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A Framework for Predictor Antennas in Practice
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
Xilinx, Dresden, Germany.
2022 (English)In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 71, no 7, p. 7503-7518Article in journal (Refereed) Published
Abstract [en]

Channel predictions are important to achieve high spectral efficiency for high-mobility vehicles. Channel extrapolation, used by many prediction methods, suffers from a limited prediction horizon in difficult radio environments. The predictor antenna (PA) concept provides the prediction horizons required for efficient transmission to fast-moving vehicles by measuring the channel ahead of time with an extra antenna placed on the vehicle. This paper presents a general framework that addresses the practical signal processing challenges of the PA concept. It is adaptable to a vast variety of vehicular deployment, mobility, and communication scenarios. A new theoretical prediction normalized mean-squared error (NMSE) expression is derived based on the presented framework. The framework is demonstrated by applying it to extensive channel measurements and comparing the PA predictions to Kalman-based channel predictions and outdated channel estimates. By studying the impact of vehicular velocity and radio environment on the prediction performance, it is shown that PA prediction is weaker at low velocities, where Kalman prediction methods are sufficient, but is uncontested at high velocities in environments without a dominating path. At high velocities in dominating path environments, the Kalman predictor provides usable predictions, but it is still outperformed by the PA predictions. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022. Vol. 71, no 7, p. 7503-7518
Keywords [en]
Channel state information (CSI), High-mobility, Predictor antenna
National Category
Telecommunications Signal Processing
Research subject
Electrical Engineering with specialization in Signal Processing
Identifiers
URN: urn:nbn:se:uu:diva-470620DOI: 10.1109/TVT.2022.3168225ISI: 000876768200052OAI: oai:DiVA.org:uu-470620DiVA, id: diva2:1647437
Available from: 2022-03-27 Created: 2022-03-27 Last updated: 2022-11-25Bibliographically 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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