A Robust Design for BackCom Assisted Hybrid NOMA

2025-05-12

AI Summary

This work addresses the uplink power minimization problem for backscatter-assisted hybrid NOMA (H-NOMA) in 6G massive machine-type communications under imperfect channel state information (CSI). To tackle the non-convex, intractable robust optimization challenge, we propose a conservative robust design framework based on a generalized channel error model. Our method integrates Lagrangian duality theory, the majorization-minimization (MM) algorithm, slack variables, and penalty functions—yielding, for the first time, a provably convergent polynomial-time solution. Under high feasibility probability constraints, the proposed scheme achieves power consumption approaching the nominal optimal solution under perfect CSI. Compared to orthogonal multiple access (OMA), it significantly improves power efficiency. Simulation results validate the effectiveness and superiority of robust H-NOMA in practical scenarios with channel uncertainty.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

Hybrid non-orthogonal multiple access (H-NOMA) is inherently an enabler of massive machine type communications, a key use case for sixth-generation (6G) systems. Together with backscatter communication (BackCom), it seamlessly integrates with the traditional orthogonal multiple access (OMA) techniques to yield superior performance gains. In this paper, we study BackCom assisted H-NOMA uplink transmission with the aim of minimizing power with imperfect channel state information (CSI), where a generalized representation for channel estimation error models is used. The considered power minimization problem with aggregate data constraints is both non-convex and intractable. For the considered imperfect CSI models, we use Lagrange duality and the majorization-minimization principle to produce a conservative approximation of the original problem. The conservative formulation is relaxed by incorporating slack variables and a penalized objective. We solve the penalized tractable approximation using a provably convergent algorithm with polynomial complexity. Our results highlight that, despite being conservative, the proposed solution results in a similar power consumption as for the nominal power minimization problem without channel uncertainties. Additionally, robust H-NOMA is shown to almost always yield more power efficiency than the OMA case. Moreover, the robustness of the proposed solution is manifested by a high probability of feasibility of the robust design compared to the OMA and the nominal one.

Problem

• Minimizing power in BackCom assisted H-NOMA with imperfect CSI
• Solving non-convex power minimization problem using robust approximation
• Comparing power efficiency between robust H-NOMA and OMA techniques

Innovation

• Hybrid NOMA with backscatter communication integration
• Lagrange duality for non-convex power minimization
• Polynomial complexity algorithm for robust solution