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COoperative DEcision making based on Slepian-Wolf/multiple Access wireless Networks

基于Slepian-Wolf/多址无线网络的协同决策

基本信息

批准号：
22560367
负责人：
KHOIRUL Anwar
金额：
$ 2.75万
依托单位：
Japan Advanced Institute of Science and Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2010
资助国家：
日本
起止时间：
2010 至 2012
项目状态：
已结题

项目摘要

In this project, we have successfully estimate and exploit the source correlation in distributed cooperative wireless network to achieve close-limit Slepian-Wolf multiple access channel (SW-MAC) rate region theoretical performance. We found that point very close to (1,1) mutual information of the extrinsic information transfer (EXIT) chart can be reached easily by using memory-1 rate-1 doped accumulator, which make the error-floor invisible (below 10^-7). For broadband transmission, our multiple input multiple output (MIMO) turbo equalizer work very efficiently to approach a performance only about 1 dB away from the theoretical MIMO/SW-MAC limit. Our system also work well even when very simple memory-1 convolutional codes are used. The potential applications are for power limited wireless multi-hops and ad-hoc wireless network, sensor/monitoring networks, where sensor collect the correlated data and transmit it to a common destination (fusion center) with higher spectrum efficiency. The main publications of the project are on IEEE Transaction on Signal Processing, IEEE Communication Letters and some major IEEE international conferences.

在本项目中，我们成功地估计和利用了分布式协作无线网络中的源相关性，以实现近极限睡眠-狼多址信道（SW-MAC）速率区域的理论性能。我们发现，使用内存-1掺杂率-1的累加器可以很容易地达到外部信息传递（EXIT）图中非常接近（1,1）互信息的点，使误差层不可见（低于10^-7）。对于宽带传输，我们的多输入多输出（MIMO）涡轮均衡器工作效率非常高，性能仅与理论MIMO/SW-MAC限制相差约1 dB。即使使用非常简单的内存-1卷积代码，我们的系统也能很好地工作。潜在的应用是功率有限的无线多跳和自组织无线网络，传感器/监控网络，传感器收集相关数据并以更高的频谱效率将其传输到共同的目的地（融合中心）。项目主要发表论文有《IEEE信号处理学报》、《IEEE通信快报》和IEEE一些重要国际会议。