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Transactions on Emerging Telecommunications Technologies

Enhancing Physical Layer Security in Millimeter Wave Multiple Input Multiple Output‐Orthogonal Frequency Division Multiplexing Networks

Parul Awasthi, Vishal Awasthi

Published May 20, 2026• 0 citations
Publisher

Abstract

ABSTRACT Millimeter‐Wave (mmWave) massive Multiple Input Multiple Output (MIMO) with Orthogonal Frequency Division Multiplexing (OFDM) is a key enabler of next‐generation wireless communication, providing high spectral efficiency and improved data rates. Massive MIMO utilizes a large number of antennas at the base station to enhance spatial diversity, while OFDM mitigates inter‐symbol interference and enhances spectral efficiency. The combination of mmWave and massive MIMO with OFDM enables high‐capacity communication with reduced latency, making it ideal for 5G and beyond networks. Physical Layer Security (PLS) provides a robust alternative to traditional cryptographic techniques, leveraging information‐theoretic principles to counter advanced eavesdropping threats, including quantum computing attacks. However, conventional PLS key generation methods suffer from reduced secrecy due to the slow variation of channel characteristics. To enhance security, a deep learning‐based key generation approach is proposed using a deep graph convolutional neural network to extract virtual angle of arrival and angle of departure features. The proposed method achieves a bit agreement ratio of 0.5767 and a key generation rate of 1856.59 bits/s, significantly improving key secrecy. Secure data transmission is ensured using a hybrid deep Q‐learning with recurrent neural network framework, incorporating on‐off transmission schemes, which enhances security by achieving a secrecy capacity of 5.37 bps/Hz, maintaining a throughput of 119.55 Mbps, and a latency of 8.00 ms, ensuring scalability. Additionally, evaluations of the secrecy rate of 5.37 bps/Hz, secrecy outage probability of 10 −2 , and 95% Eavesdropper interception capability, demonstrate significant improvements over state‐of‐the‐art techniques.

