System Level Performance Characteristics of Sub-6GHz Massive MIMO Deployments with the 3GPP New Radio

Vook, F.W.; Hillery, W.J.; Visotsky, Eugene; Tan, Jun; Shao, Xuning; Enescu, Mihai

doi:10.1109/vtcfall.2018.8690560

Cited by 11 publications

(8 citation statements)

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“…The average sum-throughput with MU-MIMO under ideal conditions with 32 and 16 ports is equivalent to a cell average spectral efficiency of 3.6 bps/Hz and 3.4 bps/Hz respectively. These results are very close to those provided in [38] with CSI-RS based Class-A feedback mechanism using the LTE codebook. The results also fall in the range of that provided in [36].…”

Section: B Simulation Setup and Resultssupporting

confidence: 86%

“…FD-MIMO supports both channel reciprocity and channel feedback mechanisms to determine the downlink precoder. 3GPP species two underlying CSI feedback mechanisms for FD-MIMO [1], [38]; non-precoded codebook and precoded or beamformed channel sounding using CSI-reference signals (CSI-RS). In the non-precoded codebook feedback mechanism, the UE selects the best index from a codebook based on channel sounding from individual antenna ports at the transmitter.…”

Section: A Channel Feedback Mechanisms and Aas Configuration In 3gppmentioning

confidence: 99%

“…In this paper, we consider the non-precoded codebook feedback mechanism for analysis. AAS configuration in 3GPP is classified based on the mapping of radiating elements to data converters, as described in [36] and [38]. We will use the same terminology throughout the rest of the paper.…”

Section: A Channel Feedback Mechanisms and Aas Configuration In 3gppmentioning

confidence: 99%

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Achieving Phase Coherency and Gain Stability in Active Antenna Arrays for Sub-6 GHz FDD and TDD FD-MIMO: Challenges and Solutions

Vaghefi¹,

Palat²,

Marzin³

et al. 2020

IEEE Access

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Full-dimension MIMO (FD-MIMO) using planar active antenna systems (AAS) is considered a critical technology for fifth-generation (5G) cellular systems to improve network capacity. An AAS is typically subject to hardware impairments that negatively impact network capacity. Hence, this paper focuses on impairments that cause phase and magnitude errors between radio frequency (RF) chains and shows why they are particularly difficult to avoid in practical AAS. Although previous investigations show these impairments to degrade performance, they are not useful in deriving measurable impairment margins for practical FD-MIMO deployments. Knowing impairment limits are critical for system designers to make hardware design tradeoffs such as AAS configuration, component selection, implementation complexity, and cost. Moreover, it also helps set conformance limits for critical lab verification. Therefore, the paper first investigates the impact of the impairments and derives their practical limits for FD-MIMO by explicitly considering the cumulative effects of the channel model, inter-cell interference, link adaptation, and channel aging due to feedback delays. It is shown that a lower number of digitized RF chains can be a better choice under lower impairments. Next, the sources of impairments are investigated by using measurements carried out in the lab and the field during live operation in a commercial LTE network. Phase drift from local oscillators (LO) and internal temperature variations are identified as two significant sources. The tradeoffs and shortcomings of some of the existing solutions in massive MIMO literature are discussed. Finally, in order to address the shortcomings, a novel and practical coherent LO distribution architecture and array calibration mechanism are proposed. This solution is shown to be applicable to both TDD and FDD FD-MIMO. Measurement results are provided to prove the high degree of coherency and stability achieved on a unique array architecture called high definition active antenna system (HDAAS). INDEX TERMS Massive MIMO, FD-MIMO, calibration, phase and magnitude error, array coherency. I. INTRODUCTION M ASSIVE multiple-input and multiple-output (MIMO) using large active antenna arrays are viewed as a critical solution to solve capacity demands in Fifth Generation New Radio (5G-NR) cellular networks operating in sub-6 GHz bands. Full-dimension MIMO (FD-MIMO) defined in the 3rd Generation Partnership Project (3GPP) [1] has adopted principles of massive MIMO. It specifies the use of planar rectangular active antenna arrays at the base station (BS) to support simultaneous data transmission to multiple user equipment (UE) terminals (referred to as multiuser MIMO or MU-MIMO) to achieve significant improvement in network capacity. This topic is of active research in academia and industry [2]. A. CHALLENGES IN FIELD OPERATION DUE TO HARDWARE IMPAIRMENTS AND RELATED WORK Antenna installations on tower tops and buildings for a macro-cell network are typically limited in space and weight due to win...

