Deep Learning for OFDM Channel Estimation in Impulsive Noise Environments

Impulsive noise suppression is essential in orthogonal frequency division multiplexing (OFDM) systems, since impulsive noise may cause a serious decline in channel estimation performance. To solve this problem, a channel estimator based on denoising autoencoder-deep neural network (DAE-DNN) is proposed in this paper. The proposed method is based on a data-driven deep learning framework. Firstly, DAE preprocesses signals to learn damaged data and recover the complete signal are used in the presence of impulsive noise. Then, the transmitted data processed by DAE are used to train the DNN in the offline training process. Finally, the estimated channel state information (CSI) is offered by the proposed DNN model in the online working process. The simulation results demonstrate that the proposed method improves OFDM channel estimation performance significantly. As expected, the proposed method has a better performance than existing ones, such as least squares, minimum mean square error and orthogonal matching pursuit algorithms. Moreover, the proposed method is robust under impulsive noise environments.

     

Theoretical Analysis of the Underwater Incremental Adaptive Network Performance Based on the VLC Technology

In this paper, the underwater implementation of the incremental adaptive networks is proposed based on the visible light communication technology. The underwater distance between transmitter and receiver nodes and the salinity and temperature levels of the considered water determines the stochastical properties of the underwater link that is modeled with the Log-normal distribution. The incremental network performance can be expressed with the excess mean square error and mean square deviation values and we used them in this paper for our theoretical analysis. Our findings showed that the distances between the nodes must not be more than 10 m or the incremental network will diverge from its estimation goal. The network performance is analyzed through multiple link distances and the results are presented with several simulations. The simulation results are devised in order to elaborate the effects of the underwater turbulent links on the performances of estimating adaptive network. Also, the impacts of different salinity and temperature levels are analyzed theoretically and the results are compared with the simulation results.

     

Reduced-rank adaptive multiuser detection in hybrid direct-sequence time-hopping ultrawide bandwidth systems

In this paper a range of reduced-rank adaptive multiuser detectors (MUDs) are proposed and investigated for the hybrid direct-sequence time-hopping ultrawide bandwidth (DS-TH UWB) systems. The adaptive MUDs are operated based on the recursive least square (RLS) principles. Three types of reduced-rank techniques are investigated, which are the principal component (PC), cross-spectral metric (CSM) and Taylor polynomial approximation (TPA). These reduced-rank adaptive techniques are beneficial to achieving low-complexity, high spectral-efficiency and robust detection in hybrid DSTH UWB systems. In this contribution bit error rate (BER) performance of the hybrid DS-TH UWB systems using proposed reduced-rank adaptive MUDs is investigated by simulations, when communicating over UWB channels modelled by the Saleh-Valenzuela (S-V) channel model. Our simulation results show that, given a sufficiently high rank of the detection subspace, the reduced-rank adaptive MUDs are capable of achieving a similar BER performance as that of the full-rank ideal minimum meansquare error MUD (MMSE-MUD) but with significantly lower detection complexity. Furthermore, the TPA-based reduced-rank adaptive MUD is capable of yielding a better BER performance than the PC- or CSM-based reduced-rank adaptive MUD, when the same but relatively low rank detection subspace is assumed.     

Joint detection for OFDM-CDMA communications in multipath fading channels

This paper proposes and compares four block joint detection algorithms for the orthogonal frequency division multiplexing (OFDM)-code division multiple access (CDMA) communication systems. The performance of the proposed OFDM-CDMA joint detectors with orthogonal and non-orthogonal spreading sequences are investigated in mobile radio channels. The proposed OFDM-CDMA detectors are zero forcing (ZF) and minimum mean square error (MMSE) detectors with decision feedback (DF) structures. The proposed joint detectors do not require discrete Fourier transform (DFT) or fast Fourier transform (FFT) operations and can be used to compensate the channel distortion. Hence for hardware implementation, the proposed detectors are more efficient than the conventional methods. The channel sorting method offers approximately 2 dB gain for the DF joint detectors and reduces the impairing effect of error propagation. The bit error rate (BER) performance of orthogonal codes is better than that of non-orthogonal ones in time varying channels. Simulation examples are given to demonstrate the effectiveness of the proposed detectors.     

