10 List Decoding
10.1 Overview
This chapter defines list-decodable codes and proves the list-decoding capacity theorem: for any \(0\lt \rho \le 1-1/q\) and \(L\ge 1\), there exist codes of rate
that are \((\rho ,L)\)-list-decodable.
10.2 List-decodable codes
A code \(C\) is \((\rho ,L)\)-list-decodable if every Hamming ball of radius \(\lfloor \rho n\rfloor \) contains at most \(L\) codewords of \(C\).
10.3 Counting lemmas
Let \(\alpha \) be a finite field, and fix natural numbers \(n\), \(L\), \(M\) and a real number \(p\) with \(0 \le p \le 1\), \(1 \le L\), \(L \lt M\), and \(M \le \left\lvert \alpha \right\rvert ^n\). Suppose \(V\) is a natural number bounding the volume of every Hamming ball of radius \(\lfloor pn\rfloor \), in the sense that each such ball around an arbitrary word \(y \in \alpha ^n\) contains at most \(V\) codewords, and suppose that
Then there exists a code \(C\) of length \(n\) over \(\alpha \) consisting of exactly \(M\) codewords that is \((p,L)\)-list-decodable.
Let \(N\), \(M\), and \(k\) be natural numbers with \(k \le M \le N\). Then the ratio of binomial coefficients satisfies
where the quantities are compared as real numbers.
Fix an integer alphabet size \(q \ge 2\), a real number \(p\), and integers \(n\) and \(L \ge 1\). Set the rate
and define
Assume that \(0 \lt M \le q^n\) and \(L \lt M\). Then
10.4 List-decoding capacity
Let \(\alpha \) be a finite field, write \(q = \left\lvert \alpha \right\rvert \) for the number of its elements, and fix a real number \(p\) with \(0 \lt p \le 1 - \tfrac {1}{q}\) together with an integer \(L \ge 1\). In this regime one considers block codes over the alphabet \(\alpha \) that admit list decoding from a \(p\)-fraction of symbol errors with output lists of size at most \(L\), and the associated rate \(r\) they can achieve.