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Section: B Simulation Setup and Resultssupporting

confidence: 86%

Section: A Channel Feedback Mechanisms and Aas Configuration In 3gppmentioning

confidence: 99%

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Achieving Phase Coherency and Gain Stability in Active Antenna Arrays for Sub-6 GHz FDD and TDD FD-MIMO: Challenges and Solutions

Vaghefi¹,

Palat²,

Marzin³

et al. 2020

IEEE Access

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show abstract

“…Channel feedback mechanism is default for FDD systems where reciprocity cannot be applied. Two basic CSI feedback mechanisms are proposed in 3GPP for FD-MIMO [1], [7]; non-precoded codebook and precoded or pre-beamformed channel sounding using CSI-reference signals (CSI-RS). In the non-precoded codebook feedback mechanism, the UE selects the best index from a codebook which can then be used to determine the downlink precoder for data transmission.…”

Section: A Fd-mimo In Tdd and Fdd Systemsmentioning

confidence: 99%

Achieving Phase Coherency and Gain Stability in Active Antenna Arrays for Sub-6 GHz FDD and TDD FD-MIMO: Challenges and Solutions

Vaghefi¹,

Palat²,

Marzin³

et al. 2019

Preprint

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Massive MIMO has been the subject of intense interest in both academia and industry for the past few years. 3GPP standardization for cellular systems have adopted the principles of massive MIMO and categorized the use of large rectangular planar arrays at the base station as full-dimension MIMO (FD-MIMO) to operate in both TDD and FDD. Operating a large antenna array base station requires the system to overcome several implementation challenges caused by hardware impairments making practical solutions non-ideal and expensive to deploy at scale. It is important to learn from existing challenges and solutions in order to prepare for larger scale deployment for example with cell free massive MIMO. Hence in this paper, we specifically study the phase and amplitude instability due to RF impairments using measurements carried out in the lab and in the field in a commercial LTE network.We investigate the effect of phase and magnitude errors on the performance of FD-MIMO systems. We discuss and characterize various sources creating these errors including time varying phase drift from low cost local oscillator (LO) and internal temperature variations affecting frequency response of RF chains.The minimum requirements and tradeoffs of different LO architectures and calibration mechanisms for practical cellular deployment are discussed. We then provide details of a novel coherent LO distribution mechanism and related novel array calibration mechanism that can be applied to both TDD and FDD systems. Measurement results are provided to validate the performance of these methods used in a 2D full-connected hybrid beamforming array architecture called High Definition Active Antenna System (HDAAS). These results showcase the efficacy of the proposed methods which can easily be extended to other array architectures including sub-array hybrid beamforming and element-level digitization.

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“…In contrast, the scheme using Type II codebook feedbacks both wideband and subband indices for a more accurate CSI representation [9], while leading to much higher overheads of communications. System-level performance evaluations for these two codebook-based schemes can be found in [10,11], and it was reported that Type II codebook based beamforming scheme could achieve up to 30% performance enhancement over the Type I codebook based counterpart.…”

Section: Introductionmentioning

confidence: 99%

Pushing The Limit of Type I Codebook For Fdd Massive Mimo Beamforming: A Channel Covariance Reconstruction Approach

Liu

Cheng

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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There is a fundamental trade-off between the channel representation resolution of codebooks and the overheads of feedback communications in the fifth generation new radio (5G NR) frequency division duplex (FDD) massive multipleinput and multiple-output (MIMO) systems. In particular, two types of codebooks (namely Type I and Type II codebooks) are introduced with different resolution and overhead. Although the Type I codebook based scheme requires lower feedback overhead, its channel state information (CSI) reconstruction and beamforming performance are not as good as those from the Type II codebook based scheme. However, since the Type I codebook based scheme has been widely used in 4G systems for many years, replacing it by the Type II codebook based scheme overnight is too costly to be an option. Therefore, in this paper, using Type I codebook, we leverage advances in cutting plane method to optimize the CSI reconstruction at the base station (BS), in order to close the gap between these two codebook based beamforming schemes. Numerical results based on channel samples from QUAsi Deterministic RadIo channel GenerAtor (QuaDRiGa) are presented to show the excellent performance of the proposed algorithm in terms of beamforming vector acquisition.

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System Level Performance Characteristics of Sub-6GHz Massive MIMO Deployments with the 3GPP New Radio

Cited by 11 publications

References 1 publication

Achieving Phase Coherency and Gain Stability in Active Antenna Arrays for Sub-6 GHz FDD and TDD FD-MIMO: Challenges and Solutions

Achieving Phase Coherency and Gain Stability in Active Antenna Arrays for Sub-6 GHz FDD and TDD FD-MIMO: Challenges and Solutions

Achieving Phase Coherency and Gain Stability in Active Antenna Arrays for Sub-6 GHz FDD and TDD FD-MIMO: Challenges and Solutions

Pushing The Limit of Type I Codebook For Fdd Massive Mimo Beamforming: A Channel Covariance Reconstruction Approach

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