments have been conducted in the Norwegian Oslofjord. Two modulation schemes are compared in a 7-kHz frequency band on a 14-kHz center frequency. The first scheme is direct-sequence spread spectrum (DSSS), using a 7-chip spreading code to achieve a raw data rate of 1000 bps on a single carrier. The second scheme is multicarrier spread spectrum (MCSS) and accomplishes spreading by using seven subbands. The DSSS receiver equalizes on the chips prior to explicit symbol despreading, whereas MCSS features joint multiband equalization and despreading. Four channels are examined, from nearly static to overspread. In slowly varying channels, MCSS offers the best performance. DSSS has the best tracking potential for rapidly varying channels, where the challenge is to obtain reliable chip decisions before symbol despreading. The tracking potential can be realized to some extent by hypothesis-feedback equalization. It is further shown that adaptive equalizers are capable of code conversion, i.e., the DSSS receiver can demodulate the MCSS waveform, and vice versa. Neither receiver requires knowledge of the spreading code in order to despread the data.     

Discrete Fourier Transform with Denoise Model Based Least Square Wiener Channel Estimator for Channel Estimation in MIMO-OFDM

Multiple-input Multiple-Output (MIMO) systems require orthogonal frequency division multiplexing to operate efficiently in multipath communication (OFDM). Channel estimation (C.E.) is used in channel conditions where time-varying features are required. The existing channel estimation techniques are highly complicated. A channel estimation algorithm is needed to estimate the received signal’s correctness. In order to resolve this complexity in C.E. methodologies, this paper developed an Improved Channel Estimation Algorithm integrated with DFT-LS-WIENER (ICEA-DA). The Least Square (L.S.) and Minimum Mean Square Error (MMSE) algorithms also use the Discrete Fourier Transform (DFT)-based channel estimation method. The DFT-LS-WIENER channel estimation approach is recommended for better BER performance. The input signal is modulated in the transmitter module using the Quadrature Phase Shift Keying (QPSK) technique, pulse modeling, and least squares concepts. The L.S. Estimation technique needs the channel consistent throughout the estimation period. DFT joined with L.S. gives higher estimation precision and limits M.S.E. and BER. Experimental analysis of the proposed state-of-the-art method shows that DFT-LS-WIENER provides superior performance in terms of symbol error rate (S.E.R.), bit error rate (BER), channel capacity (CC), and peak signal-to-noise (PSNR). At 15 dB SNR, the proposed DFT-LS-WIENER techniques reduce the BER of 48.19%, 38.19%, 14.8%, and 14.03% compared to L.S., LS-DFT, MMSE, and MMSE-DFT. Compared to the conventional algorithm, the proposed DFT-LS-WIENER outperform them.     

Efficient processing of acoustic signals for high-rate information transmission over sparse underwater channels

For underwater acoustic channels where multipath spread is measured in tens of symbol intervals at high transmission rates, multichannel equalization required for bandwidth-efficient communications may become prohibitively complex for real-time implementation. To reduce computational complexity of signal processing and improve performance of data detection, receiver structures that are matched to the physical channel characteristics are investigated. A decision-feedback equalizer is designed which relies on an adaptive channel estimator to compute its parameters. The channel estimate is reduced in size by selecting only the significant components, whose delay span is often much shorter than the multipath spread of the channel. Optimal coefficient selection (sparsing) is performed by truncation in magnitude. This estimate is used to cancel the post-cursor ISI prior to linear equalization. Spatial diversity gain is achieved by a reduced-complexity pre-combining method which eliminates the need for a separate channel estimator/equalizer for each array element. The advantages of this approach are reduction in the number of receiver parameters, optimal implementation of sparse feedback, and efficient parallel implementation of adaptive algorithms for the pre-combiner, the fractionally-spaced channel estimators and the short feedforward equalizer filters. Receiver algorithm is applied to real data transmitted at 10 kbps over 3 km in shallow water, showing excellent results.     

An efficient spread spectrum pulse position modulation scheme for point-to-point underwater acoustic communication

The underwater acoustic channel is a complicated and time-varying multipath channel, and many equalization algorithms have been researched and developed to overcome the difficulties for underwater acoustic communication. Unfortunately, many algorithms are computational intensive and prone to lose convergence due to their sensitiveness to different channel configurations. In this paper, a pulse position modulation (PPM) scheme is proposed, and it uses two M-sequences of low cross-correlation to transfer information, which are modulated on two orthogonal carriers. One is used as a reference sequence, and the other is shifted relative to the reference. Information is carried by the starting time difference between the two sequences in each symbol. Comparing with conventional direct-sequence spread spectrum technique, the proposed scheme is more spectral efficient. Two receiver designs are given, one takes advantages of M-sequences’ auto-correlation properties, and the other is motivated by passive phase conjugation (PPC) to take advantages of the channel. Combined with M-sequence, PPC performance is augmented without a receiving array to cover the water column, and it is far less complex than adaptive equalizers for receivers. Results from lake field trials are analyzed, and they verify potential applications of this PPM scheme.