In this paper, we propose a novel approach that harnesses the standard interference function, specifically tailored to address the unique challenges of non-convex optimization in wireless networks. We begin by establishing theoretical guarantees for our method under the assumption that the interference function exhibits log-concavity. Building on this foundation, we develop a Primal-Dual Algorithm (PDA) to approximate the solution to the Weighted Sum Rate (WSR) maximization problem. To further enhance computational efficiency, we leverage the deep unfolding technique, significantly reducing the complexity of the proposed algorithm. Through numerical experiments, we demonstrate the competitiveness of our method compared to the state-of-the-art fractional programming benchmark, commonly referred to as FPLinQ.