Reference number : GC-2017-Q1-00285, amendment number : , agreement type : G, recipient legal name : ShahbazPanahi, Shahram (University of Ontario Institute of Technology), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Collaborative Research and Development Grants - Project, program name french : Subventions de recherche et développement coopérative - projet, program purpose english : 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._x000D_
_x000D_
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.   _x000D_, program purpose french : 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._x000D_
_x000D_
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.   _x000D_, agreement title english : Robust signal processing for asynchronous distributed massive MIMO, agreement title french : Robust signal processing for asynchronous distributed massive MIMO, agreement number : CRDPJ, agreement value : 71428.0, agreement start date : 2017-06-14, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2019-2020), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2019-2020), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada