Secrecy Fairness Aware NOMA for Untrusted Users

TL;DR

This paper proposes a novel secure NOMA protocol for untrusted users that maximizes secrecy fairness through optimized power allocation, providing analytical insights and significant performance gains over existing methods.

Contribution

It introduces a new decoding order and power allocation scheme for secure NOMA with untrusted users, enhancing secrecy fairness and analytical understanding.

Findings

Achieved about 55% to 69% improvements in secrecy fairness metrics.

Derived closed-form SOP expressions for both users.

Developed low-complexity globally-optimized power control solutions.

Abstract

Spectrally-efficient secure non-orthogonal multiple access (NOMA) has recently attained a substantial research interest for fifth generation development. This work explores crucial security issue in NOMA which is stemmed from utilizing the decoding concept of successive interference cancellation. Considering untrusted users, we design a novel secure NOMA transmission protocol to maximize secrecy fairness among users. A new decoding order for two users' NOMA is proposed that provides positive secrecy rate to both users. Observing the objective of maximizing secrecy fairness between users under given power budget constraint, the problem is formulated as minimizing the maximum secrecy outage probability (SOP) between users. In particular, closed-form expressions of SOP for both users are derived to analyze secrecy performance. SOP minimization problems are solved using pseudoconvexity concept, and optimized power allocation (PA) for each user is obtained. Asymptotic expressions of SOPs, and optimal PAs minimizing these approximations are obtained to get deeper insights. Further, globally-optimized power control solution from secrecy fairness perspective is obtained at a low computational complexity and, asymptotic approximation is obtained to gain analytical insights. Numerical results validate the correctness of analysis, and present insights on optimal solutions. Finally, we present insights on global-optimal PA by which fairness is ensured and gains of about 55.12%, 69.30%, and 19.11%, respectively are achieved, compared to fixed PA and individual users' optimal PAs.