Receiver-Centric Hypothesis-Parallel Message Passing for Unit-Rate Transmission

This paper investigates a receiver-centric decoding framework for unit-rate transmission in which no redundancy is conveyed through the physical channel. Only k information bits are transmitted over an additive white Gaussian noise (AWGN) channel, while reliability is pursued by structured hypothesis testing and increased receiver-side computational complexity. The receiver embeds each candidate information hypothesis into a higher-dimensional (k, n) linear block code and evaluates all 2k hypotheses in parallel. For each hypothesis, a single message-passing iteration on the Tanner graph is employed as a soft refinement operator, and the final decision is obtained via an orthogonality-based constraint metric that measures the consistency of the refined estimate with the hypothesis-induced code structure. The parity-related terms used within this metric are not modeled as stochastic channel observations and do not introduce additional mutual information beyond the channel output; instead, they act as deterministic, hypothesis-conditioned constraint weights that control how strongly code consistency is enforced within the decision rule. The relationship between metric weighting, apparent horizontal shifts in bit-error-rate (BER) curves, and information-theoretic limits is explicitly clarified. Simulations for a short (8, 24) code demonstrate that near maximum-likelihood decision behavior can be approached by trading receiver complexity for reliability in a finite-hypothesis regime, without altering the physical channel model or violating established channel-capacity principles.