Optimized deep learning driven signal detection and adaptive channel estimation in underwater acoustic IoT networks

Nowadays, multiple input multiple outputs with orthogonal frequency division multiplexing (MIMO-OFDM) provide better communication performance that can be applied to the fast-growing Internet of Things (IoTs). In underwater IoT, information fades away rapidly due to varying water conditions. Therefore, the MIMO model can be applied to the OFDM acoustic system, enabling high-speed data transmission without affecting the channel effectively. However, detecting the underwater signal and estimating the channel is highly necessary for enhancing underwater acoustic communication (UWAC). Recently, many techniques have been introduced for effectively performing signal detection and channel estimation. However, those techniques face high time complexity due to increased channel interferences and noises during data transmission. Hence, this article brings a novel technique for SD and CE for the UWAC-IoT-enabled MIMO-OFDM system. An adaptive recursive least square (ARLS) technique is proposed in this study for CE that aids in evaluating the acoustic channel parameters effectively. For performing SD, a bi-directional deep pelican convolutional neural network (BDPCNN) technique is introduced to ensure the presence and absence of signals at the receiver end. The proposed method is analyzed via the MATLAB platform, and the performances are analyzed under different water types like turbid water, coastal water, clear ocean water, and pure seawater. Different performance metrics like bit error rate (BER), mean square error (MSE), energy efficiency (EE), and time complexity are analyzed with different existing techniques. The experimental section obtains the BER of 0.0086, 0.013, 0.017, and 0.021 for turbid, coastal, clear, and pure seawater, respectively.     

应用于MC-CDMA系统的块最小均方自适应合并技术

提出了一种使用块最小均方自适应滤波器进行信号合并的方案,适用于多载波码分多址（MC-CDMA）系统。在瑞利慢衰落信道下,只需要少量的训练符号就可以最大限度地消除信道噪声干扰和多址接入干扰。仿真结果表明,在一个满负荷的下行链路中,块最小均方自适应合并的性能接近非自适应的最小均方误差合并（MMSEC）;而在轻负荷（由激活用户数衡量）的情况下,性能优于MMSEC。     

Tracking Performance of an Adaptive MIMO-OFDM LS Estimator via MSE Criterion Over a Wireless Time Correlated Fading Channel

In this contribution, the performance of an adaptive least squares channel estimator for a time correlated MIMO-OFDM channel is presented in a closed form relation. As a criterion for performance evaluation, the well-known mean square error (MSE) is employed. The pilot aided channel estimation is used to extract the channel coefficients. To minimize the MSE and arrange phase shift orthogonally, the pilots are constructed with equal power and space. The model of the channel is developed by implementing a first order Markov model, and it is proved that the performance of the system is dependent upon the defined forgetting factor, the number of transmit antennas, the number of multipath channel taps, transmitted power, and Doppler shift. The effect of each parameter on the performance of the system is discussed separately. The computer simulation results follow with the analytical results comprehensively.     

一种水声信道自适应均衡算法研究

由声波传播的多途效应所引起的码间干扰及信号衰落,使得水下数据传输的速率和可靠性都大大降低。针对多途干扰严重的水声信道,将一种基于最小均方误差算法的信道自适应均衡器应用于水声信号处理中。对该均衡器的基本理论及其结构进行了归纳,结合水声多途信道的基本特点,建立了仿真模型,重点分析了步长参数、信噪比等对算法收敛速度和稳态误差等方面性能的影响,为实现高质量的水下信息传递提供技术支持。     

A new optimized least-square sparse channel estimation scheme for underwater acoustic communication

In underwater acoustic (UWA) communication, orthogonal frequency division multiplexing (OFDM) is a promising technology that is highly essential to get channel state information meant for channel estimation (CE). Nevertheless, higher complexity, slower convergence, and poor performance, which degrade the performance estimation, are the limitations of the traditional CE methodologies. Thus, by amalgamating the least square (LS)-CE algorithm along with polynomial interpolated black widow optimization (PI-BWO) model, an optimized least square sparse (OLSS) CE algorithm has been proposed to intend for a UWA-OFDM communication system. Formerly, by utilizing the 2's complement shift left turbo encoding (2CSL-TE) methodology, the input signal is encoded. After that, the modulated encoded signal is provided for inverse fast Fourier transform (IFFT) operations; subsequently, they are transferred over the UWA channel toward the receiver OFDM. By employing the OLSS methodology, the received OFDM signal's interference-free region is utilized for sparse CE at the receiver. Regarding symbol error rate (SER), bit error rate (BER), mean square error (MSE), and peak signal-to-noise ratio (PSNR), the proposed model's experiential outcome is evaluated and analogized with the other prevailing methodologies. When analogized with the conventional models, the proposed estimation methodologies achieved better performance.     

