English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Localized channel modeling is crucial for offline performance optimization of5G cellular networks, but the existing channel models are for general scenariosand do not capture local geographical structures. In this paper, we propose anovel physics-based and data-driven localized statistical channel modeling(LSCM), which is capable of sensing the physical geographical structures of thetargeted cellular environment. The proposed channel modeling solely relies onthe reference signal receiving power (RSRP) of the user equipment, unlike thetraditional methods which use full channel impulse response matrices. The keyis to build the relationship between the RSRP and the channel's angular powerspectrum. Based on it, we formulate the task of channel modeling as a sparserecovery problem where the non-zero entries of the sparse vector indicate thechannel paths' powers and angles of departure. A computationally efficientweighted non-negative orthogonal matching pursuit (WNOMP) algorithm is devisedfor solving the formulated problem. Finally, experiments based on synthetic andreal RSRP measurements are presented to examine the performance of the proposedmethod.

A Physics-based and Data-driven Approach for Localized Statistical Channel Modeling