LEOPARD: Parallel Optimal Deep Echo State Network Prediction Improves Service Coverage for UAV-Assisted Outdoor Hotspots
نویسندگان
چکیده
Unmanned aerial vehicle (UAV) base stations (BSs) can help meet the dynamic traffic demand of flash mobile crowds, but user movements also pose a significant challenge on fast-tracking for avoiding service interruption. This paper presents novel paral LE l xmlns:xlink="http://www.w3.org/1999/xlink">O ptimal dee xmlns:xlink="http://www.w3.org/1999/xlink">P echo st xmlns:xlink="http://www.w3.org/1999/xlink">A te netwo xmlns:xlink="http://www.w3.org/1999/xlink">R k pre xmlns:xlink="http://www.w3.org/1999/xlink">D iction (LEOPARD) approach that fast and accurately learn movement equipment (UE) to reduce its impact link performance from UE UAV-BS. Improving current learning technique deep state network (ESN), LEOPARD consists further three key optimization techniques. First, we develop Bayesian-Optimization Algorithm (BOA)-based hyper-parameters adjustment method improving prediction accuracy. Secondly, Message Passing Interface (MPI) is integrated into design time complexity caused by BOA. Last, Kuhn-Munkres (KM)-based matching algorithm save re-positioning energy consumption multiple UAV-BSs. As shown in our simulation results, accuracy proposed LEOPARD, combining DeepESN, BOA, MPI techniques, 78% 67% better than state-of-the-art shallow ESN original ESN, respectively.
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ژورنال
عنوان ژورنال: IEEE Transactions on Cognitive Communications and Networking
سال: 2022
ISSN: ['2332-7731', '2372-2045']
DOI: https://doi.org/10.1109/tccn.2021.3115765