Optimizing Reported Age of Information with Short Error Correction and Detection Codes

TL;DR

This paper proposes optimizing the reported age of information in 6G broadcasting systems by using short error correction and detection codes, including deep learning-based codes, to improve information freshness under power and distortion constraints.

Contribution

It introduces a novel framework for minimizing reported AoI with short-blocklength codes, combining CRC error detection with deep learning-based error correction, and derives policies with performance bounds.

Findings

Longer CRC codes increase RAoI but improve accuracy.

Deep learning-based codes reduce RAoI compared to traditional codes.

Prior finite blocklength AoI models underestimate true AoI.

Abstract

Timely sampling and fresh information delivery are important in 6G communications. This is achieved by encoding samples into short packets/codewords for transmission, with potential decoding errors. We consider a broadcasting base station (BS) that samples information from multiple sources and transmits to respective destinations/users, using short-blocklength cyclic and deep learning (DL) based codes for error correction, and cyclic-redundancy-check (CRC) codes for error detection. We use a metric called reported age of information (AoI), abbreviated as RAoI, to measure the freshness of information, which increases from an initial value if the CRC reports a failure, else is reset. We minimize long-term average expected RAoI, subject to constraints on transmission power and distortion, for which we obtain age-agnostic randomized and age-aware drift-plus-penalty policies that decide which user to transmit to, with what message-word length and transmit power, and derive bounds on their performance. Simulations show that longer CRC codes lead to higher RAoI, but the RAoI achieved is closer to the true, genie-aided AoI. DL-based codes achieve lower RAoI. Finally, we conclude that prior AoI optimization literature with finite blocklengths substantially underestimates AoI because they assume that all errors can be detected perfectly without using CRC.