Compressed Sensing of Sparse Multipath MIMO Channels with Superimposed Training Sequence

Abstract

Recent advances in multiple-input multiple-output (MIMO) systems have renewed the interests of researchers to further explore this area for addressing various dynamic challenges of emerging radio communication networks. Various measurement campaigns reported recently in the literature show that physical multipath MIMO channels exhibit sparse impulse response structure in various outdoor radio propagation environments. Therefore, a comprehensive physical description of sparse multipath MIMO channels is presented in first part of this paper. Superimposing a training sequence (low power, periodic) over the information sequence offers an improvement in the spectral efficiency by avoiding the use of dedicated time/frequency slots for the training sequence, which is unlike the traditional schemes. The main contribution of this paper includes three superimposed training (SiT) sequence based channel estimation techniques for sparse multipath MIMO channels. The proposed techniques exploit the compressed sensing (CS) theory and prior available knowledge of channel's sparsity. The proposed sparse MIMO channel estimation techniques are named as, SiT based compressed channel sensing (SiT-CCS), SiT based hardlimit thresholding with CCS (SiT-ThCCS), and SiT training based match pursuit (SiT-MP). Bit error rate (BER) and normalized channel mean square error (NCMSE) are used as metrics for the simulation analysis to gauge the performance of proposed techniques. A comparison of the proposed schemes with a notable first order statistics based SiT least squares (SiT-LS) estimation technique is presented to establish the improvements achieved by the proposed schemes. For sparse multipath time-invariant MIMO communication channels, it is observed that SiT-CCS, SiT-MP, and SiT-ThCCS can provide an improvement up to 2 dB, 3.5 dB, and 5.2 dB in the MSE at signal to noise ratio (SNR) of 12 dB when compared to SiT-LS, respectively. Moreover, for BER = 10 −1.9 , the proposed SiT-CCS, SiT-MP, and SiT-ThCCS, compared to SiT-LS, can offer a gain of about 1 dB, 2.5 dB, and 3.5 dB in the SNR, respectively. The performance gain in MSE and BER is observed to improve with an increase in the channel sparsity.

Location-Aware and Superimposed-Pilot Based Channel Estimation of Sparse HAP Radio Communication Channels

“…In air-to-ground and ground-to-air propagation environments, typically only a few sparsely located dominant scattering objects correspond in arrival of signal at the receiver, the channel in delay and angular domains is thus sparse [18,19]. In such scenarios, the time-invariant channel impulse response vector w.r.t.…”

Equivalent MIMO Channel Matrix Sparsification for Enhancement of Sensor Capabilities

“…In this case, the number of combinations for one observation will be significantly less than the number of combinations for the entire set of symbols. This makes it possible to use the sequential (optimal) processing of each observation and transfer the received information for the processing of the next observation [8,16,19,[28][29][30][31][32]. Similar algorithms are used in Markov processes with finite fixed connectivity.…”

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

“…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 [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.…”