MIMO and massive MIMO — Analysis for a local area scenario

Zirwas, Wolfgang; Jäger, M.; Panzner, Berthold; Kramer, Gerhard

doi:10.1109/eusipco.2015.7362825

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…Our results help guide design choices for future mobile radio communication systems, e.g., Long Term Evolution-Advanced (LTE-Advanced) and 5G. We presented preliminary results in [11], [12], [13], and we add the following results.…”

Section: Introductionmentioning

confidence: 95%

“…For few BS antennas, the gap between the capacity upper bound and network MIMO is large, but the gap could be reduced by more advanced scheduling. The channels harden for more BS antennas: It becomes optimal to schedule all UEs on each subcarrier [5], and advanced scheduling strategies provide diminishing gains [13]. With an increasing number of BS antennas, the gap decreases and vanishes completely under a total power constraint, while a gap remains under per-BS power constraints.…”

Section: Comparison To Capacity Upper Boundmentioning

confidence: 99%

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Information rates of precoding for massive MIMO and base station cooperation in an indoor scenario

Kramer

Panzner

Zirwas

2020

J Wireless Com Network

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The performance of centralized and distributed massive MIMO deployments are analyzed for indoor office scenarios. The distributed deployments use one of the following precoding methods: (1) local precoding with local channel state information (CSI) to the user equipments (UEs) that it serves; (2) large-scale MIMO with local CSI to all UEs in the network; (3) network MIMO with global CSI. For the distributed deployments (2) and (3), it is shown that using twice as many base station antennas as data streams provides many of the massive MIMO benefits in terms of spectral efficiency and fairness. This is in contrast to the centralized deployment and the distributed deployment (1) where more antennas are needed. Two of the main conclusions are that distributing base stations helps to overcome wall penetration loss; however, a backhaul is required to mitigate inter-cell interference. The effect of estimation errors on the performance is also quantified.

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Section: Introductionmentioning

confidence: 95%

Section: Comparison To Capacity Upper Boundmentioning

confidence: 99%

Information rates of precoding for massive MIMO and base station cooperation in an indoor scenario

Kramer

Panzner

Zirwas

2020

J Wireless Com Network

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“…TDD-based massive MIMO: Due to channel hardening (because of sufficient randomness and many antennas), almost no channel quality variations are observed in massive MIMO systems. Scheduling is also seldom needed in case of variations in user load and is left as optional which means that advanced scheduling algorithms may not provide the desired gains [32]. Cell-edge performance is also improved by a factor of number of antennas.…”

Section: Tdd and Fdd Mimo Systemsmentioning

confidence: 99%

Performance comparisons of FDD MIMO and 2.6 GHz TDD massive MIMO: An experimental analysis

Zeydan

Dedeoglu

Türk³

2021

Physical Communication

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Massive multiple-input multiple-output (MIMO) is considered as a breakthrough technology in 5G and beyond 5G systems. Some of its main advantages are providing high spectral efficiency to many users simultaneously in the same timefrequency blocks, strong directive signals towards short-range areas and little interference leaks. However, while massive MIMO exhibits interesting benefits, it is important to investigate its main gains through real deployment scenarios in an operator's infrastructure. In this paper, we focus on performance comparisons of traditional frequency-division duplex (FDD)-based MIMO and 2.6 GHz Time-division duplex (TDD)-based massive MIMO deployments through

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Improved Spectral Efficiency Based on Double Spatially Sparse in Millimeter Wave MIMO System

Zhu

Guo

Sun

et al. 2021

Lecture Notes in Electrical Engineering

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MIMO and massive MIMO — Analysis for a local area scenario

Cited by 8 publications

References 7 publications

Information rates of precoding for massive MIMO and base station cooperation in an indoor scenario

Information rates of precoding for massive MIMO and base station cooperation in an indoor scenario

Performance comparisons of FDD MIMO and 2.6 GHz TDD massive MIMO: An experimental analysis

Improved Spectral Efficiency Based on Double Spatially Sparse in Millimeter Wave MIMO System

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