A proposed resource allocation （RA） scheme is given to device-to-device （D2D） communication underlaying cellular networks from an end-to-end energy-efficient perspective, in which, the end-to-end energy consumptio...A proposed resource allocation （RA） scheme is given to device-to-device （D2D） communication underlaying cellular networks from an end-to-end energy-efficient perspective, in which, the end-to-end energy consumptions were taken into account. Furthermore, to match the practical situations and maximize the energy-efficiency （EE）, the resource units （RUs） were used in a complete-shared pattern. Then the energy-efficient RA problem was formulated as a mixed integer and non-convex optimization problem, extremely difficult to be solved. To obtain a desirable solution with a reasonable computation cost, this problem was dealt with two steps. Step 1, the RU allocation policy was obtained via a greedy search method. Step 2, after obtaining the RU allocation, the power allocation strategy was developed through quantum-behaved particle swarm optimization （QPSO）. Finally, simulation was presented to validate the effectiveness of the proposed RA scheme.展开更多
基金supported by the National Science Foundation for Young Scientists of China (61302080)
文摘A proposed resource allocation （RA） scheme is given to device-to-device （D2D） communication underlaying cellular networks from an end-to-end energy-efficient perspective, in which, the end-to-end energy consumptions were taken into account. Furthermore, to match the practical situations and maximize the energy-efficiency （EE）, the resource units （RUs） were used in a complete-shared pattern. Then the energy-efficient RA problem was formulated as a mixed integer and non-convex optimization problem, extremely difficult to be solved. To obtain a desirable solution with a reasonable computation cost, this problem was dealt with two steps. Step 1, the RU allocation policy was obtained via a greedy search method. Step 2, after obtaining the RU allocation, the power allocation strategy was developed through quantum-behaved particle swarm optimization （QPSO）. Finally, simulation was presented to validate the effectiveness of the proposed RA scheme.
