Dynamic Carrier to MCPA Allocation for Energy Efficient Communication: Convex Relaxation Versus Deep Learning

Abstract

The rapid increasing demand of wireless transmission has caused mobile broadband to continuously evolve through multiple frequency bands, massive antennas, and other multi-stream processing schemes. Together with the improved data transmission rate, the power consumption for multi-carrier transmission and processing is proportionally increasing, which contradicts with the energy efficiency requirements of 5G wireless systems. To meet this challenge, multi-carrier power amplifier (MCPA) technology, e.g., to support multiple carriers through a single power amplifier, is widely deployed in practice. With so many carriers required for 5G communication and limited number of carriers supported per MCPA, a key question to ask is how to allocate those carriers into multiple MCPAs and whether we shall dynamically adjust this allocation strategy. In this paper, we have theoretically formulated the dynamic carrier to MCPA allocation problem to jointly optimize the traditional separated baseband and radio frequency processing. On top of that, we have proposed both convex relaxation and deep learning-based algorithms. From our simulation results, the proposed algorithms achieve most of the power saving gain compared with the optimal exhaustive search-based algorithm. Furthermore, the deep learning-based approach can greatly reduce computational time, which is of vital importance in the practical deployment.