  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.
