Sparse channel estimation using orthogonal matching pursuit in frequency domain

In order to solve the problem that noise and signal interference have an effect on the channel estimation of receiver, a channel estimation method based on integrated neural network has been proposed in this paper. Firstly, improved neural network would be used to extract nonlinear relationship between pilot signal with noise and original pilot signal. Secondly, the integrated neural network model would be obtained by selective integrating according to diversity factor, then channel estimation results could be obtained by model. Compared with the traditional methods, not only this method could reduce the computational complexity and obtain more accurate channel estimation results to reduce the bit error rate of communication system, but also reduce the cost of pilot and increase the bandwidth utilization rate. The simulation results show that the mean square error of channel estimation and bit error rate of communication are reduced by about 5% and 3% respectively, when compared with the traditional channel estimation method under the same signal-to-noise ratio.

Orthogonal frequency division multiplexing (OFDM) combined with time division multiplexing (TDM), called OFDM/TDM, can bridge the conventional OFDM and single-carrier (SC) transmission by using frequency-domain equalization (FDE). Accurate channel estimation is required for FDE. A well-known channel estimation scheme is pilot-based channel estimation that uses periodically transmitted pilot signals. If a channel estimation error is present, intersymbol interference (ISI) is produced in OFDM/TDM. We apply a Gaussian approximation to the channel estimation error and theoretically investigate the impact of imperfect channel estimation on the average bit error rate (BER) performance of OFDM/TDM. It is shown by numerical evaluation that the channel estimation error degrades the BER performance more when OFDM/TDM approaches SC.

In this paper, a fast orthogonal matching pursuit (OMP) algorithm based on optimized iterative process is proposed for sparse time-varying underwater acoustic (UWA) channel estimation. The channel estimation consists of calculating amplitude, delay and Doppler scaling factor of each path using the received multi-path signal. This algorithm, called as OIP-FOMP, can reduce the computationally complexity of the traditional OMP algorithm and maintain accuracy in the presence of severe inter-carrier interference that exists in the time-varying UWA channels. In this algorithm, repeated inner product operations used in the OMP algorithm are removed by calculating the candidate path signature Hermitian inner product matrix in advance. Efficient QR decomposition is used to estimate the path amplitude, and the problem of reconstruction failure caused by inaccurate delay selection is avoided by optimizing the Hermitian inner product matrix. Theoretical analysis and simulation results show that the computational complexity of the OIP-FOMP algorithm is reduced by about 1/4 compared with the OMP algorithm, without any loss of accuracy.

SummaryThis paper introduces the concept of spatial and media‐based modulated (SMBM) orthogonal frequency division multiplexing (OFDM) as a potential candidate for highly mobile next generation beyond 5G (B5G) wireless communications. The proposed SMBM‐OFDM technique utilizes not only the transmit antenna and channel state indices but also OFDM subcarriers to improve the system performance under high mobility. In addition, this study sheds light on challenging fast time‐varying channel estimation problem of MBM‐based systems by using the linear minimum mean square error (LMMSE) approach due to its optimality to investigate the achievable system performance with the aid of basis expansion modeling. The minimum lower bound on the channel estimation error (Bayesian Cramer–Rao bound) is derived theoretically and shown to be attainable by the considered LMMSE estimator. Moreover, symbol detection performance is provided for different modulation types and higher mobile velocities. Simulation results demonstrate that SMBM‐OFDM system under high mobility is able to provide around 12‐dB performance gains in terms of both channel estimation and symbol detection error compared to conventional spatial modulation (SM)‐OFDM systems without MBM. The presented framework is important due to addressing the high mobility support of SMBM‐OFDM systems for B5G wireless communications in terms of achievable channel estimation and data detection performance.

In this paper, the estimation of doubly spread underwater acoustic (UWA) channels is investigated. The UWA channels are characterized by severe delay spread and significant Doppler effects, and can be well modeled as a multi-scale multi-lag (MSML) channel. Furthermore, exploiting the sparsity of UWA channels, MSML channel estimation can be transformed into the estimation of parameter sets (amplitude, Doppler scale factor, time delay). Based on this, the orthogonal matching pursuit (OMP) algorithm has been widely used. However, the estimation accuracy of OMP depends on the size of the dictionary, which is related with both delay spread and Doppler spread. Thus it requires high computational complexity. This paper proposes a new method, called wavelet transform (WT) based algorithm, for the UWA channel estimation. Different from OMP algorithm which needs to search in both time domain and Doppler domain, WT-based algorithm only needs to search in time domain by using the Doppler invariant characteristic of hyperbolic frequency modulation (HFM) signal. The performance of the proposed algorithm is evaluated by computer simulations based on BELLHOP. The simulation results show that WT-based algorithm performs slightly better than OMP algorithm in low signal to noise ratio (SNR) while can greatly reduce computational complexity.