IRS辅助MU-MISO系统中基于深度残差学习的信道估计

针对智能反射表面辅助多用户通信系统中传统信道估计方法性能下降的问题,将信道估计问题转化为信道去噪问题,利用深度残差学习方法学习残差噪声,从含噪导频信号中恢复信道系数。同时为提升信道估计精度,设计信道估计网络进一步提升去噪性能。网络主体包含两个模块：拼接信息保留模块将每一层卷积输出相融合,防止信道特征丢失,有效提取信道噪声的主体特征;扩张卷积稀疏模块通过扩大感受野范围获得信道的重要结构和细节特征,恢复信道噪声的边缘细节特征。仿真结果表明,归一化均方误差约等于0.45时,所提方法在不明显增加复杂度情况下,相比于线性最小均方误差算法获得3.7 dB的信噪比增益,更为接近最小均方误差信道估计器的性能,表现出了更好的性能和可用性。     

Mitigation of Multipath Effects Based on a Robust Fractional Order Bidirectional Least Mean Square (FOBLMS) Beamforming Algorithm for GPS Receivers

Consideration on positioning and location services among the public has been increasing in the recent years with their applications in most of the anticipating milieus such as automobile navigation system etc. This insists for a development of high recitation global navigation satellite system such as global positioning system (GPS). Multipath effects, interference, signal jamming etc. are the major sources of error influencing the performance of the GPS receiver. Literature presents many of the multipath mitigation techniques. Among them, adaptive processing technology based beamforming algorithms appears a viable solution for multipath mitigation. The least mean square (LMS) beamforming algorithms were sensitive to dynamic environments thus affecting the accuracy of GPS. In this paper, an adaptive beamforming algorithm called fractional order bidirectional least mean square (FOBLMS) algorithm is proposed to mitigate the multipath effects and to conceal the jammer signal in a GPS receiver. The FOBLMS is an integration of the fractional calculus and bidirectional least mean square algorithm. The effectiveness of the proposed algorithm is validated using the bit error rate and experimentation gain results over the existing beamforming algorithms. Experimental results demonstrated that the performance of the proposed beamforming algorithm is better than LMS algorithm with maximal relative antenna gain of 28.92 dB, 32.84 dB for two and four element antenna arrays at ??60° and 10°, direction of arrivals respectively. The outcome of this work would be useful for developing a robust technique for multipath mitigation in GPS receivers.

     

Estimation of Nonlinear Wireless Channel Based on Incremental Spline Adaptive Filtering

Channel estimation in a wireless sensor network is imperative to error-free information dissemination and data collection. The estimation procedure is challenging if there exists a nonlinear distortion to the communication signal due to the radio-frequency components in the transmitting or receiving entity. It has drawn attention to nonlinear system modeling for channel estimation, where lately, one of the most important methods has been spline adaptive filter (SAF). The necessity of updating both linear filter coefficients and nonlinear control points makes the adaptation process slow. Hence, we propose an incremental spline adaptive filter using the least mean square algorithm (ISAF-LMS), which acquires faster convergence while estimating non-linearity along with linear filter coefficients. The steady-state performance of the proposed method is carried out by following the energy conservation approach. The simulation result shows faster convergence in the distributed case than in non-cooperative estimation. Further, the performance is compared with diffusion SAF and incremental version of conventional Volterra adaptive filter-based nonlinear channel estimation (IVLMS). The proposed algorithm performance is better than IVLMS.

     

Performance of adaptive MC-CDMA detectors in rapidly fading Rayleigh channels

Multicarrier code-division multiple access (MC-CDMA) combines multicarrier transmission with direct sequence spread spectrum. Different approaches have been adopted which do not assume a perfectly known channel. We examine the forward-link performance of decision-directed adaptive detection schemes, with and without explicit channel estimation, for MC-CDMA systems operating in fast fading channels. We analyze theoretically the impact of channel estimation errors by first considering a simpler system employing a threshold orthogonality restoring combining (TORC) detector with a Kalman channel estimator. We show that the performance deteriorates significantly as the channel fading rate increases and that the fading rate affects the selection of system parameters. We examine the performance of more realistic schemes based on the minimum mean square error (MMSE) criterion using least mean square (LMS) and recursive least square (RLS) adaptation. We present a discussion which compares the decision-directed and pilot-aided approaches and explores the tradeoffs between channel estimation overhead and performance. We find that there is a fading rate range where each method provides a good tradeoff between performance and overhead. We conclude that the MMSE per carrier decision-directed detector with RLS estimation combines good performance in low to moderate fading rates, robustness in parameter variations, and relatively low complexity and overhead. For higher fading rates, however, only pilot-symbol-aided detectors are appropriate.     

Structure-Based Low Complexity MMSE Channel Estimator for OFDM Wireless Systems

Wireless communication systems utilizing orthogonal frequency division multiplexing (OFDM) transmissions are capable of delivering high data rates over multipath frequency selective channels. This paper deals with joint estimation/interpolation of wireless channel using pilot symbols transmitted concurrently with the data. We propose a low complexity, spectrally efficient minimum mean square error channel estimator which exploits the correlation structure of channel frequency response for reducing the complexity. Specifically, it is shown that if pilots are inserted appropriately across OFDM subcarriers, the proposed algorithm requires no matrix inversion, thereby significantly relieving the computational burden without deteriorating the performance. Moreover, the knowledge of channel correlation is also not required for the proposed estimator. Simulation results validate that the proposed technique outperforms existing low-complexity variants in terms of mean square error and computational complexity.

