On Error Exponents of Almost-Fixed-Length Channel Codes and Hypothesis Tests

Anusha Lalitha, Tara Javidi

We examine a new class of channel coding strategies, and hypothesis tests referred to as almost-fixed-length strategies that have little flexibility in the stopping time over fixed-length strategies. The stopping time of these strategies is allowed to be slightly large only on a rare set of sample paths with an exponentially small probability. We show that almost-fixed-length channel coding strategies can achieve Burnashev's optimal error exponent. Similarly, almost-fixed length hypothesis tests are shown to bridge the gap between hypothesis testing with fixed sample size and sequential hypothesis testing and improve the trade-off between type-I and type-II error exponents.

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