Channel-State Information-Driven Data Rate Optimization for Multi-UAV IoT Networks

One of the primary requirements in cellular-enabled multiunmanned aerial vehicle (UAV) Internet of Things (IoT) networks is to preserve data rates according to the IoT users' (IUs) requirements. The mobility of IUs, 3-D movement of UAVs, environmental conditions, and bandwidth allocation to the IUs increase the challenges to maintain the data rates due to the continual change in the channel state. A constant extraction of channel state is crucial in this regard. We construct a sum-rate maximization problem considering the channel-state information (CSI). Unlike previous work, we propose CSI-driven data rate optimization for multi-UAV IoT networks (CARTEL). First, it allocates optimized bandwidth to IUs and accomplishes UAV-IU associations by adopting the matrix minima method. Subsequently, it maximizes the sum-rate invoking four modules: 1) parameter selector (PS); 2) IU tracker (IT); 3) path-loss estimator (PE); and 4) policy generator (PG). PS, IT, and PE help to extract the CSI by selecting suitable environmental parameters, tracking the IU mobility, and estimating the path loss, respectively. Finally, PG maximizes the data rates by adjusting the 3-D position and transmitting the power of a UAV. Extensive simulation results depict that the sum-rate in CARTEL improves by 26.03% and 65.46% than learn-as-you-fly (LAYF) and random selection (RS), respectively.