     

Modified leaky LMS algorithm for channel estimation in DS-CDMAsystems

A simple adaptive least mean square (LMS) type algorithm for channel estimation is developed based on certain modifications to finite-impulse response (FIR) Wiener filtering. The proposed algorithm is nearly blind since it does not require any training sequence or channel statistics, and it can be implemented using only noise variance knowledge. A condition guaranteeing the convergence of the algorithm and theoretical mean square error (MSE) values are also derived. Computer simulation results demonstrate that the proposed algorithm can yield a smaller MSE than existing techniques, and that its performance is close to that of optimal Wiener filtering     

Channel‐estimate‐based frequency‐domain equalization (CE‐FDE) for broadband single‐carrier transmission

A channel‐estimate‐based frequency‐domain equalization (CE‐FDE) scheme for wireless broadband single‐carrier communications over time‐varying frequency‐selective fading channels is proposed. Adaptive updating of the FDE coefficients are based on the timely estimate of channel impulse response (CIR) to avoid error propagation that is a major source of performance degradation in adaptive equalizers using least mean square (LMS) or recursive least square (RLS) algorithms. Various time‐domain and frequency‐domain techniques for initial channel estimation and adaptive updating are discussed and evaluated in terms of performance and complexity. Performance of uncoded and coded systems using the proposed CE‐FDE with diversity combining in different time‐varying, multi‐path fading channels is evaluated. Analytical and simulation results show the good performance of the proposed scheme suitable for broadband wireless communications. For channels with high‐Doppler frequency, diversity combining substantially improves the system performance. For channels with sparse multi‐path propagation, a tap‐selection strategy used with the CE‐FDE systems can significantly reduce the complexity without sacrificing the performance. Copyright © 2004 John Wiley & Sons, Ltd.     

Low-complexity adaptive decision-feedback equalization of MIMO channels

A new adaptive MIMO channel equalizer is proposed based on adaptive generalized decision-feedback equalization and ordered-successive interference cancellation. The proposed equalizer comprises equal-length subequalizers, enabling any adaptive filtering algorithm to be employed for coefficient updates. A recently proposed computationally efficient recursive least squares algorithm based on dichotomous coordinate descents is utilized to solve the normal equations associated with the adaptation of the new equalizer. Convergence of the proposed algorithm is examined analytically and simulations show that the proposed equalizer is superior to the previously proposed adaptive MIMO channel equalizers by providing both enhanced bit error rate performance and reduced computational complexity. Furthermore, the proposed algorithm exhibits stable numerical behavior and can deliver a trade-off between performance and complexity.     

基于整体最小二乘的联合信道估计及OFDM信号检测算法

针对现有的联合信道估计及OFDM信号检测算法性能不高的问题,该文提出一种基于整体最小二乘的联合信道估计及OFDM信号检测算法。该算法首先通过导频估计初始的信道信息,在此基础上不断地采用整体最小二乘进行OFDM信号检测及信道估计,有效缓解迭代模型误差的影响,加快了算法迭代的收敛速度,提高了信道估计的精度,从而降低了OFDM系统的误码率。该文推导的信道估计克拉美罗界及仿真结果均表明所提出的算法在时变信道环境下优于现有的联合信道估计及OFDM信号检测算法。     

Channel estimation and error correction for UWOC system with vertical non-line-of-sight channel

Recently, underwater wireless optical communication (UWOC) system has placed more attention towards increasing data rate, high accuracy, higher bandwidth and providing highly secured transmission. Generally, light propagation in underwater medium is disturbed due to some degrading effects such as turbulence, scattering and absorption which will degrade the performance. Therefore, channel estimation is essential and need to adapt suitable correction techniques that compensate the errors due to those effects. In this work, channel estimation and error correction technique are proposed for the UWOC system with vertical non-line-of-sight channel based on MIMO-OFDM approach. Initially, input sequence error is eliminated by HVD Turbo coding method and error rectified signal is modulated with M-ary OAM-PPM modulation technique. Modulated signal is transmitted through Vertical NLOS channel. During transmission, the signal undergoes scattering, absorption and channel fading. In receiver side, channel characteristics are estimated by invariant embedding method. Finally, estimated signal is demodulated and decoded by same techniques. MATLAB environment is used to implement the proposed work. Resultant performances like channel capacity, BER, SNR, data rate, receiving power and MSE for different water types like pure sea water, ocean water, costal water and turbid water are compared and results are examined.

     

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