Title
On error exponents of modulo lattice additive noise channels
Abstract
Modulo lattice additive noise (MLAN) channels appear in the analysis of structured binning codes for Costa's dirty-paper channel and of nested lattice codes for the additive white Gaussian noise (AWGN) channel. In this paper, we derive a new lower bound on the error exponents of the MLAN channel. With a proper choice of the shaping lattice and the scaling parameter, the new lower bound coincides with the random-coding lower bound on the error exponents of the AWGN channel at the same signal-to-noise ratio (SNR) in the sphere-packing and straight-line regions. This result implies that, at least for rates close to channel capacity, 1) writing on dirty paper is as reliable as writing on clean paper; and 2) lattice encoding and decoding suffer no loss of error exponents relative to the optimal codes (with maximum-likelihood decoding) for the AWGN channel.
Year
DOI
Venue
2006
10.1109/TIT.2005.862077
IEEE Transactions on Information Theory
Keywords
Field
DocType
mlan channel,lattice encoding,lattice decoding,random codes,random-coding,error exponent,awgn channels,dirty paper,optimal code,dirty-paper channel,error analysis,additive white gaussian noise (awgn) channel,structured binning code,channel coding,additive white gaussian noise channel,error exponents,awgn channel,modulo lattice additive noise,sphere-packing,channel capacity,clean paper,additive white gaussian noise,nested lattice codes,modulo lattice additive noise (mlan) channel,costa dirty-paper channel,scaling parameter,modulo lattice additive noise channel,costa's dirty-paper channel,nested lattice code,exponential distribution,sphere packing,lower bound,signal to noise ratio
Discrete mathematics,Combinatorics,Lattice (order),Modulo,Upper and lower bounds,Communication channel,Exponential distribution,Decoding methods,Additive white Gaussian noise,Channel capacity,Mathematics
Journal
Volume
Issue
ISSN
52
2
0018-9448
Citations 
PageRank 
References 
9
0.60
11
Authors
3
Name
Order
Citations
PageRank
T. Liu1385.41
P. Moulin245568.97
Ralf Koettery35019456.62