On estimating sparse and frequency modulated channels for MIMO underwater acoustic communications

Abstract

Effective channel estimation plays a critical role in the overall performance of multi-input multi-output (MIMO) underwater acoustic communications (UAC). This paper compares two closely related channel estimation algorithms developed under different models for sparse and frequency modulated acoustic channels. More specifically, the recently proposed channel estimation algorithm, referred to as the generalization of the sparse learning via iterative minimization (GoSLIM), assumes that each receiver has its own Doppler frequency. This channel model is further simplified in the present paper by assuming that all the receivers experience the same Doppler frequency, and the corresponding channel estimation algorithm is modified accordingly. Both channel estimation algorithms considered address sparsity through a hierarchical Bayesian model. They are user parameter free algorithms and are easy to use in practical applications. By analyzing the in-water experimental measurements recently acquired during the MACE10 experiment, we demonstrate that the employment of the GoSLIM variation not only reduces the overall complexity involved in the channel estimation stage, but also slightly improves the detection performance, as compared with its original GoSLIM counterpart. This paper signifies the importance of establishing proper channel models for enhanced UAC performance.

Enhanced Mobile Multiple-Input Multiple-Output Underwater Acoustic Communications

“…Thêobtained using ( 12) is more robust against the noise contamination than the one from (11). We will show later on in Section 6.2.1 via the MACE10 in-water experimental data that the method in (12) works well in practice.…”

“…Like GoSLIM, GoSLIM-V addresses sparsity through a hierarchical Bayesian model, and because GoSLIM-V is user parameter free, it is easy to use in practical applications. It is demonstrated in [11] that the employment of GoSLIM-V not only reduces the overall complexity in the channel estimation stage but also slightly improves the detection performance compared to its GoSLIM counterpart. Due to this reason, GoSLIM-V is used as the channel estimation algorithm in the present paper.…”

“…As a consequence, the impact of the Doppler frequency shift on the received measurements across all the receivers is taken into account through one unknown common frequency. Accordingly, a variation of GoSLIM, referred to as GoSLIM-V (V stands for variation), is proposed for channel estimation in [11]. Like GoSLIM, GoSLIM-V addresses sparsity through a hierarchical Bayesian model, and because GoSLIM-V is user parameter free, it is easy to use in practical applications.…”

“…In [11], the aforementioned channel model is further simplified by assuming that the channel taps for all the transmitter and receiver pairs experience the same Doppler frequency. As a consequence, the impact of the Doppler frequency shift on the received measurements across all the receivers is taken into account through one unknown common frequency.…”