Weighted Sum Secrecy Rate Maximization via Minorization–Maximization

This paper studies the weighted sum secrecy rate (WSSR) maximization problem in single-input single-output interference wiretap channels, which is a fundamental yet challenging problem in physical layer security. Given the inherent nonconvexity of the WSSR objective, we develop a novel fast iterative algorithm based on the minorization–maximization (MM) framework. By exploiting the flexibility of the MM principle in surrogate function design, the proposed algorithm admits closed-form updates at each iteration. From a unified perspective, we show that many existing algorithms can be interpreted as instances of MM methods. We further analyze the surrogate functions employed by several state-of-the-art approaches together with the proposed method, clarifying their intrinsic connections and highlighting the advantages of the proposed method. Numer-ical results demonstrate that the proposed algorithm achieves comparable convergence behavior to baseline methods while significantly reducing computational time across a wide range of network configurations and signal-to-noise ratio regimes.