An expurgating linear function (ELF) is an outer code that disallows low-weight codewords of the inner code. ELFs can be designed either to maximize the minimum distance or to minimize the codeword error rate (CER) of the expurgated code. A list-decoding sieve can efficiently identify ELFs that maximize the minimum distance of the expurgated code. For convolutional inner codes, this paper provides analytical distance spectrum union (DSU) bounds on the CER of the concatenated code.
For short codeword lengths, ELFs transform a good inner code into a great concatenated code. For a constant message size of K = 64 bits or constant codeword blocklength of N = 152 bits, an ELF can reduce the gap at CER 10−6 between the DSU and the random-coding union (RCU) bounds from over 1 dB for the inner code alone to 0.23 dB for the concatenated code. The DSU bounds can also characterize puncturing that mitigates the rate overhead of the ELF while maintaining the DSU-to-RCU gap. List Viterbi decoding guided by the ELF achieves maximum likelihood (ML) decoding of the concatenated code with a sufficiently large list size. The rate-K/(K+m) ELF outer code reduces rate and list decoding increases decoder complexity. As SNR increases, the average list size converges to 1 and average complexity is similar to Viterbi decoding on the trellis of the inner code. For rare large-magnitude noise events, which occur less often than the FER of the inner code, a deep search in the list finds the ML codeword.
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This content will become publicly available on January 30, 2025
Pseudorandom Linear Codes Are List-Decodable to Capacity
We introduce a novel family of expander-based error correcting codes. These codes can be sampled with randomness linear in the block-length, and achieve list decoding capacity (among other local properties). Our expander-based codes can be made starting from any family of sufficiently low-bias codes, and as a consequence, we give the first construction of a family of algebraic codes that can be sampled with linear randomness and achieve list-decoding capacity. We achieve this by introducing the notion of a pseudorandom puncturing of a code, where we select n indices of a base code C ⊂ 𝔽_q^m in a correlated fashion. Concretely, whereas a random linear code (i.e. a truly random puncturing of the Hadamard code) requires O(n log(m)) random bits to sample, we sample a pseudorandom linear code with O(n + log (m)) random bits by instantiating our pseudorandom puncturing as a length n random walk on an exapnder graph on [m]. In particular, we extend a result of Guruswami and Mosheiff (FOCS 2022) and show that a pseudorandom puncturing of a small-bias code satisfies the same local properties as a random linear code with high probability. As a further application of our techniques, we also show that pseudorandom puncturings of Reed-Solomon codes are list-recoverable beyond the Johnson bound, extending a result of Lund and Potukuchi (RANDOM 2020). We do this by instead analyzing properties of codes with large distance, and show that pseudorandom puncturings still work well in this regime.
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- Award ID(s):
- 2310818
- NSF-PAR ID:
- 10494228
- Publisher / Repository:
- 15th Innovations in Theoretical Computer Science Conference, ITCS 2024
- Date Published:
- Journal Name:
- 15th Innovations in Theoretical Computer Science Conference, ITCS 2024
- Page Range / eLocation ID:
- 90:1-90:21
- Format(s):
- Medium: X
- Location:
- Berkeley, California
- Sponsoring Org:
- National Science Foundation
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