We consider beyond-diagonal reconfigurable intelligent surfaces (BD-RISs) whose elements are connected in groups and aim at optimizing their configuration to maximize the achievable rate of the cascade channel. We propose two suboptimal approaches (i.e., semidefinite programming (SDP) and projected gradient ascent (PGA) solutions) to first find the BD-RIS configuration that maximizes the composite channel trace and then locally maximizes the achievable rate by a randomization approach. We impose a constraint on the choice of the coefficients to ensure that the BD-RIS is passive, i.e., it does not emit more power than that received. Still, our solution has a high communication overhead for a large number of connections among the BD-RIS elements. We then propose a dynamic mapping between the BD-RIS configuration and a small number of control variables. The mapping is provided by the encoder part of an autoencoder, trained to minimize a suitable loss function on the optimal configurations in the specific deployment. We also design the BD-RIS configuration directly in the latent space of the autoencoder, reducing the complexity. By simulations in a typical cellular communication scenario, we show that the group-connected BD-RIS can achieve up to 95% of the rate obtained for a fully-connected BD-RIS with two orders of magnitude lower complexity, while the autoencoder compression and configuration optimization in the latent space reduces the control rate by 90% with negligible rate loss.

This paper focuses on the challenge-response physical-layer authentication (CR-PLA) scheme where a reflecting intelligent surface (RIS) is under the control of a receiving base station (BS) (Bob) who aims at checking if received messages come from a legitimate user equipment (UE) Alice or from an impersonating device (Trudy). To this end, Bob sets a random configuration of the RIS which remains secret to the attacker, and verifies that the channel estimated on the received message corresponds to the set configuration. We design the probability distribution of RIS configurations chosen by the verifier to maximize average capacity while satisfying an upper bound on missed detection (MD) probability for a given false alarm (FA) probability. The balance of communication and security metrics demonstrated by the numerical results shows the effectiveness and potential of the CR-PLA scheme.

Due to their ability to manipulate (EM) fields with high flexibility and low-cost implementation, metasurfaces have emerged as a promising technology to enhance the performance of cellular networks. We propose a novel reconfigurable antenna using transmitting metasurfaces for cellular base stations. This antenna is formed by surrounding radiating elements with multiple metasurfaces that can be configured to be transparent or absorptive to electromagnetic waves. This increases the directionality of the resulting emitted signal, improves reception, and limits interference. We present a baseband equivalent channel model for downlink transmission that incorporates the reconfigurable antenna and describes the diffraction phenomena resulting from the metasurfaces’ specific configuration. Next, we optimize the metasurface configuration, the transmit and receive beamformers, and the transmit power at multiple coordinated base stations to maximize the network sum-rate. Numerical results in a (5G) networks confirm that the proposed structure considerably increases the sum-rate compared to traditional antenna arrays.

Reconfigurable intelligent surfaces (RISs) are seen as a promising technology to improve cellular network coverage, due to their ability to steer the impinging signals in desired directions. The design of the RIS can be easily addressed by assuming full channel knowledge. Nevertheless, estimating the channels to and from the RIS is a challenging problem, as it requires a huge training overhead. This paper proposes an efficient configuration optimization jointly with channel estimation by exploiting deep learning tools. In particular, we propose an algorithm that works in two steps. The first step is based on a decision tree that requires few end-to-end channel estimates with different RIS configurations. The configurations are iteratively selected based on an estimate of the mutual information between the obtained rates and the optimal configuration. The second step instead provides the minimum mean-square-error estimate of the optimal RIS configuration based on the data rates estimated on the channels obtained in the first step through a neural network (NN) trained with a supervised approach. Numerical results confirm that the proposed solution provides a configuration close to the optimal, with achievable rates approaching the upper bound obtained with perfect channel knowledge.

