IRS-assisted NOMA-MEC Communication Resource Allocation Optimization Based on Deep Reinforcement Learning

To solve the problem of blind spot edge user offloading tasks where direct communication links cannot be established with edge servers, an intelligent reflecting surface (IRS)-assisted non-orthogonal multiple access (NOMA) communication resource allocation optimization algorithm based on deep reinforcement learning (DRL) was designed. The algorithm aimed to obtain the maximum system benefit weighted by system sum rate and energy efficiency (EE) for green and efficient communication. The deep deterministic policy gradient (DDPG) algorithm was adopted to jointly optimize the power allocation and phase-shift matrix. The simulation results show that DDPG algorithm is superior to other comparative experimental algorithms in dealing with the communication resource allocation of mobile edge computing (MEC).