Title
Block Markov superposition transmission of convolutional codes with minimum shift keying signalling
Abstract
In this study, the authors' present a scheme, denoted as BMST-MSK, which combines the block Markov superposition transmission (BMST) with the minimum shift keying (MSK) signalling. The BMST-MSK can be implemented in two forms - the BMST with recursive MSK (BMST-RMSK) and the BMST with non-recursive MSK (BMST-NRMSK). The BMST-MSK admits a sliding-window decoding/demodulation algorithm, where two schedules with or without iterative processing between the BMST and MSK (referred to as outer iteration) are discussed. To analyse the asymptotic performance of BMST-MSK, the authors' first assume a genie-aided decoder and then derive the union bound for the equivalent genie-aided system. Numerical results show that the performances of the BMST-MSK match well with the derived lower bounds in the low error rate regions. From simulations, the authors' found that the outer iterations can provide performance improvement for the BMST-RMSK, but not for the BMST-NRMSK. Taking a (2,1,2) convolutional code with input length of 10 000 bits as the basic code, the BMST-NRMSK achieves a bit-error-rate of 10-5 at Eb/N0 = 0.45 dB over additive white Gaussian noise channels, which is away from the Shannon limit about 0.25 dB.
Year
DOI
Venue
2015
10.1049/iet-com.2014.0751
IET Communications
Keywords
Field
DocType
additive white gaussian noise channels,modulation coding,shannon limit,iterative processing,awgn channels,bmst-msk,minimum shift keying signalling,block markov superposition transmission,bit-error-rate,minimum shift keying,markov processes,bmst-nrmsk,genie-aided decoder,error statistics,sliding-window demodulation algorithm,iterative methods,convolutional codes,sliding-window decoding algorithm,bit error rate
Demodulation,Superposition principle,Convolutional code,Minimum-shift keying,Markov chain,Algorithm,Theoretical computer science,Real-time computing,Decoding methods,Noisy-channel coding theorem,Mathematics,Bit error rate
Journal
Volume
Issue
ISSN
9
1
1751-8628
Citations 
PageRank 
References 
2
0.37
8
Authors
3
Name
Order
Citations
PageRank
Xiying Liu120.71
Chulong Liang210312.50
Xiao Ma348764.77