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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

Antenna Selection and Artificial Noise-based Security Transmission for Large-Scale MIMO Systems

Shiguo Wang, Jie Ma

Published Nov 14, 2025• 0 citations
Publisher

Abstract

For large-scale multiple-input multiple-output (MIMO) systems, antenna selection (AS) schemes are usually used to reduce hardware cost and energy consumption, in which only the antennas with low correlation each other and high gain are activated. However, such existing AS schemes may not necessarily ensure the security of information transmission when eavesdroppers are involved as the channels between legitimate users and eavesdroppers may correlate each other due to the inadequate scattering conditions in the propagation. To this end, a novel AS and artificial noise (AN) injection strategy is proposed to enhance system security. Specifically, integrating the correlation between the paths of the legitimate user (LU) and the eavesdropper with channel gains, a comprehensive metric is defined, based on which the optimal antennas used to transmit signals are obtained. Then, based on a deep Q-network (DQN), the subspaces for precoding confidential messages and injecting AN are alternately optimized to maximize the system secrecy rate. Simulation results illustrate that the proposed scheme can obtain much higher secrecy rate compared to the existing ones.

DOI
10.1109/trustcom66490.2025.00053

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For large-scale multiple-input multiple-output (MIMO) systems, antenna selection (AS) schemes are usually used to reduce hardware cost and energy consumption, in which only the antennas with low correlation each other and high gain are activated. However, such existing AS schemes may not necessarily ensure the security of information transmission when eavesdroppers are involved as the channels between legitimate users and eavesdroppers may correlate each other due to the inadequate scattering conditions in the propagation. To this end, a novel AS and artificial noise (AN) injection strategy is proposed to enhance system security. Specifically, integrating the correlation between the paths of the legitimate user (LU) and the eavesdropper with channel gains, a comprehensive metric is defined, based on which the optimal antennas used to transmit signals are obtained. Then, based on a deep Q-network (DQN), the subspaces for precoding confidential messages and injecting AN are alternately optimized to maximize the system secrecy rate. Simulation results illustrate that the proposed scheme can obtain much higher secrecy rate compared to the existing ones.
Owing to the openness of wireless channels, ensuring the security of communication for massive multiple-input multiple-output (MIMO) systems faces severe challenges. For the massive MIMO system in the presence of a full-duplex (FD) multi-antenna eavesdropper, a dual artificial-noise (AN) injection scheme is proposed to enhance communication security in this paper. Specifically, AN is superimposed onto the transmitted pilot signals to interfere with the eavesdropper from estimating the legitimate user channel in pilot training phases. In the phase of data transmission, the AN generated in the null space of the legitimate user is introduced to prevent the eavesdropper from receiving the confidential messages. Meanwhile, an iterative optimization algorithm is presented to obtain the optimal power allocation between the information-bearing signals and the AN for the potential countermeasures taken by the eavesdropper. Numerical results are presented to validate the superior performance of the proposed scheme in security ensurance compared to the conventional ones with AN injection schemes.
This paper considers a massive MIMO network that includes one multiple-antenna base station, one multiple-antenna eavesdropper, and K single-antenna users. The eavesdropper operates in passive mode and tries to overhear the confidential information from one of the users in the down-link transmission. In order to secure the confidential information, two artificial noise (AN)-aiding schemes are proposed. In the first scheme, AN is injected into the downlink training signals to prevent the eavesdropper from obtaining the correct channel state information of the eavesdropping link. In the second scheme, AN is deployed in both downlink training phase and payload data transmission phase to further degrade the eavesdropping channel. Analytical expressions and tight approximations of the achievable secrecy rate of the considered systems are derived with taking imperfect channel estimation and two types of precoding, i.e., maximum-ratio-transmission and zero-forcing, into consideration. Optimization algorithms for power allocation are proposed to enhance the secrecy performance of the proposed AN-aiding schemes. The results reveal that deploying AN in the downlink training phase of massive MIMO networks does not affect the downlink channel estimation process at users while enabling the system to suppress the downlink channel estimation process at eavesdropper. As a consequence, the proposed AN-aided schemes improve the system performance significantly. Furthermore, implementing AN in both phases allows the considered system having a flexible solution to maximize its secrecy performance at the price of higher complexity.