Physical-layer authentication (PLA) mechanisms exploit signals exchanged at the physical layer of communication systems to confirm the sender of a received message. In this paper, we propose a novel challenge-response PLA (CR-PLA) mechanism for a cellular system that leverages the reconfigurability property of a reconfigurable intelligent surface (RIS) (under the control of the verifier) in an authentication mechanism. In CR-PLA, the verifier base station (BS) sets a random RIS configuration, which remains secret to the intruder, and then checks that the resulting estimated channel is modified correspondingly. In fact, for a message sent by an attacker in a different location than the legitimate user equipment (UE), the BS will estimate a different channel and the message will be rejected as fake. Such a solution reduces the communication and computational overhead with respect to higher-layer cryptographic authentication. We derive the maximum a-posteriori attack when the attacker observes a correlated channel and the reconfigurable intelligent surface (RIS) has many elements, and the attacker transmits to Bob either directly or through the RIS. Using a generalized likelihood ratio test to test the authenticity at the base station (BS), we derive approximate expressions of the false alarm and misdetection probabilities when both the BS and the UE have a single antenna each, while the RIS has a large number of elements. We also evaluate the trade-off between security and communication performance, since choosing a random RIS configuration reduces the data rate. Moreover, we investigate the impact of various parameters (e.g., the RIS randomness, the number of RIS elements, and the operating signal-to-noise ratio) on security and communication performance.

Reconfigurable intelligent surfaces (RISs) are a promising solution to improve the coverage of cellular networks, thanks to their ability to steer impinging signals in desired directions. However, they introduce an overhead in the communication process since the optimal configuration of a RIS depends on the channels to and from the RIS, which must be estimated. In this paper, we propose a novel fast iterative configuration (FIC) protocol to determine the optimal RIS configuration that exploits the small number of paths of millimetre-wave (mmWave) channels and an adaptive choice of the explored RIS configurations. In particular, we split the elements of the RIS into a number of subsets equal to the number of channel taps. For each subset, then an iterative procedure finds at each iteration the optimal RIS configuration in a codebook exploring a two-dimensional grid of possible angles of arrival and departure of the path at the RIS. Over the iterations, the grid is made finer around the point identified in previous iterations. Numerical results obtained using an urban channel model confirm that the proposed solution is fast and provides a configuration close to the optimal in a shorter time than other existing approaches.

Wireless body area networks (WBANs) are becoming increasingly popular as they allow individuals to continuously monitor their vitals and physiological parameters remotely from the hospital. With the spread of the SARS-CoV-2 pandemic, the availability of portable pulse-oximeters and wearable heart rate detectors has boomed in the market. At the same time, in 2020 we assisted to an unprecedented increase of healthcare breaches, revealing the extreme vulnerability of the current generation of WBANs. Therefore, the development of new security protocols to ensure data protection, authentication, integrity and privacy within WBANs are highly needed. Here, we targeted a WBAN collecting ECG signals from different sensor nodes on the individual's body, we extracted the inter-pulse interval (i.e., R-R interval) sequence from each of them, and we developed a new information theoretic key agreement protocol that exploits the inherent randomness of ECG to ensure authentication between sensor pairs within the WBAN. After proper pre-processing, we provide an analytical solution that ensures robust authentication; we provide a unique information reconciliation matrix, which gives good performance for all ECG sensor pairs; and we can show that a relationship between information reconciliation and privacy amplification matrices can be found. Finally, we show the trade-off between the level of security, in terms of key generation rate, and the complexity of the error correction scheme implemented in the system.

We consider the problem of detecting spoofing attacks for a GNSS receiver in space, orbiting around the Earth. Since a receiver in space cannot leverage the presence of so called signals of opportunity, it must rely on detecting anomalies in the signal itself and checking the consistency of its measurements with the computed orbital position. We consider three different consistency checks: on the overall received GNSS signal power at the front-end; on the estimated carrier-to-noise ratio (C/N0) for the signal coming from each satellite in view; on the final computed position at the receiver output. Moreover, we devise a fusion method that combines soft outputs from the three checks to provide a more reliable and robust detection. The proposed techniques are tested in a realistic simulation environment showing that, although the position consistency check is by far the most reliable, the proper fusion of the soft information from all three allow to further improve the detection rates in different conditions significantly.

