The Impact of Interference Cognition on the Reliability and Capacity of Industrial Wireless Communications

Abstract

Interference significantly impacts the performance of industrial wireless networks, particularly n severe interference environments with dense networks reusing spectrum resources intensively. Although delicate interference information is often unavailable in conventional networks, emerging interference cognition techniques can compensate this critical problem with possibly different precision. This paper investigates the relationship between precision of interference cognition and system performance. We propose a novel performance analysis framework that quantifies the impact of varying interference information precision on achievable rate. Specifically, leveraging the Nakagami-$\mathbf{m}$ fading channel model, we analytically and asymptotically analyze the average achievable rate in the finite blocklength regime for different precision levels of signal and interference information. Our findings reveal the critical importance of identifying per-link interference information for achieving optimal performance. Additionally, obtaining instantaneous information is more beneficial for signal links.