Summary
Wireless networks using millimeter waves can be interfered with by jammers, which can disrupt signals and reduce data rates. The authors present a method that uses a digital twin—a simulated model of the network—to compare expected and actual behavior, helping the system identify jammers and adjust signal beams accordingly. They combine this with a special type of learning called belief-guided Q-learning to make decisions about where to aim signals and how much power to use. Their approach improves detection accuracy and maintains better data rates compared to other standard methods, at the cost of longer computation. This work shows a promising way to keep future wireless networks secure and efficient.
What this means in practice
- •For network engineers: Improve wireless network resilience by detecting jammers and adjusting beam power in 28-GHz millimeter wave systems using model-based learning.
- •For wireless security teams: Enhance intrusion detection and adaptive response for security threats in future 6G millimeter wave deployments through belief-guided decision making.
Abstract
Digital Twin (DT) combined with reinforcement learning (RL) can support adaptive resource allocation and security-aware decision-making by comparing expected and observed network behavior. This paper presents a simulation-based DT reference and a belief-guided Q-learning (BGQL) for joint jammer identification and beam-power control in a site-specific 28-GHz network. For each action, the same channel simulator is evaluated under an attack-free and an attacked configuration. Receiver-level residuals are combined with spatial jamming footprints and accumulated to form a normalized suspicion vector. BGQL incorporates this vector into an aggregated state, a belief-concentration reward, and exploratory beam-selection rule. The BGQL scheme is compared with SARSA, Greedy, and Random under matched attacker sets. Across 30 paired Monte Carlo runs in four configurations, with the number of active jammers supplied to every method, BGQL achieves mean aggregate full-band link-rate scores of approximately 437.4--564.2~Mbit/s and rate-to-device-power scores of 60.37--76.79~Mbit/J. Its detection accuracy is 100\% in one-jammer configurations and approximately 78\%--81\% in two-jammer configurations. BGQL requires approximately 80--130~s per run, compared with 5--14~s for SARSA, reflecting evaluation of 625 beam pairs during exploratory steps. These findings provide a matched-model simulation proof of concept for DT-referenced belief-guided learning for security-aware beam-power control in 6G networks.