Operator Data Driven Cell-Selection in LTE-LAA Coexistence Networks

Kala, Srikant Manas; Sathya, Vanlin; Yamatsuta, Eitaro; Yamaguchi, Hirozumi; Higashino, Teruo

doi:10.1145/3427796.3427818

Cited by 8 publications

(28 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The PCI scenarios that emerge from the network configurations of coexistence deployments impact performance, network feature relationships, and performance prediction. However, recent works that have analyzed the performance of public LAA deployments, have only focused on the data from Same PCI cell-sites [10], [11]. They have not analyzed the impact of Different PCI, the network performance trends in the two scenarios, how they coexist with Wi-Fi, or proposed solutions for the identification of the PCI scenarios [10], [11].…”

Section: A Research Problemsmentioning

confidence: 99%

“…As a result, cell selection and handover decisions made at the UE trigger frequent disconnections and re-attachments, degrading LAA performance. Thus, for efficient cell selection and radio resource allocation in LAA, additional information about the small cell is necessary to determine cell quality [10], [17].…”

Section: B New Challenges In the Unlicensed Bandmentioning

confidence: 99%

“…Although solutions for LTE HetNets/LTE-LAA cell association have been put forward, they are either simulation based or validated on network prototypes [8], [9]. More recently, a comprehensive analysis of the impact of cell-selection on the performance of LTE-LAA network and its Licensed and Unlicensed components has been done through real-world LAA operator data [10], [11]. Further, cell-quality metrics derived from operator data have been proposed to improve the existing LAA cellselection [10], [11].…”

mentioning

confidence: 99%

See 2 more Smart Citations

Identification and Analysis of a Unique Cell Selection Phenomenon in Public Unlicensed Cellular Networks Through Machine Learning

et al. 2022

Self Cite

View full text Add to dashboard Cite

Cellular operators deploy 4G License Assisted Access (LAA) and 5G NR-U base stations in the unlicensed spectrum, to enhance the overall network capacity. This work highlights a unique Physical Cell Id (PCI) related phenomenon observed in the public LAA operator deployments. Notably, the licensed and unlicensed carriers of a device may have the same PCI or different PCIs. The phenomenon is triggered by the combined effect of unlicensed deployment architectures and cell selection mechanisms. Consequently, the phenomenon will intensify manifold in 5G NR-U whose public deployment will soon commence. Unfortunately, the impact of this phenomenon on coexistence network performance is unexplored. It is also desirable to accurately identify the PCI scenarios at the device for improved cell selection and network performance. However, the data imbalance makes the classification problem challenging. This work addresses these problems through the following approach. Operator data from three LAA cellular providers is gathered and analyzed using machine learning algorithms. The impact of the phenomenon on LTE, LAA, and Wi-Fi components is demonstrated in three steps: First, the variation in network performance prediction accuracy in the PCI scenarios is examined. Second, the efficacy of numerosity reduction techniques used in data-driven cell selection is evaluated in both PCI scenarios. The third step entails a comparison of operator data analysis with network measurements. On-site experiments are conducted at the same PCI and different PCI sites to study the differences in real-time network performance. A controlled LTE-WiFi coexistence environment is created and multiple traffic categories are considered. Finally, a class-weight-based solution is proposed for PCI scenario identification. F-score of 0.75 and AUC-ROC of 0.84 is achieved for LAA, with a minimalist feature-set of SINR and Throughput.

show abstract

Section: A Research Problemsmentioning

confidence: 99%

Section: B New Challenges In the Unlicensed Bandmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Identification and Analysis of a Unique Cell Selection Phenomenon in Public Unlicensed Cellular Networks Through Machine Learning

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…An ML inspired channel assignment by analyzing the collected LAA data was proposed for the LAA BS and was shown to have a minimal adverse impact on Wi-Fi users. Likewise, deployment data of three LAA operators was analyzed to ascertain the impact of cell-selection mechanisms on LAA performance [111]. Further, a data-driven network hybrid optimization model for LAA networks was proposed in [112].…”

Section: Asymmetric Vs Symmetric Ed Threshold On Laa and Wi-fimentioning

confidence: 99%

“…Machine learning (ML) techniques offer efficient solutions to complex engineering problems and tasks such as video and image recognition, genome analysis, recommender systems, logistics, and automation of industrial operations. Given the potential of ML tools in solving non-linear problems, it is desirable to apply to the challenges encountered in spectrum sharing between unlicensed LTE (LAA/LTE-U) and Wi-Fi [47,48,49,55,111,112,113]. For example, LTE-U Forum has standardized the duty-cycle approach for fair coexistence of LTE-U and Wi-Fi.…”

Section: Optimal Scaling Of Lte-u Duty Cycle In Lte-u/wi-fi Coexistencementioning

confidence: 99%

Heterogenous Networks: From small cells to 5G NR-U

Sathya¹,

Kala²,

Narendran³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

With the exponential increase in mobile users, the mobile data demand has grown tremendously. To meet these demands, cellular operators are constantly innovating to enhance the capacity of cellular systems. Consequently, operators have been reusing the licensed spectrum "spatially," by deploying 4G/LTE small cells (e.g., Femto Cells) in the past. However, despite the use of small cells, licensed spectrum will be unable to meet the consistently rising data traffic because of data-intensive applications such as augmented reality/virtual reality (AR/VR) and on-the-go high-definition video streaming. Applications such AR/VR and online gaming not only place extreme data demands on the network, but are also latency-critical. To meet the QoS guarantees, cellular operators have begun leveraging the unlicensed spectrum by coexisting with Wi-Fi in the 5 GHz band. The standardizing body 3GPP, has prescribed cellular standards for fair unlicensed coexistence with Wi-Fi, namely LTE Licensed Assisted Access (LAA), New Radio in unlicensed (NR-U), and NR in Millimeter. The rapid roll-out of LAA deployments in developed nations like the US, offers an opportunity to study and analyze the performance of unlicensed coexistence networks through real-world ground truth. Thus, this paper presents a high-level overview of past, present, and future of the research in small cell and unlicensed coexistence communication technologies. It outlines the vision for future research work in the recently allocated unlicensed spectrum: The 6 GHz band, where the latest Wi-Fi standard, IEEE 802.11ax, will coexist with the latest cellular technology, 5G New Radio (NR) in unlicensed.

show abstract