無線電力驅動通訊網路可以使用混合存取點和傳感器節點執行無線功率傳輸
與無線資訊傳輸。但是，尚未對無線電力驅動網路應用於精準農業的一些問題進
行深入研究: 對網路中通道信息的瞭解不完善，存在通道不確定性的情況下電池供
電型的傳感器節點的吞吐量公平性以及電池壽命的延長。為了解決上述問題，本
研究開發了一種基於學習的用於無線功率傳輸和無線資料傳輸的時隙分配演算法。
所提出的方法學習吞吐量與時隙分配之間的關係，而無需任何有關通道的相關資
訊。然後在學習過程中調整時間分配，解決受傳感器節點最低吞吐量限制的優化
問題，並最大化網路吞吐量和電池壽命。數值分析表明，在存在通道不確定性的
情況下，所提出的方法可以產生比現有方法梗高的網路吞吐量，並可以確保吞吐
量公平性，同時延長電池壽命。

Wireless powered communication networks (WPCNs) can perform wireless power
transfer and energy harvest using a hybrid access point and sensor nodes. However, some
problems arising from the applications of WPCNs to precision agriculture have not been
well investigated: imperfect knowledge on channel information in the network, fairness
throughput of batterypowered
sensor nodes in the presence of channel uncertainty, and
battery lifetime extension. To solve the aforementioned problems, this study develops a
learning based algorithm for allocating time slots for wireless power transfer and energy
harvest. The proposed approach learns the relationship between throughput and time allocations
without any knowledge on channel information. Time allocations are then adjusted
during the learning course in order to maximize an optimization problem constrained by
lowest throughput of sensor nodes and maximizing the network throughput and battery
lifetime. Numerical analysis reveals that the proposed approach can yield higher network
throughput than existing ones in the presence of channel uncertainty and ensure the
throughput fairness while extending battery lifetime.

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