DOI
10.1002/ett.70443

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ABSTRACT Millimeter‐Wave (mmWave) massive Multiple Input Multiple Output (MIMO) with Orthogonal Frequency Division Multiplexing (OFDM) is a key enabler of next‐generation wireless communication, providing high spectral efficiency and improved data rates. Massive MIMO utilizes a large number of antennas at the base station to enhance spatial diversity, while OFDM mitigates inter‐symbol interference and enhances spectral efficiency. The combination of mmWave and massive MIMO with OFDM enables high‐capacity communication with reduced latency, making it ideal for 5G and beyond networks. Physical Layer Security (PLS) provides a robust alternative to traditional cryptographic techniques, leveraging information‐theoretic principles to counter advanced eavesdropping threats, including quantum computing attacks. However, conventional PLS key generation methods suffer from reduced secrecy due to the slow variation of channel characteristics. To enhance security, a deep learning‐based key generation approach is proposed using a deep graph convolutional neural network to extract virtual angle of arrival and angle of departure features. The proposed method achieves a bit agreement ratio of 0.5767 and a key generation rate of 1856.59 bits/s, significantly improving key secrecy. Secure data transmission is ensured using a hybrid deep Q‐learning with recurrent neural network framework, incorporating on‐off transmission schemes, which enhances security by achieving a secrecy capacity of 5.37 bps/Hz, maintaining a throughput of 119.55 Mbps, and a latency of 8.00 ms, ensuring scalability. Additionally, evaluations of the secrecy rate of 5.37 bps/Hz, secrecy outage probability of 10 −2 , and 95% Eavesdropper interception capability, demonstrate significant improvements over state‐of‐the‐art techniques.
ABSTRACT Multiple input multiple output‐orthogonal frequency division multiplexing (MIMO‐OFDM) communication, allowing high data rates and low latency, is a crucial enabler for 5G and beyond wireless networks. However, its sensitivity to eavesdropping and physical‐layer attacks poses serious security challenges. This research proposes a novel framework that enhances physical layer security in MIMO by integrating deep learning–based key generation and federated learning–assisted secure transmission. Capitalizing on the spatial uniqueness of mmWave propagation, we employ a cross‐layer mobilenetV2 pyramid mutual attention network to extract rich physical‐layer features—such as angle of arrival and angle of departure, for robust key generation without requiring prior key exchange. To enhance the security of the transmission process, we introduce a multichannel squeeze‐and‐excitation combined network trained in a federated learning setting, allowing distributed devices to jointly learn a secure transmission model without compromising data privacy. Experimental results demonstrate that the proposed method significantly enhances resistance to passive eavesdropping and ensures low bit error rates, even under dynamic channel conditions and high secrecy rate, key generation rate of about 1.63 at 40 dB signal‐to‐noise ratio and bit error rate of 0.1 at 25 dB. The proposed approach enables lightweight, scalable, and privacy‐preserving security in mmWave‐enabled communication systems.
In this work, we present a scheme of physical layer secret key generation for Millimeter wave (mmWave) Massive MIMO system. Our scheme is compatible with current hardware structure and protocols including Analog Beamforming, Massive MIMO, and Beam Sweep. We add a small perturbation angle into the Angle of Arrival (AoA) of the transmitter as the common randomness, which significantly improved the secret key rate without being constrained by the complexity of link initialization protocols and low dynamic of the channel. Therefore, the secret key rate can be enhanced by increasing the number of perturbations. In addition, our scheme can combat co-located eavesdropper (Eve) by utilizing the high directionality of Massive MIMO antenna. Numerical results show that our scheme has a high bit agreement ratio (BAR) between legitimate users while the co-located Eve only gets the BAR around 50%, which indicates that the secrecy of the generated key is well achieved.
Physical layer security (PLS) has grown in popularity as it guarantees secure and reliable communication between wireless devices without restricting the ability of data analysts to eavesdrop. PLS benefits from the randomization property of the wireless channel, which improves immunity and protects against many dangers of information breaches from eavesdroppers. MIMO and Massive MIMO, which use numerous antennas at the transmitter and receiver ends, are fundamental technologies for improving efficiency in 5G and future network systems. The PLS still requires a significant amount of work before it can be employed in practical systems. The interception of eavesdroppers can be made difficult by adding artificial noise. This paper focuses on the physical layer security considering maximal ratio combining, and artificial noise with beamforming techniques to further enhance the secrecy rate. The proposed work demonstrates that the use of transmitter beamforming with artificial noise can significantly improve the degree of confidentiality of information being transmitted between a transmitter and an intended receiver while making it more difficult for eavesdroppers to intercept. The simulation results in terms of bit error rate show that the proposed method has a significant impact on the error rate performance of the eavesdropper.
Open nature of wireless networks leads to being sensitive to eavesdropping. Hence, a tremendous growth of literature has carried out challenges in privacy and security in wireless networks. Conventional techniques would be highly susceptible to technological imperfections, over-exposed to eavesdroppers and dependent on complicated mathematics, leading to failure for deploying secure transmission wireless channels. In this study, we have significantly boosted the performance of the physical layer security in the 5G downlink transmission system by implementing the Orthogonal Frequency Division Multiplexing (OFDM) technique twice in the wiretap channel. We have fully compared the performance of exploiting the OFDM technique twice in the wiretap channel with its counterpart available approach with the one OFDM technique. This study has a major contribution to compare the performance of the two aforementioned techniques in terms of the secrecy capacity, the success rate of an eavesdropper, and the OFDM-modulation efficiency. The simulation results have verified that an extreme security rate and a very low success rate for the eavesdropper in the proposed technique. In addition, we have confirmed that the OFDM-64-QAM3/4 technique shows the ultimate performance in terms of a smaller number of errors, less value of BERs, and high rate modulation compared to the other modulation techniques. In the following, we have represented the simulation results of the active user throughput for the further evaluation of the proposed channel model.
Physical layer security is emerging as a promising means of ensuring secrecy in wireless communications. The key idea is to exploit the characteristics of wireless channels such as fading or noise to transmit a message from the source to the intended receiver while keeping this message confidential from eavesdroppers. Topics covered in Trusted Communications with Physical Layer Security for 5G and Beyond include secrecy metrics for physical layer security over fading channels; trusted wireless communications with spatial multiplexing; directional modulation enabled physical layer wireless security; secure waveform for 5G systems; confidential and energy efficient communications using physical layer security; secure data networks with channel uncertainty; antenna selection strategies for wiretap channels; physical layer security for massive MIMO systems, millimeter wave cellular networks, non-orthogonal multiple access, multiuser relay networks, cognitive radio networks, MIMOME-OFDM systems; wirelessly powered communication systems and D2D-enabled cellular networks; and security solutions and applications at the physical layer, including case studies of secret key generation and secrecy coding in communication nodes and terminals.
Shared secret key generation from wireless channel characteristics has attracted a lot of research attention in recent years due to its low computational overhead and potentials to achieve information-theoretic security. In this paper, for millimeter wave (mmWave) Massive MIMO channel, we propose to use new channel characteristics - virtual AoA (Angle of Arrival) and AoD (Angle of Departure), to generate a shared secret key between two devices. Other than reciprocity, AoA and AoD of the virtual channel present sparsity and their estimation is efficient and robust against noise. Through theoretical analysis and extensive simulations, we show that the proposed key generation using virtual AoA and AoD achieves much higher bit agreement ratio than existing mechanisms directly using channel state information. Our method can achieve above 99% bit agreement ratio even under very low SNR (e.g., -10dB), which is not achievable by the existing mechanisms. Besides, in our proposed mechanism, the bit agreement ratio becomes higher when number of antennas increases, along with higher key generate rates achieved per channel sounding.