
FOLLOWUS
1.Zhengzhou Information Science and Technology Institute, Zhengzhou 450000, China
2.National Digital Switching System Engineering & Technological Research Center, Zhengzhou 450002, China
[ "Na-e ZHENG, E-mail: zhengnaecshz@163.com" ]
收稿:2014-02-14,
修回:2014-10-15,
纸质出版:2014-11
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多用户分布式MIMO-OFDM系统子信道共享的资源分配算法[J]. 信息与电子工程前沿(英文), 2014,15(11):1048-1057.
Na-e ZHENG, You ZHOU, Han-ying HU, et al. Sub-channel shared resource allocation for multi-user distributed MIMO-OFDM systems[J]. Frontiers of Information Technology & Electronic Engineering, 2014, 15(11): 1048-1057.
多用户分布式MIMO-OFDM系统子信道共享的资源分配算法[J]. 信息与电子工程前沿(英文), 2014,15(11):1048-1057. DOI: 10.1631/jzus.C1400049.
Na-e ZHENG, You ZHOU, Han-ying HU, et al. Sub-channel shared resource allocation for multi-user distributed MIMO-OFDM systems[J]. Frontiers of Information Technology & Electronic Engineering, 2014, 15(11): 1048-1057. DOI: 10.1631/jzus.C1400049.
研究目的
2
良好的资源分配算法对提高多天线OFDM系统性能具有重要作用。已有研究多针对传统集中式MIMO-OFDM系统,且通常假设子载波或子信道被用户独占,限制了部分容量性能。为进一步提升系统容量性能,针对多用户分布式MIMO-OFDM系统,提出一种子信道共享的资源分配算法。
创新要点
2
不同于已有算法,本文的算法允许每个子信道同时被多个用户使用。允许子信道共享和干扰抑制,确保容量最大化。很好地利用了分布式MIMO系统的信道特性和架构特点,通过静态端口选择,有效降低了复杂度和反馈开销。
研究方法
2
假设用户只与系统中的两个端口进行通信,并为每个用户选择信道状况最好的两个端口作为通信端口,进而在每个子载波上采用逐减方法为用户分配子信道。逐减过程中,对每个用户而言,每次删除一个使信漏噪比(SLNR)减少量最小的子信道,直到剩余子信道数与接收天线数相等。所有用户处理完毕后,若仍有子信道未被分配,则将其分给能使信漏噪比增量最大的用户。
重要结论
2
仿真结果表明,相较于对比算法,所提算法容量性能更优,且在同样容量性能要求下,该算法能使系统容纳更多用户同时进行通信。
Well-controlled resource allocation iscrucial for promoting the performance of multiple input multiple output orthogonalfrequency division multiplexing (MIMO-OFDM) systems. Recent studies havefocused primarily on traditional centralized systems or distributed antenna systems(DASs)
and usually assumed that one sub-carrier or sub-channel is exclusivelyoccupied by one user. To promote system performance
we propose a sub-channelshared resource allocation algorithm for multi-user distributed MIMO-OFDM systems. Each sub-channel can be shared by multipleusers in the algorithm
which is different from previous algorithms. Thealgorithm assumes that each user communicates with only two best ports in thesystem. 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 tothat of receiving antennas. If there are still sub-channels after all users areprocessed
these sub-channels will be allocated to users who can maximize theSLNR gain. Simulations show that compared to other algorithms
our proposedalgorithm has better capacity performance and enables the system to provideservice to more users under the same capacity constraints.
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