Compressed Sensing Maximum Likelihood Channel Estimation for Ultra-Wideband Impulse Radio

Abstract

Abstract-One of the most attractive features of ultrawideband impulse radio is the collection of rich multipath with the transmission of ultra-short pulses. Exploiting the rich multipath diversity with channel estimating Rake receivers enables significant energy capture, higher performance and flexibility than suboptimal receivers. Although data-aided (DA) maximum likelihood (ML) channel estimator shows a promising performance, its implementation is restricted by the Nyquist sampling criterion. The emerging theory of compressed sensing (CS) describes a novel framework to jointly compress and detect a sparse signal with fewer samples than the traditional Nyquist criterion. In this paper, we propose a CS-ML channel estimator which combines the compression framework of CS for sampling rate reduction while retaining the noise statistics formulation of ML to achieve a reliable performance. Simulation assessment indicates that, with far fewer measurements, the performance of our proposed scheme supersedes that of the 1-norm minimization estimator of CS and can be as close as the ML, but with a reduction in complexity.

Compressive sensing for ultra‐wideband channel estimation: on the sparsity assumption of ultra‐wideband channels

“…For the CS-based transceiver design, the main goal is to detect the UWB signal with reduced sampling rate and yet with negligible performance degradation [15][16][17]. For the CS-based channel estimation, the main goal is to estimate the sparse channel with reduced number of observations [18][19][20][21][22][23][24].…”

Pilot-assisted channel estimation for coexisting heterogeneous wireless personal area networks

“…This work solely focuses on channel estimation for one pulse position modulation (PPM) time-hopping (TH) UWB transceiver and does not take into account the effects of multi-user interference (MUI) and other external sources of interference. The work in [9] proposes a compressed sensing maximum likelihood (CSML) channel estimator in order to reduce the Nyquist sampling rate, which renders the implementation of UWB systems easier. The authors have shown that while retaining the noise statistics formulation of ML to achieve a reliable performance, the sampling rate is reduced significantly.…”

Compressed Sensing: Ultra-Wideband Channel Estimation Based on FIR Filtering Matrix

“…As the IR-UWB signals have resolvable multipath with a sparse structure at the receiver, the application of CS theory to UWB channel estimation has also found wide interest in the UWB community. For the CS based UWB channel estimation, the main goal has been to estimate the sparse channel with reduced number of observations (Paredes et al, 2007;Liu & Lu, 2009;Naini et al, 2009).That is equivalent to reducing the sampling rate at the receiver. In (Paredes et al, 2007), a channel detection method based on the Matching Pursuit algorithm is proposed, where the path delays and gains are calculated iteratively.…”

“…In (Paredes et al, 2007), a channel detection method based on the Matching Pursuit algorithm is proposed, where the path delays and gains are calculated iteratively. In (Liu & Lu, 2009), the authors combine the maximum likelihood (ML) approach with the CS theory. In (Naini et al, 2009), a spread spectrum modulation structure is placed before the measurement matrix to enhance the estimation performance.…”

“…In (Naini et al, 2009), a spread spectrum modulation structure is placed before the measurement matrix to enhance the estimation performance. The common assumption of the studies in (Paredes et al, 2007;Liu & Lu, 2009;Naini et al, 2009) is that the UWB channels are sparse. However, depending on the environment (e.g., an industrial environment may have dense multipath), the sparsity assumption of the channels may not hold.…”