This paper investigates the threat of passive eavesdropping on downlink cell-free massive MIMO (CF-MaMIMO) systems, examining a particular system under the effects of both hardware impairments (HWIs) and pilot contamination. Physical layer security (PLS) techniques and power allocation algorithms are typically adopted to deteriorate the quality of eavesdropped signals. In the downlink stream, artificial noise (AN) is broadcasted simultaneously with the users’ data streams, with the aim of jamming the eavesdropper’s signal without sacrificing the quality of service (QoS). The obtained results prove that mitigating the impact of both pilot contamination and HWIs on both the system’s capacity and PLS is crucial for the deployment of practical CF-MaMIMO systems.

This work addresses two innovative and challenging aspects in the pursuit of an accurate localization process, which can be achieved through a complex and nonlinear approach such as neural networks (NNs). This study specifically focuses on developing a low-complexity localization method using NNs, along with presenting the associated Cramèr-Rao lower bound (CRB). The research considers real-world scenarios where system performance is corrupted by imperfect synchronization and hardware impairments (HWIs), which cause significant accuracy reduction when using traditional estimators. The NN approach offers a promising solution to counteract the effects of HWI and synchronization issues, by using substantial prior information during the training phase. However, incorporating this prior information and dealing with the nonlinearity of NNs presents challenges in deriving the CRB of the neural localization process. To overcome these challenges, the research utilizes the f-divergence function and the concept of mutual information to derive the CRB, making it applicable to various HWI, nonlinear activation functions in NNs and statistical models. The study further simplifies the optimization problem traditionally associated with this approach used in calculating the CRB. The simulation results demonstrate that the proposed approach achieves a low-complexity and accurate localization process even in asynchronous and impaired systems. Furthermore, the simulations validate the proposed CRB driving technique and provide the CRB values for various neural localization scenarios.

This paper investigates the threat of passive eavesdropping on downlink cell-free massive MIMO (CF-MaMIMO) systems, examining in a particular system under the effects of imperfect channel estimation. Physical layer security (PLS) techniques and power allocation algorithms are typically adopted to deteriorate the quality of eavesdropped signals. In the downlink stream, artificial noise (AN) is broadcasted simultaneously with the users’ data streams, with the aim of jamming the eavesdropper’s signal without sacrificing the quality of service (QoS). However, imperfect channel estimation results in AN leakage to legitimate users. Our work considers this condition, and the power allocation algorithms we present determine the minimum power that is needed to maintain the user’s Qos even in the presence of leaked AN. We first propose the cooperative PLS algorithm (COP), which facilitates AN broadcasting via access point (AP) cooperation. Unfortunately, this cooperation wastes some of the power used to send the AN and, in most cases, increases the computational complexity. We then present the independent PLS algorithm (IND), where the goal is to regain this power by allowing each AP to broadcast AN independently. However, to meet the feasibility criterion, this occurs at the cost of increasing the number of used antennas. This technique reduces the power allocation complexity but increases the computational complexities of the precoder design and the channel estimation process. Our results reveal that the proposed algorithms enhance the security performance of CF-MaMIMO. We evaluate such performance from numerous angles including the number of users, number of APs, QoS, beamforming, AN leakage, and channel estimation techniques.

Reconfigurable intelligent surfaces (RISs) are a recent yet revolutionary development in communications systems. Particularly applicable to millileter wave (mmWave) systems, these surfaces can increase localization performance and decrease vulnerability to environmental influences, all by adjusting the incoming signals’ phase. At the same time, manufacturing ideal hardware to be deployed at the transceivers is not feasible nor practical. These non-linearities in hardware, collectively known as hardware impairments (HWIs), cause signal degradation and adversely affect localization. In this paper, the effect of HWIs on RIS-aided localization is examined. Towards that, the mean squared error (MSE) of the user’s position is found through a maximum likelihood estimator (MLE) and its functionality is verified by the position error bounds (PEB), derived from Cramér-Rao lower bounds (CRLB). Our numerical results show that active RISs mitigate the deteriorating effect of HWIs on the user’s PEB. Based on our outcome, increasing the inter-RISs space generally creates more resolvable paths and leads to improved localization.

In this study, the authors secure the mixed radio frequency/free space optical (FSO) relay‐aided interference aligned system using a proposed physical layer security algorithm. This algorithm reduces the quality of the received signal at the eavesdropper through two procedures. First, it minimises the data transmission power from the legitimate users and the relays. Second, it jams the eavesdropper by broadcasting artificial noise from the users and the relays. Therefore, a joint optimisation problem is formulated to degrade the received signal at the eavesdropper from the users and the relays and to maximise the jamming artificial noise power, which is solved using an iterative optimisation algorithm beside a semi‐definitive programming algorithm. Furthermore, the pre‐coding and decoding matrices of the users and the relay are designed to enable the legitimate users to cancel the artificial noise, while the eavesdropper is disabled from distinguishing the artificial noise from the real streams. Moreover, the security performance of the proposed algorithm is analysed, and the impact of the FSO link's state on the security performance is studied. The extensive simulation results show the efficiency of the proposed algorithm and illustrate the role of the FSO link's state on the security performance.

This paper proposes a novel physical layer security algorithm in interference alignment (IA) based multiuser communication system with a single amplify-and-forward (AF) relay in the coexistence of an eavesdropper that tries to hide its existence. Thus, the relay and the users cannot estimate the eavesdroppers channel state information (CSI). In the proposed algorithm, the users broadcasts artificial noise in the null of the relay at the multiple access (MAC) phase, and the relay jams the eavesdropper by broadcasting the artificial noise in the null of users at the broadcast (BC) phase. Therefore, the proposed algorithm is formulated as a joint optimization problem consisting of two semidefinite programming (SDP) problems aiming at maximizing the power of the artificial noise and preserving the quality- of-services (QoS) of the users. An iterative optimization algorithm is proposed to solve the joint problem. Extensive simulation results are provided to show the effectiveness of the proposed algorithm for IA based relaying networks.