Capacity of Wireless Ad Hoc Networks with Opportunistic Collaborative Communications

Optimal multihop routing in ad hoc networks requires the exchange of control messages at the MAC and network layer in order to set up the (centralized) optimization problem. Distributed opportunistic space-time collaboration (OST) is a valid alternative that avoids this drawback by enabling opportunistic cooperation with the source at the physical layer. In this paper, the performance of OST is investigated. It is shown analytically that opportunistic collaboration outperforms (centralized) optimal multihop in case spatial reuse (i.e., the simultaneous transmission of more than one data stream) is not allowed by the transmission protocol. Conversely, in case spatial reuse is possible, the relative performance between the two protocols has to be studied case by case in terms of the corresponding capacity regions, given the topology and the physical parameters of network at hand. Simulation results confirm that opportunistic collaborative communication is a promising paradigm for wireless ad hoc networks that deserves further investigation

HMM-Based tracking of moving terminals in dense multipath indoor environments

This paper deals with the problem of radio localization of moving terminals (MTs) for indoor applications with mixed line-of sight/non-line-of-sight (LOS/NLOS) conditions. To reduce false localizations, a Bayesian approach is proposed to estimate the MT position. The tracking algorithm is based on a Hidden Markov Model (HMM) that permits to jointly track both the MT position and the sight condition. Numerical results show that the proposed HMM method improves the localization accuracy in LOS/NLOS indoor environments

Vehicular blockage modelling and performance analysis for mmwave v2v communications

Vehicle-to-Everything (V2X) communications are revolutionizing the connectivity of transportation systems supporting safe and efficient road mobility. To meet the growing bandwidth eagerness of V2X services, millimeter-wave (e.g., 5G new radio over spectrum 26.50 - 48.20 GHz) and sub-THz (e.g., 120 GHz) frequencies are being investigated for the large available spectrum. Communication at these frequencies requires beam-type connectivity as a solution for the severe path loss attenuation. However, beams can be blocked, with negative consequences for communication reliability. Blockage prediction is necessary and challenging when the blocker is dynamic in high mobility scenarios such as Vehicle-to-Vehicle (V2V). This paper presents an analytical model to derive the unconditional probability of blockage in a highway multi-lane scenario. The proposed model accounts for the traffic density, the 3D dimensions of the vehicles, and the position of the antennas. Moreover, by setting the communication parameters and a target quality of service, it is possible to predict the signal-to-noise ratio distribution and the service probability, which can be used for resource scheduling. Exhaustive numerical results confirm the validity of the proposed model.Comment: 6 page

Classification of Sensory Neural Signals through Deep Learning Methods

The recording and analysis of peripheral neural signals can be beneficial to provide feedback to prosthetic limbs and recover the sensory functionality in people with nerve injuries. Nevertheless, the interpretation of sensory recordings extracted from the nerve is not trivial, and only few studies have applied classifiers on sequences of neural signals without previous feature extraction. This paper evaluates the classification performance of two deep learning (DL) models (CNN and ConvLSTM) applied to the electroneurographic (ENG) activity recorded from the sciatic nerve of rats. The ENG signals, available from two public datasets, were recorded using multi-channel cuff electrodes in response to four sensory inputs (plantarflexion, dorsiflexion, nociception, and touch) elicited in response to mechanical stimulation applied to the hind paw of the rats. Different temporal lengths of the signals were considered (2.5 s, 1 s, 500 ms, 200 ms, and 100 ms), Both the two DL models proved to correctly discriminate sensory stimuli without the need of hand-engineering feature extraction. Moreover, ConvLSTM outperformed state-of-the-art results in classifying sensory ENG activity (more than 90% F1-score for sequences greater than 500 ms), and it showed promising results for real-time application scenarios