Joint spectrum allocation and power control for D2D communication and sensing in 6G networks using DRL-based hyper-heuristics

Abstract

The ongoing evolution of mobile communication systems, particularly toward the sixth generation (6G), has opened new frontiers in the integration of communication and sensing technologies. In this context, Industry 4.0 demands efficient and intelligent solutions for supporting a growing number of interconnected devices while ensuring low latency and high spectral efficiency.

This study addresses the complex problem of joint resource allocation in systems that integrate primary communications, device-to-device (D2D) communication, and sensing, with a special focus on power control and spectrum sharing.It proposes a novel hyper-heuristic (HH) strategy powered by Deep Reinforcement Learning (DRL) that dynamically allocates resources and optimizes spectral usage in a 6G-enabled environment. Unlike traditional heuristic-based approaches that rely on fixed rules, DRL-based HH learns from interactions with the environment and selects appropriate low-level heuristics (LLHs) for managing interference, meeting performance constraints, and improving D2D and sensor operations. A realistic simulation scenario inspired by industrial environments was modeled for evaluations of the strategy’s effectiveness.

The results show the method can effectively balance the competing demands of different system components, dynamically adapt to environmental changes, and maintain compliance with detection and transmission constraints. By extending existing models to including D2D communication, channel uncertainties, and spectrum reallocation over time, the study contributes with a scalable and intelligent solution for future wireless systems in complex industrial settings.