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We consider energy-efficient wireless resource management in cellular networks where BSs are equipped with energy harvesting devices, using statistical information for traffic intensity and harvested energy. The problem is formulated as adapting BSs on-off states, active resource blocks (e.g. subcarriers) as well as power allocation to minimize the average grid power consumption in a given time period while satisfying the users quality of service (blocking probability) requirements. It is transformed into an unconstrained optimization problem to minimize a weighted sum of grid power consumption and blocking probability. A two-stage dynamic programming (DP) algorithm is then proposed to solve this optimization problem, by which the BSs on-off states are optimized in the first stage, and the active BSs resource blocks are allocated iteratively in the second stage. Compared with the optimal joint BSs on-off states and active resource blocks allocation algorithm, the proposed algorithm greatly reduces the computational complexity, while at the same time achieves close to the optimal energy saving performance.
The explosive wireless data service requirement accompanied with carbon dioxide emission and consumption of traditional energy has put pressure on both industry and academia. Wireless networks powered with the uneven and intermittent generated renewa
We characterize the rate coverage distribution for a spectrum-shared millimeter wave downlink cellular network. Each of multiple cellular operators owns separate mmWave bandwidth, but shares the spectrum amongst each other while using dynamic inter-o
Non-orthogonal multiple access (NOMA) has attracted much recent attention owing to its capability for improving the system spectral efficiency in wireless communications. Deploying NOMA in heterogeneous network can satisfy users explosive data traffi
This work presents a new resource allocation optimization framework for cellular networks using neighborhood-based optimization. Under this optimization framework resources are allocated within virtual cells encompassing several base-stations and the
This work proposes a new resource allocation optimization framework for cellular networks using fog or neighborhood-based optimization rather than fully centralized or fully decentralized methods. In neighborhood-based optimization resources are allo