MEC-Assisted NOMA Network With Layered Division Multiplexing for Power Consumption Minimization

Mobile edge computing (MEC)-assisted non-orthogonal multiple access (NOMA) network has great potential to support real-time computing with the lower power consumption and network latency, since it leverages the computation capacities at the access point and power domain multiplexing technique. However, with the explosive growth of users in massive access, the power consumption and network latency for data offloading will inevitably increase. Conventional network ignoring the specific service requirement from different levels of users could severely jeopardize quality of service for the massive access NOMA network. To fill this gap, this work adopts the layered-division-multiplexing (LDM) structure, and jointly optimizes the beamforming, computation frequency, and power allocation to minimize the system power consumption under the alternating minimization framework. Simulation results demonstrate the convergence behavior of the proposed algorithm, and the performance gains from the proposed MEC-assisted NOMA network with LDM technique over the other benchmark networks.