We consider the problem of jointly detecting a known signal and estimating the channel in the frequency range used by underwater acoustic communication systems. A multi-scale multi-lag propagation environment is considered and no prior knowledge of the channel order is assumed. The proposed detection/estimation method is based on the framework of multifamily generalized likelihood ratio tests applied to a signal lying in a union of subspaces. The result is a ”tuning-free” orthogonal matching pursuit algorithm with a stopping criterion that does not require the knowledge of the number of channel taps or the noise variance. The performance is illustrated with replay simulations using real shallow-water channels measured in the Mediterranean Sea. Numerical results show that the proposed method outperforms competing algorithms in terms of both detection probability and channel estimation error. In addition, channel estimation does not exhibit a performance floor as observed with fixed-order-based approaches.

In this thesis we consider the performance degradation of three telecommunication transceivers with respect to parameters uncertainties. The major contribution of our analysis is the derivation of simple and informative approximations to the excess mean square error, which uncover the basic factors that determine the performance of each transceiver under the pres- ence of parameter uncertainties. In Chapter 1, we brie°y present the channel models and the transceiver structures used in this thesis. In Chapters 2 and 3, we consider the case of the Multiple-Input Multiple-Output (MIMO) °at fading channel and two widely known transceiver schemes, i.e., the trans- mit MIMO Wiener ?ltering and the Tomlinson-Harashima (TH) precoding. For the case of the transmit MIMO Wiener ?ltering, degradation is due to channel estimation errors and time-variations, and noise second-order statistics estimation errors. For the case of the TH precoder, degradation is due to channel estimation errors and time-variations. For both cases, our ?nal expressions uncover the factors that determine the performance degradation in practice, and the relative importance between di®erent error sources. In Chapter 4, we consider the case of the Single-Input Single-Output (SISO) frequency selective channel and the minimum mean square error (MMSE) linear equalizer. The error sources that cause the system degradation is the channel and CFO estimation errors. Our aim is to uncover the relative importance of these error sources. It turns out that the CFO estimation error is much more important than the channel estimation error. This fact leads to useful conclusions concerning the optimal training sequence design for joint CFO and channel estimation.

Due to the channel with characteristic of sparse multi-path in the 60 GHZ wireless communication system, the channel estimation problem can be attributed to that of sparse signals recovery. And with the consideration of the subspace pursuit (SP) algorithm is superior to the orthogonal matching pursuit (OMP) at reconstruction precision, the channel estimation technique based on the SP algorithm is presented in the 60 GHZ wireless communication system, First, design the OFDM multi-carrier modulation communication system. Then, establish the channel estimation mathematical model with indoor Line-of-sight. Finally, complete the reconstruction of sparse signals using SP algorithm. The experimental results and comparison analysis show that the presented technique based on the SP algorithm provides better channel estimation performances in the same pilot conditions, and it is superior to the technique based on OMP algorithm and the technique based on least square (LS) algorithm.

In this paper, a new joint timing and channel estimation scheme is proposed for orthogonal frequency division multiplexing and single carrier block transmission ultra-wideband (UWB) systems. In particular, a new preamble pattern is used for both coarse timing and channel estimation. Despite of the presence of coarse timing error, the estimated channel impulse response (CIR) is simply the cyclic shifted version of the real CIR, thanks to the unique structure of the preamble. Then a new method is proposed to search for the first channel tap in the CIR vector, which can be employed to fine tune the timing position and adjust the channel estimation for later data block detection. The proposed scheme has low complexity, saves preamble overhead by performing joint synchronization and channel estimation, and outperforms existing timing acquisition methods in the literature by a large margin in dense multipath UWB channels. Moreover, this paper presents an analysis of the effect of residual timing error on the performance of single carrier systems with frequency domain equalization.

