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.

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.

We consider a cost minimization problem for High Volume Servers (HVS) equipped with Virtual Machines (VMs) to serve Virtual Network Functions (VNF) demands for resources. Given a scheduling period, a central scheduler decides which VM to use for each VNF demand. Each VM can be activated or disabled with different costs. Each VNF has a delay-weighted pricing function to indicate its completion time tolerance. We prove the NP-completeness of the problem and develop an algorithm based on Lagrangian relaxation and subgradient optimization to deal with this computational complexity. Finally, our numerical results demonstrate our algorithm’s effectiveness compared to two benchmarks.

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. 

Information age is a recently introduced metric to represent the freshness of information in communication systems. We investigate age minimization in a wireless network and propose a novel approach of optimizing the scheduling strategy to deliver all messages as fresh as possible. Specifically, we consider a set of links that share a common channel. The transmitter at each link contains a given number of packets with time stamps from an information source that generated them. We address the link transmission scheduling problem with the objective of minimizing the overall age. This minimum age scheduling problem (MASP) is different from minimizing the time or the delay for delivering the packets in question. We model the MASP mathematically and prove it is NP-hard in general. We also identify tractable cases as well as optimality conditions. An integer linear programming formulation is provided for performance benchmarking. Moreover, a steepest age descent algorithm with better scalability is developed. Numerical study shows that, by employing the optimal schedule, the overall age is significantly reduced in comparison to other scheduling strategies.

In this work, we study the benefit of partial relay cooperation. We consider a two-node system consisting of one source and one relay node transmitting information to a common destination. The source and the relay have external traffic and in addition, the relay is equipped with a flow controller to regulate the incoming traffic from the source node. The cooperation is performed at the network level. A collision channel with erasures is considered. We provide an exact characterization of the stability region of the system and we also prove that the system with partial cooperation is always better or at least equal to the system without the flow controller. 

We address the maximum link activation problem in wireless networks with new features, namely when the transmitters can perform cooperative transmission, and the receivers are able to perform successive interference cancellation. In this new problem setting, which transmitters should transmit and to whom, as well as the optimal cancellation patterns at the receivers, are strongly intertwined. We present contributions along three lines. First, we provide a thorough tractability analysis, proving the NP-hardness as well as identifying tractable cases. Second, for benchmarking purposes, we deploy integer linear programming for achieving global optimum using off-theshelf optimization methods. Third, to overcome the scalability issue of integer programming, we design a sub-optimal but efficient optimization algorithm for the problem in its general form, by embedding maximum-weighted bipartite matching into local search. Numerical results are presented for performance evaluation, to validate the benefit of cooperative transmission and interference cancellation for maximum link activation and to demonstrate the effectiveness of the proposed algorithm.

Long Term Evolution (LTE)-Wireless Local Area Network (WLAN) Path Aggregation (LWPA) based on Multi- path Transmission Control Protocol (MPTCP) has been under standardization procedure as a promising and cost-efficient solution to boost Downlink (DL) data rate and handle the rapidly increasing data traffic. This paper aims at providing tractable analysis for the DL performance evaluation of large-scale LWPA networks with the help of tools from stochastic geometry. We consider a simple yet practical model to determine under which conditions a native WLAN Access Point (AP) will work under LWPA mode to help increasing the received data rate. Using stochastic spatial models for the distribution of WLAN APs and LTE Base Stations (BSs), we analyze the density of active LWPA- mode WiFi APs in the considered network model, which further leads to closed-form expressions on the DL data rate and area spectral efficiency (ASE) improvement. Our numerical results illustrate the impact of different network parameters on the performance of LWPA networks, which can be useful for further performance optimization. 

Deploying direct device-to-device (D2D) links is a promising technology for vehicle-to-X (V2X) applications. However, intracell interference, along with stringent requirements on latency and reliability, are challenging issues. In this paper, we study the radio resource management problem for D2D-based safety-critical V2X communications. We first transform the V2X requirements into the constraints that are computable using slowly varying channel state information only. Secondly, we formulate an optimization problem, taking into account the requirements of both vehicular users (V-UEs) and cellular users (C-UEs), where resource sharing can take place not only between a V-UE and a C-UE but also among different V-UEs. The NP-hardness of the problem is rigorously proved. Moreover, a heuristic algorithm, called Cluster-based Resource block sharing and pOWer allocatioN (CROWN), is proposed to solve this problem. Finally, simulation results indicate promising performance of the CROWN scheme.

With the densification of nodes in cellular networks, free space optics (FSO) connections are becoming an appealing low cost and high rate alternative to copper and fiber backhaul solutions for wireless communication systems. To en- sure a reliable cellular backhaul, provisions for redundant, disjoint paths between the nodes must be made in the design phase. This chapter aims at finding a cost- effective solution to upgrade the cellular backhaul with pre-deployed optical fibers using FSO links and mirror components. A novel integer linear programming model to approach optimal FSO backhaul design, guaranteeing K-disjoint paths connecting each node pair is presented. Next, a column generation method to a path-oriented mathematical formulation is developed. Applying the method in a sequential man- ner enables high computational scalability. Realistic scenarios are used to demon- strate the proposed approaches which efficiently provide optimal or near-optimal solutions, and thereby allow for accurately dealing with the trade-off between cost and reliability.