Narrowband interference parameterization for sparse Bayesian recovery

Anum Ali; Hesham Elsawy; Tareq Y. Al-Naffouri; Mohamed-Slim Alouini

doi:10.1109/ICC.2015.7249036

Narrowband interference parameterization for sparse Bayesian recovery

Anum Ali, Hesham Elsawy, Tareq Y. Al-Naffouri, Mohamed-Slim Alouini

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

This paper addresses the problem of narrowband interference (NBI) in SC-FDMA systems by using tools from compressed sensing and stochastic geometry. The proposed NBI cancellation scheme exploits the frequency domain sparsity of the unknown signal and adopts a Bayesian sparse recovery procedure. This is done by keeping a few randomly chosen sub-carriers data free to sense the NBI signal at the receiver. As Bayesian recovery requires knowledge of some NBI parameters (i.e., mean, variance and sparsity rate), we use tools from stochastic geometry to obtain analytical expressions for the required parameters. Our simulation results validate the analysis and depict suitability of the proposed recovery method for NBI mitigation. © 2015 IEEE.

Original language	English (US)
Title of host publication	2015 IEEE International Conference on Communications (ICC)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	4530-4535
Number of pages	6
ISBN (Print)	9781467364324
DOIs	https://doi.org/10.1109/ICC.2015.7249036
State	Published - Sep 11 2015

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10.1109/ICC.2015.7249036

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title = "Narrowband interference parameterization for sparse Bayesian recovery",

abstract = "This paper addresses the problem of narrowband interference (NBI) in SC-FDMA systems by using tools from compressed sensing and stochastic geometry. The proposed NBI cancellation scheme exploits the frequency domain sparsity of the unknown signal and adopts a Bayesian sparse recovery procedure. This is done by keeping a few randomly chosen sub-carriers data free to sense the NBI signal at the receiver. As Bayesian recovery requires knowledge of some NBI parameters (i.e., mean, variance and sparsity rate), we use tools from stochastic geometry to obtain analytical expressions for the required parameters. Our simulation results validate the analysis and depict suitability of the proposed recovery method for NBI mitigation. {\textcopyright} 2015 IEEE.",

author = "Anum Ali and Hesham Elsawy and Al-Naffouri, {Tareq Y.} and Mohamed-Slim Alouini",

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T1 - Narrowband interference parameterization for sparse Bayesian recovery

AU - Ali, Anum

AU - Elsawy, Hesham

AU - Al-Naffouri, Tareq Y.

AU - Alouini, Mohamed-Slim

N1 - KAUST Repository Item: Exported on 2020-10-01

PY - 2015/9/11

Y1 - 2015/9/11

N2 - This paper addresses the problem of narrowband interference (NBI) in SC-FDMA systems by using tools from compressed sensing and stochastic geometry. The proposed NBI cancellation scheme exploits the frequency domain sparsity of the unknown signal and adopts a Bayesian sparse recovery procedure. This is done by keeping a few randomly chosen sub-carriers data free to sense the NBI signal at the receiver. As Bayesian recovery requires knowledge of some NBI parameters (i.e., mean, variance and sparsity rate), we use tools from stochastic geometry to obtain analytical expressions for the required parameters. Our simulation results validate the analysis and depict suitability of the proposed recovery method for NBI mitigation. © 2015 IEEE.

AB - This paper addresses the problem of narrowband interference (NBI) in SC-FDMA systems by using tools from compressed sensing and stochastic geometry. The proposed NBI cancellation scheme exploits the frequency domain sparsity of the unknown signal and adopts a Bayesian sparse recovery procedure. This is done by keeping a few randomly chosen sub-carriers data free to sense the NBI signal at the receiver. As Bayesian recovery requires knowledge of some NBI parameters (i.e., mean, variance and sparsity rate), we use tools from stochastic geometry to obtain analytical expressions for the required parameters. Our simulation results validate the analysis and depict suitability of the proposed recovery method for NBI mitigation. © 2015 IEEE.

UR - http://hdl.handle.net/10754/621281

UR - http://ieeexplore.ieee.org/document/7249036/

UR - http://www.scopus.com/inward/record.url?scp=84953747523&partnerID=8YFLogxK

U2 - 10.1109/ICC.2015.7249036

DO - 10.1109/ICC.2015.7249036

M3 - Conference contribution

SN - 9781467364324

SP - 4530

EP - 4535

BT - 2015 IEEE International Conference on Communications (ICC)

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

Narrowband interference parameterization for sparse Bayesian recovery

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