2011/07/07 by Yann Barbotin, Ali Hormati, Sundeep Rangan +1 · 191 citations
Computer Science · Engineering · Mathematics · #Additive white Gaussian noise #Advanced MIMO Systems Optimization #Algorithm #Bandwidth (computing) #Channel (broadcasting) #Computer science #Direction-of-Arrival Estimation Techniques #Fading #MIMO #MIMO-OFDM #Mathematics #Orthogonal frequency-division multiplexing #Parametric statistics #Rayleigh fading #Sparse and Compressive Sensing Techniques #Statistics #Telecommunications #cs.NI
paper · pdf · doi:10.1109/tcomm.2012.091112.110439
published in IEEE Transactions on Communications 60(12), 3705-3716 (IEEE Communications Society) · 12 pages / 7 figures. Submitted to IEEE Transactions on Communication
arxiv created 2011/07/07 · openalex publication_date 2012/09/21 · arxiv updated 2016/11/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We consider the problem of estimating sparse communication channels in the MIMO context. In small to medium bandwidth communications, as in the current standards for OFDM and CDMA communication systems (with bandwidth up to 20 MHz), such channels are individually sparse and at the same time share a common support set. Since the underlying physical channels are inherently continuous-time, we propose a parametric sparse estimation technique based on finite rate of innovation (FRI) principles. Parametric estimation is especially relevant to MIMO communications as it allows for a robust estimation and concise description of the channels. The core of the algorithm is a generalization of conventional spectral estimation methods to multiple input signals with common support. We show the application of our technique for channel estimation in OFDM (uniformly/contiguous DFT pilots) and CDMA downlink (Walsh-Hadamard coded schemes). In the presence of additive white Gaussian noise, theoretical lower bounds on the estimation of sparse common support (SCS) channel parameters in Rayleigh fading conditions are derived. Finally, an analytical spatial channel model is derived, and simulations on this model in the OFDM setting show the symbol error rate (SER) is reduced by a factor 2 (0 dB of SNR) to 5 (high SNR) compared to standard non-parametric methods - e.g. lowpass interpolation.