In this paper, we investigate secure and reliable transmission strategies for multi-cell multi-user massive multiple-input multiple-output systems with a multi-antenna active eavesdropper. We consider a time-division duplex system where uplink training is required and an active eavesdropper can attack the training phase to cause pilot contamination at the transmitter. This forces the precoder used in the subsequent downlink transmission phase to implicitly beamform toward the eavesdropper, thus increasing its received signal power. Assuming matched filter precoding and artificial noise (AN) generation at the transmitter, we derive an asymptotic achievable secrecy rate when the number of transmit antennas approaches infinity. For the case of a single-antenna active eavesdropper, we obtain a closed-form expression for the optimal power allocation policy for the transmit signal and the AN, and find the minimum transmit power required to ensure reliable secure communication. Furthermore, we show that the transmit antenna correlation diversity of the intended users and the eavesdropper can be exploited in order to improve the secrecy rate. In fact, under certain orthogonality conditions of the channel covariance matrices, the secrecy rate loss introduced by the eavesdropper can be completely mitigated.
This paper investigates the power allocation problem for artificial noise (AN) secure precoding systems, and proposes closed-form solutions for maximizing the achievable secrecy rate. It is assumed that the transmitter knows the full channel information at the legitimate receiver, and knows only the statistics of the channel information at the eavesdropper. Lower bounds are derived for the secrecy rates in multiple-input single-output channels with single or multiple eavesdroppers and multiple-input multiple-output channels with multiple eavesdroppers. When the number of transmit antennas is sufficiently large, the bounds are tight, and closed-form solutions can be derived from these bounds. The analytical results suggest simple and yet informative solutions as follows: Let the numbers of receive antennas at the legitimate receiver and at the eavesdropper beNrN_{r}Nr​andNr,eN_{r,e}Nr,e​, respectively. The system should distributeNr,e/(Nr+Nr,e)N_{r,e}/(N_{r}+N_{r,e})Nr,e​/(Nr​+Nr,e​)of the power to AN in the high SNR regime, and distribute zero power to AN in the low SNR regime; the rate loss due to the eavesdropper is−Nrlog⁡(Nr/(Nr+Nr,e))−Nr,elog⁡(Nr,e/(Nr+Nr,e))-N_{r}\log(N_{r}/(N_{r}+N_{r,e}))-N_{r,e}\log(N_{r,e}/(N_{r}+N_{r,e}))−Nr​log(Nr​/(Nr​+Nr,e​))−Nr,e​log(Nr,e​/(Nr​+Nr,e​))bits/sec/Hz in the high SNR regime and nearly negligible in the low SNR regime. The derived results also show that equal power and water-filling power allocations lead to similar solutions and rate loss. Simulation results corroborate the theoretical results.
Artificial noise (AN) can be used to confuse eavesdroppers in a physical layer security system. One of the main issues concerned in AN schemes is how to improve secrecy capacities. Most existing AN schemes were proposed based on an assumption that the number of transmit antennas t is larger than that of receiver antennas r, such that they can utilize all r eigen-subchannels of a multiple-output multiple-input (MIMO) system to send messages, and use remaining t - r null spaces for transmitting AN signals. These AN signals null out legitimate receivers and degrade eavesdropper channels. However, transmitting messages in all eigen-subchannels is not always a good strategy. In particular, when the number of transmit antennas is constrained or even smaller than those of receivers, the secrecy capacities of legitimate receivers will be impaired significantly if using all eigen-subchannels for message transmission. To improve secrecy capacity, we propose an AN scheme where messages are encoded in s (which is a variable) strongest eigen-subchannels based on ordered eigenvalues of Wishart matrices, while AN signals are generated in remaining t - s spaces. We derive the average secrecy capacity of a single-user MIMO wiretap channel in the presence of an eavesdropper with multiple antennas. We show that the numerical results are in a good agreement with simulation results. The secrecy capacity of the proposed AN scheme can be improved by approximately 20% ~ 40% if compared with existing AN schemes.
The matter of information security within healthcare systems holds paramount importance. Therefore, the purpose of this letter is to enhance the secure communication for the uplink multiple-input multiple-output (MIMO) wiretap channel. Our objective is to augment the secrecy rate for legitimate users (LUs). We present a novel system that incorporates uplink rate splitting (RS) and a cooperative jamming scheme. The base station operates as a jammer, introducing artificial noise (AN) to prevent the leakage of information. Specifically, we optimize the transmit beamforming for the LUs and the AN covariance, with the aim of maximizing the minimum secrecy rate across all LUs. For this, we develop precise surrogate functions for the non-convex secrecy rates and propose an iterative alternating optimization (AO) algorithm. This algorithm updates auxiliary variables and transmit beamforming iteratively. Simulation results demonstrate the superiority of our proposed scheme over existing benchmarks, resulting in significant gains in secrecy rates.