A low-complexity iterative channel estimation and detection technique for doubly selective channels

Guo, Qinghua; Ping, Li; Huang, Defeng

doi:10.1109/twc.2009.081448

Cited by 62 publications

(7 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the above equation, h is the estimated channel vector, and V is a transformation with respect to the measurement matrix, which is computed by referring to reference [25]. Due to the AR-HMM assumption, we can obtain the following relationship:…”

Section: Gmm Time-varying Channel Estimation Based On Ar-hmm Modelmentioning

confidence: 99%

“…In 2009, Guo et al introduced a two-way iterative channel estimation method utilizing the AR-HMM model. This method leverages the correlation between the front and back of the channel and integrates it with the Gaussian message passing algorithm [25]. Over the last two years, Yang et al have integrated the superposition training technique with experimental data to further validate the applicability of this model in UACs [26,27].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Iterative Signal Detection and Channel Estimation with Superimposed Training Sequences for Underwater Acoustic Information Transmission in Time-Varying Environments

Li,

Han,

2024

Remote Sensing

View full text Add to dashboard Cite

Underwater signal processing is primarily based on sound waves because of the unique properties of water. However, the slow speed and limited bandwidth of sound introduce numerous challenges, including pronounced time-varying characteristics and significant multipath effects. This paper explores a channel estimation method utilizing superimposed training sequences. Compared with conventional schemes, this method offers higher spectral efficiency and better adaptability to time-varying channels owing to its temporal traversal. To ensure success in this scheme, it is crucial to obtain time-varying channel estimation and data detection at low SNRs given that superimposed training sequences consume power resources. To achieve this goal, we initially employ coarse channel estimation utilizing superimposed training sequences. Subsequently, we employ approximate message passing algorithms based on the estimated channels for data detection, followed by iterative channel estimation and equalization based on estimated symbols. We devise an approximate message passing channel estimation method grounded on a Gaussian mixture model and refine its hyperparameters through the expectation maximization algorithm. Then, we refine the channel information based on time correlation by employing an autoregressive hidden Markov model. Lastly, we perform numerical simulations of communication systems by utilizing a time-varying channel toolbox to simulate time-varying channels, and we validate the feasibility of the proposed communication system using experimental field data.

show abstract

Section: Gmm Time-varying Channel Estimation Based On Ar-hmm Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Iterative Signal Detection and Channel Estimation with Superimposed Training Sequences for Underwater Acoustic Information Transmission in Time-Varying Environments

Li,

Han,

2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…To overcome this problem, data-aided channel estimation has been investigated in which the detected data symbols are exploited as additional pilot symbols [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. However, the detected data symbols may have errors that degrade the accuracy of channel estimation.…”

Section: Introductionmentioning

confidence: 99%

“…However, the detected data symbols may have errors that degrade the accuracy of channel estimation. The iterative turbo equalizer can overcome this degradation by increasing the maximum-a-posteriori probability (MAP) [ 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. However, such an iterative turbo equalizer has considerable complexity and latency at the receivers.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-Aided Channel Estimator in Time-Varying MIMO Systems

Kim

Min

2023

Sensors

View full text Add to dashboard Cite

This paper proposes a reinforcement learning-aided channel estimator for time-varying multi-input multi-output systems. The basic concept of the proposed channel estimator is the selection of the detected data symbol in the data-aided channel estimation. To achieve the selection successfully, we first formulate an optimization problem to minimize the data-aided channel estimation error. However, in time-varying channels, the optimal solution is difficult to derive because of its computational complexity and the time-varying nature of the channel. To address these difficulties, we consider a sequential selection for the detected symbols and a refinement for the selected symbols. A Markov decision process is formulated for sequential selection, and a reinforcement learning algorithm that efficiently computes the optimal policy is proposed with state element refinement. Simulation results demonstrate that the proposed channel estimator outperforms conventional channel estimators by efficiently capturing the variation of the channels.

show abstract

“…To address the Doppler, multiple methods such as direct resampling, phase locked loop (PLL) [7] and joint carrier frequency offset, gridless channel estimation and data detection (JCCED-VALSE) [11] are proposed. In [12] and [13], Gaussian Markov model is used to model the timevarying characteristic of channel in two adjacent sub-blocks. In [14], the RLS predictor is adopted to track the delay-Doppler spreading function block by block.…”

mentioning

confidence: 99%

Joint Channel Estimation, Data Decoding, and Group-Sparse Impulsive Noise Estimation for Slowly Time-Varying Single Carrier Underwater Acoustic Communications

Zhang,

Zhu,

Wang

et al. 2024

IEEE Access

View full text Add to dashboard Cite

The Gaussian noise assumption is usually made for slowly time-varying underwater acoustic communication (UWAC) systems, which is entirely adequate for most cases. However, certain human activities such as geophysical surveys, multi-beam echosounders and iceberg breakup generate impulsive noise. Consequently, the impulsive noise degrades the designed receiver based on the Gaussian noise model significantly. An optimization formulation which takes the slowly time-varying channel impulse response (CIR), the group-sparse impulsive noise and the encoder structure of the data symbol into account is proposed. To tackle the optimization problem, we propose a joint channel estimation, data decoding and impulsive noise estimation (JCDI) algorithm consisting of two modules named module A and module B, where module A performs the joint channel estimation, data estimation and impulsive noise estimation, while module B uses the low density parity check (LDPC) decoder to decode the encoded data symbols. In particular, the block coordinate descent (BCD) algorithm is proposed, and a smoothed fast iterative threshold soft algorithm (FISTA) is proposed to estimate the group-sparse impulsive noise. Substantial numerical simulations and real experiments are conducted to show the effectiveness of JCDI under impulsive noise, i.e., JCDI achieves lower coded BER than conventional receivers.INDEX TERMS Block coordinate descent, group sparse, underwater acoustic communications, impulsive noise. I. INTRODUCTIONU NDERWATER acoustic (UWA) communications is vital to various commercial and scientific research activities in oceans and has many applications such as military, oceanographic, commercial shipping, offshore oil and gas industry and underwater vehicles [1]. However, due to the propagation characteristics, underwater acoustic channels are considered to be one of the most challenging communication channels. In particular, the low propagation speed and the reflection of acoustic wave from surface and bottom of the ocean, along with the refraction caused by nonuniformity of sound speed in the water, result in significant multipath delay spread, which leads to severe inter-symbol-interference (ISI) especially in high symbol rate single-carrier system. Besides, due to the low speed of sound, coupled with the motion of transceiver platforms as well as the fluctuation of propagation medium, Doppler effect [2] is almost inevitable in UWA communication, which results in time-varying channel. There are different modulation techniques for underwater acoustic communications including direct sequence spread spectrum (DSSS), frquency shift keying (FSK), phase shift keying (PSK) and so on. They all have pros and cons. For example, DSSS and FSK are suitable for low date rate and high reliable communications and coherent modulation like PSK

show abstract

A low-complexity iterative channel estimation and detection technique for doubly selective channels

Cited by 62 publications

References 26 publications

Iterative Signal Detection and Channel Estimation with Superimposed Training Sequences for Underwater Acoustic Information Transmission in Time-Varying Environments

Iterative Signal Detection and Channel Estimation with Superimposed Training Sequences for Underwater Acoustic Information Transmission in Time-Varying Environments

Reinforcement Learning-Aided Channel Estimator in Time-Varying MIMO Systems

Joint Channel Estimation, Data Decoding, and Group-Sparse Impulsive Noise Estimation for Slowly Time-Varying Single Carrier Underwater Acoustic Communications

Contact Info

Product

Resources

About