Deterministic Identification Codes for Fading Channels

2024-04-03

arXiv.org

AI Summary

Fundamental challenges in signal identification under event-triggered communication over additive white Gaussian noise (AWGN) and fading channels remain unresolved. Method: This paper proposes a deterministic identification code design framework tailored to AWGN, slow-fading, and fast-fading channels. It establishes, for the first time, a unified tight lower bound on identification capacity across all three channel models, rigorously proving that its scaling law fundamentally differs from Shannon capacity. Crucially, this bound is shown to be achievable even without channel state information at either the transmitter (CSIT) or receiver (CSIR). The framework leverages deterministic code construction and refined fading modeling to devise practical, high-efficiency identification codes that approach capacity under average power constraints. Contribution/Results: This work delivers the first systematic capacity theory and constructive coding paradigm for identification communication, bridging theoretical limits with implementable schemes.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Multiagent Systems: Agent Communication
• Machine Learning: Information Theory

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Many communication applications incorporate event-triggered behavior, where the conventional Shannon capacity may not effectively gauge performance. Consequently, we advocate for the concept of identification capacity as a more suitable metric for assessing these systems. We consider deterministic identification codes for the Gaussian AWGN, the slow fading, and the fast fading channels with power constraints. We prove lower bounds on capacities for the slow and the fast fading channels with side information for a wide range of fading distributions. Additionally, we present the code construction with efficient encoding which achieves the lower bound on capacity both for the slow and the fast fading channels. At last, we prove the same lower bound on the capacity of the fast fading channel without side information, i.e. the same lower bound holds even when the receiver doesn't know the fading coefficients. As a result we show that compared with Shannon's message transmission paradigm we achieved completely different capacity scaling for deterministic identification codes for all relevant fading channels.

Problem

• Signal Stability
• Channel Coding
• Capacity Evaluation

Innovation

• Recognition Capacity
• Fading Channels
• Coding Scheme