mmKey: Channel-Aware Beam Shaping for Reliable Key Generation in mmWave Wireless Networks

2025-08-26

AI Summary

In millimeter-wave (mmWave) communications, physical-layer key generation (PLKG) suffers from insufficient randomness and reciprocity due to channel sparsity, high phase noise, and severe path loss. To address this, we propose a novel PLKG framework integrating active randomness injection with intelligent beam steering. Specifically, we leverage multi-antenna beamforming to deliberately introduce controllable randomness into the channel probing process, and design a genetic algorithm-based dynamic beamweight optimization mechanism that jointly suppresses line-of-sight (LOS) components, adapts to channel sparsity, and enhances signal-to-noise ratio (SNR) robustness. Experimental results demonstrate that our method achieves average improvements of 39.4% and 34.0% in secret key rate over random and null beamforming baselines, respectively—significantly enhancing key agreement consistency and secrecy. This work establishes a deployable, physics-informed PLKG paradigm for secure mmWave communication.

Technology Category

• Machine Learning: Active Learning
• Multiagent Systems: Agent Communication
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Measurement, analysis, and circumvention of Web censorship

Abstract

Physical-layer key generation (PLKG) has emerged as a promising technique to secure next-generation wireless networks by exploiting the inherent properties of the wireless channel. However, PLKG faces fundamental challenges in the millimeter wave (mmWave) regime due to channel sparsity, higher phase noise, and higher path loss, which undermine both the randomness and reciprocity required for secure key generation. In this paper, we present mmKey, a novel PLKG framework that capitalizes on the availability of multiple antennas at mmWave wireless nodes to inject randomness into an otherwise quasi-static wireless channel. Different from prior works that sacrifice either the secrecy of the key generation or the robustness, mmKey balances these two requirements. In particular, mmKey leverages a genetic algorithm to gradually evolve the initial weight vector population toward configurations that suppress the LOS component while taking into account the channel conditions, specifically, the sparsity and the signal-to-noise ratio (SNR). Extensive simulations show that mmKey improves the secrecy gap by an average of 39.4% over random beamforming and 34.0% over null beamforming, outperforming conventional schemes.

Problem

• Enhancing key randomness in sparse mmWave channels
• Balancing secrecy and robustness in PLKG systems
• Suppressing LOS components using genetic algorithm optimization

Innovation

• Genetic algorithm optimizes beamforming weights
• Suppresses LOS component using multiple antennas
• Balances secrecy and robustness in key generation