Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Yichen Guo, Tao Peng, Yujie Zhao, Yijing Niu, Wenbo Wang

TL;DR

This work analyzes how interference cognition precision affects achievable rate in industrial wireless networks operating in the finite blocklength (FBL) regime under Nakagami-$m$ fading. It introduces a Deteministic Communication Capacity (DCC) framework that quantifies average rate under varying levels of instantaneous and statistical knowledge of signal and interference powers, and develops analytical and asymptotic results across cognition levels I, D, A, and M. The key finding is that per-link interference information is crucial for maintaining usable performance and reliability, while instantaneous signal power information yields the greatest benefits for stringent QoS; the paper provides upper bounds and asymptotic expressions that guide rate allocation and network planning, with numerical results validating the theory. The results have practical implications for grant-free and dense industrial deployments, informing resource management and analysis for future 6G HRLLC/HRLLC-type industrial networks.

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.

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

TL;DR

This work analyzes how interference cognition precision affects achievable rate in industrial wireless networks operating in the finite blocklength (FBL) regime under Nakagami- fading. It introduces a Deteministic Communication Capacity (DCC) framework that quantifies average rate under varying levels of instantaneous and statistical knowledge of signal and interference powers, and develops analytical and asymptotic results across cognition levels I, D, A, and M. The key finding is that per-link interference information is crucial for maintaining usable performance and reliability, while instantaneous signal power information yields the greatest benefits for stringent QoS; the paper provides upper bounds and asymptotic expressions that guide rate allocation and network planning, with numerical results validating the theory. The results have practical implications for grant-free and dense industrial deployments, informing resource management and analysis for future 6G HRLLC/HRLLC-type industrial networks.

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- 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.
Paper Structure (42 sections, 7 theorems, 68 equations, 12 figures, 2 tables)

This paper contains 42 sections, 7 theorems, 68 equations, 12 figures, 2 tables.

Table of Contents

  1. Introduction
  2. System Model
  3. System Setup
  4. Channel Model
  5. Deterministic Communication Capacity (DCC)
  6. Instantaneous Capacity and BLER in the FBL Regime
  7. DCC Analysis Framework
  8. Modeling signal and interference, and projecting SINR distribution
  9. Deciding rate allocating strategy
  10. Evaluating BLER
  11. Projecting average DCC
  12. Evaluating actual performance
  13. Differences from existing approaches
  14. Interference Cognition and SINR Distribution
  15. Precision of Interference Cognition
  16. ...and 27 more sections

Key Result

Theorem 1

The upper bound of average DCC in the I/I case, with $m_I = 0.5$, is where $\mathcal{C}_1$ is defined in eq_C1 with $m_I=0.5$, and $\mathcal{C}_2$ is defined in eq_C2.

Figures (12)

  • Figure 1: The system model. For brevity, only the interference links of the upper right cell is depicted.
  • Figure 2: Framework for analyzing DCC in industrial wireless networks.
  • Figure 3: The four levels of precision of the interference.
  • Figure 4: One of the generated scenarios.
  • Figure 5: Evaluation of Theorem \ref{['th_avgRate']} with $m_I=0.5$. (a) Exact value of the average DCC, calculated by numerical integration and Theorem \ref{['th_avgRate']}. (b) The relative error between the value of numerical integration and Theorem \ref{['th_avgRate']} with 31-, 62-, and 126-digit precision.
  • ...and 7 more figures

Theorems & Definitions (13)

  • Theorem 1
  • proof
  • Theorem 2
  • proof
  • Lemma 1
  • proof
  • Corollary 1
  • Theorem 3
  • proof
  • Theorem 4
  • ...and 3 more