Compressive Sensing in Channel Estimation for SISO and MIMO OFDM Systems

Munshi, Ami; Unnikrishnan, Srija

doi:10.1109/tencon.2018.8650423

Cited by 10 publications

(5 citation statements)

References 8 publications

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“…On analyzing Table VIII and Fig 7, the fact that sparser channel gives better performance in terms of BER established in [22] is affirmed. Channel 3 is the sparsest with only 10% significant non-zero channel coefficients.…”

Section: B Performance Analysis Of Ls Ls-cs Mmse Mmse-cs In 2x2 Mmentioning

confidence: 70%

“…1. shows the block diagram of LS-CS estimation technique which was implemented in [22] and Table I shows its algorithm.…”

Section: B Compressive Sensing Theorem In Channel Estimation Algorithmmentioning

confidence: 99%

“…Moreover, an appropriate recovery algorithm will be able to recreate the original signal from its compressed structure. In [22], LS-CS channel estimation technique is implemented in SISO and MIMO OFDM system which takes into account sparsity of the channel and explores the effectiveness of compressive sensing theory in channel estimation . In [23], the effect of varying Fast Fourier Transform (FFT) size in OFDM on the performance of LS-CS channel estimation algorithm is observed.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we have developed and implemented MMSE-CS channel estimation algorithm. We have compared the performance of MMSE-CS algorithm with LS-CS algorithm implemented in [22] and its performance to recover audio data was analyzed in [24]. To accentuate the importance of compressive sensing in channel estimation, we have also compared the performance of MMSE-CS in terms of BER with classical LS and MMSE channel estimation algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…In section II we briefly describe the design and implementation of LS-CS channel estimation algorithm that was implemented in [22], followed by implementation of MMSE-CS channel estimation algorithm. Section III gives the simulation results of LS, MMSE, LS-CS and MMSE-CS channel estimation techniques and its analysis, followed by conclusion in section IV.…”

Section: Introductionmentioning

confidence: 99%

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Performance Analysis of Compressive Sensing based LS and MMSE Channel Estimation Algorithm

Munshi

Unnikrishnan²

2021

JCOMSS

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Section: B Performance Analysis Of Ls Ls-cs Mmse Mmse-cs In 2x2 Mmentioning

confidence: 70%

“…1. shows the block diagram of LS-CS estimation technique which was implemented in [22] and Table I shows its algorithm.…”

Section: B Compressive Sensing Theorem In Channel Estimation Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Performance Analysis of Compressive Sensing based LS and MMSE Channel Estimation Algorithm

Munshi

Unnikrishnan²

2021

JCOMSS

Self Cite

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A Hybrid Optimization-Based Technique for Channel Estimation in OFDM System: A Parametric Approach

Arora

Chawla²

2022

Wireless Pers Commun

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The performance of OFDM system depends on the how efficient channel estimation is, which directly impacts its bit error rate (BER). In this research work, the novel technique of pseudo-pilot aided OFDM system is used its channel estimation with least square (LS) and Recursive Least Squares (RLS) approach. In proposed technique, for channel estimation Pseudo-pilot technique is used in OFDM system over AWGN fading channels. A hybridization technique has been implemented to alleviate the error rate in the signals. The hybrid optimization approach is implemented on the estimated output for the optimum solution. The combination of the two optimization techniques is PSO (particle swarm optimization) and MFO (Moth Flame Optimization) method that is used for optimization of the performance rate. To get more optimum solutions (reducing BER) OFDM system is trained with neural network (NN). The performance of the proposed techniques and the weighted scheme are compared and verified using computerized simulation carried out using Matrix Laboratory (MATLAB) software. The hybrid approach is able to achieve low BER of the network. The BER of PSO is 1.004 x 10 -7 whereas for hybrid optimization (PSO+MFO) BER is 6.275 x 10 -8 at 18 dB SNR. After training of the whole system is done with BPNN which will further reduce the BER while increasing SNR. By using BPNN, BER will further reduce to 1.243 x 10 -8 at 18 dB SNR. It means hybrid optimization is done to optimize the performance of the channel and reduce the BERs, which will help to increase the channel estimation process as well as channel capacity and further reduces the losses while transmission from one end to the other end.

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A survey on improving the wireless communication with adaptive antenna selection by intelligent method

Lai

2022

Computer Communications

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Compressive Sensing in Channel Estimation for SISO and MIMO OFDM Systems

Cited by 10 publications

References 8 publications

Performance Analysis of Compressive Sensing based LS and MMSE Channel Estimation Algorithm

Performance Analysis of Compressive Sensing based LS and MMSE Channel Estimation Algorithm

A Hybrid Optimization-Based Technique for Channel Estimation in OFDM System: A Parametric Approach

A survey on improving the wireless communication with adaptive antenna selection by intelligent method

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