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Sub-channel shared resource allocation for multi-user distributed MIMO-OFDM systems

Sub-channel shared resource allocation for multi-user distributed MIMO-OFDM systems

作     者:Na-e ZHENG You ZHOU Han-ying HU Sheng WANG 

作者机构:Zhengzhou Information Science and Technology Institute National Digital Switching System Engineering & Technological Research Center 

出 版 物:《Journal of Zhejiang University-Science C(Computers and Electronics)》 (浙江大学学报C辑(计算机与电子(英文版))

年 卷 期:2014年第15卷第11期

页      面:1048-1057页

核心收录:

学科分类:0810[工学-信息与通信工程] 080904[工学-电磁场与微波技术] 0809[工学-电子科学与技术(可授工学、理学学位)] 08[工学] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 0805[工学-材料科学与工程(可授工学、理学学位)] 081001[工学-通信与信息系统] 0812[工学-计算机科学与技术(可授工学、理学学位)] 

基  金:Project supported by the National High-Tech R&D Program(863) of China(Nos.2012AA01A502 and 2012AA01A505) 

主  题:MIMO-OFDM Multi-user Resource allocation Sub-channel shared Signal to leakage plus noise ratio(SLNR) 

摘      要:Well-controlled resource allocation is crucial for promoting the performance of multiple input multiple output orthogonal frequency division multiplexing(MIMO-OFDM) systems. Recent studies have focused primarily on traditional centralized systems or distributed antenna systems(DASs), and usually assumed that one sub-carrier or sub-channel is exclusively occupied by one user. To promote system performance, we propose a sub-channel shared resource allocation algorithm for multiuser distributed MIMO-OFDM systems. Each sub-channel can be shared by multiple users in the algorithm, which is different from previous algorithms. The algorithm assumes that each user communicates with only two best ports in the system. On each sub-carrier, it allocates a sub-channel in descending order, which means one sub-channel that can minimize signal to leakage plus noise ratio(SLNR) loss is deleted until the number of remaining sub-channels is equal to that of receiving antennas. If there are still sub-channels after all users are processed, these sub-channels will be allocated to users who can maximize the SLNR gain. Simulations show that compared to other algorithms, our proposed algorithm has better capacity performance and enables the system to provide service to more users under the same capacity constraints.

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