Paper Info
Title
Accurate channel estimation based on Bayesian Compressive Sensing for next-generation wireless broadcasting systems
Abstract
Digital television terrestrial broadcasting (DTTB) industry has achieved rapid development in recent years. It is now facing new development opportunities for next-generation wireless broadcasting systems. As one of the DTTB standards, digital terrestrial multimedia/television broadcasting (DTMB) uses the time domain synchronous OFDM (TDS-OFDM) as its basic core technology. TDS-OFDM has higher spectral efficiency and faster synchronization but it cannot support high-order modulation and high-definition television (HDTV) delivery in fast fading channels. Recently, compressive sensing (CS) methods have been used for the accurate channel estimation. However, the classical CS algorithms require the channel sparsity in prior and the signal recovery accuracy is unacceptable when the signal-to-noise ratio (SNR) is low. To solve this problem, in this paper we exploit the Bayesian Compressive Sensing (BCS) based on statistical learning theory (SLT) and relevance vector machines (RVM) to improve the sparse channel estimation accuracy. Besides, we propose to reduce the correlation of the measurement matrix columns for a further performance improvement. Simulation results demonstrate that the proposed BCS-based channel estimation has better performance than conventional solutions.
Year
DOI
Venue
2014
10.1109/BMSB.2014.6873548
BMSB
Keywords
Field
DocType
measurement matrix,next generation wireless broadcasting systems,bayesian compressive sensing,hdtv,television broadcasting,dtmb,bcs,fading channels,ofdm modulation,matrix algebra,dttb industry,statistical learning theory,digital terrestrial multimedia-television broadcasting,fast fading channels,accurate channel estimation,compressed sensing,channel sparsity,sparse channel estimation accuracy,high definition television,snr,rvm,tds-ofdm,signal recovery accuracy,time domain synchronous ofdm,multimedia communication,digital television terrestrial broadcasting,relevance vector machines,slt,channel estimation,cs methods,dttb standards,signal-to-noise ratio,broadcasting,signal to noise ratio,estimation,vectors,correlation,ofdm
Broadcasting,Synchronization,Fading,Computer science,Computer network,Communication channel,Spectral efficiency,Orthogonal frequency-division multiplexing,Compressed sensing,Performance improvement
Conference
ISSN
Citations 
PageRank 
2155-5044
1
0.36
References 
Authors
13
3
Name
Order
Citations
PageRank
Zhenkai Fan110.36
Zhaohua Lu210.36
Yanjun Han341.09