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Register a new userBase Station Antenna Uptilt Optimization for Cellular-Connected Drone Corridors
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Added by
Sung Joon Maeng
Publication date
2021
fields
Electronic Engineering
and research's language is
English
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The concept of drone corridors is recently getting more attention to enable connected, safe, and secure flight zones in the national airspace. To support beyond visual line of sight (BVLOS) operations of aerial vehicles in a drone corridor, cellular base stations (BSs) serve as a convenient infrastructure, since such BSs are widely deployed to provide seamless wireless coverage. However, antennas in the existing cellular networks are down-tilted to optimally serve their ground users, which results in coverage holes if they are also used to serve drones. In this letter, we consider the use of additional uptilted antennas at cellular BSs and optimize the uptilt angle to minimize outage probability for a given drone corridor. Our numerical results show how the beamwidth and the maximum drone corridor height affect the optimal value of the antenna uptilt angle.
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Autonomous unmanned aerial vehicles (UAVs) with on-board base station equipment can potentially provide connectivity in areas where the terrestrial infrastructure is overloaded, damaged, or absent. Use cases comprise emergency response, wildfire suppression, surveillance, and cellular communications in crowded events to name a few. A central problem to enable this technology is to place such aerial base stations (AirBSs) in locations that approximately optimize the relevant communication metrics. To alleviate the limitations of existing algorithms, which require intensive and reliable communications among AirBSs or between the AirBSs and a central controller, this paper leverages stochastic optimization and machine learning techniques to put forth an adaptive and decentralized algorithm for AirBS placement without inter-AirBS cooperation or communication. The approach relies on a smart design of the network utility function and on a stochastic gradient ascent iteration that can be evaluated with information available in practical scenarios. To complement the theoretical convergence properties, a simulation study corroborates the effectiveness of the proposed scheme.
Multiple-input multiple-output (MIMO) techniques can help in scaling the achievable air-to-ground (A2G) channel capacity while communicating with drones. However, spatial multiplexing with drones suffers from rank deficient channels due to the unobstructed line-of-sight (LoS), especially in millimeter-wave (mmWave) frequencies that use narrow beams. One possible solution is utilizing low-cost and low-complexity metamaterial-based intelligent reflecting surfaces (IRS) to enrich the multipath environment, taking into account that the drones are restricted to fly only within well-defined drone corridors. A hurdle with this solution is placing the IRSs optimally. In this study, we propose an approach for IRS placement with a goal to improve the spatial multiplexing gains, and hence to maximize the average channel capacity in a predefined drone corridor. Our results at 6 GHz, 28 GHz and 60 GHz show that the proposed approach increases the average rates for all frequency bands for a given drone corridor, when compared with the environment where there are no IRSs present, and IRS-aided channels perform close to each other at sub-6 and mmWave bands.
The use of millimeter-wave (mmWave) bands in 5G networks introduce a new set of challenges to network planning. Vulnerability to blockages and high path loss at mmWave frequencies require careful planning of the network to achieve the desired service quality. In this paper, we propose a novel 3D geometry-based framework for deploying mmWave base stations (gNBs) in urban environments by considering first-order reflection effects. We also provide a solution for the optimum deployment of passive metallic reflectors (PMRs) to extend radio coverage to non-line-of-sight (NLoS) areas. In particular, we perform visibility analysis to find the direct and indirect visibility regions, and using these, we derive a geometry-and-blockage-aided path loss model. We then formulate the network planning problem as two independent optimization problems, placement of gNB(s) and PMRs, to maximize the coverage area with a certain quality-of-service constraint and minimum cost. We test the efficacy of our proposed approach using a generic map and compare our simulation results with the ray-tracing solution. Our simulation results show that considering the first-order reflections in planning the mmWave network helps reduce the number of PMRs required to cover the NLoS area and the gNB placement aided with PMRs requires fewer gNBs to cover the same area, which in turn reduces the deployment cost.
Integrating unmanned aerial vehicle (UAV) into the existing cellular networks that are delicately designed for terrestrial transmissions faces lots of challenges, in which one of the most striking concerns is how to adopt UAV into the cellular networks with less (or even without) adverse effects to ground users. In this paper, a cellular-connected UAV network is considered, in which multiple UAVs receive messages from terrestrial base stations (BSs) in the down-link, while BSs are serving ground users in their cells. Besides, the line-of-sight (LoS) wireless links are more likely to be established in ground-to-air (G2A) transmission scenarios. On one hand, UAVs may potentially get access to more BSs. On the other hand, more co-channel interferences could be involved. To enhance wireless transmission quality between UAVs and BSs while protecting the ground users from being interfered by the G2A communications, a joint time-frequency resource block (RB) and beamforming optimization problem is proposed and investigated in this paper. Specifically, with given flying trajectory, the ergodic outage duration (EOD) of UAV is minimized with the aid of RB resource allocation and beamforming design. Unfortunately, the proposed optimization problem is hard to be solved via standard optimization techniques, if not impossible. To crack this nut, a deep reinforcement learning (DRL) solution is proposed, where deep double duelling Q network (D3QN) and deep deterministic policy gradient (DDPG) are invoked to deal with RB allocation in discrete action domain and beamforming design in continuous action regime, respectively. The hybrid D3QN-DDPG solution is applied to solve the outer Markov decision process (MDP) and the inner MDP interactively so that it can achieve the sub-optimal result for the considered optimization problem.
This paper explores the effects of three-dimensional (3D) antenna radiation pattern and backhaul constraint on optimal 3D path planning problem of an unmanned aerial vehicle (UAV), in interference prevalent downlink cellular networks. We consider a cellular-connected UAV that is tasked to travel between two locations within a fixed time and it can be used to improve the cellular connectivity of ground users by acting as a relay. Since the antenna gain of a cellular base station changes significantly with the UAV altitude, the UAV can increase the signal quality in its backhaul link by changing its height over the course of its mission. This problem is non-convex and thus, we explore the dynamic programming technique to solve it. We show that the 3D optimal paths can introduce significant network performance gain over the trajectories with fixed UAV heights.
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