A generic top-level mechanism for accelerating signal recovery in compressed sensing

Rateb, Ahmad M.; Yusof, Sharifah Kamilah Syed; Rashid, Rozeha A.

doi:10.1016/j.dsp.2017.06.026

Cited by 4 publications

(3 citation statements)

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“…Therefore, we may be able to scale more sub-carriers, and hence further reduce PAPR, without affecting spectral efficiency. This approach is highly appealing especially in the presence of recent fast compressed sensing decoding mechanisms [74], in addition to its ability to perform efficiently in a highly noisy environment [75], [76].…”

Section: Discussionmentioning

confidence: 99%

An Optimal Low Complexity PAPR Reduction Technique for Next Generation OFDM Systems

Rateb

Labana

2019

IEEE Access

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Orthogonal frequency division multiplexing (OFDM) is an efficient multi-carrier modulation technique that underlies most of the current and probably future high-speed wireless communication systems. However, the OFDM waveform is characterized by a high peak-to-average power ratio (PAPR), especially when a large number of subcarriers are used. A high PAPR is a major waveform defect since it leads to non-linear distortion when passing through the transmitter's power amplifier. Most of the PAPR reduction techniques found in the literature reduce the PAPR mainly at the cost of either excessive computational complexity or degrading the transmission bit error rate (BER). We propose a low-complexity technique for PAPR reduction based on linear scaling of a portion of signal coefficients by an optimal factor. This paper is backed up by the extensive analysis of various performance metrics, which leads to optimal choices of key parameters and hence maximum achievable gains. The analytic and simulated results show that the proposed technique is capable of reducing the PAPR effectively with negligible effect on BER in return for a slight reduction in data rate. For example, for 1024 subcarriers, the PAPR can be reduced from 13 dB to below 7.4 or 6.9 dB, in return for only 1% or 2% reduction in data rate, respectively. In addition, the achievable PAPR varies very slightly in response to increasing the number of subcarriers. This offers a highly competitive and flexible tradeoff compared with those provided by current techniques found in the literature. Therefore, this technique has a very good potential for practical application in current and future OFDM-based systems, especially those which employ a very large number of subcarriers, such as LTE, DVB-T2, and 5G systems. INDEX TERMS Orthogonal frequency division multiplexing (OFDM), peak to average power ratio (PAPR), optimization, 5G, distortionless techniques, signal distortion techniques, companding, peak clipping, DVB-T2, LTE, low-complexity.

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Section: Discussionmentioning

confidence: 99%

An Optimal Low Complexity PAPR Reduction Technique for Next Generation OFDM Systems

Rateb

Labana

2019

IEEE Access

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“…The literature provides many synthesis methods for reconfigurable antenna arrays, as, for example, [6][7][8] . Here, it is worth noticing that the advantages provided by beam design are also recognized in related works of other research topics, such as, for example, imaging [9,10] , remote sensing [11,12] , signal recovery and reconstruction [13][14][15] and wireless sensor networks [16][17][18] .…”

Section: Introductionmentioning

confidence: 94%

Gaussian approach for the synthesis of phase-only beam-scanning linear aperiodic antenna arrays

Buttazzoni

Vescovo

2019

Signal Processing

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“…Several authors have investigated the use of RP for convex optimization. For example, Rateb et al [13] employed a fixed point precision quadratic programming (QP) 1 -Solver, and a half floating point ADMM was investigated by Wills et al [14]. An approximate stochastic gradient descent (SGD) method was introduced [15] with fewer quantisation steps; this allowed a faster implementation with a slight reduction in performance.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Approximate 3D Image Reconstruction

Aßmann

Stewart

et al. 2022

IEEE Trans. Emerg. Topics Comput.

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We demonstrate an efficient and accelerated parallel, sparse depth reconstruction framework using compressed sensing (CS) and approximate computing. Employing data parallelism for rapid image formation, the depth image is reconstructed from sparsely sampled scenes using convex optimization. Coupled with faster imaging, this sparse sampling reduces significantly the projected laser power in active systems such as LiDAR to allow eye safe operation at longer range. We also demonstrate how reduced precision is leveraged to reduce the number of logic units in FPGA implementations for such sparse imaging systems. It enables significant reduction in logic units, memory requirements and power consumption by over 80% with minimal impact on the quality of reconstruction. To further accelerate processing, pre-computed, important components of the lower-upper (LU) decomposition and other linear algebraic computations are used to solve the convex optimization problems. Our methodology is demonstrated by the application of the alternating direction method of multipliers (ADMM) and proximal gradient descent (PGD) algorithms. For comparison, a fully discrete least square reconstruction method (dSparse) is also presented. This demonstrates the feasibility of novel, high resolution, low power and high frame rate LiDAR depth imagers based on sparse illumination for use in applications where resources are strictly limited.

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A generic top-level mechanism for accelerating signal recovery in compressed sensing

Cited by 4 publications

References 50 publications

An Optimal Low Complexity PAPR Reduction Technique for Next Generation OFDM Systems

An Optimal Low Complexity PAPR Reduction Technique for Next Generation OFDM Systems

Gaussian approach for the synthesis of phase-only beam-scanning linear aperiodic antenna arrays

Energy Efficient Approximate 3D Image Reconstruction

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