Joint offloading design and bandwidth allocation for RIS-aided multiuser MEC networks

This article examines a multiuser intelligent reflecting surface (RIS) aided mobile edge computing (MEC) system, where multiple edge nodes (ENs) with powerful calculating resources at the network can help compute the calculating tasks from the users through wireless channels. We evaluate the system performance by using the performance metric of communication and computing delay. To enhance the system performance by reducing the network delay, we jointly optimize the unpacking design and wireless bandwidth allocation, whereas the task unpacking optimization is solved by using the deep deterministic policy gradient (DDPG) algorithm. As to the bandwidth allocation, we propose three analytical solutions, where criterion I performs an equal bandwidth allocation, criterion II performs the allocation based on the transmission data rate, while criterion III performs the allocation based on the transmission delay. We finally provide simulation results to show that the proposed optimization on the task unpacking and bandwidth allocation is effective in decreasing the network delay.