Abstract | ||
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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 |
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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. Liu | 1 | 38 | 5.41 |
P. Moulin | 2 | 455 | 68.97 |
Ralf Koettery | 3 | 5019 | 456.62 |