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Frontiers of Information Technology & Electronic Engineering

On optimization of cooperative MIMO for underlaid secrecy Industrial Internet of Things

Xinyao Wang, Xuyan Bao, Yuzhen Huang, Zhong Zheng, Zesong Fei

Published Feb 1, 2023• 3 citations
Publisher

Abstract

In this paper, physical layer security techniques are investigated for cooperative multi-input multi-output (C-MIMO), which operates as an underlaid cognitive radio system that coexists with a primary user (PU). The underlaid secrecy paradigm is enabled by improving the secrecy rate towards the C-MIMO receiver and reducing the interference towards the PU. Such a communication model is especially suitable for implementing Industrial Internet of Things (IIoT) systems in the unlicensed spectrum, which can trade off spectral efficiency and information secrecy. To this end, we propose an eigenspace-adaptive precoding (EAP) method and formulate the secrecy rate optimization problem, which is subject to both the single device power constraint and the interference power constraint. This precoder design is enabled by decomposing the original optimization problem into eigenspace selection and power allocation sub-problems. Herein, the eigenvectors are adaptively selected by the transmitter according to the channel conditions of the underlaid users and the PUs. In addition, a simplified EAP method is proposed for large-dimensional C-MIMO transmission, exploiting the additional spatial degree of freedom for a low-complexity secrecy precoder design. Numerical results show that by transmitting signal and artificial noise in the properly selected eigenspace, C-MIMO can eliminate the secrecy outage and outperforms the fixed eigenspace precoding methods. Moreover, the proposed simplified EAP method for the large-dimensional C-MIMO can significantly improve the secrecy rate.

