Joint Sparse Channel Estimation Research Articles

Joint Channel Estimation and Data Detection for IRS-Assisted OFDM Systems

Intelligent reconfigurable surface (IRS) becomes a promising technology for improving spectrum efficiency and reducing the energy consumption in the next-generation networks. To reap the passive beamforming gain of IRS, accurate channel estimation is essentially required. However, the existing channel estimation methods for the IRS-assisted orthogonal frequency division multiplexing (OFDM) systems suffer from serious performance loss, due to using the least-squares (LS) estimator and ignoring the channel sparsity in the time domain. Another drawback of the existing methods is that the channel estimation and data detection are separated from each other so that any inaccurate channel estimation would cause an error accumulation problem for the data detection. To overcome these drawbacks, we propose a novel joint sparse channel estimation and data detection method for the IRS-assisted OFDM systems. The novelties of the proposed method are twofold: (i) present a new common sparse representation model for the joint channel estimation and data detection, which can utilize the common channel sparsity between the pilots and data streams to significantly improve the channel estimation performance and reduce the training overhead; and (ii) propose an efficient variational Bayesian inference (VBI) framework to simultaneously perform the Bayesian inference for the sparse channel recovery and data detection, where the coupling effect caused by the reflection pattern is trickly evaded by adopting an additional column-independent factorization. Besides exploiting the common sparsity for the channel estimation and data detection, the proposed method also avoids using the LS estimator, and thus its performance can be greatly enhanced. Simulation results verify the superiority of the proposed method.

DCD-Based Joint Sparse Channel Estimation for OFDM in Virtual Angular Domain

Massive Multiple Input Multiple Output (MIMO) is a promising technique for communications due to the high data transmission rate. To harvest the benefit from the massive MIMO, it is necessary to have accurate channel estimates. Such channels often exhibit sparsity in the virtual angular domain. This paper proposes a dichotomous coordinate descent (DCD) based algorithm for joint sparse channel estimation in the virtual angular domain for the orthogonal-frequency-division-multiplexing massive MIMO. We show that compared to the distributed sparsity adaptive matching pursuit algorithm previously proposed for this purpose, the DCD-based algorithm has significantly lower complexity and better channel estimation performance.

Joint sparse channel estimation based on angle domain and delay domain

Joint sparse channel estimation based on angle domain and delay domain

Multi-array iterative receiver for underwater acoustic OFDM communications with EXIT chart evaluation

In this paper, a multi-array iterative receiver based on log-likelihood ratio (LLR)-combining detection involving joint sparse channel estimation and decoding is proposed for underwater acoustic OFDM communication. First, Extrinsic information transfer (EXIT) chart analysis is applied to evaluate the convergence behavior of the iterative receiver using the real data collected from the Kauai Acomms MURI 2011 (KAM11) experiment. This experiment was conducted in about 106m-depth shallow water west of Kauai, HI, in June 2011, with a 20kHz bandwidth (12–32kHz) at range up to 3km. It helps to explain the impact of different data configurations, detectors, and the diversity combinations in a highly inhomogeneous underwater environment and to predict the bit-error rate (BER) performance of the proposed receiver. Then the BERs as a function of the number of combined elements are illustrated to verify the prediction and analysis via the EXIT chart. Data transmission using 16QAM modulation achieves a BER of 10−4 at a data rate of 21kb/s. The results provide guidance for the design of system parameters including the data configurations, the number of iterations for both iterative processing and low density parity check (LDPC) decoding, which are beneficial to achieve a good efficiency-performance tradeoff.

Time–Frequency Joint Sparse Channel Estimation for MIMO-OFDM Systems

This letter proposes a time-frequency joint sparse channel estimation for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems under the framework of structured compressive sensing (CS). The proposed scheme first relies on a pseudorandom preamble, which is identical for all transmit antennas, to acquire the partial common support by utilizing the sparse common support property of the MIMO channels. Then, a very small amount of frequency-domain orthogonal pilots are used for the accurate channel recovery. Simulation results show that the proposed scheme demonstrates better performance and higher spectral efficiency than the conventional MIMO-OFDM schemes. Moreover, the obtained partial common support can be further utilized to reduce the complexity of the CS algorithm and improve the signal recovery probability under low signal-to-noise-ratio conditions.