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

Massive MIMO is widely touted as an enabling technology for 5th generation(5G) mobile communications and beyond. On paper, the large excess of basestation (BS) antennas promises unprecedented spectral efficiency gains.Unfortunately, during the initial phase of industrial testing, a practicalchallenge arose which threatens to undermine the actual deployment of massiveMIMO: user mobility-induced channel Doppler. In fact, testing teams reportedthat in moderate-mobility scenarios, e.g., 30 km/h of user equipment (UE)speed, the performance drops up to 50% compared to the low-mobility scenario, aproblem rooted in the acute sensitivity of massive MIMO to this channelDoppler, and not foreseen by many theoretical papers on the subject. In orderto deal with this "curse of mobility", we propose a novel form of channelprediction method, named Prony-based angular-delay domain (PAD) prediction,which is built on exploiting the specific angle-delay-Doppler structure of themultipath. In particular, our method relies on the high angular-delayresolution which arises in the context of 5G. Our theoretical analysis showsthat when the number of base station antennas and the bandwidth are large, theprediction error of our PAD algorithm converges to zero for any UE velocitylevel, provided that only two accurate enough previous channel samples areavailable. Moreover, when the channel samples are inaccurate, we propose tocombine the PAD algorithm with a denoising method for channel estimation phasebased on the subspace structure and the long-term statistics of the channelobservations. Simulation results show that under a realistic channel model of3GPP in rich scattering environment, our proposed method is able to overcomethis challenge and even approaches the performance of stationary scenarioswhere the channels do not vary at all.

Addressing the curse of mobility in massive MIMO with Prony-based angular-delay domain channel predictions