Deep convolutional neural networks for massive MIMO fingerprint-based positioning

Vieira, João; Leitinger, Erik; Sarajlić, Muris; Li, Xuhong; Tufvesson, Fredrik

doi:10.1109/pimrc.2017.8292280

Cited by 167 publications

(159 citation statements)

References 13 publications

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“…The channel samples obtained from QuaDRiGa does not have any idealistic feature, e.g., perfect directional reciprocity between the UL and DL channels. This is quite different from previous works where channel samples are obtained based on analytical channel models [15], [16], [18]- [22], [25], [26].…”

Section: A Generating Channel Samplescontrasting

confidence: 68%

Downlink Extrapolation for FDD Multiple Antenna Systems Through Neural Network Using Extracted Uplink Path Gains

Choi

2020

IEEE Access

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When base stations (BSs) are deployed with multiple antennas, they need to have downlink (DL) channel state information (CSI) to optimize downlink transmissions by beamforming. The DL CSI is usually measured at mobile stations (MSs) through DL training and fed back to the BS in frequency division duplexing (FDD). The DL training and uplink (UL) feedback might become infeasible due to insufficient coherence time interval when the channel rapidly changes due to high speed of MSs. Without the feedback from MSs, it may be possible for the BS to directly obtain the DL CSI using the inherent relation of UL and DL channels even in FDD, which is called DL extrapolation. Although the exact relation would be highly nonlinear, previous studies have shown that a neural network (NN) can be used to estimate the DL CSI from the UL CSI at the BS. Most of previous works on this line of research trained the NN using full dimensional UL and DL channels; however, the NN training complexity becomes severe as the number of antennas at the BS increases. To reduce the training complexity and improve DL CSI estimation quality, this paper proposes a novel DL extrapolation technique using simplified input and output of the NN. It is shown through many measurement campaigns that the UL and DL channels still share common components like path delays and angles in FDD. The proposed technique first extracts these common coefficients from the UL and DL channels and trains the NN only using the path gains, which depend on frequency bands, with reduced dimension compared to the full UL and DL channels. Extensive simulation results show that the proposed technique outperforms the conventional approach, which relies on the full UL and DL channels to train the NN, regardless of the speed of MSs.

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Section: A Generating Channel Samplescontrasting

confidence: 68%

Downlink Extrapolation for FDD Multiple Antenna Systems Through Neural Network Using Extracted Uplink Path Gains

Choi

2020

IEEE Access

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“…Therefore, for a given static communication environment (including the geometry, materials, antenna positions, etc. ), there exists a deterministic mapping function from the position x u to the channel h u,m (f 1 ) at every antenna element m [15]. More formally, if {x u } represents the set of all candidate user positions, with the sets {h u,M1 (f 1 )} and {h u,M2 (f 2 )} assembling the corresponding channels at antenna sets M 1 and M 2 , then we define the position-tochannel mapping functions g M1,f1 (.)…”

Section: The Existence Of Channel Mappingmentioning

confidence: 99%

“…While it is hard to guarantee the bijectiveness of g M1,f1 (. ), it is important to note that this mapping is actually bijective with high probability in many practical wireless communication scenarios [15]. Now, we define the channel-to-position mapping function g −1 M1,f1 (.)…”

Section: The Existence Of Channel Mappingmentioning

confidence: 99%

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Deep Learning for TDD and FDD Massive MIMO: Mapping Channels in Space and Frequency

Alrabeiah

Alkhateeb

2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

163

110

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Can we map the channels at one set of antennas and one frequency band to the channels at another set of antennaspossibly at a different location and a different frequency band? If this channel-to-channel mapping is possible, we can expect dramatic gains for massive MIMO systems. For example, in FDD massive MIMO, the uplink channels can be mapped to the downlink channels or the downlink channels at one subset of antennas can be mapped to the downlink channels at all the other antennas. This can significantly reduce (or even eliminate) the downlink training/feedback overhead. In the context of cellfree/distributed massive MIMO systems, this channel mapping can be leveraged to reduce the fronthaul signaling overhead as only the channels at a subset of the distributed terminals need to be fed to the central unit which can map them to the channels at all the other terminals. This mapping can also find interesting applications in mmWave beam prediction, MIMO radar, and massive MIMO based positioning.In this paper, we introduce the new concept of channel mapping in space and frequency, where the channels at one set of antennas and one frequency band are mapped to the channels at another set of antennas and frequency band. First, we prove that this channel-to-channel mapping function exists under the condition that the mapping from the candidate user positions to the channels at the first set of antennas is bijective; a condition that can be achieved with high probability in several practical MIMO communication scenarios. Then, we note that the channel-to-channel mapping function, even if it exists, is typically unknown and very hard to characterize analytically as it heavily depends on the various elements of the surrounding environment. With this motivation, we propose to leverage the powerful learning capabilities of deep neural networks to learn (approximate) this complex channel mapping function. For a case study of distributed/cell-free massive MIMO system with 64 antennas, the results show that acquiring the channels at only 4-8 antennas can be efficiently mapped to the channels at all the 64 distributed antennas, even if the 64 antennas are at a different frequency band. Further, the 3D ray-tracing based simulations show that the achievable rates with the predicted channels achieve near-optimal data rates when compared to the upper bound with perfect channel knowledge. This highlight a novel solution for reducing the training and feedback overhead in mmWave and massive MIMO systems thanks to the powerful learning capabilities of deep neural networks.

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“…To evaluate the efficacy of our approach, we consider a massive MU-MIMO-OFDM localization scenario in LoS and non-LoS scenarios with a single basestation containing 32 antennas operating at 2.68 GHz with a bandwidth of 20 MHz and localizing 2000 transmitters distributed uniformly at random in an area of 40, 000 m 2 ; the noisy channel vectors are generated using channel models from [28]. The CSI features are D = 256 dimensional (32 antennas and 8 maximallyspaced subcarriers) and correspond to the absolute value of beamspace/delay domain channel vectors as in [16], [19].…”

Section: A Simulated Scenariomentioning

confidence: 99%

Reducing the Complexity of Fingerprinting-Based Positioning using Locality-Sensitive Hashing

Tang

Ghods

Studer

2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

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Localization of wireless transmitters based on channel state information (CSI) fingerprinting finds widespread use in indoor as well as outdoor scenarios. Fingerprinting localization first builds a database containing CSI with measured location information. One then searches for the most similar CSI in this database to approximate the position of wireless transmitters. In this paper, we investigate the efficacy of locality-sensitive hashing (LSH) to reduce the complexity of the nearest neighborsearch (NNS) required by conventional fingerprinting localization systems. More specifically, we propose a low-complexity and memory efficient LSH function based on the sum-to-one (STOne) transform and use approximate hash matches. We evaluate the accuracy and complexity (in terms of the number of searches and storage requirements) of our approach for line-of-sight (LoS) and non-LoS channels, and we show that LSH enables low-complexity fingerprinting localization with comparable accuracy to methods relying on exact NNS or deep neural networks.

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Deep convolutional neural networks for massive MIMO fingerprint-based positioning

Cited by 167 publications

References 13 publications

Downlink Extrapolation for FDD Multiple Antenna Systems Through Neural Network Using Extracted Uplink Path Gains

Downlink Extrapolation for FDD Multiple Antenna Systems Through Neural Network Using Extracted Uplink Path Gains

Deep Learning for TDD and FDD Massive MIMO: Mapping Channels in Space and Frequency

Reducing the Complexity of Fingerprinting-Based Positioning using Locality-Sensitive Hashing

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