Joint decoding improves detection of user messages in random access communication
Exact Joint MAP Decoding for Unknown Load and Message Counts in Orthogonal Unsourced Random Access
Information Theory
Summary
In wireless communication where many users send very short messages without identifying themselves, it's hard to know how many are active and what exactly they sent. The authors developed a method to better guess both the number of active users and their messages at the same time. Their method works efficiently and reduces mistakes, especially when few or no users are active. This helps improve reliability when signals overlap or when user activity is uncertain.
What this means in practice
- •For wireless network engineers: Improve detection and decoding of unknown numbers of user messages in dense, short-burst random access uplink communication.
- •For industrial iot system designers: Enable more reliable message recovery in systems with many low-power sensors sending short, sporadic data bursts without user-specific IDs.
Authors
Mohammad Kazemi, Mert Ozates
Abstract
We study joint recovery of the load (the number of active users) and message-count vector in type-based unsourced random access with very short messages. Each user's $k$-bit message activates one of $M=2^k$ orthogonal resources; coherent collisions therefore produce a noisy, unnormalized histogram of the transmitted messages. For arbitrary load and message priors, we propose an exact joint maximum-a-posteriori (MAP) decoder. Known-load allocation is separable and convex; our contribution is to show that the solutions for all candidate loads are nested prefixes of one greedy chain. Scoring these prefixes with the Bayesian posterior terms jointly estimates the load and message-count vector in $O(M+K_{\max}\log M)$ time. We compare the proposed joint MAP decoder with marginal MAP, three scalar-count two-stage receivers, oracle-load MAP, a sum-only receiver, and a soft-pilot receiver. For $K\sim\operatorname{Unif}\{0{:}60\}$ at 2 dB, the proposed decoder cuts the no-activity false-alarm probability from $25.9\%$ to $3.5\%$ relative to the Bayesian-count two-stage receiver and, conditioned on $K\ge1$, reduces normalized count error by $21.4\%$. The advantage concentrates under idle or sparse uncertain activity, whereas support-count processing approaches joint MAP when the possible loads are well separated. We also prove that the sum-only statistic is insufficient and quantify prior and amplitude mismatch. The model targets synchronized, power-controlled, very-short payloads rather than general URA.