Robust Multiuser Physical Layer Security for Grant-Free mMTC in Beyond 5G/6G Networks

Industry 5.0 introduces human-machine collaboration and resilient automation, demanding secure, low-latency connectivity for ultra-dense Industrial IoT (IIoT). Grant-free massive machine-type communications (mMTC) supports such connectivity but faces challenges including dense multiuser access, passive eavesdropping, and imperfect channel state information (CSI), which undermine physical layer security (PLS). This paper proposes a robust and low-complexity multiuser PLS scheme tailored for grant-free mMTC under CSI uncertainty. The scheme leverages dynamic user clustering based on spatial correlation and real-time interference to enable scalable, interference-aware beamforming. Furthermore, a joint optimisation of receive beamforming and adaptive artificial noise injection is performed, and enhanced by a regularised minimum mean square error (MMSE) framework to mitigate bounded CSI errors. Simulation results show that the scheme consistently outperforms existing benchmarks across secrecy capacity, bit error rate, and secrecy outage probability under different channel models, together with analyses of SOP sensitivity to CSI error and scalability to dense users/eavesdroppers, confirms its robustness, efficiency, and applicability to large-scale, secure IIoT communications in beyond 5G/6G networks aligned with Industry 5.0 requirements.