Intelligent Wiretap Code Design: Exploiting Wireless Endogenous Security via Information Theory and Deep Learning Integration
2026-08-10 • Information Theory
Information Theory
AI summaryⓘ
The authors studied ways to keep wireless communications secure by using the natural randomness of wireless signals instead of traditional encryption. They developed a coding method that works with semantic communication, which uses meaningful discrete symbols, to improve both security and reliability. They looked at two cases where an eavesdropper tries to listen in: one where the eavesdropper uses a smart guessing decoder and another where the eavesdropper has the same decoder as the intended user. In each case, the authors used different mathematical measures to design codes that keep messages safe while ensuring good communication quality.
wireless endogenous securitysemantic communicationwiretap codingdiscrete semantic representationsmaximum a posteriori (MAP) decodermutual informationgeneralized mutual information (GMI)digital modulationinformation leakagecode optimization
Authors
Haibin Zhang, Xiangnan Zhou, Chao Wang, Liang Jin, Hao Xu, Yao Sun, Chonghua Wang, Derrick Wing Kwan Ng, Giuseppe Caire
Abstract
Recent advancements in wireless endogenous security have explored leveraging the inherent randomness of wireless channels to enhance communication security, providing an effective alternative to traditional encryption methods. This paper proposes a wiretap coding scheme within the semantic communication framework, which leverages discrete semantic representations compatible with conventional digital modulation to jointly enhance communication security and reliability. We investigate two eavesdropping scenarios: (i) the eavesdropper employs a maximum a posteriori (MAP) decoder, and (ii) the eavesdropper has access to a decoder identical to that of the legitimate receiver. In the first scenario, we exploit mutual information as a metric to guide the design of an optimized coding strategy, minimizing information leakage while enhancing communication reliability. In the second scenario, considering the limitations of the eavesdropper's decoding capability, we employ generalized mutual information (GMI) to characterize recoverability under the prescribed decoding rule and guide reliability-aware code optimization.