Weave and Conquer: A Measurement-based Analysis of Dense Antenna Deployments

Guevara, Andrea P.; Bast, Sibren De; Pollin, Sofie

doi:10.1109/icc42927.2021.9500612

Cited by 9 publications

(7 citation statements)

References 11 publications

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“…In order to train and test our CSI recreation framework, we use the MaMIMO dataset provided by KU Leuven [33]. The MaMIMO dataset is a set of indoor MIMO channel measurements collected under line of sight (LOS) and non-line of sight (NLOS) conditions with recordings of the measurement location relative to the BS.…”

Section: Simulations and Resultsmentioning

confidence: 99%

“…The LOS channels have smoother variations; therefore, the UNN takes better advantage of the correlations between the measured snapshots which leads to better estimation results. Overall, our UNN CSI-estimators provide more than 10 dB estimation gain if compared with the normalized received signal strength (RSS) of the MaMIMO measurements [33], see Table I.…”

Section: Simulations and Resultsmentioning

confidence: 99%

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Machine Learning for CSI Recreation in the Digital Twin Based on Prior Knowledge

Boas

Zirwas

Haardt

2022

IEEE Open J. Commun. Soc.

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Knowledge of channel state information (CSI) is fundamental to many functionalities for mobile communication systems. With the advance of machine learning (ML) and digital maps, i.e., digital twins, we have a big opportunity to learn the propagation environment and design novel methods to derive and report CSI. In this work, we propose to combine untrained neural networks (UNNs) and conditional generative adversarial networks (cGANs) for MIMO channel recreation based on prior knowledge. The UNNs learn the prior-CSI for some locations which are used to build the input to a cGAN. Based on the prior-CSI estimates, their locations and the location of the desired channel, the cGAN is trained to output the channel expected at the desired location. This combined approach can be used for low overhead CSI reporting as, after training, we only need to report the desired location. Our results show that our CSI recreation method is successful in modelling the wireless channel under different configurations of prior-CSI spatial sampling. In addition, the results consider a real world measurement campaign for indoor line of sight and non-line of sight channels. The signal to noise ratio (SNR) achieved by our CSI recreation is better than the SNR reported by the measured campaign providers. Moreover, our CSI recreation provides means for low overhead CSI reporting as the UNN structure is underparameterized compared to the full explicit CSI, and only the desired location is needed for the cGAN to recreate the desired CSI.

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Section: Simulations and Resultsmentioning

confidence: 99%

Section: Simulations and Resultsmentioning

confidence: 99%

Machine Learning for CSI Recreation in the Digital Twin Based on Prior Knowledge

Boas

Zirwas

Haardt

2022

IEEE Open J. Commun. Soc.

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“…A more extensive measurement campaign was conducted at KU Leuven [26,32,33]. An automated massive MIMO experiment analyzed three-antenna configurations and the corresponding leakage power impact in an indoor scenario.…”

Section: Minimum Mean Squared Error (Mmse) [30]mentioning

confidence: 99%

Internet of Things Networks: Enabling Simultaneous Wireless Information and Power Transfer

et al. 2022

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The number of sensors deployed in the world is expected to explode in the near future. At this moment, nearly 30 billion Internet of Things (IoT) devices are connected and this number is expected to double in the next four years. While not all of these are battery powered, as technology becomes smaller and mobility becomes more important to consumers, soon a larger portion will be. This forecast predicts that the number of machine to machine (M2M) devices will have the largest increase, representing nearly 50% of all devices in 2023 [1,2]. These devices are typically small and therefore ideal candidates to be powered wirelessly. Typical examples are healthcare monitoring, smart homes, and industrial 1

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“…A cell-free system is divided in space by distributing the BS into multiple access points (AP) and spreading the APs over the targeted coverage area. It has been shown [4] [5], that the distribution of the antennas increases the spatial diversity, which decreases the channel correlation between users. As a result, inter-user interference decreases and the spectral efficiency of the system increases.…”

Section: A Distributed Localisationmentioning

confidence: 99%

Expert-Knowledge-Based Data-Driven Approach for Distributed Localization in Cell-Free Massive MIMO Networks

2022

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Massive Multiple-Input Multiple-Output (MaMIMO) communication networks are recently being investigated for hltheir high potential for localisation services. This is enabled by the high-dimensional channel state information (CSI) captured by the many antennas in the system. Previously, it has been shown that these systems can achieve a very high localisation accuracy. However, many challenges still remain, we identified two of them. First, the recent trend towards cell-free MaMIMO with many highly distributed Access Points (AP), leads to the question of how this impacts the localisation methods. Current localisation methods process the signals in a central processing unit (CPU), resulting in a high fronthaul requirement when deploying these algorithms in a distributed network, limiting the deployment and scalability. Second, there exists a trade-off between using model-driven and data-driven localisation methods. In this work, we propose two new localisation methods which employ a distributed processing strategy and compare them against two centralised localisation methods. In addition, the four analysed methods explore the tradeoff between being model-and data-driven. Moreover, the proposed ML-MUSIC method blurs the lines between the two by combining Machine Learning and traditional signal processing. Next to comparing the localisation accuracy, we evaluate the performance in a dynamic setting, the scalability and fronthaul requirement of the methods. The proposed Machine Learning-enhanced Multiple Signals Classification method, ML-MUSIC, reaches a median error of 34.2 mm on the test set while only using 500 training samples. Due to ML-MUSICs distributed design, the fronthaul throughput requirement is reduced 1200fold in comparison to the centralised methods. Furthermore, ML-MUSIC has the lowest computational complexity of all analysed methods, making it an ideal method to localise users in upcoming distributed cell-free MaMIMO networks.

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Weave and Conquer: A Measurement-based Analysis of Dense Antenna Deployments

Cited by 9 publications

References 11 publications

Machine Learning for CSI Recreation in the Digital Twin Based on Prior Knowledge

Machine Learning for CSI Recreation in the Digital Twin Based on Prior Knowledge

Internet of Things Networks: Enabling Simultaneous Wireless Information and Power Transfer

Expert-Knowledge-Based Data-Driven Approach for Distributed Localization in Cell-Free Massive MIMO Networks

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