Beam management procedures needed to support highly directional transmission links in wireless systems have been shown to be susceptible to replay attacks that can induce beam alignment failure. This paper proposes a new replay attack against beam management procedures using passive reconfigurable intelligent surfaces (RISs). For launching the proposed attack, we propose a combinatorial multi-armed bandit (CMAB)-based adversarial RIS that smartly controls which reference signals enter a certain indoor network service area to manipulate indoor user equipments (UEs) into poor beam selection. Our results indicate that our adversarial RIS can degrade outdoor-to-indoor communication by several orders of magnitude, potentially disrupting indoor communication.

While the robustness of rate-splitting multiple access (RSMA) to imperfect channel state information (CSI) is well-documented, its susceptibility to attacks launched with malicious reconfigurable intelligent surfaces (RISs) remains unexplored. This paper fills this gap by investigating three potential RIS-induced attacks against RSMA in a multi-user multiple-input multiple-output (MIMO) network: random interference, aligned interference, and mitigation attack. The random interference attack employs random RIS coefficients to disrupt RSMA. The other two attacks are triggered by optimizing the RIS through weighted-sum strategies based on the projected gradient method. Simulation results reveal significant degradation caused by all the attacks under perfect CSI conditions. Remarkably, when imperfect CSI is considered, RSMA, owing to its flexible power allocation strategy designed to counter CSI-related interference, can be robust to the attacks even when the base station is blind to them. It is also shown that RSMA can significantly outperform conventional space-division multiple access (SDMA).

As virtualization of Radio Access Networks (RAN) gains momentum, understanding the impact of hardware and software disaggregation on resiliency becomes critical to meet the high availability requirements of mobile networks. Our paper presents an analytical model, using continuous time Markov chains, to study the impact of virtualization and disaggregation on RAN availability. Our evaluation, assuming typical parameter value ranges for failure and recovery rates, points to container-ized platform reliability as a constraint on vRAN availability. We also find that with active-passive replication, increasing hardware replication factor beyond 2 may not bring any benefits unless failover times are reduced. We also compare the reliability of centralized and distributed virtualized central units.

Bone marrow edema (BME) or bone marrow lesion is the term attributed to an observed signal change within the bone marrow in magnetic resonance imaging (MRI). BME can be originated from multiple mechanisms, with pain being the main symptom. The presence of BME is an unspecific but sensitive sign with a wide differential diagnosis, that may act as a guide that leads to a systematic and correct interpretation of the magnetic resonance examination. An automatic approach for BME detection and quantification aims to reduce the overload of clinicians, decreasing human error and accelerating the time to the correct diagnosis. In this work, the bone region on the MRI slice was split into several patches and a CNN-based model was trained to detect BME in each patch from the MRI slice. The learning model developed achieved an AUC of 0.853 ± 0.056, showing that the CNN-based model can be used to detect BME in the MRI and confirming the patch strategy implemented to deal with the small data size and allowing the neural network to learn the specific information related with the classification task by reducing the region of the image to be considered. A learning model that can help clinicians with BME identification will decrease the time and the error for the diagnosis, and represent the first step for a more objective assessment of the BME.

A key distinction between today’s and future networks is the appetite for reliable communication to support emerging critical-communication services. In this paper, we study multi-operator connectivity as a form of redundancy to support the design of reliable networks and investigate its trade-offs. This approach is motivated by 3GPP standardisation initiatives of dual-connectivity and similar techniques in industrial wired networks. We deploy a risk awareness performance metric to assess reliability: this superquantile metric accounts for periods of connectivity shortfalls. Our analysis shows that multi-operator connectivity brings significant reliability gains, in particular when network deployments by different operators exhibit high complementarity in coverage. We also explore the effects of multi-connectivity on spectral efficiency in times of high demand for bandwidth. Our study is based on a real-world dataset comprising signal strength indicators of three mobile operators in Dublin, Ireland.

The next generation of mobile networks will bring an appetite for reliable communication, which enables emerging critical-communication services. In this paper, we present network sharing between operators as a way to provide increased reliability while using the already existing mobile network infrastructure. Our results indicate that network sharing significantly improves network performance during periods of connectivity shortfalls, benefiting the design of reliable networks. Our conclusions are drawn from a real-world dataset of signal quality indicators for three mobile operators in Dublin, Ireland.