Networked MIMO with Fractional Joint Transmission in Energy Harvesting Systems

TL;DR

This paper introduces a fractional joint transmission strategy for energy-harvesting MIMO systems, optimizing energy and time allocation to maximize sum-rate, with a novel dynamic programming approach for energy management.

Contribution

It proposes a new fractional joint transmission scheme with a closed-form power solution and a dynamic programming method for energy allocation in energy-harvesting MIMO networks.

Findings

Fractional JT significantly improves system performance.

Optimal power and time fraction can be efficiently computed.

Policy exploration enhances energy management effectiveness.

Abstract

This paper considers two base stations (BSs) powered by renewable energy serving two users cooperatively. With different BS energy arrival rates, a fractional joint transmission (JT) strategy is proposed, which divides each transmission frame into two subframes. In the first subframe, one BS keeps silent to store energy while the other transmits data, and then they perform zero-forcing JT (ZF-JT) in the second subframe. We consider the average sum-rate maximization problem by optimizing the energy allocation and the time fraction of ZF-JT in two steps. Firstly, the sum-rate maximization for given energy budget in each frame is analyzed. We prove that the optimal transmit power can be derived in closed-form, and the optimal time fraction can be found via bi-section search. Secondly, approximate dynamic programming (DP) algorithm is introduced to determine the energy allocation among frames. We adopt a linear approximation with the features associated with system states, and determine the weights of features by simulation. We also operate the approximation several times with random initial policy, named as policy exploration, to broaden the policy search range. Numerical results show that the proposed fractional JT greatly improves the performance. Also, appropriate policy exploration is shown to perform close to the optimal.