To integrate unmanned aerial vehicles (UAVs) in future large-scale deployments, a new wireless communication paradigm, namely, the cellular-connected UAV has recently attracted interest. However, the line-of-sight dominant air-to-ground channels along with the antenna pattern of the cellular ground base stations (GBSs) introduce critical interference issues in cellular-connected UAV communications. In particular, the complex antenna pattern and the ground reflection (GR) from the down-tilted antennas create both coverage holes and patchy coverage for the UAVs in the sky, which leads to unreliable connectivity from the underlying cellular network. To overcome these challenges, in this paper, we propose a new cellular architecture that employs an extra set of co-channel antennas oriented towards the sky to support UAVs on top of the existing down-tilted antennas for ground user equipment (GUE). To model the GR stemming from the down-tilted antennas, we propose a path-loss model, which takes both antenna radiation pattern and configuration into account. Next, we formulate an optimization problem to maximize the minimum signal-to-interference ratio (SIR) of the UAVs by tuning the up-tilt (UT) angles of the up-tilted antennas. Since this is an NP-hard problem, we propose a genetic algorithm (GA) based heuristic method to optimize the UT angles of these antennas. After obtaining the optimal UT angles, we integrate the 3GPP Release-10 specified enhanced inter-cell interference coordination (eICIC) to reduce the interference stemming from the down-tilted antennas. Our simulation results based on the hexagonal cell layout show that the proposed interference mitigation method can ensure higher minimum SIRs for the UAVs over baseline methods while creating minimal impact on the SIR of GUEs.
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Reinforcement Learning Based Power-Optimal Usage of Beamforming Antenna Array for Multi-Way Wireless Communication in Vehicular Traffic Environments
There has been recent work on the design of antenna arrays for beamforming in dynamic evolving environments such as in vehicle-to-vehicle communication systems. A key problem is that of determining how to optimally use a large antenna array to communicate with multiple spatially located vehicles in dynamically changing channel conditions with minimal co-channel interference while minimizing overall power consumption of the wireless system. We envision disjoint subsets of antennas in the array being used to direct beams concurrently to different vehicles. The number of antennas, gain and phase of each RF-chain driving an antenna are optimized dynamically using a constrained quadratic cost formulation encompassing channel quality, interference and power consumption. This quadratic optimization problem is solved using behavior constrained bandit algorithm, a reinforcement learning based technique. A gaussian kernel is used to perform data clustering of vehicle environment and resulting solutions, allowing quick bootstrapping of the bandit solver to find optimal array configurations in real-time vehicle environments. Simulation studies prove the viability of the proposed scheme.
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- Award ID(s):
- 1815653
- PAR ID:
- 10410094
- Date Published:
- Journal Name:
- Midwest Symposium on Circuits and Systems
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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