Energy-Efficient Power Control for Multiple-Task Split Inference in
  UAVs: A Tiny Learning-Based Approach
Chenxi Zhao Min Sheng Junyu Liu Tianshu Chu Jiandong Li
The limited energy and computing resources of unmanned aerial vehicles (UAVs)
hinder the application of aerial artificial intelligence. The utilization of
split inference in UAVs garners significant attention due to its effectiveness
in mitigating computing and energy requirements. However, achieving
energy-efficient split inference in UAVs remains complex considering of various
crucial parameters such as energy level and delay constraints, especially
involving multiple tasks. In this paper, we present a two-timescale approach
for energy minimization in split inference, where discrete and continuous
variables are segregated into two timescales to reduce the size of action space
and computational complexity. This segregation enables the utilization of tiny
reinforcement learning (TRL) for selecting discrete transmission modes for
sequential tasks. Moreover, optimization programming (OP) is embedded between
TRL's output and reward function to optimize the continuous transmit power.
Specifically, we replace the optimization of transmit power with that of
transmission time to decrease the computational complexity of OP since we
reveal that energy consumption monotonically decreases with increasing
transmission time. The replacement significantly reduces the feasible region
and enables a fast solution according to the closed-form expression for optimal
transmit power. Simulation results show that the proposed algorithm can achieve
a higher probability of successful task completion with lower energy
consumption.