Robust signal processing for asynchronous distributed massive MIMO

用于异步分布式大规模 MIMO 的鲁棒信号处理

• 批准号: 502013-2016
• 负责人: Shahbazpanahi, Shahram
• 金额: $ 3.9万
• 依托单位: University of Ontario Institute of Technology
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=664815
• 关键词: Robust; signal; processing; asynchronous; distributed

In this NSERC Collaborative Research and Development Grant proposal, we propose to study centralized and de-centralized signal processing techniques for asynchronous distributed massive multiple input multiple output (MIMO) systems. Distributed massive MIMO systems are comprise of many base stations (BSs) each of which is equipped with multiple antennas. Such systems benefit from the advantages offered by massive MIMO systems (where the effects of fast fading and uncorrelated noise are vanished) while allowing distributed processing and exploiting user diversity.****In our study, we focus on three aspects of distributed massive MIMO systems, namely energy efficiency, latency, and computational complexity. In traditional models for massive MIMO communication systems, it is often assumed that different users/base stations in the network are time-synchronized at the symbol level. This assumption, however, may not be realistic due to the fact that different users are located at different distances from the massive MIMO base stations. To the best of our knowledge, the published results on distributed massive MIMO ignore two facts. The first fact which is often ignored is that the signals transmitted by one user device will very likely arrive at different massive MIMO BSs with different delays. The second fact which is also ignored is that signals transmitted by different massive MIMO BSs also arrive at one receiver with different delays. For example, in coordinated multi-point (CoMP) scheme with many base stations and with possibly many antennas at each base station, these aforementioned delays need to be taken into account. Due to these delays, the end-to-end channel can no longer be modeled as a frequency-flat link. Indeed, the end-to-end channel is best modeled with a multi-path (frequency selective) impulse response. Such a view point on the end-end-end channel makes the equalization of this channel inevitable, whether it will be a single-carrier equalization scheme or a multi-carrier one. **

在这项NSERC协作研究与发展资助计划中，我们建议研究异步分布式大规模多输入多输出(MIMO)系统的集中式和非集中式信号处理技术。分布式海量MIMO系统由多个基站组成，每个基站配备多个天线。这样的系统得益于大规模MIMO系统(消除了快衰落和不相关噪声的影响)所提供的优势，同时允许分布式处理和利用用户分集。*在我们的研究中，我们重点研究了分布式大规模MIMO系统的三个方面，即能量效率、延迟和计算复杂性。在大规模MIMO通信系统的传统模型中，通常假设网络中的不同用户/基站在符号级别上是时间同步的。然而，由于不同的用户位于距离大规模MIMO基站的不同距离，该假设可能不现实。就我们所知，已发表的关于分布式大规模MIMO的结果忽略了两个事实。经常被忽略的第一个事实是，由一个用户设备发送的信号将很可能以不同的延迟到达不同的大规模MIMO BS。同样被忽略的第二个事实是，由不同的大规模MIMO BS发送的信号也以不同的延迟到达同一接收器。例如，在具有许多基站并且在每个基站处可能具有许多天线的协调多点(CoMP)方案中，需要考虑这些前述延迟。由于这些延迟，端到端信道不能再建模为频率平坦链路。事实上，端到端信道最好用多路径(频率选择性)脉冲响应来建模。端-端-端信道的这种观点使得无论是单载波均衡方案还是多载波均衡方案，该信道的均衡都是不可避免的。**