DOI
10.1631/fitee.2200188

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In this paper, physical layer security techniques are investigated for cooperative multi-input multi-output (C-MIMO), which operates as an underlaid cognitive radio system that coexists with a primary user (PU). The underlaid secrecy paradigm is enabled by improving the secrecy rate towards the C-MIMO receiver and reducing the interference towards the PU. Such a communication model is especially suitable for implementing Industrial Internet of Things (IIoT) systems in the unlicensed spectrum, which can trade off spectral efficiency and information secrecy. To this end, we propose an eigenspace-adaptive precoding (EAP) method and formulate the secrecy rate optimization problem, which is subject to both the single device power constraint and the interference power constraint. This precoder design is enabled by decomposing the original optimization problem into eigenspace selection and power allocation sub-problems. Herein, the eigenvectors are adaptively selected by the transmitter according to the channel conditions of the underlaid users and the PUs. In addition, a simplified EAP method is proposed for large-dimensional C-MIMO transmission, exploiting the additional spatial degree of freedom for a low-complexity secrecy precoder design. Numerical results show that by transmitting signal and artificial noise in the properly selected eigenspace, C-MIMO can eliminate the secrecy outage and outperforms the fixed eigenspace precoding methods. Moreover, the proposed simplified EAP method for the large-dimensional C-MIMO can significantly improve the secrecy rate.
In this paper, a secure communication model for cognitive multi-user massive multiple-input multiple-output (MIMO) systems with underlay spectrum sharing is investigated. A secondary (cognitive) multi-user massive MIMO system is underlaid within a primary (licensed) multi-user massive MIMO system. A passive multi-antenna eavesdropper is eavesdropping upon either the primary or secondary transmission. To this end, physical layer secure transmissions are provisioned for both primary and secondary systems by exploiting linear precoders and artificial noise (AN) generation. The excess degrees-of-freedom at the primary massive MIMO base-station are exploited to transmit the AN sequence by using random AN shaping matrices for the purpose of degrading the signal decodability at the eavesdropper. Achievable asymptotic secrecy rates for infinitely many base-station antennas are derived, and these secrecy rates are shown to be independent of the eavesdropper channels. Moreover, for infinitely many secondary base-station antennas, the secondary system becomes independent of the primary system allowing it to operate at its maximum transmission power without disturbing the primary system.
Smart homes can improve the quality of life and be implemented by Internet of Things (IoT) technologies. However, security is a very important issue in smart homes. For this reason, we propose a secrecy transmission protocol for primary user (PU) by selecting friendly jammer in cognitive IoT model. In particular, a secondary transmitter (ST) is selected to transmit secondary signals by the PU's frequency spectrum, while another ST is chosen to transmit artificial noise to protect the transmission confidentiality of the PU against eavesdropping. Moreover, two selection schemes are presented to confirm the former and the latter ST, and the goal is to optimize the secondary transmission performance and the primary security performance, respectively. For the non-security model and the proposed protocol, we derive the closed-form expressions of the intercept probability and the outage probability for the PU. We also obtain the closed-form expression of outage probability for the secondary user. The numerical results show that the security performance of the PU is significantly enhanced in our protocol compared to the non-security model. In addition, the outage performance of the secondary users is also improved in high secondary transmit SNR region.
This paper presents the optimal physical-layer security scheme of both primary user (PU) and secondary user (SU) in the underlay multiple-input multiple-output cognitive-radio network (MIMO-CRN) by use of a bi-directional zero-forcing beamformer. The proposed new method enables the PU to communicate along with the SUs through a relay node without sacrificing their individual secrecy capacity, i.e., without causing interference to each other even in the presence of eavesdropper(s). In the first phase, a transmitting beamformer at the PU/SU transmitters and a receiving beamformer at the relay have been adopted to separate PU and SU data. In the second phase, a bi-directional beamformer has been applied to eradicate the necessity of involving artificial noise for preventing the active eavesdropper(s). To maximize the total secrecy capacity, a generalized non-linear optimization problem has been formulated and converted to a simplified constrained optimization problem by utilizing the beamformer and the associated subspace restriction. Then, this problem is solved by the Lagrangian method. Both theoretical and numerical analyses of the total and individual ergodic secrecy rates are provided to demonstrate the effectiveness of our proposed method. The secrecy outage probability is also derived to evaluate the performance variations of our proposed scheme when the channel-estimation error occurs.
In this paper, a secure communication model for cognitive multi-user massive multiple-input multiple-output (MIMO) systems with underlay spectrum sharing is investigated. A secondary (cognitive) multi-user massive MIMO system is operated by using underlay spectrum sharing within a primary (licensed) multi-user massive MIMO system. A passive multi-antenna eavesdropper is assumed to be eavesdropping upon either the primary or secondary confidential transmissions. To this end, a physical layer security strategy is provisioned for the primary and secondary transmissions via artificial noise (AN) generation at the primary base-station (PBS) and zero-forcing precoders. Specifically, the precoders are constructed by using the channel estimates with pilot contamination. In order to degrade the interception of confidential transmissions at the eavesdropper, the AN sequences are transmitted at the PBS by exploiting the excess degrees-of-freedom offered by its massive antenna array and by using random AN shaping matrices. The channel estimates at the PBS and secondary base-station (SBS) are obtained by using non-orthogonal pilot sequences transmitted by the primary user nodes (PUs) and secondary user nodes (SUs), respectively. Hence, these channel estimates are affected by intra-cell pilot contamination. In this context, the detrimental effects of intra-cell pilot contamination and channel estimation errors for physical layer secure communication are investigated. For this system set-up, the average and asymptotic achievable secrecy rate expressions are derived in closed-form. Specifically, these performance metrics are studied for imperfect channel state information (CSI) and for perfect CSI, and thereby, the secrecy rate degradation due to inaccurate channel knowledge and intra-cell pilot contamination is quantified. Our analysis reveals that a physical layer secure communication can be provisioned for both primary and secondary massive MIMO systems even with the channel estimation errors and pilot contamination.
This paper studies the achievable rates of the multi-antenna or multiple-input multiple-output (MIMO) secrecy channel with multiple single-/multi-antenna eavesdroppers. It is known that the maximum achievable secrecy rate is obtained with the optimal transmit covariance matrix that maximizes the minimum difference between the channel mutual information of the secrecy user and those of the eavesdroppers. The maximum secrecy rate computation can thus be formulated as a non-convex max-min problem, which cannot be solved efficiently by existing methods. To handle this difficulty, this paper explores a new relationship between the secrecy channel and the recently developed cognitive radio (CR) channel, in which the secondary user transmits over the same spectrum simultaneously with multiple primary users, subject to the received interference power constraints at the primary users, or the so-called ¿interference temperature (IT)¿ constraints. By constructing an auxiliary multi-antenna CR channel that has the same channel responses as the secrecy channel, this paper shows that the optimal transmit covariance to achieve the maximum secrecy rate is the same as that to achieve the CR spectrum sharing capacity with properly selected IT constraints. Thereby, finding the optimal complex transmit covariance matrix for the secrecy channel becomes equivalent to searching over a set of real IT constraints in the auxiliary CR channel. Based on this relationship, efficient algorithms are proposed to solve the non-convex secrecy rate maximization problem by transforming it into a sequence of convex CR spectrum sharing capacity computation problems, under various setups of the secrecy channel.
This paper studies the achievable rates of the multi-antenna or multiple-input multiple-output (MIMO) secrecy channel with multiple single-/multi-antenna eavesdroppers. By assuming Gaussian input, the maximum achievable secrecy rate is obtained with the optimal transmit covariance matrix that maximizes the minimum difference between the channel mutual information of the secrecy user and those of the eavesdroppers. The maximum secrecy rate computation can thus be formulated as a non-convex max-min problem, which cannot be solved efficiently by existing methods. To handle this difficulty, this paper explores a new relationship between the secrecy channel and the recently developed cognitive radio (CR) channel, in which the secondary user transmits over the same spectrum simultaneously with multiple primary users, subject to the received interference power constraints at the primary users, or the so-called "interference temperature (IT)" constraints. By constructing an auxiliary multi-antenna CR channel that has the same channel responses as the secrecy channel, this paper shows that the optimal transmit covariance to achieve the maximum secrecy rate is the same as that to achieve the CR spectrum sharing capacity with properly selected IT constraints. Thereby, finding the optimal complex transmit covariance matrix for the secrecy channel becomes equivalent to searching over a set of real IT constraints in the auxiliary CR channel. Based on this relationship, efficient algorithms are proposed to solve the non-convex secrecy rate maximization problem by transforming it into a sequence of convex CR spectrum sharing capacity computation problems, under various setups of the secrecy channel.