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Homomorphic Encryption (HE) has become a wellknown tool for privacy-preserving recognition in biometric systems. Despite some important advantages of HE (such as preservation of recognition accuracy), there are two main drawbacks in the application of HE to biometric recognition systems: first, the security of the system solely depends on the secrecy of the private (decryption) key; second, the computational costs of the operations on the ciphertexts are expensive. To address these challenges, in this paper we propose a hybrid scheme for the protection of biometric templates, which combines cancelable biometrics (CB) methods and HE. Applying CB prior to HE enhances both the security and privacy of the overall system, since the protected templates remain irreversible even if the secret keys are leaked (commonly referred to as the full disclosure scenario). In addition, we can reduce the dimensionality of templates using CB before applying HE, which speeds up the computation over the ciphertexts. We use BioHashing, Multi-Layer Perceptron (MLP) hashing, and Index-ofMaximum (IoM) hashing as different CB methods, and for each of these schemes, we propose a method for computing scores between hybrid-protected templates in the encrypted domain. We evaluate our proposed hybrid scheme using different state-of-the-art face recognition models (ArcFace, ElasticFace, and FaceNet) on the MOBIO and LFW datasets. The source code of our experiments is publicly available, so our work can be fully reproduced.

Multi-connectivity emerges as a useful feature to handle the traffic in heterogeneous cellular scenarios and fulfill the demanding requirements in terms of data rate and reliability. It allows a device to be simultaneously connected to multiple cells belonging to different radio access network nodes from a single or multiple radio access technologies. This paper addresses the problem of optimally splitting the traffic among cells when multi-connectivity is used. For this purpose, it proposes the use of deep learning to determine the optimum amount of traffic of a device that needs to be sent through one or another cell depending on the current traffic and radio conditions. Obtained results reveal a promising capability of the proposed Deep Q Network solution to select quasi optimum traffic splits in the considered scenario. This paper is part of ARTIST project (ref. PID2020- 115104RB-I00) funded by MCIN/AEI/10.13039/ 501100011033. The work is also funded by the Spanish Ministry of Science and Innovation under grant ref. PRE2018-084691.

Deep Reinforcement Learning (DRL) has recently emerged as a promising technique to deal with different problems in the 5G and beyond Radio Access Network (RAN). The practical implementation of DRL solutions in the real network embraces a training process that is fundamental to materialize the expected benefits associated to these techniques. However, little effort has been devoted in the literature to analyze this training process when applying DRL in the RAN. In an effort to contribute to fill this gap, this paper presents an impact analysis of the training process on the obtained performance by a DRL solution for RAN slicing. To this end, a methodology to specify the training dataset is introduced together with the definition of relevant metrics. Then, the paper presents different simulation results to determine the features of the training dataset that allow a satisfactory training and a high performance of the obtained policy when applied during the inference stage.

Along the successive generations, mobile network infrastructure deployments have required continuous expansion and upgrades to respond to the ever increasing traffic demand. At the same time, user equipment (UE) has evolved with the introduction of powerful and advanced communication and computational capabilities. Moreover, several industrial initiatives are promoting the principles of RAN disaggregation through the split of RAN functionalities among multiple interoperable components in contrast to traditional monolithic solutions. Embracing these trends, this paper presents the vision of a Beyond 5G (B5G) scenario where the UE takes a more active role in the communication service provisioning and is used to augment the RAN infrastructure as a source of distributed capacity and network intelligence. This is expected to bring benefits for Mobile Network Operators by reducing the number of base stations to deploy and by providing the RAN with a new degree of flexibility for smartly and dynamically adapting the RAN to the specific needs. In this direction, after discussing the main enabling technologies of the envisaged RAN augmentation concept, this paper presents a simulation-based assessment to quantify the performance improvements and the achievable savings in terms of infrastructure deployment. This paper is part of ARTIST project (ref. PID2020- 115104RB-I00) funded by MCIN/AEI/10.13039/ 501100011033.

This paper describes a 4.3e- RMS low noise 4.6µm Time-of-Flight pixel based on charge domain with kTC noise removal designed to enhance depth camera image quality. The pixel takes advantage of 6µm gradually doped epitaxial layer for 88.5% demodulation contrast at 200MHz and 18.5% QE at 940nm. Buried channel transfer gates are used to enable low capacitive switching and allowing best-in-class 1.4µW/pixel power consumption at 200MHz. The pixel is fully isolated due to deep trench isolation and the pixel bulk is biased at several hundreds of mV for low power Time-of-Flight sensor and system operation. The paper describes the design of 0.54Mpix camera (672 x 804 pixels) implementing demodulation circuits robust against EMI and multi-devices interferences. The performance of an indirect Time-of-Flight system is demonstrated with 1.0% depth noise under 40kLux at 30fps.

This paper introduces two novel control solutions, which allow localised delay compensation for grid-connected and standalone inverters. As the prediction horizon of the existing controllers are quite small as compared to large communication delays and information unavailability due to denial-of-service (DoS) attacks, the proposed strategy offers a robust delay mitigation range using localized dynamics. Its design philosophy is leveraged via a prediction policy using the inner control loop dynamics. Based on different control objectives in grid-connected and standalone mode, the proposed solutions have been augmented into the control system accordingly. Finally, its robustness under various communication delay and DoS attacks have been tested.

This paper investigates the impact of the delay resulting from a blockchain, a promising security measure, for a hierarchical control system of inverters connected to the grid. The blockchain communication network is designed at the secondary control layer for resilience against cyberattacks. To represent the latency in the communication channel, a model is developed based on the complexity of the blockchain framework. Taking this model into account, this work evaluates the plant’s performance subject to communication delays, introduced by the blockchain, among the hierarchical control agents. In addition, this article considers an optimal model-based control strategy that performs the system’s internal control loop. The work shows that the blockchain’s delay size influences the convergence of the power supplied by the inverter to the reference at the point of common coupling. In the results section, real-time simulations on OPAL-RT are performed to test the resilience of two parallel inverters with increasing blockchain complexity.

This work is developed for the identification of a denial-of-service cyberattack on the secondary controller of inverter systems which is connected to the power grid. The identification is made through the dynamic response of the reactive power (Q) of the system under attack. By observing the dynamic characteristic of Q, it is possible to correlate the attack with the nominal response of the hierarchical controller. The article shows that the occurrence of the attack can be identified through a supervisory control that runs a model in parallel. The article argues that after an early identification of the attack, a local controller can take action to mitigate its effects on the system’s response.