A Compressed Sensing Approach to Block-Iterative Equalizers

Pinto, Rafael; Merched, R.

doi:10.1109/tsp.2017.2781648

Cited by 5 publications

(6 citation statements)

References 24 publications

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“…A simple choice for the ridge parameter in the above context can be considered by recalling that, under ergodicity arguments, the empirical cost (36) is commonly motivated from their regularized (desired) stochastic analog:…”

Section: E Stochatic Analoguesmentioning

confidence: 99%

“…Hence, for large i, the first term in (38) can be approximated by }y i ´Hi W} 2 {i, which suggests that we can set η o i " iη o approximately in (36), considering that Λ i " I.…”

Section: E Stochatic Analoguesmentioning

confidence: 99%

“…5. This diagram is similar to a block decision feedback equalizer (DFE) that projects the estimate p W i onto the "decisions" W i according to some data-fusion procedure [36]. The roles of the corresponding feedforward and feedback matrices tG i , B i u are played by a constantly redesigned, data-driven adaptation algorithm, which performs self-learning with the help of nearby agents; the role of the decision device is played by the combination strategy, which further refines the agent estimates via social-learning.…”

Section: Adaptive Combination Matrix Designsmentioning

confidence: 99%

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Optimal Diffusion Learning Over Networks—Part I: Single-Task Algorithms

Merched

2022

IEEE Open J. Signal Process.

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We revisit the theory of distributed networks of cooperative agents under a broader perspective of diffusion adaptation, by exploiting proximity concepts. This leads to two main families of algorithms with enhanced convergence rate and mean-square-error performance. Part I of this work considers mainly single-task scenarios, which are based on formulating optimal learning and fusion steps via an adaptive network penalty function. The main recursions, which we refer to as Adapt-and-Fuse (AAF) diffusion, are reminiscent of a reweighted network regularized algorithm, usually seen in standalone formulations. This is in line with early approaches that promote proximity among agents in cooperative networks. The AAF strategy employs exact fusion in the leastsquares sense, and outperforms the exact global least-squares solution that ignores the topology of the network. It also suggests simplified LMS-complexity algorithms, and motivates us to develop a normalized version of the relative variance diffusion algorithm, which also learns combination weights. It is verified that even when agents do not share estimates, but only their uncertainties, the simplified AAF improves accuracy over the NLMS-RV algorithm in the presence of intruders, and becomes more robust to noisy links. In order to cope with the computational burden associated with long parameter vectors and correlated inputs, an overlapped block multidelay adaptive frequency-domain (FD) version of each new algorithm is derived. It turns out that for correlated inputs, these FD-LMS versions outperform the exact fullband RLS solutions. In the accompanying Part II of this work, we pursue extensions to the multitask scenario. Extensive simulations illustrate the superiority of the new approaches.

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Section: E Stochatic Analoguesmentioning

confidence: 99%

“…Hence, for large i, the first term in (38) can be approximated by }y i ´Hi W} 2 {i, which suggests that we can set η o i " iη o approximately in (36), considering that Λ i " I.…”

Section: E Stochatic Analoguesmentioning

confidence: 99%

Section: Adaptive Combination Matrix Designsmentioning

confidence: 99%

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Optimal Diffusion Learning Over Networks—Part I: Single-Task Algorithms

Merched

2022

IEEE Open J. Signal Process.

Self Cite

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“…It is essential to design a measurement process or sensing matrix that matches the data, and learning of the sparse dictionary increases the complexity of the system when the signal cannot be expressed simply by sparse [9]. Although CS block equalizers are proposed as the iterative solution for minimizing redundancy [10], the CS-based receiver design still has considerable limitations. With the development of artificial intelligence technology and the improvement of computing power, deep learning assisted communication systems have been widely concerned [11].…”

Section: Introductionmentioning

confidence: 99%

Pilot-Assisted MIMO-V-OFDM Systems: Compressed Sensing and Deep Learning Approaches

Zhang

Gao

et al. 2020

IEEE Access

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In this paper, we investigate the channel estimation and decoding methods exploiting the channel sparsity in pilot-assisted Multiple-Input Multiple-Output (MIMO) Vector Orthogonal Frequency Division Multiplexing (V-OFDM) systems. Based on the sparse multipath channels, we utilize orthogonal and non-orthogonal pilot schemes to design the compressed sensing (CS) measurement process. For the optimization of the sensing matrix, we discuss the influence of pilot search algorithms and evaluation criteria and propose a particle swarm optimization (PSO) based pilot search algorithm with the simplified evaluation criterion to improve the pilot design procedure. Meanwhile, the effect of pilot insertion on the Peak-to-Average Power Ratio (PAPR) is reduced by a particular precoding matrix method without affecting the decoding complexity. Simulation data are used to evaluate the classical sparsity adaptive matching (SAMP) algorithms and the proposed Variable Threshold SAMP (VTSAMP) algorithm, and the results show that the improved method has higher channel estimation accuracy with unknown sparsity. On the other hand, to overcome the complexity of CS-based decoding, we design the fully connected Deep Neural Network (FC-DNN) decoders, which combine the results of channel estimation results with the prevalent neural network technology. We observe that when the sparse channels are estimated accurately by CS methods, the proposed FC-DNN can achieve the same performance as the high-precision linear decoder by using the time-domain pilots and channel estimation results. INDEX TERMS MIMO, OFDM, compressed sensing, channel estimation, peak to average power ratio, decoding, neural networks.

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“…The compressed sensing theory is a new concept to acquire data at a low sampling rate [4][5][6]. In 2010, the MWC structure was proposed by Mishali and Eldar to achieve sub-Nyquist sampling [7].…”

Section: Introductionmentioning

confidence: 99%

A Sub-Nyquist Sampling Digital Receiver System Based on Array Compression

Chen¹,

Han²,

Yu³

2020

PIER Letters

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In order to obtain the carrier frequency (CF) and direction-of-arrival (DOA) estimation, a uniform linear array (ULA)-based modulated wideband converter (MWC) discrete compressed sampling (CS) digital receiver system is proposed. It can achieve sub-Nyquist sampling, save the storage space, and obtain the CF and DOA estimation by processing the CS data directly. However, the existing method for this system needs more branches to get better performance. In this paper, a compressed ULA (CULA)-based MWC discrete CS digital receiver system is proposed. First, a compression matrix is used to reduce the number of branches behind the antennas. Then, the MWC discrete CS structure is used to reduce the data volume. Finally, the multiple signal classification (MUSIC) algorithm is used to jointly estimate the CF and DOA by processing the CS data directly. The simulation results validate the effectiveness of the proposed system and the proposed method for the joint CF and DOA estimation.

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A Compressed Sensing Approach to Block-Iterative Equalizers

Cited by 5 publications

References 24 publications

Optimal Diffusion Learning Over Networks—Part I: Single-Task Algorithms

Optimal Diffusion Learning Over Networks—Part I: Single-Task Algorithms

Pilot-Assisted MIMO-V-OFDM Systems: Compressed Sensing and Deep Learning Approaches

A Sub-Nyquist Sampling Digital Receiver System Based on Array Compression

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