The proposed framework for UAV corridor-assisted RF-powered IoT networks can be extended to include multiple UAV lanes in the 2D plane by adopting a multi-dimensional stochastic geometry approach. This involves modeling the UAVs as a two-dimensional (2D) point process rather than a one-dimensional (1D) binomial point process (BPP). Each UAV lane can be represented as a parallel line segment in the 2D plane, with specific altitude levels assigned to each lane to prevent vertical overlap and potential collisions.
To achieve this, the following steps can be taken:
Lane Definition: Define multiple UAV lanes, each with a designated altitude and width, ensuring that the lanes are spaced adequately to minimize interference and collision risks.
Spatial Distribution: Model the spatial distribution of UAVs within each lane using a 2D point process, such as a Poisson point process (PPP) or a BPP, to capture the randomness in UAV placement.
Inter-lane Interference: Analyze the inter-lane interference effects on both energy harvesting and communication coverage probabilities. This requires deriving new expressions for the signal-to-interference-plus-noise ratio (SINR) that account for interference from UAVs in adjacent lanes.
Coordination Mechanisms: Implement coordination mechanisms among UAVs in different lanes to optimize resource allocation and minimize interference. This could involve dynamic frequency allocation or time-division multiple access (TDMA) strategies.
Performance Metrics: Extend the performance metrics to evaluate the joint energy and communication coverage probabilities across multiple lanes, considering the unique challenges posed by the increased complexity of the network.
By incorporating these elements, the framework can effectively model and analyze the performance of UAV-assisted IoT networks with multiple UAV lanes, leading to enhanced coverage and efficiency.