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

For millimeter-wave networks, this paper presents a paradigm shift forleveraging time-consecutive camera images in handover decision problems. Whilemaking handover decisions, it is important to predict future long-termperformance---e.g., the cumulative sum of time-varying data rates---proactivelyto avoid making myopic decisions. However, this study experimentally noticesthat a time-variation in the received powers is not necessarily informative forproactively predicting the rapid degradation of data rates caused by movingobstacles. To overcome this challenge, this study proposes a proactiveframework wherein handover timings are optimized while obstacle-caused datarate degradations are predicted before the degradations occur. The key idea isto expand a state space to involve time consecutive camera images, whichcomprises informative features for predicting such data rate degradations. Toovercome the difficulty in handling the large dimensionality of the expandedstate space, we use a deep reinforcement learning for deciding the handovertimings. The evaluations performed based on the experimentally obtained cameraimages and received powers demonstrate that the expanded state spacefacilitates (i) the prediction of obstacle-caused data rate degradations from500 ms before the degradations occur and (ii) superior performance to ahandover framework without the state space expansion

Handover Management for mmWave Networks with Proactive Performance Prediction Using Camera Images and Deep Reinforcement Learning