Explainable Deep Learning for Secrecy Energy-Efficiency Maximization in Ambient Backscatter Multi-User NOMA Systems

TL;DR

This paper explores optimizing secrecy energy-efficiency in multi-user NOMA systems with ambient backscatter devices, proposing analytical solutions, heuristic algorithms, and an explainable deep learning model that enhances performance and interpretability.

Contribution

It introduces novel analytical solutions for two BDs, scalable optimization techniques, and an explainable neural network for SEE maximization in AmBC-NOMA systems.

Findings

AmBC significantly improves SEE, with gains up to 615%.

The FNN predictor achieves over 95% accuracy.

SHAP analysis reveals key channel features influencing the model.

Abstract

In this paper, we investigate the secrecy energy-efficiency (SEE) of a multi-user downlink non-orthogonal multiple access (NOMA) system assisted by multiple ambient backscatter communications (AmBC) in the presence of a passive eavesdropper. We analyze both the trade-off and the ratio between the achievable secrecy sum-rate and total power consumption. In the special case of two backscatter devices (BDs), we derive closed-form solutions for the optimal reflection coefficients and power allocation by exploiting the structure of the SEE objective and the Pareto boundary of the feasible set. When more than two BDs are present, the problem becomes analytically intractable. To address this, we propose two efficient optimization techniques: (i) an exhaustive grid-based benchmark method, and (ii) a scalable particle swarm optimization algorithm. Furthermore, we design a deep learning-based predictor using a feedforward neural network (FNN), which closely approximates the optimal solutions. Numerical results show that the inclusion of AmBC significantly improves SEE, with gains up to 615% compared to conventional NOMA in high-noise regimes. Additionally, the FNN model achieves more than 95% accuracy compared to the optimal baseline, while reducing complexity. Finally, we employ SHAP (SHapley Additive exPlanations) to interpret the learned model, revealing that the most influential features correspond to the dominant composite channel components, in accordance with the theoretical system model. This demonstrates the potential of explainable artificial intelligence to build trust in energy-efficient and secure AmBC-NOMA systems for next-generation internet of things applications.