
TL;DR
This paper analyzes the capacity of bit-interleaved coded modulation (BICM), focusing on optimal labelings and input distributions, especially at low SNR, and introduces first-order asymptotics and conditions for optimal constellations.
Contribution
It develops first-order asymptotics for BICM capacity and characterizes FOO constellations using the Hadamard transform, providing new insights into optimal input distributions and labelings.
Findings
Proper input distributions can nearly eliminate the 1 dB gap in BICM capacity.
Only 72 classes of labelings differ in asymptotic behavior for 8-PAM, reduced to 26 for 8-PSK.
FOO constellations are linear projections of hypercubes, with specific conditions for PAM, QAM, and PSK.
Abstract
Optimal binary labelings, input distributions, and input alphabets are analyzed for the so-called bit-interleaved coded modulation (BICM) capacity, paying special attention to the low signal-to-noise ratio (SNR) regime. For 8-ary pulse amplitude modulation (PAM) and for 0.75 bit/symbol, the folded binary code results in a higher capacity than the binary reflected gray code (BRGC) and the natural binary code (NBC). The 1 dB gap between the additive white Gaussian noise (AWGN) capacity and the BICM capacity with the BRGC can be almost completely removed if the input symbol distribution is properly selected. First-order asymptotics of the BICM capacity for arbitrary input alphabets and distributions, dimensions, mean, variance, and binary labeling are developed. These asymptotics are used to define first-order optimal (FOO) constellations for BICM, i.e. constellations that make BICM…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
