论文部分内容阅读
To minimize the total transmit power for multicast service in an orthogonal frequency division multiplexing(OFDM) downlink system,resource allocation algorithms that adaptively allocate subcarriers and bits are proposed.The proposed algorithms select users with good channel conditions for each subcarrier to reduce the transmit power,while guaranteeing each user’s instantaneous minimum rate requirement.The resource allocation problem is first formulated as an integer programming(IP) problem,and then,a full search algorithm that achieves an optimal solution is presented.To reduce the computation load,a suboptimal algorithm is proposed.This suboptimal algorithm decouples the joint resource allocation problem by separating subcarrier and bit allocation.Greedy-like algorithms are employed in both procedures.Simulation results illustrate that the proposed algorithms can significantly reduce the transmit power compared with the conventional multicast approach and the performance of the suboptimal algorithm is close to the optimum.
To minimize the total transmit power for multicast service in an orthogonal frequency division multiplexing (OFDM) downlink system, resource allocation algorithms that adaptively allocate subcarriers and bits are proposed. The proposed algorithms select users with good channel conditions for each subcarrier to reduce the transmit power , while guaranteeing each user’s instantaneous minimum rate requirement. The resource allocation problem is first formulated as an integer programming (IP) problem, and then, a full search algorithm that achieves an optimal solution is presented. To reduce the computation load, a suboptimal algorithm is proposed. This suboptimal algorithm decouples the joint resource allocation problem by separating subcarrier and bit allocation. Gredy-like algorithms are employed in both procedures. Simulation results illustrate that the proposed algorithms can significantly reduce the transmit power compared with the conventional multicast approach and the performance of the subopt imal algorithm is close to the optimum.