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

Multiple-input multiple-output (MIMO) is a key for the fifth generation (5G)and beyond wireless communication systems owing to higher spectrum efficiency,spatial gains, and energy efficiency. Reaping the benefits of MIMO transmissioncan be fully harnessed if the channel state information (CSI) is available atthe transmitter side. However, the acquisition of transmitter side CSI entailsmany challenges. In this paper, we propose a deep learning assisted CSIestimation technique in highly mobile vehicular networks, based on the factthat the propagation environment (scatterers, reflectors) is almost identicalthereby allowing a data driven deep neural network (DNN) to learn thenon-linear CSI relations with negligible overhead. Moreover, we formulate andsolve a dynamic network slicing based resource allocation problem for vehicularuser equipments (VUEs) requesting enhanced mobile broadband (eMBB) andultra-reliable low latency (URLLC) traffic slices. The formulation considers athreshold rate violation probability minimization for the eMBB slice whilesatisfying a probabilistic threshold rate criterion for the URLLC slice.Simulation result shows that an overhead reduction of 50% can be achieved with12% increase in threshold violations compared to an ideal case with perfect CSIknowledge.

Deep Learning Assisted CSI Estimation for Joint URLLC and eMBB Resource Allocation