background
logo
ArxivPaperAI

Channel Coding with Mean and Variance Cost Constraints

Author:
Adeel Mahmood, Aaron B. Wagner
Keyword:
Computer Science, Information Theory, Information Theory (cs.IT)
journal:
--
date:
2024-01-29 00:00:00
Abstract
We consider channel coding for discrete memoryless channels (DMCs) with a novel cost constraint that constrains both the mean and the variance of the cost of the codewords. We show that the maximum (asymptotically) achievable rate under the new cost formulation is equal to the capacity-cost function; in particular, the strong converse holds. We further characterize the optimal second-order coding rate of these cost-constrained codes; in particular, the optimal second-order coding rate is finite. We then show that the second-order coding performance is strictly improved with feedback using a new variation of timid/bold coding, significantly broadening the applicability of timid/bold coding schemes from unconstrained compound-dispersion channels to all cost-constrained channels. Equivalent results on the minimum average probability of error are also given.
PDF: Channel Coding with Mean and Variance Cost Constraints.pdf
Empowered by ChatGPT