The future sixth generation (6G) of communication systems is envisioned to provide numerous applications in safety-critical contexts, e.g., driverless traffic, modular industry, and smart cities, which require outstanding performance, high reliability and fault tolerance, as well as autonomy. Ensuring criticality awareness for diverse functional safety applications and providing fault tolerance in an autonomous manner are essential for future 6G systems. Therefore, this paper proposes jointly employing the concepts of resilience and mixed criticality. In this work, we conduct physical layer resource management in cloud-based networks under the rate-splitting paradigm, which is a promising factor towards achieving high resilience. We recapitulate the concepts individually, outline a joint metric to measure the criticality-aware resilience, and verify its merits in a case study. We, thereby, formulate a non-convex optimization problem, derive an efficient iterative algorithm, propose four resilience mechanisms differing in quality and time of adaption, and conduct extensive numerical simulations. Towards this end, we propose a highly autonomous rate-splitting-enabled physical layer resource management algorithm for future 6G networks respecting mixed-critical quality of service (QoS) levels and providing high levels of resilience. Results emphasize the considerable improvements of incorporating a mixed criticality-aware resilience strategy under channel outages and strict QoS demands. The rate-splitting paradigm is particularly shown to overcome state-of-the-art interference management techniques, and the resilience and throughput adaption over consecutive outage events reveals the proposed schemes contribution towards enabling future 6G networks.

In cyber-physical systems (CPSs) and internet-of-things applications, various sensor-actuator pairs are deployed for control purposes which require timely online communication. The sensors are measuring information about the CPS, e.g., process systems, whereas the actuators are using the information to take control actions. These sensor-actuator pairs usually communicate via the same wireless medium and thus their transmissions need to be scheduled in time. When transmitting the process data, a short blocklength source-channel coding approach is employed to reduce data errors. We investigate the influence of the decision policy consisting of communication parameters and scheduling design on data freshness and accuracy of process monitoring systems. An age-of-information (AoI) metric is used to assess data timeliness, while the mean square error (MSE) is used to assess the precision of the predicted process values. We characterize the AoI and MSE with closed-form expressions for the blocklengths and accuracy levels, for special types of scheduling strategies, namely, round-robin and maximum-age scheduling. We optimize the coding strategies by showing an achievability region of AoI and MSE. Other priority-based scheduling policies are also investigated. It is shown that the maximum-age policy provides excellent results in terms of AoI, while priority-based scheduling performs better in terms of MSE.

In fifth-generation (5G) cellular networks, users feed back to the base station the index of the precoder (from a codebook) to be used for downlink transmission. The precoder is strongly related to the user channel and in turn to the user position within the cell. We propose a method by which an external attacker determines the user position by passively overhearing this unencrypted layer-2 feedback signal. The attacker first builds a map of fed back precoder indices in the cell. Then, by overhearing the precoder index fed back by the victim user, the attacker finds its position on the map. We focus on the type-I single-panel codebook, which today is the only mandatory solution in the 3GPP standard. We analyze the attack and assess the obtained localization accuracy against various parameters. We analyze the localization error of a simplified precoder feedback model and describe its asymptotic localization precision. We also propose a mitigation against our attack, wherein the user randomly selects the precoder among those providing the highest rate. Simulations confirm that the attack can achieve a high localization accuracy, which is significantly reduced when the mitigation solution is adopted, at the cost of a negligible rate degradation.

The fifth generation (5G) of cellular networks improves the precision of user localization and provides the means to disclose location information to over-the-top (OTT) service providers. The network data analytics function (NWDAF) can further elaborate this information at an aggregated level using artificial intelligence techniques. These powerful features may lead to the improper use of user location information by mobile network operators (MNOs) and OTT service providers. Moreover, vulnerabilities at various layers may also leak user location information to eavesdroppers. Hence, the privacy of users is likely at risk, as location is part of their sensitive data. In this paper, we first go through the evolution of localization in cellular networks and investigate their effects on location privacy. Then, we propose a location-privacy-preserving integrated solution comprising virtual private mobile networks, an independent authentication and billing authority, and functions to protect wireless signals against location information leakage. Moreover, we advocate the continuous and detailed control of localization services by the user.

In cyber-physical systems, as in 5G and beyond, multiple physical processes require timely online monitoring at a remote device. There, the received information is used to estimate current and future process values. When transmitting the process data over a communication channel, source-channel coding is used in order to reduce data errors. During transmission, a high data resolution is helpful to capture the value of the process variables precisely. However, this typically comes with long transmission delays reducing the utilizability of the data, since the estimation quality gets reduced over time. In this paper, the trade-off between having recent data and precise measurements is captured for a Gauss-Markov process. An Age-of-Information (AoI) metric is used to assess data timeliness, while mean square error (MSE) is used to assess the precision of the predicted process values. AoI appears inherently within the MSE expressions, yet it can be relatively easier to optimize. Our goal is to minimize a time-averaged version of both metrics. We follow a short blocklength source-channel coding approach, and optimize the parameters of the codes being used in order to describe an achievability region between MSE and AoI.

One of the great potentials to improve the confidentiality in mmWave/THz at the physical layer of technical communication, measured by the secrecy rate, lies in the use of reconfigurable intelligent surfaces (RISs). However, an important open problem arises when the eavesdropper is aligned with the legitimate user or in proximity to the RIS or legitimate user. The limitation comes, on one hand, from the high directional gain caused by the dominant line-of-sight (LOS) path in high-frequency transmission, and, on the other hand, from the high energy leakage in the proximity of the RIS and the legitimate user. To address these issues, we employ the concept of frequency diverse arrays (FDA) at the base station (BS) associated with random inverted transmit beamforming and reflective element subset selection (RIBES). More specifically, we consider a passive eavesdropper with unknown location and design the transmit beamforming and RIS configuration based on the channel information of the legitimate user only. In this context, the secrecy rate with the proposed transmission technique is evaluated in the case of deterministic eavesdropper channel, demonstrating that we can ensure a secure transmission regarding both direction and range. Furthermore, assuming no prior information about the eavesdropper, we describe the wiretap region and derive the worst-case secrecy rate in closed form. The latter is further optimized by determining the optimal subset sizes of the transmit antennas and reflective elements. Simulations verify the correctness of the closed-form expressions and demonstrate that we can effectively improve the secrecy rate, especially when the eavesdropper is close to the RIS or the legitimate user.