To provide a reliable wireless uplink for users in a given ground area, one can deploy Unmanned Aerial Vehicles (UAVs) as base stations (BSs). In another application, one can use UAVs to collect data from sensors on the ground. For a power efficient and scalable deployment of such flying BSs, directional antennas can be utilized to efficiently cover arbitrary 2-D ground areas. We consider a large-scale wireless path-loss model with a realistic angle-dependent radiation pattern for the directional antennas. Based on such a model, we determine the optimal 3-D deployment of N UAVs to minimize the average transmit-power consumption of the users in a given target area. The users are assumed to have identical transmitters with ideal omnidirectional antennas and the UAVs have identical directional antennas with given half-power beamwidth (HPBW) and symmetric radiation pattern along the vertical axis. For uniformly distributed ground users, we show that the UAVs have to share a common flight height in an optimal power-efficient deployment, by simulations. We also derive in closed-form the asymptotic optimal common flight height of N UAVs in terms of the area size, data-rate, bandwidth, HPBW, and path-loss exponent.
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Self-Organizing mm Wave Networks: A Power Allocation Scheme Based on Machine Learning
Millimeter-wave (mmWave) communication is anticipated to provide significant throughout gains in urban scenarios. To this end, network densification is a necessity to meet the high traffic volume generated by smart phones, tablets, and sensory devices while overcoming large pathloss and high blockages at mmWaves frequencies. These denser networks are created with users deploying small mm Wave base stations (BSs) in a plug-and-play fashion. Although, this deployment method provides the required density, the amorphous deployment of BSs needs distributed management. To address this difficulty, we propose a self-organizing method to allocate power to mm Wave BSs in an ultra dense network. The proposed method consists of two parts: clustering using fast local clustering and power allocation via Q-learning. The important features of the proposed method are its scalability and self-organizing capabilities, which are both important features of 5G. Our simulations demonstrate that the introduced method, provides required quality of service (QoS) for all the users independent of the size of the network.
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- Award ID(s):
- 1642865
- PAR ID:
- 10076909
- Date Published:
- Journal Name:
- 2018 11th Global Symposium on Millimeter Waves (GSMM)
- Page Range / eLocation ID:
- 1 to 4
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Despite the promising attributes of the 12 GHz band for expanding terrestrial 5G network’s capacity and coverage, interference between coexisting networks remains a major issue. This paper develops a simulation-based evaluation framework and investigates the harmful interference between the 5G radio links and incumbent fixed non-geostationary satellite orbit (NGSO) fixed satellite services (FSS) receivers of the 12 GHz band. A variety of features including actual deployment locations of 5G base stations (BSs) and fixed NGSO FSS receivers, industry standardized beamforming at BSs, directional signal reception at FSS receivers, realistic propagation channels with obstruction from buildings, and channel scheduling at 5G BSs are incorporated in the interference study. Simulation results conducted in a realistic urban-micro deployment scenario confirm that the terrestrial 5G networks with directional BSs can coexist in the 12GHz band by suitably selecting exclusion zone’s radius around the FSS receiver. Simulation results also show that interference in the coexisting network can be notably reduced by appropriately activating BSs in the 12 GHz band based on their locations and surroundings.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/GSMM.2018.8439323