In this study, the authors presented discrete Fourier transform (DFT)-based channel estimation algorithms for a coherent optical orthogonal frequency division multiplexing (CO-OFDM) transmission system. Influence of the proposed algorithms to the performance and computational complexity of the CO-OFDM system was analysed and compared with other published algorithms. Their simulation results demonstrated that the improved DFT-based channel estimation algorithms are significantly efficient in our experimental conditions. Those algorithms can be considered as efficiently compromised systems in balance out the outcome of performance and the requirement on computational complexity. Such relatively economic and practical approaches have provided an optimistic prospect in studies of transmission systems for future optical communications.

Magnetic Resonance Sounding (MRS) equipment for groundwater exploration has high receiving sensitivity, and it is vulnerably affected by high-level power-line harmonic when worked near the city and village. In order to effectively extract the weak signal, variable step-size LMS algorithm in frequency domain is studied to eliminate noise based on the principle of adaptive noise cancellation. Simulation results show that the algorithm can increase the signal to noise ratio more than 10dB, and the fitting error of parameters is about 10% in the condition that the frequency offset between signals and power-line harmonic is greater than 7Hz and with different SNR and water content. Therefor the variable step-size LMS algorithm in frequency domain can obtained better de-noising effect and parameter extraction accuracy.

A novel asymmetrical massive multiple-input multiple-output (MIMO) system has recently emerged as a crucial solution for reducing hardware complexity and alleviating the data processing pressure. However, the absence of channel reciprocity in this system presents unique challenges when directly applying traditional channel estimation methods, inevitably leading to performance loss. Deep learning approaches hold promise for achieving improved channel estimation performance by implicitly learning channel features. Unfortunately, deep learning approaches are often designed empirically. It is critical to utilize prior knowledge to develop an efficient deep neural network (DNN), especially for wireless communication systems. This paper explores a knowledge-driven DNN design approach and introduces a deep learning-based channel estimation framework for asymmetrical transceivers. The channel estimation problem is decoupled into channel denoising and information inference problems. Two novel DNNs are proposed to eliminate noise and exploit correlative features for reconstructing the missing channel information, respectively. Extensive simulations demonstrate that our proposed channel estimation framework can significantly eliminate noise effects, even in low signal-to-noise ratio regimes, and outperform traditional estimators and other deep learning-based methods. Moreover, ablation studies also validate the effectiveness of our knowledge-driven network structure design approach.

In time division duplexing (TDD) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, downlink channel state information (CSI) can be obtained from uplink channel estimation thanks to channel reciprocity. However, under high-mobility scenarios, frequent uplink channel estimation is needed due to channel aging. Additionally, large amounts of antennas and subcarriers result in high-dimensional CSI matrices, aggravating pilot training overhead. To address this, we propose a three-domain (3D) channel extrapolation framework across spatial, frequency, and temporal domains. First, considering the effectiveness of traditional knowledge-driven channel estimation methods and the marginal effects of pilots in the spatial and frequency domains, a knowledge-and-data driven spatial-frequency channel extrapolation network (KDD-SFCEN) is proposed for uplink channel estimation via joint spatial-frequency channel extrapolation to reduce spatial-frequency domain pilot overhead. Then, leveraging channel reciprocity and temporal dependencies, we propose a temporal uplink-downlink channel extrapolation network (TUDCEN) powered by generative artificial intelligence for slot-level channel extrapolation, aiming to reduce the tremendous temporal domain pilot overhead caused by high mobility. Numerical results demonstrate the superiority of the proposed framework in significantly reducing the pilot training overhead by 16 times and improving the system’s spectral efficiency under high-mobility scenarios compared with state-of-the-art channel estimation/extrapolation methods.

In this paper, the performance of time division duplex (TDD) orthogonal frequency division multiplexing (OFDM) systems over slow Rayleigh channels with phase and amplitude and phase pre-equalization are analyzed. The hit error rate (BER) formulas of binary phase-shift keying (BPSK) modulated TDD OFDM systems using ideal and nonideal pre-equalization are provided. Computer simulations are performed and the results verify corresponding formulas. The system performance of phase pre-equalization is decided by E/sub b//N/sub 0/ and channel estimation(CE) error. With the diminishing of CE error, system performance will reach towards the theoretical performance of a BPSK system in Rayleigh channels. In amplitude and phase pre-equalization systems, appropriate pre-equalization factor should he selected carefully to avoid the boost effect of transmission power and CE error on those subcarriers suffering deep fading. The corresponding BER formulas are presented, which base upon the E/sub b//N/sub 0/, mean square error (MSE) and pre-equalization factor.

FOMP algorithm for Direction of Arrival estimation