Exploiting Deep Learning in Limited-Fronthaul Cell-Free Massive MIMO Uplink - CentraleSupélec Accéder directement au contenu
Article Dans Une Revue IEEE Journal on Selected Areas in Communications Année : 2020

Exploiting Deep Learning in Limited-Fronthaul Cell-Free Massive MIMO Uplink

Manijeh Bashar
Ali Akbari
  • Fonction : Auteur
Kanapathippillai Cumanan
Hien Quoc Ngo
Alister Burr
Pei Xiao
Josef Kittler

Résumé

COVID-19 abatement strategies have risks and uncertainties which could lead to repeating waves of infection. We show—as proof of concept grounded on rigorous mathematical evidence—that periodic, high-frequency alternation of into, and out-of, lockdown effectively mitigates second-wave effects, while allowing continued, albeit reduced, economic activity. Periodicity confers (i) predictability, which is essential for economic sustainability, and (ii) robustness, since lockdown periods are not activated by uncertain measurements over short time scales. In turn—while not eliminating the virus—this fast switching policy is sustainable over time, and it mitigates the infection until a vaccine or treatment becomes available, while alleviating the social costs associated with long lockdowns. Typically, the policy might be in the form of 1-day of work followed by 6-days of lockdown every week (or perhaps 2 days working, 5 days off) and it can be modified at a slow-rate based on measurements filtered over longer time scales. Our results highlight the potential efficacy of high frequency switching interventions in post lockdown mitigation. All code is available on Github at https://github.com/V4p1d/FPSP_Covid19 . A software tool has also been developed so that interested parties can explore the proof-of-concept system.

Dates et versions

hal-04470524 , version 1 (21-02-2024)

Identifiants

Citer

Manijeh Bashar, Ali Akbari, Kanapathippillai Cumanan, Hien Quoc Ngo, Alister Burr, et al.. Exploiting Deep Learning in Limited-Fronthaul Cell-Free Massive MIMO Uplink. IEEE Journal on Selected Areas in Communications, 2020, Lecture Notes in Computer Science, 38 (8), pp.1678-1697. ⟨10.1109/JSAC.2020.3000812⟩. ⟨hal-04470524⟩
8 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More