Superimposed training-based compressed sensing of sparse multipath channels

Abstract

In a number of wireless communication applications, the impulse response of multipath communication channels has sparse nature. In this study, physical model for various propagation environments exhibiting sparse channel structure is considered. A superimposed (SI) training-based compressed channel sensing (SI-CCS) technique is proposed for such sparse multipath channels. A non-random periodic pilot sequence is SI over the information sequence at the transmitter, which avoids the use of dedicated time slots for training sequence. At the receiver, first-order statistics and the theory of compressed sensing is applied to estimate the wireless communication channels with sparse impulse response. A simulation analysis is presented to demonstrate the effectiveness of the proposed-channel estimation technique, where mean-square error and biterror rate are used as the performance measures. Exploiting the proposed SI-CCS technique, the simulation results along with the observations are presented, which illustrate the effect of various channel parameters on the performance of the proposed technique. Furthermore, obtained simulation results for the proposed SI-CCS technique along with its comparison with other techniques in literature are also presented. It is established that for the cases of sparse multipath channels, the proposed SI-CCS technique can potentially achieve significant improvement in the performance of channel estimator over the existing estimation techniques of such sparse channels.

GA Based Sensing of Sparse Multipath Channels with Superimposed Training Sequence

“…In this section, the derivation of first order statistics based channel estimators with superimposed pilot sequence proposed in [3], [14], and Error! Reference source not found.…”

“…Moreover, for the case of sparse multipath channels, this estimation method do not exploit the available prior knowledge of channel's sparsity, thus fails to correctly estimate the zero valued channel taps. Therefore, an extension of this method is proposed in [14] for the case of sparse multipath channels, which propose the following solution to obtain the channel's estimate Dantzig Selector (DS) has been casted to optimize the estimate in (13). This method performs betters than (11) for the cases of sparse multipath channels [14].…”

Massive-MIMO Sparse Uplink Channel Estimation Using Implicit Training and Compressed Sensing

“…The authors in [27,28] have established the fact that a finite-dimensional sparse signal can be exactly reconstructed from fewer, linear and nonadaptive measurements. The CS approach has been established as an efficient solution to estimate sparse multipath channels-see e.g., [14,29]. Computing the sparse solution requires solving a 0 -minimization problem, which is computationally non-deterministic polynomial-time (NP) hard.…”

“…By exploiting the sparsity of wireless multipath channels, SiT sequence based compressive channel sensing methods have been studied in various contexts such as single-input single-output (SISO) systems [14,46], sparse MIMO channels [47,48], and underwater acoustic channels [49]. In [46], a genetic algorithm (GA) based channel estimation method is proposed using an SiT sequence for SISO systems.…”

“…In [46], a genetic algorithm (GA) based channel estimation method is proposed using an SiT sequence for SISO systems. In [14], a Dantzig selector (DS) algorithm based method is proposed for estimation of SISO sparse multipath channels using a known SiT sequence. This study is further extended in [47,48] for the case of multiuser MIMO systems, where SiT based DS and MP algorithms are proposed.…”