Cooperative relaying is emerging as an effective technology to fulfill requirements on high data rate coverage in next-generation cellular networks, like long term evolution-advanced （LTE-Advanced）. In this paper, w...Cooperative relaying is emerging as an effective technology to fulfill requirements on high data rate coverage in next-generation cellular networks, like long term evolution-advanced （LTE-Advanced）. In this paper, we propose a distributed joint relay node （RN） selection and power allocation scheme over multihop relaying cellular networks toward LTE-Advanced, taking both the wireless channel state and RNs＇ residual energy into consideration. We formulate the multihop relaying cellular network as a restless bandit system. The first-order finite-state Markov chain is used to characterize the time-varying channel and residual energy state transitions. With this stochastic optimization formulation, the optimal policy has indexability property that dramatically reduces the computational complexity. Simulation results demonstrate that the proposed scheme can efficiently enhance the expected system reward, compared with other existing algorithms.展开更多
基金supported by the National Major Science and Technology Project (2009ZX03002-014)the National Natural Science Foundation of China (60832009)+1 种基金the Beijing Municipal Natural Science Foundation (4102044)the National Natural Science Foundation for Distinguished Young Scholar (61001115)
文摘Cooperative relaying is emerging as an effective technology to fulfill requirements on high data rate coverage in next-generation cellular networks, like long term evolution-advanced （LTE-Advanced）. In this paper, we propose a distributed joint relay node （RN） selection and power allocation scheme over multihop relaying cellular networks toward LTE-Advanced, taking both the wireless channel state and RNs＇ residual energy into consideration. We formulate the multihop relaying cellular network as a restless bandit system. The first-order finite-state Markov chain is used to characterize the time-varying channel and residual energy state transitions. With this stochastic optimization formulation, the optimal policy has indexability property that dramatically reduces the computational complexity. Simulation results demonstrate that the proposed scheme can efficiently enhance the expected system reward, compared with other existing algorithms.
