Soft-feedback OMP for the recovery of discrete-valued sparse signals

Sparrer, Susanne; Fischer, Robert F. H.

doi:10.1109/eusipco.2015.7362626

Cited by 13 publications

(11 citation statements)

References 17 publications

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“…The rest of the procedure is the same as the common support identification. Afterward, the complex pilot at each support index is recomputed and updated (lines 13 and 14 for common support and lines 16 and 17 for individual support in algorithm 1) in a design similar to [43], although the overall procedure and problem are different from the proposed. Finally, the channel is estimated using the LS approach (lines 18 step-4 in algorithm 1).…”

Section: ) Channel Recovery With Q-pjomp (Algorithm 1)mentioning

confidence: 99%

Joint Sparse Channel Recovery With Quantized Feedback for Multi-User Massive MIMO Systems

et al. 2020

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Accurate channel state information (CSI) at the transmitter is an essential prerequisite for transmit beamforming in massive multiple input multiple output (MIMO) systems. However, due to a large number of antennas in massive MIMO systems, the pilot training and feedback overhead become a bottleneck. To resolve this issue, the research work presents a novel framework for frequency division duplex (FDD) based multiuser massive MIMO system. A 2-step quantization technique is employed at the user equipment (UE) and the CSI is recovered at the base station (BS) by applying the proposed compressed sensing (CS) based algorithms. The received compressed pilots are quantized by preserving 1 bit per dimension direction information as well as the partial amplitude information. Subsequently, this information is fed back to the BS, which employs the proposed quantized partially joint orthogonal matching pursuit (Q-PJOMP) or quantized partially joint iterative hard thresholding (Q-PJIHT) CS algorithms to recover the CSI from a limited and quantized feedback. Indeed, an appropriate dictionary and the hidden joint channel sparsity structure among users is exploited by the CS methods, resulting in the reduction of the feedback information required for channel estimation. Simulations are performed using singular value decomposition (SVD) and minimum mean square error (MMSE) beamforming utilizing the estimated channel. The results confirm that the proposed 2-step quantization approaches the system with channel knowledge without quantization, thus overcoming the training and feedback overhead problem. Moreover, the proposed 2-step quantization outperforms 1-bit quantization, at the cost of slightly higher complexity. INDEX TERMS Compressed sensing, joint channel estimation, quantization, channel state information (CSI), multiple input multiple output (MIMO), sparse channel estimation, dictionary.

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Section: ) Channel Recovery With Q-pjomp (Algorithm 1)mentioning

confidence: 99%

Joint Sparse Channel Recovery With Quantized Feedback for Multi-User Massive MIMO Systems

et al. 2020

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“…In the extensive CS literature, some recent work is dedicated to the subcase of finite-valued signals, i.e., x ∈ A n where A is a known alphabet, that is, a finite set of symbols. This is a problem encountered in a number of sparse/CS applications, such as digital image recovery [3], security [4], digital communications [5,6], and discrete control signal design [7]. In many localization problems [8,9], the localization area is split into cells and the goal is to verify which cells are occupied or not, the number of occupied cells being generally much smaller than the total: this can be interpreted as the recovery of a binary sparse signal in {0, 1} n .…”

Section: Finite-valued Sparse Signalsmentioning

confidence: 99%

Non-convex Lasso-kind approach to compressed sensing for finite-valued signals

Fosson

2018

Preprint

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In this paper, we bring together two trends that have recently emerged in sparse signal recovery: the problem of sparse signals that stem from finite alphabets and the techniques that introduce concave penalties. Specifically, we show that using a minimax concave penalty (MCP) the recovery of finite-valued sparse signals is enhanced with respect to Lasso, in terms of estimation accuracy, number of necessary measurements, and run time. We focus on problems where sparse signals can be recovered from few linear measurements, as stated in compressed sensing theory. We start by proposing a Lasso-kind functional with MCP, whose minimum is the desired signal in the noisefree case, under null space conditions. We analyze its robustness to noise as well. We then propose an efficient ADMM-based algorithm to search the minimum. The algorithm is proven to converge to the set of stationary points, and its performance is evaluated through numerical experiments, both on randomly generated data and on a real localization problem. Furthermore, in the noise-free case, it is possible to check the exactness of the solution, and we test a version of the algorithm that exploits this fact to look for the right signal.

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“…Since this approach takes the apriori distribution of x into account, it depends on the alphabet; an adaptation to any alphabet is straightforward. This approach is also used in other algorithms for (discrete) CS, cf., e.g., [15], [22], [24], [13]. All variables of the second (soft-value calculating) step are indicated by the index "S".…”

Section: A Approximate Lmmse Tsrmentioning

confidence: 99%

“…Some algorithms for the solution of problem (2) have been proposed over the last few years. Besides the most obvious approach of a standard CS algorithm with subsequent quantizer [11], the quantization can be included inside OMP [13], which equals the so-called model-based Compressed Sensing [12] if it is applied to discrete CS. This algorithm has been further improved by the application of a method which preserves reliability information [13].…”

Section: Introductionmentioning

confidence: 99%

“…Besides the most obvious approach of a standard CS algorithm with subsequent quantizer [11], the quantization can be included inside OMP [13], which equals the so-called model-based Compressed Sensing [12] if it is applied to discrete CS. This algorithm has been further improved by the application of a method which preserves reliability information [13]. Another improved variant of OMP has been introduced in [14], where a minimum mean-squared error estimator has been applied.…”

Section: Introductionmentioning

confidence: 99%

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Unveiling bias compensation in turbo-based algorithms for (discrete) compressed sensing

Sparrer

Fischer

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In Compressed Sensing, a real-valued sparse vector has to be recovered from an underdetermined system of linear equations. In many applications, however, the elements of the sparse vector are drawn from a finite set. Adapted algorithms incorporating this additional knowledge are required for the discrete-valued setup. In this paper, turbo-based algorithms for both cases are elucidated and analyzed from a communications engineering perspective, leading to a deeper understanding of the algorithm. In particular, we gain the intriguing insight that the calculation of extrinsic values is equal to the unbiasing of a biased estimate and present an improved algorithm.

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Soft-feedback OMP for the recovery of discrete-valued sparse signals

Cited by 13 publications

References 17 publications

Joint Sparse Channel Recovery With Quantized Feedback for Multi-User Massive MIMO Systems

Joint Sparse Channel Recovery With Quantized Feedback for Multi-User Massive MIMO Systems

Non-convex Lasso-kind approach to compressed sensing for finite-valued signals

Unveiling bias compensation in turbo-based algorithms for (discrete) compressed sensing

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