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The application of renewable energy is a promising solution to realize theGreen Communications. However, if the cellular systems are solely powered bythe renewable energy, the weather dependence of the renewable energy arrivalmakes the systems unstable. On the other hand, the proliferation of the smartgrid facilitates the loads with two-way energy trading capability. Hence, ahybrid powered cellular system, which combines the smart grid with the basestations, can reduce the grid energy expenditure and improve the utilizationefficiency of the renewable energy. In this paper, the long-term grid energyexpenditure minimization problem is formulated as a stochastic optimizationmodel. By leveraging the stochastic optimization theory, we reformulate thestochastic optimization problem as a \mbox{per-frame} grid energy plus weightedpenalized packet rate minimization problem, which is NP-hard. As a result, twosuboptimal algorithms, which jointly consider the effects of the channelquality and the packet reception failure, are proposed based on the successiveapproximation beamforming (SABF) technique and the \mbox{zero-forcing}beamforming (ZFBF) technique. The convergence properties of the proposedsuboptimal algorithms are established, and the corresponding computationalcomplexities are analyzed. Simulation results show that the proposed SABFalgorithm outperforms the ZFBF algorithm in both grid energy expenditure andpacket delay. By tuning a control parameter, the grid energy expenditure can betraded for the packet delay under the proposed stochastic optimization model.

Dynamic Cross-Layer Beamforming in Hybrid Powered Communication Systems With Harvest-Use-Trade Strategy