Predictive Cross-Layer Anti-Interference Optimization for IoT Data Transmission over Unstable Channels in Power Transmission Environments

INTRODUCTION: Transmission-line Internet of Things (IoT) networks provide continuous sensing for power-grid monitoring, but their wireless links are vulnerable to channel fluctuation, burst interference, sparse relay deployment, and limited node energy.

Objectives

This paper aims to improve reliable IoT data transmission over unstable transmission-line channels by jointly considering prediction uncertainty, packet urgency, reliability, latency, and energy constraints.

Methods

A Predictive Cross-layer Anti-interference Optimization (PCAO) method is proposed. PCAO predicts near-future channel and interference states through uncertainty-aware temporal modeling, evaluates packet priority from event severity and information freshness, and jointly optimizes channel allocation, transmission power, redundancy, and relay preference through constrained cross-layer optimization.

Results

Experiments on DeepMIMO, RadioML, POWDER, and FlockLab show that PCAO consistently outperforms DQN, DDPG, PPO, and SAC, improving average packet delivery ratio by 2.55 percentage points over SAC while reducing delay by about 17.0%.

Conclusion

The results indicate that predictive, priority-aware, and robust cross-layer control can enhance IoT transmission reliability under unstable channels in power transmission environments.