Intelligent Optimization of Base Station Array Orientations via Scenario-Specific Modeling

He, Weiliang; Zhang, Cheng; Huang, Yongming; You, Xiaohu

doi:10.1109/tcomm.2021.3135532

Cited by 5 publications

(2 citation statements)

References 43 publications

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“…Non-RT AI models at CoNs mainly serve the network layer, involving optimization tasks such as antenna tuning, which are naturally suitable for AI methods due to unknown or imprecise underlying models. To reduce the cost of road testing for the optimization of array orientation and antenna weight in massive MIMO systems, we recently proposed a hierarchical intelligent framework [32]. Specifically, a deep Gaussian process regression (DGPR) model was utilised for the prediction of reference signal received power (RSRP) from limited data, and a domain-knowledge driven multibranch deep neural network (DNN) made up of convolutional neural network (CNN) and multi-layer perceptron (MLP) was designed to provide a high-precision KPI mapping and subsequent antenna optimization.…”

Section: Network Data Knowledge Graph Constructionmentioning

confidence: 99%

Toward 6G TK$μ$ Extreme Connectivity: Architecture, Key Technologies and Experiments

You¹,

Huang²,

Liu³

et al. 2022

Preprint

Self Cite

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networks are evolving towards new features and order-of-magnitude enhancement of systematic performance metrics compared to the current 5G. In particular, the 6G networks are expected to achieve extreme connectivity performance with Tbps-scale data rate, Kbps/Hzscale spectral efficiency, and µs-scale latency. To this end, an original three-layer 6G network architecture is designed to realise uniform full-spectrum cell-free radio access and provide taskcentric agile proximate support for diverse applications. The designed architecture is featured by super edge node (SEN) which integrates connectivity, computing, AI, data, etc. On this basis, a technological framework of pervasive multi-level (PML) AI is established in the centralised unit to enable task-centric near-real-time resource allocation and network automation. We then introduce a radio access network (RAN) architecture of full spectrum uniform cell-free networks, which is among the most attractive RAN candidates for 6G TKµ extreme connectivity. A few most promising key technologies, i.e., cell-free massive MIMO, photonics-assisted Terahertz wireless access and spatiotemporal two-dimensional channel coding are further discussed. A testbed is implemented and extensive trials are conducted to evaluate innovative technologies and methodologies. The proposed 6G network architecture and technological framework demonstrate exciting potentials for full-service and full-scenario applications.

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Section: Network Data Knowledge Graph Constructionmentioning

confidence: 99%

Toward 6G TK$μ$ Extreme Connectivity: Architecture, Key Technologies and Experiments

You¹,

Huang²,

Liu³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Furthermore, the CSI prior is difficult to analytically characterize especially in complex scenarios, thus the model-driven methods inherently are inappropriate for the implicit CSI prior. Deep learning (DL) [7] has been identified as an enabling technology for future wireless mobile networks [8,9], and it has received extensive attention for precoding [10]- [12], positioning [13,14], CSI compression and reconstruction [15,16], beam management [17]- [21], network optimization [22]. Data-driven or -aided BA/T is a promising technique that automatically learns and exploits the underlying correlations of CSI across different times, frequencies, spaces or other out-of-band information [23,24], to reduce CSI acquisition overhead and improve system spectral efficiency and robustness.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Private Enterprises Based on Deep Learning

Meng

Zhang

2022

Mobile Information Systems

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In this paper, we conduct an in-depth research on the corresponding enterprises, combined with some problems existing in the process of data processing and use. We establish a deep learning model on the extensive collection and comprehensive investigation of the research results of domestic and foreign enterprises in all aspects of the process of data processing and use, and determine the research directions. Firstly, in view of the increasing complexity and dimension of enterprise data, and the difficulties of enterprise data application, this paper studies the related data preprocessing methods. Secondly, aiming at the problems of enterprise cost control and customer relationship management, this paper studies the prediction based on enterprise data through the analysis of practical problems and the processing of corresponding data. Finally, in order to progress and advance the efficiency and scientific usefulness of enterprise management, we in this paper study the evaluation based on enterprise data. The model is verified through simulations and compared with several models i.e. cross hybrid and sequential hybrid models. Using certain assumptions, the attained outcomes confirm that the accuracy of the deep learning structure of the single model is sophisticated and greater than that of the cross hybrid model, but lower than that of the sequential hybrid model.

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An Efficient Codebook Based Radio Parameter Optimization Method for Mobile Networks

Shen

2022

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

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Intelligent Optimization of Base Station Array Orientations via Scenario-Specific Modeling

Cited by 5 publications

References 43 publications

Toward 6G TK$μ$ Extreme Connectivity: Architecture, Key Technologies and Experiments

Toward 6G TK$μ$ Extreme Connectivity: Architecture, Key Technologies and Experiments

Evaluation of Private Enterprises Based on Deep Learning

An Efficient Codebook Based Radio Parameter Optimization Method for Mobile Networks

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