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Numerically computable lower bounds on the capacity of the (1, �)-rll input-constrained binary erasure channel

Arvind Rameshwar, V and Kashyap, N (2021) Numerically computable lower bounds on the capacity of the (1, �)-rll input-constrained binary erasure channel. In: 27th National Conference on Communications, 27-30 jul 2021, Kanpur.

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Official URL: https://doi.org/10.1109/NCC52529.2021.9530025


The paper considers the binary erasure channel (BEC) with the inputs to the channel obeying the (1, �)-runlength limited (RLL) constraint, which forbids input sequences with consecutive ones. We derive a lower bound on the capacity of the channel, by considering the mutual information rate between the inputs and the outputs when the input distribution is first-order Markov. Further, we present a numerical algorithm for numerically computing the lower bound derived. The algorithm is based on ideas from stochastic approximation theory, and falls under the category of two-timescale stochastic approximation algorithms. We provide numerical evaluations of the lower bound, and characterize the input distribution that achieves the bound. We observe that our numerical results align with those obtained using the sampling-based scheme of Arnold et al. (2006). Furthermore, we note that our lower bound expression recovers the series expansion type lower bound discussed in Corollary 5 of Li and Han (2018). We also derive an alternative single-parameter optimization problem as a lower bound on the capacity, and demonstrate that this new bound is better than the linear lower bound shown in Li and Han (2018) and Rameshwar and Kashyap (2020), for 0.77, where is the erasure probability of the channel. © 2021 IEEE.

Item Type: Conference Paper
Publication: 2021 National Conference on Communications, NCC 2021
Publisher: Institute of Electrical and Electronics Engineers Inc.
Additional Information: The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords: Approximation algorithms; Codes (symbols); Computation theory; Stochastic systems, Binary erasure channel; Consecutive ones; First order; Input distributions; Input sequence; Low bound; Mutual information rates; Numerical algorithms; Run-length limited constraints; Stochastic approximations, Approximation theory
Department/Centre: Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited: 07 Dec 2021 10:22
Last Modified: 07 Dec 2021 10:22
URI: http://eprints.iisc.ac.in/id/eprint/70380

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