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: $ 1.3万
• 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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=618697
• 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 协作研究与开发资助提案中，我们建议研究异步分布式大规模多输入多输出 (MIMO) 系统的集中式和分散式信号处理技术。分布式大规模 MIMO 系统由许多基站 (BS) 组成，每个基站配备多个天线。此类系统受益于大规模 MIMO 系统提供的优势（快速衰落和不相关噪声的影响消失），同时允许分布式处理和利用用户多样性。在我们的研究中，我们重点关注分布式大规模 MIMO 系统的三个方面，即能源效率、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/基站在符号级别上时间同步。然而，由于不同用户距大规模 MIMO 基站的距离不同，这一假设可能并不现实。据我们所知，已发表的分布式大规模 MIMO 的结果忽略了两个事实。第一个经常被忽视的事实是，一个用户设备发送的信号很可能会以不同的延迟到达不同的大规模 MIMO 基站。第二个同样被忽视的事实是，不同大规模 MIMO 基站发送的信号到达一个接收器时的延迟也不同。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，需要考虑上述这些延迟。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，端到端信道最好使用多路径（频率选择性）脉冲响应进行建模。这种端到端信道的观点使得该信道的均衡是不可避免的，无论是单载波均衡方案还是多载波均衡方案。