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.
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.
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.
Due to the broadcast nature of wireless radio propagation channels, 5G wireless networks face serious security threats. Security mechanisms that leverage physical layer characteristics have been considered as potential complements to enhance 5G wireless network security. This article proposes physical layer security enhancements to defend against security attacks from the three most significant aspects: wireless secure communication, physical layer assisted authentication and secret key distribution. By integrating physical layer security with novel techniques and application scenarios in 5G communication, we elaborate several promising strategies, including massive MIMO beamforming with security code communication, self-adaptive mobile and cooperative secure communication with dynamic channel prediction, physical layer assisted authentication, malicious node detection and key generation of massive MIMO millimeter wave system. Research opportunities and future challenges are further discussed.
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.
The fifth generation (5G) network will serve as a key enabler in meeting the continuously increasing demands for future wireless applications, including an ultra-high data rate, an ultrawide radio coverage, an ultra-large number of devices, and an ultra-low latency. This article examines security, a pivotal issue in the 5G network where wireless transmissions are inherently vulnerable to security breaches. Specifically, we focus on physical layer security, which safeguards data confidentiality by exploiting the intrinsic randomness of the communications medium and reaping the benefits offered by the disruptive technologies to 5G. Among various technologies, the three most promising ones are discussed: heterogenous networks, massive multiple-input multiple-output, and millimeter wave. On the basis of the key principles of each technology, we identify the rich opportunities and the outstanding challenges that security designers must tackle. Such an identification is expected to decisively advance the understanding of future physical layer security.
The fifth generation (5G) and beyond wireless communications will transform many exciting applications and trigger massive data connections with private, confidential, and sensitive information. The security of wireless communications is conventionally established by cryptographic schemes and protocols in which the secret key distribution is one of the essential primitives. However, traditional cryptography-based key distribution protocols might be challenged in the 5G and beyond communications because of special features such as device-to-device and heterogeneous communications, and ultra-low latency requirements. Channel reciprocity-based key generation (CRKG) is an emerging physical layer-based technique to establish secret keys between devices. This article reviews CRKG when the 5G and beyond networks employ three candidate technologies: duplex modes, massive multiple-input multiple-output (MIMO) and mmWave communications. We identify the opportunities and challenges for CRKG and provide corresponding solutions. To further demonstrate the feasibility of CRKG in practical communication systems, we overview existing prototypes with different IoT protocols and examine their performance in real-world environments. This article shows the feasibility and promising performances of CRKG with the potential to be commercialized.
The fifth generation (5G) network would be a key facilitator to keeping up to the future wireless application demands such as ultra-high data rate, ultra-wide radio coverage, ultra — low latency, large number of devices connected etc. Security is a crucial issue in the 5G network as wireless transmissions have the innate quality of vulnerability towards unfriendly breach or hacks. In this paper, security issue in the 5G network, specifically the physical layer security, has been analyzed. The physical layer safeguards confidentiality of data by capitalizing on the inherent random nature of the communication medium. There are various technologies prevalent in 5G, the three most prominent ones-heterogeneous networks, massive multiple-input multiple-output, and millimeter wave have been considered and discussed. The potential areas for improvement and issues in each of the technologies have been identified and analyzed. This analysis would help in better understanding of physical layer security in the future.