M. Arnold, S. Dörner, S. Cammerer, S. Yan, J. Hoydis, and S. ten Brink, “Enabling FDD Massive MIMO through Deep Learning-based Channel Prediction.” 2019 [Online]. Available:
http://arxiv.org/abs/1901.03664Abstract
A major obstacle for widespread deployment of frequency division duplex
(FDD)-based Massive multiple-input multiple-output (MIMO) communications is the
large signaling overhead for reporting full downlink (DL) channel state
information (CSI) back to the basestation (BS), in order to enable closed-loop
precoding. We completely remove this overhead by a deep-learning based channel
extrapolation (or "prediction") approach and demonstrate that a neural network
(NN) at the BS can infer the DL CSI centered around a frequency $f_DL$
by solely observing uplink (UL) CSI on a different, yet adjacent frequency band
around $f_UL$; no more pilot/reporting overhead is needed than with a
genuine time division duplex (TDD)-based system. The rationale is that
scatterers and the large-scale propagation environment are sufficiently similar
to allow a NN to learn about the physical connections and constraints between
two neighboring frequency bands, and thus provide a well-operating system even
when classic extrapolation methods, like the Wiener filter (used as a baseline
for comparison throughout) fails. We study its performance for various
state-of-the-art Massive MIMO channel models, and, even more so, evaluate the
scheme using actual Massive MIMO channel measurements, rendering it to be
practically feasible at negligible loss in spectral efficiency when compared to
a genuine TDD-based system.BibTeX
M. Arnold, J. Hoydis, and S. t. Brink, “Novel Massive MIMO Channel Sounding Data applied to Deep Learning-based Indoor Positioning,” in
SCC 2019; 12th International ITG Conference on Systems, Communications and Coding, 2019, pp. 1–6 [Online]. Available:
https://ieeexplore.ieee.org/document/8661318Abstract
With a significant increase in area throughput, Massive MIMO has become an enabling technology for fifth generation (5G) wireless mobile communication systems. Although prototypes were built, an openly available dataset for channel impulse responses to verify assumptions, e.g., regarding channel sparsity, is not yet available. In this paper, we introduce a novel channel sounder architecture capable of measuring multi-antenna and multi-subcarrier channel state information (CSI) at different frequency bands, antenna geometries and propagation environments. The channel sounder has been verified by evaluating channel data from first measurements. Such datasets can be used to study various deep-learning (DL) techniques in different applications, e.g., for indoor user positioning in three dimensions, as is done in this paper. Not only do we achieve an accuracy better than 75cm for line of sight (LoS), as is comparable to stateof-the-art conventional positioning techniques, but also obtain similar precision for the much more challenging case of non-line of sight (NLoS). Further extensive indoor/outdoor measurement campaigns will provide a more comprehensive open CSI dataset, tagged with positions, for the scientific community to further test various algorithms.BibTeX
M. Arnold and S. t. Brink, “Properties of Measured Massive MIMO Channels using Different Antenna Geometries,” in
WSA 2019; 23rd International ITG Workshop on Smart Antennas, 2019, pp. 1–5 [Online]. Available:
https://ieeexplore.ieee.org/document/8727203Abstract
With a significant increase in data throughput, massive MIMO has become an enabling technology for fifth generation (5G) wireless mobile communication systems. Evaluating achievable throughputs in massive MIMO propagation environments using actual channel measurements is an important task. In this paper we characterize three measurement scenarios over four key channel parameters in typical urban environments. We show that different antenna geometries result in different dominating channel properties due to angular resolution and diversity, leading to antenna geometries favoring particular scenarios. A good compromise in antenna geometry is the unconventional "L"-structure for separating channels even in near proximity of the basestation. It is shown that the performance for a practical system in a Rician fading environment can be better approximated with the average K-factor and channel order NpBibTeX
X. Wang, M. Arnold, and S. t. Brink, “TDD and FDD Massive MIMO: Channel Coherence and Operation Region,” in
WSA 2019; 23rd International ITG Workshop on Smart Antennas, 2019, pp. 1–5 [Online]. Available:
https://ieeexplore.ieee.org/document/8727218Abstract
We analyze the achievable rates of time division duplex (TDD) and frequency division duplex (FDD) operations in massive MIMO systems depending on the coherence time and bandwidth of the underlying channel. In particular, an interlaced FDD (IFDD) scheme is considered due to both its simplicity and low pilot overhead. We establish the operational region of TDD and IFDD schemes for channels with different properties in the time and frequency domain. We prove that IFDD shall be preferred when the channel has large coherence bandwidth while TDD performs better if the channel has large coherence time. Furthermore, we evaluate the performance of TDD and IFDD systems for time-varying and frequency-selective channels via numerical simulations, showing that IFDD is an attractive alternative in high speed scenarios.BibTeX
M. Arnold, S. Dörner, S. Cammerer, J. Hoydis, and S. T. Brink, “Towards Practical FDD Massive MIMO: CSI Extrapolation Driven by Deep Learning and Actual Channel Measurements,” 2019.
BibTeX
M. Widmaier, M. Arnold, S. Dorner, S. Cammerer, and S. ten Brink, “Towards Practical Indoor Positioning Based on Massive MIMO Systems,” in
2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), 2019, pp. 1–6 [Online]. Available:
https://ieeexplore.ieee.org/document/8891273Abstract
We showcase the practicability of an indoor positioning system (IPS) solely based on neural networks (NNs) and the channel state information (CSI) of a (Massive) multiple-input multiple-output (MIMO) communication system, i.e., only build on the basis of data that is already existent in today's systems. As such our IPS system promises both, a good accuracy without the need of any additional protocol/signaling overhead for the user localization task. In particular, we propose a tailored NN structure with an additional phase branch as feature extractor and (compared to previous results) a significantly reduced amount of trainable parameters, leading to a minimization of the amount of required training data. We provide actual measurements for indoor scenarios with up to 64 antennas covering a large area of 80m2. In the second part, several robustness investigations for real-measurements are conducted, i.e., once trained, we analyze the recall accuracy over a period of several days. Further, we analyze the impact of pedestrians walking in-between the measurements and show that finetuning and pre-training of the NN helps to mitigate effects of hardware drifts and alterations in the propagation environment over time. This reduces the amount of required training samples at equal precision and, thereby, decreases the effort of the costly training data acquisition.BibTeX