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Channel Interpolation of Fading Channels and the Pilot Density Required for Predictor Antennas
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Electrical Engineering, Signals and Systems.ORCID iD: 0000-0002-6254-3348
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Electrical Engineering, Signals and Systems.ORCID iD: 0000-0003-0981-2665
Orange Labs, Châttillon, France.
Rohde & Schwarz, Dresden, Germany.
2024 (English)In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 73, no 5, p. 6765-6779Article in journal (Refereed) Published
Abstract [en]

Predictor antennas (PAs) are a potential solution to severe channel aging that can occur at high vehicular velocities in non line-of-sight (NLOS) environments. Channel aging reduces the performance of many advanced communication schemes based on channel state information at the transmitter (CSIT). Although PAs have been shown to work in combination with dense pilots in time and space, prediction performance can be reduced when channel estimates are sparse. This paper answers how densely pilots must be placed for PAs to be feasible when performing basic interpolation between channel estimates. This is important, especially for establishing upper limits on the length of the downlink (DL) frames required in a time-division duplex (TDD) system with PAs. Nearest-neighbor, linear, and spline interpolation are analyzed when applied to stochastic radio channels. A theoretical expression is derived for the power of the expected interpolation error for any interpolation method that can be expressed as a linear function of a set of measured values. The interpolation methods are evaluated on three theoretical channels with Rayleigh, flat, and Rician fading, and on two sets of channel measurements. The results indicate that linear and spline interpolation can be used with down to five and three samples per wavelength, respectively, without affecting the PA-based prediction NMSE. At two samples per wavelength, the prediction NMSE is still at a level that can be useful for precoding design in massive multiple-input multiple-output (M-MIMO) systems.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024. Vol. 73, no 5, p. 6765-6779
Keywords [en]
Channel interpolation, Channel prediction, Channel state information (CSI), Predictor antenna, Spline interpolation
National Category
Signal Processing Telecommunications
Research subject
Electrical Engineering with specialization in Signal Processing
Identifiers
URN: urn:nbn:se:uu:diva-470623DOI: 10.1109/TVT.2023.3343617ISI: 001224392800082OAI: oai:DiVA.org:uu-470623DiVA, id: diva2:1647442
Available from: 2022-03-27 Created: 2022-03-27 Last updated: 2024-10-30Bibliographically approved
In thesis
1. Predictor Antennas: Enabling channel prediction for fast-moving vehicles in wireless broadband systems
Open this publication in new window or tab >>Predictor Antennas: Enabling channel prediction for fast-moving vehicles in wireless broadband systems
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Many advanced transmission techniques utilize channel state information (CSI) at the transmitter (CSIT) to improve throughput, spectral efficiency, power efficiency, and other performance metrics. Estimating CSI accurately is important to fully benefit from many of these techniques. In situations where users travel at high speed, the channel can change rapidly, especially in small-scale fading environments. In many systems, there is also a delay between measuring CSI and using it for transmission. If the channel changes significantly during this delay, CSI becomes outdated and the benefits of advanced transmission techniques are typically negatively affected. Long-range channel prediction can be used to counteract this delay and enable advanced transmission to vehicles that travel at high velocity. Conventional prediction methods use channel extrapolation and have a limited prediction horizon that does not support high vehicular velocities for the current size of these delays. The predictor antenna concept has been shown to increase the prediction horizon by at least an order-of-magnitude. It does so by placing an antenna array on the exterior of a vehicle, in the direction of travel. The first antenna can then measure the channel at positions that the following antennas will visit later.

This thesis uses channel measurements to investigate how practical aspects affect the prediction performance of predictions based on predictor antennas. It also develops a general framework that can be used to calculate the predictions in a real system. This includes addressing the causality of all the processing methods involved and adapting these methods to the design of the system and the radio environment. In a massive multiple-inputmultiple-output (MIMO) system, multi-user transmission is enabled by channel prediction and increases the sum capacity by 100% compared to 1 ms old channel estimates at a velocity of 150 km/h. This is achieved with relatively dense pilots in time. The prediction performance of the proposed framework is shown to degrade if pilots are spread further than 0.3–0.5 wavelengths in space, if spline interpolation is used to interpolate between the channel estimates.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2022. p. 52
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 2128
Keywords
Predictor antenna, Channel prediction, Long-range prediction, Massive MIMO, Spline interpolation, Channel aging, Outdated CSI
National Category
Telecommunications Signal Processing
Research subject
Electrical Engineering with specialization in Signal Processing
Identifiers
urn:nbn:se:uu:diva-470624 (URN)978-91-513-1453-2 (ISBN)
Public defence
2022-05-13, Häggsalen, Ångstromlaboratoriet, Lägerhyddsvägen 1, Uppsala, 09:00 (English)
Opponent
Supervisors
Available from: 2022-04-22 Created: 2022-03-27 Last updated: 2022-06-14

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Björsell, JoachimSternad, Mikael

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