Networked MIMO With Fractional Joint Transmission in Energy Harvesting Systems

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 separately. First, the sum-rate maximization for given energy budgets 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. Second, an approximate dynamic programming 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 policy exploration, to broaden the policy search range. Numerical results show that the proposed fractional JT greatly improves the performance. In addition, appropriate policy exploration is shown to perform close to the optimal.