Internet of Things is rapidly spreading across several fields, including healthcare, posing relevant questions related to communication capabilities, energy efficiency and sensors unobtrusiveness. Particularly, in the context of recognition of gestures, e.g., grasping of different objects, brain and muscular activity could be simultaneously recorded via EEG and EMG, respectively, and analyzed to identify the gesture that is being accomplished, and the quality of its performance. This paper proposes a new algorithm that aims (i) to robustly extract the most relevant features to classify different grasping tasks, and (ii) to retain the natural meaning of the selected features. This, in turn, gives the opportunity to simplify the recording setup to minimize the data traffic over the communication network, including Internet, and provide physiologically significant features for medical interpretation. The algorithm robustness is ensured both by consensus clustering as a feature selection strategy, and by nested cross-validation scheme to evaluate its classification performance. Although Feature Selection with Consensus (FeSC) implements a very robust architecture for feature selection and classification, results are still negatively affected by the limited size of the dataset. In the future, further investigations could determine to what extent size could cause a drop in the performance of FeSC in this and other gesture recognition applications.

In this paper, we propose a new method for jointly compressing EEG and EMG biosignals based on the so-called cortico-muscular coherence, a function that takes into account the simultaneous frequency changes of the brain and the muscles activity, and can be used, e.g., to classify different kinds of movement. It is shown that this method increases the achievable compression rate compared to transmitting EEG and EMG samples separately, while trading-off with the accuracy of the classification. This can be exploited in several kinds of life and health applications e.g., motor rehabilitation and drivers attention monitoring; it could be especially useful for low-power wireless technologies, such as Bluetooth Low Energy or IEEE 802.15.6, whose transmission resources are limited.

This work presents an innovative application of the well-known concept of cortico-muscular coherence for the classification of various motor tasks, i.e., grasps of different kinds of objects. Our approach can classify objects with different weights (motor-related features) and different surface frictions (haptics-related features) with high accuracy (over 0.8). The outcomes presented here provide information about the synchronization existing between the brain and the muscles during specific activities; thus, this may represent a new effective way to perform activity recognition.

In this paper, we analyze hybrid automatic repeat request applied to the transmission of video content over the wireless channel. Retransmission-based techniques are usually applied to queueing systems assuming a homogeneous flow of identical packets, which are all transmitted and possibly retransmitted in the same way. However, multimedia packets are encoded with incremental methods leveraging spatial and temporal redundancy and as such, they have different roles and should be treated differently by the retransmission mechanism. Therefore, our work considers a selective retransmission scheme with unequal error protection applied to a multimedia flow subdivided into distinguishable packets. We assume a binary channel with memory and non-zero round-trip time. We utilize discrete-time Markov chains to model the channel and the transmission/retransmission system. This enables a closed-form derivation of performance metrics via Markov analysis. Numerical results are discussed and possible implications on multimedia communications are evaluated.

We investigate a jamming problem in an underwater acoustic sensor network, where nodes try to communicate in spite of an adversary that is attempting to block their communications. We take into account that the attenuation of underwater acoustic channels is strongly dependent on the communication distance and the signal frequency. We frame the problem in a game theoretic setup, as a Bayesian zero-sum game where the sensor network acts as the maximizer of the transmission capacity, while the jammer is the minimizer. In particular, we are interested in evaluating the effect of the nodes' position on the resulting equilibrium. The Bayesian character comes into play to represent the uncertainty on the position information of the nodes. Our evaluations show that for many network configurations, the equilibrium strategy of the jammer is pure. Thus, the transmitters can act as though the jammer only causes a higher level of interference. This allows us to identify positions where the damage caused by a jammer is easier to quantify, but the jammer itself is harder to detect.

This paper presents a study of the automatic repeat request (ARQ) technique applied to the transmission of multimedia traffic, e.g., video content. In the literature, retransmission-based techniques are usually investigated by means of queueing theory and assuming a homogeneous flow of identical packets, which are sent and possibly retransmitted all in the same way. However, multimedia packets are the result of an incremental encoding that leverages spatial and temporal redundancy, which is naturally present in the raw data. As a result, the flow is inherently made of packets with different roles, which should also be treated differently by the ARQ mechanism. Thus, we assume that different levels of error protection are applied, and also we model the decoding process at the receiver as accounting for a dependence relationship among the packets. Moreover, since error correlation has a strong impact on the performance, we consider a transmission over a Markov channel where we tune not only the error probability but also the average error burst size. This enables the derivation of several performance metrics in an entirely analytical manner via Markov analysis. Finally, some numerical results are explored and possible applications on the development of guidelines for multimedia transmission are discussed.