Abstract-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 coordination 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.

The massive exploitation of robots for industry 4.0 needs advanced wireless solutions that replace less flexible and 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 connectivity. We propose an algorithm that can solve MAPP in polynomial time and it is able to numerically approach the global optimum. We show that the proposed solution is able to guarantee network connectivity and to dramatically reduce the number of handovers in comparison to minimizing only the path lengths.

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Machine learning will play a major role in handling 1 the complexity of future mobile wireless networks by improving 2 network management and orchestration capabilities. Due to the 3 large number of parameters that can be monitored and config-4 ured in the network, collecting and processing high volumes of 5 data is often unfeasible or too expensive at network runtime, 6 which calls for taking resource management and service orches-7 tration decisions with only a partial view of the network status. 8 Motivated by this fact, this paper proposes a transfer learning 9 framework for reconstructing the radio map corresponding to a 10 target antenna tilt configuration by transferring the knowledge 11 acquired from another tilt configuration of the same antenna, 12 when no or very limited measurements are available from the 13 target. The performance of the framework is validated against 14 standard machine learning techniques on a data set collected 15 from a 4G commercial base stations. In most of the tested scenar-16 ios, the proposed framework achieves notable prediction accuracy 17 with respect to classical machine learning approaches, with a 18 mean absolute percentage error below 8%. 19 Index Terms-Radio map prediction, antenna tilt, transfer 20 learning. 21 I. INTRODUCTION 22 F IFTH generation wireless networks (5G) are expected to 23 improve the performance of cellular systems, achieving 24 higher data rates, reduced latency, higher reliability and sup-25 port for greater numbers of users. To achieve this, 5G resorts 26 to dense and heterogeneous deployments, coupled with higher 27 flexibility in the network access and core domains, which can 28 be dynamically managed in either a centralized or distributed 29 manner. To cope with such a complex scenario, it is foreseen 30 that machine learning tools will play a major role in enabling 31

Relaying techniques for millimeter-wave wireless networks represent a powerful solution for improving the transmission performance. In this work, 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 to 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 take into account the beam alignment phase that occurs every time a transmitter node changes the destination node. We show how the beam alignment duration, as well as position and number of transmitting nodes, significantly affect the network performance. We additionally 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. Index TermsMillimeter-waves, network cooperative relaying, beam alignment, random access networks, directional communications.cooperative full-duplex relay that is equipped with a queue. We analyze the impact of directional communications by evaluating two possible transmission schemes: broadcast (BR) and fully directional (FD). Using the former, the UEs transmit simultaneously to both the mmAP and the relay by means of wider beams at lower beamforming gains, whereas, with the FD scheme, the UEs transmit either to the mmAP or to the relay by using narrow beams. Moreover, we take into account the beam alignments that occur every time the transmitters change receiver and scheme. A. Related WorkSeveral works have been proposed for evaluating the benefits of relaying techniques in mmwave communications, e.g, [25]- [34]. In [25], the authors propose a physical layer analysis of cooperative communications for frequencies above 10 GHz and evaluate the outage probability of several multiple access protocols, combining techniques, and relay transmission techniques.The study shows that the use of relays drastically improves the coverage probability and the correlation between the source-relay and relay-destination links can be exploited to improve the performance. The authors of [26], [27] use stochastic geometry to show the improvements in the signal-to-interference-plus-noise ratio (SINR) distribution and coverage probability for a mm-wave cellular network...

Communications using frequency bands in the millimeter-wave range can play a key role in future generations of mobile networks. By allowing large bandwidth allocations, high carrier frequencies will provide high data rates to support the ever-growing capacity demand. The prevailing challenge at high frequencies is the mitigation of large path loss and link blockage effects. Highly directional beams are expected to overcome this challenge. In this paper, we propose a stochastic model for characterizing beam coverage probability. The model takes into account both line-of-sight and first-order non-lineof-sight reflections. We model the scattering environment as a stochastic process and we derive an analytical expression of the coverage probability for any given beam. The results derived are validated numerically and compared with simulations to assess the accuracy of the model.

In this work, we analyze the throughput of random access multi-user relay-assisted millimeter-wave wireless networks, in which both the destination and the relay have multipacket reception capability. We consider a full-duplex network cooperative relay that stores the successfully received packets in a queue, for which we analyze the performance. Moreover, we study the effects on the network throughput of two different schemes, by which the source nodes transmit either a packet to both the destination and the relay in the same timeslot by using wider beams (broadcast scheme) or to only one of these two by using narrower beams (fully directional scheme). Numerical results show how the network throughput varies according to specific system parameters, such as positions and number of nodes. The analysis allows us also to understand the optimal transmission scheme for different network scenarios and shows that the choice to use transmissions with narrow beams does not always represent the best strategy, as wider beams provide a lower beamforming gain, but they allow to transmit simultaneously both at the relay and the destination.

Fifth generation wireless networks (5G) will face key challenges caused by diverse patterns of traffic demands and massive deployment of heterogeneous access points. In order to handle this complexity, machine learning techniques are expected to play a major role. However, due to the large space of parameters related to network optimization, collecting data to train models for all possible network configurations can be prohibitive. In this paper, we analyze the possibility of performing a knowledge transfer, in which a machine learning model trained on a particular network configuration is used to predict a quantity of interest in a new, unknown setting. We focus on the tilt-dependent received signal strength maps as quantities of interest and we analyze two cases where the knowledge acquired for a particular antenna tilt setting is transferred to (i) a different tilt configuration of the same antenna or (ii) a different antenna with the same tilt configuration. Promising results supporting knowledge transfer are obtained through extensive experiments conducted using different machine learning models on a real dataset.