Channel Charting Aided Pilot Reuse for Massive MIMO Systems With Spatially Correlated Channels

Ribeiro, Lucas Eduardo; Leinonen, Markus; Al-Tous, Hanan; Tirkkonen, Olav; Juntti, Markku

doi:10.1109/ojcoms.2022.3225054

Cited by 6 publications

(7 citation statements)

References 56 publications

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“…i (t) are the RSRP measurements from the i-th BS at devices 1 and 2 in the logarithmic domain and are given in (5). The correlation ρ i is a deterministic function of the BSs spatial distribution Φ and the position of the devices and is given as…”

Section: ) Likelihoodmentioning

confidence: 99%

“…Furthermore, social networking, Device-to-Device (D2D) communications [1], [2], and Vehicle-to-Vehicle (V2V) communications [3] are among the emerging applications that motivate research on efficient proximity estimation algorithms and solutions. Knowledge of the proximity between cellular devices allows for interference management [4], [5], which is critical for achieving high throughputs in spatially-and spectrallydense future 6G networks. Other applications of proximity estimation include but are not limited to advertising, content creation [6], and crowd counting.…”

Section: Introductionmentioning

confidence: 99%

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CSI-Based Proximity Estimation: Data-Driven and Model-Based Approaches

Bezerra,

Kouzayha,

Elsawy

et al. 2024

IEEE Open J. Commun. Soc.

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Proximity estimation forms the backbone of contact tracing solutions, as quarantining potentially infected individuals is essential for controlling the spread of epidemics. It is also essential for device discovery in Device-to-Device (D2D) and Vehicle-to-Vehicle (V2V) communications, which will be critical in future 6G networks. Despite the widespread coverage of cellular networks, no previous work has evaluated cellular-based proximity estimation in an experimental setting. In this paper, we collect Channel State Information (CSI) measurements from an actual cellular network and utilize them to train and evaluate two proposed solutions. Capitalizing on CSI spatial correlation, we propose a data-driven method to classify devices based on their respective proximity. The proposed neural network has an accuracy of 91.18% when classifying devices as within 5 meters from one another or not, from only 10 seconds of CSI measurements. This method is complemented by a model-based approach that provides a solid theoretical model for estimating proximity. The model-based approach uses Bayesian inference of the conditional distribution of power correlation across devices, assuming spatially-correlated shadowing and stochastic geometry tools. The proposed Bayesian regressor fits our dataset better than the standard exponentially-decaying correlation model, reaching a 2.8× lower RMSE while requiring fine-tuning of only two more parameters. A Bayesian classifier is also proposed and reached a 91.67% accuracy on the binary case while also outperforming the data-driven model significantly at higher distances.INDEX TERMS Bayesian methods, cellular networks, channel models, channel state information, machine learning, proximity detection.

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Section: ) Likelihoodmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CSI-Based Proximity Estimation: Data-Driven and Model-Based Approaches

Bezerra,

Kouzayha,

Elsawy

et al. 2024

IEEE Open J. Commun. Soc.

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“…Since a smaller difference in power is more likely to be caused by small-scale fading (and not by distance), we would be less confident in deducing which AP might be closer. Hence, observing that the AP-side receive power is not perfectly inversely proportional to distance motivates the margin M p in (3). By setting M p > 0, we can avoid some false AP pairs, i.e., pairs (a c , a f ) ∈ P (n) for which x(n) − x(ac) > x(n) − x(a f ) .…”

Section: B Is Received Power Inversely Proportional To Distance?mentioning

confidence: 99%

“…The key idea is to apply dimensionality reduction to a large database of CSI features that represent large-scale properties of the wireless channel; this leads to a low-dimensional latent space-the socalled channel chart-that is tied to UE position. This UE pseudo-position information can be used to assist a wide range of applications in wireless communication systems, such as pilot allocation [3], beam management [4], [5], channel capacity prediction [6], and many more [7].…”

Section: Introductionmentioning

confidence: 99%

Channel Charting in Real-World Coordinates

Taner,

Palhares,

Studer

2023

GLOBECOM 2023 - 2023 IEEE Global Communications Conference

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Channel charting is an emerging self-supervised method that maps channel state information (CSI) to a lowdimensional latent space, which represents pseudo-positions of user equipments (UEs). While this latent space preserves local geometry, i.e., nearby UEs are nearby in latent space, the pseudopositions are in arbitrary coordinates and global geometry is not preserved. In order to enable channel charting in real-world coordinates, we propose a novel bilateration loss for multipoint wireless systems in which only the access point (AP) locations are known-no geometrical models or ground-truth UE position information is required. The idea behind this bilateration loss is to compare the received power at pairs of APs in order to determine whether a UE should be placed closer to one AP or the other in latent space. We demonstrate the efficacy of our method using channel vectors from a commercial ray-tracer.

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“…Reducing pilot contamination is critical as it degrades channel estimation accuracy and communications quality. Although the problem can be alleviated by an intelligent assignment of the pilots [2], the performance is limited by the overlapping on the direction of the UEs' incoming signals to the BS [3]. Thus, the number of sequences, i.e., their length, directly affects the channel estimation accuracy and the SE.…”

Section: Introductionmentioning

confidence: 99%

Spectral Efficiency Maximization for Massive MIMO Uplink With Intra-Cell Pilot Reuse

Ribeiro,

Juntti

2024

IEEE Wireless Commun. Lett.

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Massive multiple-input multiple-output (mMIMO) and spatial multiplexing of devices are crucial technologies to support massive connectivity. As the number of users increases, it becomes impossible to assign unique orthogonal pilot sequences to each device, making the necessary channel state estimation challenging. Hence, intra-cell pilot reuse becomes necessary. In this letter, we propose a novel method for determining the optimal pilot length that maximizes spectral efficiency (SE) in mMIMO networks with pilot reuse. To that end, we develop an approximated expression for the signal-to-interference-plus-noise ratio, which is a function of the pilot length. The proposed method achieves accurate SE for different degrees of channel correlation and block length. This allows for efficient evaluation of the SE for different pilot lengths, without having to resort to lengthy network simulations.

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Channel Charting Aided Pilot Reuse for Massive MIMO Systems With Spatially Correlated Channels

Cited by 6 publications

References 56 publications

CSI-Based Proximity Estimation: Data-Driven and Model-Based Approaches

CSI-Based Proximity Estimation: Data-Driven and Model-Based Approaches

Channel Charting in Real-World Coordinates

Spectral Efficiency Maximization for Massive MIMO Uplink With Intra-Cell Pilot Reuse

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