Distributed Massive MIMO Systems With Non-Reciprocal Channels: Impacts and Robust Beamforming

Minasian, Arin; Shahbazpanahi, Shahram; Adve, Raviraj

doi:10.1109/tcomm.2018.2859937

Cited by 18 publications

(12 citation statements)

References 41 publications

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“…To analyze the system performance, we refer to the technique in [33] which is widely used in the field of massive MIMO [8], [9], [34]. This technique only utilize the statistical channel to detect the received signals, when UEs do not have any channel estimate.…”

Section: A Performance Of Existing Linear Reciprocity Mismatchmentioning

confidence: 99%

Impact and Calibration of Nonlinear Reciprocity Mismatch in Massive MIMO Systems

Nie

Chen

Zhao

et al. 2021

IEEE Trans. Wireless Commun.

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Section: A Performance Of Existing Linear Reciprocity Mismatchmentioning

confidence: 99%

Impact and Calibration of Nonlinear Reciprocity Mismatch in Massive MIMO Systems

Nie

Chen

Zhao

et al. 2021

IEEE Trans. Wireless Commun.

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“…When M N is large, (G T UL G * UL ) −1 can be approximated as a diagonal matrix in the following form [8]…”

Section: Appendix a Proof Of Proposition 1 A Derivation Of βmentioning

confidence: 99%

“…This is because the overall channel consists of not only the wireless propagation channel, but also the radio frequency (RF) gains due to the frequency responses of hardwares [7], e.g., analog-todigital converters (ADC), digital-to-analog converters (DAC), filters and amplifiers. Although uplink and downlink wireless propagation channels may be reciprocal, the radio frequency (RF) gains of different hardwares are asymmetric, which causes the reciprocity mismatch [8].…”

Section: Introductionmentioning

confidence: 99%

Relaying Systems With Reciprocity Mismatch: Impact Analysis and Calibration

Nie

Chen

Zhao

et al. 2020

IEEE Trans. Commun.

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“…Prior investigations [17]- [25] have analyzed the impact of channel non-reciprocity caused by RF impairments. Although the results provide useful insights into the nature of performance degradation, the system models considered are much simpler than actual deployment.…”

mentioning

confidence: 99%

“…For example, [17]- [24] only consider single-cell with small-scale fading. Multicell with large scale fading, channel estimation errors, and vector normalization was only considered in [25]. How-ever, even in this case and others, the effects of temporal and spatial multipath clustering [26], [27], the effects of channel aging from feedback or scheduling delays, and link adaptation are not considered.…”

mentioning

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. 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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Distributed Massive MIMO Systems With Non-Reciprocal Channels: Impacts and Robust Beamforming

Cited by 18 publications

References 41 publications

Impact and Calibration of Nonlinear Reciprocity Mismatch in Massive MIMO Systems

Impact and Calibration of Nonlinear Reciprocity Mismatch in Massive MIMO Systems

Relaying Systems With Reciprocity Mismatch: Impact Analysis and Calibration

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

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