Sparse channel estimation algorithms for OTFS system

Abstract

Orthogonal time-frequency space (OTFS) modulation, which has recently been proposed in the literature, is one of the promising techniques designed in the 2D Delay-Doppler domain adapted to combat high Doppler fading channels. However, channel estimation in high Doppler scenarios in advanced mobile-communication systems is still a challenging task. In this paper, the problem of channel estimation in the Delay-Doppler domain of the OTFS is focused on. First, a simple adaptation of the generalized orthogonal matching pursuit procedure, which will serve as a baseline method in this work, is proposed. Then, iterative algorithms are derived beneficiating from the sparsity of the channel. The unknown channel vector is separated into an unknown sparse support vector corresponding to the delay and Doppler taps, and an unknown vector of channel gains. These algorithms involve 𝓁 1 -norm minimization and a two-stage iterative procedure to recover alternatively the channel support and its coefficients. The estimation problem is also addressed from a Bayesian point of view. The sparse representation is reformulated as a specific marginalization of the maximum a posteriori problem on the support of the channel. To deal with the intractability of this problem, two existing techniques are adapted to this context, namely: The Monte Carlo Markov chain with the Gibbs sampler and variational mean-field approximation with the variational Bayesian expectation-maximization procedure. Finally, to assess the performance of the proposed algorithms, their complexity and performance are compared against existing methods. Experimental tests, conducted in high-mobility scenarios and low-latency applications, show that the proposed schemes are slightly more expensive in terms of complexity load but perform significantly better in terms of normalized mean square error and bit error rate.

Joint Channel Estimation and Symbol Detection for OTFS System Using a Two-Stage Algorithm

“…where Z p ∈ C Lp×L represents the pilots matrix, Γ is an additional phase shift matrix, L = (2k ν + 1)(l τ + 1), and h is a sparse vector containing only P non-zero elements, which are the channel gains {h i } i=1:P . To estimate the channel vector h, we perform the Generalized Orthogonal Matching Pursuit algorithm (GOMP) [6] on (8). The positions of the non-zero entries of h correspond to delay and Doppler taps, and their values correspond to channel gains.…”

“…The first one concerns conventional pilot-aided (CPA) schemes in which a superframe architecture is used: the first frame is used for channel estimation, and the subsequent frames are used for data detection [4], [5]. The second category of algorithms, called embedded pilots (EP), consists of channel estimation and data detection in the same frame [1], [6]- [15]. This is achieved by arranging pilots and data symbols in the same DD grid.…”

“…In this category, guard intervals are placed between pilots and data symbols to avoid interference between them. The third category called superimposed pilots (SP) includes schemes that superimpose pilots and data symbols in the same locations in the DD domain [16]- [19]. This category requires high-performance iterative algorithms for efficient channel estimation and accurate data detection.…”

Spatially correlated channel estimation for RIS‐assisted MIMO systems with correlated gaussian perturbation

“…To reduce the pilot and communication overhead, the authors in [8] utilize the inherent sparsity of mmWave channels to transfer composite channels into a sparse signal recovery problem and thus acquire composite channel estimation. To further reduce the pilot overhead, some works have been done by exploiting superimposed pilots [11–14]. In [11], the authors develop a superimposed pilot‐based channel estimation approach for RIS‐assisted systems.…”

“…Following this line, an iterative channel estimation and data detection algorithm in delay‐Doppler domain for OTFS system is proposed by using the sparse nature of the channel [13]. Subsequently, the authors further explore an iterative algorithm channel estimation and symbol detection by exploiting a superimposed pilot pattern MIMO OTFS systems to improve the spectral efficiency of the system [14].…”