Multi-connectivity is emerging as promising solution to provide reliable communications and seamless connectivity at the millimeter-wave frequency range. Due to the obstacles that cause frequent interruptions at such high frequency range, connectivity to multiple cells can drastically increase the network performance in terms of throughput and reliability by coordi- nation among the network elements. In this paper, we propose an algorithm for the link scheduling optimization that maximizes the network throughput for multi-connectivity in millimeter-wave cellular networks. The considered approach exploits a centralized architecture, fast link switching, proactive context preparation and data forwarding between millimeter-wave access points and the users. The proposed algorithm is able to numerically approach the global optimum and to quantify the potential gain of multi-connectivity in millimeter-wave cellular networks. 

Multi-connectivity is emerging as a promising solution to provide reliable communications and seamless connectivity for the millimeter-wave frequency range. Due to the blockage sensitivity at such high frequencies, connectivity with multiple cells can drastically increase the network performance in terms of throughput and reliability. However, an inefficient link scheduling, i.e., over and under-provisioning of connections, can lead either to high interference and energy consumption or to unsatisfied user's quality of service (QoS) requirements. In this work, we present a learning-based solution that is able to learn and then to predict the optimal link scheduling to satisfy users' QoS requirements while avoiding communication interruptions. Moreover, we compare the proposed approach with two base line methods and the genie-aided link scheduling that assumes perfect channel knowledge. We show that the learning-based solution approaches the optimum and outperforms the base line methods.

Relaying techniques for millimeter-wave wireless networks represent a powerful solution for improving the transmission performance. In this paper, we quantify the benefits in terms of delay and throughput for a random-access multi-user millimeter-wave wireless network, assisted by a full-duplex network cooperative relay. The relay is equipped with a queue for which we analyze the performance characteristics (e.g., arrival rate, service rate, average size, and stability condition). Moreover, we study two possible transmission schemes: fully directional and broadcast. In the former, the source nodes transmit a packet either to the relay or to the destination by using narrow beams, whereas, in the latter, the nodes transmit both the destination and the relay in the same timeslot by using a wider beam but with lower beamforming gain. In our analysis, we also consider the beam alignment phase that occurs every time, a transmitter node changes the destination node. We show the duration of how the beam is aligned, as well as position and a number of transmitting nodes, significantly affect the network performance. In addition, we discuss the impact of beam alignment errors and imperfect self-interference cancellation technique at the relay for full-duplex communications. Moreover, we illustrate the optimal transmission scheme (i.e., broadcast or fully directional) for several system parameters and show that a fully directional transmission is not always beneficial, but in some scenarios, broadcasting and relaying can improve the performance in terms of throughput and delay.

The massive exploitation of robots for Industry 4.0 needs advanced wireless solutions that replace more costly wired networks. In this regard, millimeter-waves (mm-waves) can provide high data rates, but they are characterized by a spotty coverage requiring dense radio deployments. In such scenarios, coverage holes and numerous handovers may decrease the communication throughput and reliability. In contrast to conventional multi-robot path planning (MPP), we define a type of multi-robot association-path planning (MAPP) problems aiming to jointly optimize the robots’ paths and the robots-access points (APs) associations. In MAPP, we focus on minimizing the path lengths as well as the number of handovers while sustaining the wireless connectivity of the robots. We propose an algorithm that can solve MAPP in polynomial time presenting results close to the global optimum. The proposed solution is able to guarantee the robots’ connectivity and to dramatically reduce the number of handovers in comparison to minimizing only the path lengths.