A Sparsity-Aware Approach for NBI Estimation in MIMO-OFDM

Gomaa, Ahmad; Al-Dhahir, Naofal

doi:10.1109/twc.2011.040411.101118

Cited by 74 publications

(29 citation statements)

References 25 publications

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“…As a result, we can exploit this sparse signal structure with ISLs to design recovery algorithms that outperform the conventional CS recovery algorithms in [6], [7], [8], [9]. The above system model covers many interesting application scenarios in wireless communications [11] and other fields involving CS signal processing [3], [5].…”

Section: System Modelmentioning

confidence: 99%

A sparse MLE approach for joint interference mitigation and data recovery

Liu

Lau

Kong

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Consider the scenario where a receiver acquires information (data) corrupted by interference and noise. Both the information and interference have a sparse structure. To fully exploit the individual sparse structure of the information and interference, the joint interference mitigation and data recovery is formulated as a sparse maximum likelihood estimation (MLE) problem which maximizes the associated likelihood function under individual sparsity levels (ISLs) constraints. We propose an alternating optimization (AO) recovery algorithm to solve the nonconvex sparse MLE problem. Under certain restricted isometry property (RIP) conditions, we show that the proposed AO algorithm converges to the optimal solution of the sparse MLE problem. We also derive an upper bound of the corresponding estimation error for the information. Simulations show that the proposed solution achieves significant gain over various baselines.

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Section: System Modelmentioning

confidence: 99%

A sparse MLE approach for joint interference mitigation and data recovery

Liu

Lau

Kong

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…All the signals we employ in the following study have a length of N = 1024. We let the sparsity level be S = N 20 in our simulation 3 . We calculate the Fréchet mean from the randomly sampled signal of different SNs with λ 1 = .…”

Section: Algorithm 2 Precognition Matching Pursuit (Pmp)mentioning

confidence: 99%

“…Compressive sensing (CS) is a new sampling paradigm, which leverages the compressibility of signals to reduce the number of samples required for reconstruction and have been applied to a wide range of applications [1]- [3]. Using the CS technique as the data acquisition approach in WSNs can significantly reduce the energy consumed in the process of sampling and transmission through the network, and also lower the wireless bandwidth requirements for communication.…”

Section: Introductionmentioning

confidence: 99%

A Frechet Mean Approach for Compressive Sensing Date Acquisition and Reconstruction in Wireless Sensor Networks

Chen

Rodrigues

Wassell

2012

IEEE Trans. Wireless Commun.

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Abstract-Compressive sensing leverages the compressibility of natural signals to trade off the convenience of data acquisition against computational complexity of data reconstruction. Thus, CS appears to be an excellent technique for data acquisition and reconstruction in a wireless sensor network (WSN) which typically employs a smart fusion center (FC) with a high computational capability and several dumb front-end sensors having limited energy storage. This paper presents a novel signal reconstruction method based on CS principles for applications in WSNs. The proposed method exploits both the intra-sensor and inter-sensor correlation to reduce the number of samples required for reconstruction of the original signals. The novelty of the method relates to the use of the Fréchet mean of the signals as an estimate of their sparse representations in some basis. This crude estimate of the sparse representation is then utilized in an enhanced data recovering convex algorithm, i.e., the penalized ℓ1 minimization, and an enhanced data recovering greedy algorithm, i.e., the precognition matching pursuit (PMP). The superior reconstruction quality of the proposed method is demonstrated by using data gathered by a WSN located in the Intel Berkeley Research lab.

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“…Assuming knowledge 1 of Λ and, hence,Λ at the receiver, we multiply Y by W such that WΛ = 0. To 1 Channel estimation is investigated in [14] this end, W is designed to be the projection matrix on the left null-subspace ofΛ as follows 2…”

Section: Compressive Sensing Backgroundmentioning

confidence: 99%

“…This technique is labelled as "with estimating α" in Section VII. In [14], we use another technique to deal with asynchronous jammers, viz. we use receiver windowing to spectrally contain the jammer.…”

Section: B Asynchronous Jammermentioning

confidence: 99%

A Compressive Sensing Approach to NBI Cancellation in Mobile OFDM Systems

Gomaa¹,

Al-Dhahir²

2010

2010 IEEE Global Telecommunications Conference GLOBECOM 2010

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We propose a novel algorithm based on compressive sensing (CS) theory to estimate narrow band interference (NBI) signals experiencing time-varying frequency-selective fading channels in orthogonal frequency division multiplexing (OFDM) systems. In addition, we investigate the case of asynchronous jamming where there is a frequency offset between the NBI and desired signals. Furthermore, we propose a reducedcomplexity implementation for our proposed algorithm with negligible performance loss. Finally, we show that our proposed approach can be applied to both cyclic-prefix and zero-padding OFDM systems. Simulation results show the effectiveness of our proposed algorithm in mitigating NBI.

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A Sparsity-Aware Approach for NBI Estimation in MIMO-OFDM

Cited by 74 publications

References 25 publications

A sparse MLE approach for joint interference mitigation and data recovery

A sparse MLE approach for joint interference mitigation and data recovery

A Frechet Mean Approach for Compressive Sensing Date Acquisition and Reconstruction in Wireless Sensor Networks

A Compressive Sensing Approach to NBI Cancellation in Mobile OFDM Systems

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