In this paper, we consider a relay-enabled wireless network and optimize the weighted proportional fair (wPF) bandwidth allocation by means of exact algorithms with linear complexity on the number of users and relays. Complex architectures typical of relay-enabled systems pose the need of developing efficient techniques that account for the intertwined nature of all the network agents, as resources must be split not only between users, but also between relays and relay-served users, altogether constrained by backhaul capacities and the traffic bottleneck present at the wired base stations. Here, traditional schemes for bandwidth allocation cannot be applied, as resources from one point of the network cannot be allocated regardless the allocation performed at other entities of the same network. Hence, we develop a compact weighted proportional fair resource management with very lightweight complexity, able to jointly allocate access and backhaul resources optimally in real time. We benchmark the results on network capacity and fairness with state-of-art proposals and show that the wPF exact algorithms proposed yield the best trade-off between capacity and fairness in relay networks.

Aiding the ground cellular network with aerial base stations carried by drones has experienced an intensive raise of interest in the past years. Reconfigurable air-to-ground channels enable aerial stations to enhance users’ access links by means of seeking good line-of-sight connectivity while hovering in the air. In this article, we propose an analytical framework for the 3D placement of a fleet of coordinated drone relays. This framework optimizes network performance in terms of user throughput fairness, expressed through the α-fairness metric. The optimization problem is formulated as a mixed-integer non-convex program, which is intractable. Hence, we propose an extremal-optimization-based algorithm, Parallelized Alpha-fair Drone Deployment (PADD), which solves the problem online, in low-degree polynomial time. We evaluate our proposal by means of numerical simulations over the real topology of a dense city. We discuss the advantages of integrating drone relay stations in current networks and test several resource scheduling approaches in both static and dynamic scenarios, including with progressively larger and denser crowds.

With the current advances in unmanned aerial vehicle (UAV) technologies, aerial vehicles are becoming very attractive for many purposes. However, currently the bottleneck in their adoption is no longer due to architectural and protocol challenges and constraints, but rather to the limited energy that they can rely on. In this article, we design two power resupply schemes under the assumption of a fleet of homogeneous UAVs. Such schemes are designed to minimize the size of the fleet to be devoted to apersistentservice (i.e., carried out at all times) of a set of aerial locations. First, we consider the case where the aerial locations to be served are equidistant from an energy supply station. In that scenario, we design a simple scheduling scheme, that we name homogeneous rotating resupply (HoRR), which we prove to be feasible and exact in the sense that it uses the minimum possible number of UAVs to guarantee the permanent coverage of the aerial service locations. Then, we extend that work for the case of nonevenly distributed aerial locations. In this new scenario, we demonstrate that the problem becomes NP-hard, and design a lightweight scheduling scheme, partitioned heterogeneous rotating resupply (PHeRR), which extends the operation ofHoRRto the heterogeneous case. Through numerical analysis, we show thatPHeRRprovides near-exact resupply schedules.

The adoption and integration of drones in commu-nication networks is becoming reality thanks to the deployment of advanced solutions for IoT and cellular communication relay schemes. However, using drones introduces new energy con-straints and scheduling issues in the dynamic management of the network topology, due to the need to call back and recharge, or substitute, drones that run out of energy. In this paper, we describe the design of a drone recharging scheme for realisti-cally limited flight time of drones, and leverage the presence of recharging stations. Indeed, drones need to be recharged periodically, and maximizing the operational time of drones is paramount to minimize the size of the fleet of drones to be devoted to a drone mission, hence its cost. We design Homogeneous Rotating Recharge (HRR), an optimal drone recharging scheduling that extends the coverage of a cellular network. HRR minimizes the number of back-up drones needed to guarantee a fixed number of operational drones, so as to support the operation of an underlying cellular network. Results show that operating a network of drones with our scheme provides reliable and stable performance over time.

Millimeter wave (mmWave) communication not only provides ultra-high speed radio access but is also ideally suited for efficient and flexible wireless backhauling. Specifically for dense deployments, a mmWave macro base station (MBS) that serves a large number of mmWave micro base stations (μBSs) is much more cost effective than legacy cellular architectures which connect μBSs to the core network through fibers. In addition, μBSs can cooperate with each other by acting as relay nodes. The directional nature of mmWave communication allows for spatial reuse, even in the presence of interference, which can be exploited to optimize mmWave wireless backhaul performance. The optimization opportunistically prioritizes the use of good connections at the MBS and further leverages compact and concurrent transmissions between μBS. Relays and directional antennas speed up communication, but increase the complexity of the scheduling problem. In this work, we study the mmWave backhaul scheduling problem and derive an MILP formulation for it as well as upper and lower bounds. We prove that the problem is NP-hard and can be approximated, but only if interference is negligible. By means of numerical simulations, we compare theoretical results with heuristics in small system sizes. Results validate the analysis and demonstrate the high performance of our heuristics in realistic cellular settings.

The integration of aerial base stations carried by drones in cellular networks offers promising opportunities to enhance the connectivity enjoyed by ground users. In this paper, we propose an optimization framework for the 3-D placement and repositioning of a fleet of drones with a realistic inter-drone interference model and drone connectivity constraints. We show how to maximize network coverage by means of an extremal-optimization algorithm. The design of our algorithm is based on a mixed-integer non-convex program formulation for a coverage problem that is NP-Complete, as we prove in the paper. We not only optimize drone positions in a 3-D space in polynomial time, but also assign flight routes solving an assignment problem and using a strong geometrical tool, namely Bézier curves, which are extremely useful for non-uniform and realistic topologies. Specifically, we propose to fly drones following Bézier curves to seek the chance of approaching to clusters of ground users. This enhances coverage over time while users and drones move. We assess the performance of our proposal for synthetic scenarios as well as realistic maps extracted from the topology of a capital city. We demonstrate that our framework is near-optimal and using Bézier curves increases coverage up to 47 percent while drones move.

Device-to-Device (D2D) communications potentially allow users placed in a cell to establish direct connections among them using several connection modes. In this paper, we propose Multi-Path D2D (MPD2D), a mathematical optimisation framework that accounts for the availability of D2D modes under flow demand requirements. MPD2D selects the combination of cellular and D2D links that fits better for boosting the network benefit. We consider the Underlay and Overlay as Inband D2D modes over cellular technology (LTE) and the Outband D2D mode exploiting WLAN technology (WiFi). Throughput, energy consumption, interference, and flow requirements are managed in order to maximise a network utility function accounting for cell capacity and power efficiency. We also derive a user satisfaction metric that accounts for the history of users within the cell. Integrating such a metric is lightweight yet very effective to drive towards almost complete fairness for the system over time. Our optimisation scheme is formulated as a Binary Non-Linear Program which results in very high performance in terms of throughput gain in comparison to other benchmarks for the D2D mode selection problem. Finally, we propose two effective heuristics whose performance and complexity we compare with optimal results and that largely outperform state of the art solutions.