2 research outputs found
Dynamic Cross-Layer Beamforming in Hybrid Powered Communication Systems With Harvest-Use-Trade Strategy
The application of renewable energy is a promising solution to realize the
Green Communications. However, if the cellular systems are solely powered by
the renewable energy, the weather dependence of the renewable energy arrival
makes the systems unstable. On the other hand, the proliferation of the smart
grid facilitates the loads with two-way energy trading capability. Hence, a
hybrid powered cellular system, which combines the smart grid with the base
stations, can reduce the grid energy expenditure and improve the utilization
efficiency of the renewable energy. In this paper, the long-term grid energy
expenditure minimization problem is formulated as a stochastic optimization
model. By leveraging the stochastic optimization theory, we reformulate the
stochastic optimization problem as a \mbox{per-frame} grid energy plus weighted
penalized packet rate minimization problem, which is NP-hard. As a result, two
suboptimal algorithms, which jointly consider the effects of the channel
quality and the packet reception failure, are proposed based on the successive
approximation beamforming (SABF) technique and the \mbox{zero-forcing}
beamforming (ZFBF) technique. The convergence properties of the proposed
suboptimal algorithms are established, and the corresponding computational
complexities are analyzed. Simulation results show that the proposed SABF
algorithm outperforms the ZFBF algorithm in both grid energy expenditure and
packet delay. By tuning a control parameter, the grid energy expenditure can be
traded for the packet delay under the proposed stochastic optimization model.Comment: accepted by IEEE Trans. Wireless Commu
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 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.Comment: 33 pages, 7 figures, accepted by IEEE Transactions on Communication