Physical Layer Secrecy in Multi-Eavesdropper Wiretap Channels With Transmitter-Side Information Over Correlated Log-Normal Shadowing

Abstract

In this paper, under correlated shadowing channels-a propagation model that accurately reflects real-world wireless environment-we consider a multi-eavesdropper wiretap channel, in which several passive eavesdroppers independently try to intercept the legitimate transmission over the primary communication path from the transmitter (Tx) to the authorized receiver (Rx), constituting a realistic threat model, and analyze the achievable physical layer security (PLS) of wireless system, where, motivated by the proven positive impact of side information (SI) at the Tx on PLS performance, SI is assumed to be available at the Tx. Unlike previous studies that either considered (i) a single eavesdropper under correlated fading channels, with SI present or absent, or (ii) multiple eavesdroppers under independent fading channels, in the absence of SI, this work jointly incorporates SI at the Tx and multiple eavesdroppers under correlated shadowing conditions, yielding a more realistic and effective system model for secure wireless communication. There is no intercorrelation among the eavesdroppers, and the probability of information leakage is zero if none of them can successfully eavesdrop on the transmission. The shadowing experienced over the Tx–authorized Rx channel and the eavesdropper channels (the link between the Tx and each eavesdropper) is modeled using a Log-normal (LN) probability model, which is widely used in communication platforms that rely on wireless transmission to characterize shadowing caused by obstacles and environmental inhomogeneities, based on which we derive fast and accurate approximate closed-form expressions using the Holtzman method (HM) for the average secrecy capacity (ASC), secrecy outage probability (SOP), and the probability of non-zero secrecy capacity (PNSC).The resulting analytical expressions are mathematically tractable, and their validity is confirmed by benchmarking them against Monte Carlo (MC) simulations, and they offer reduced computational complexity and faster execution compared to simulation-based outcomes.