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Victor Valls

Researcher at Yale University

Publications -  37
Citations -  289

Victor Valls is an academic researcher from Yale University. The author has contributed to research in topics: Subgradient method & Lagrange multiplier. The author has an hindex of 8, co-authored 34 publications receiving 216 citations. Previous affiliations of Victor Valls include Hamilton Institute & Pompeu Fabra University.

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Maximizing LTE Capacity in Unlicensed Bands (LTE-U/LAA) While Fairly Coexisting With 802.11 WLANs

TL;DR: A channel access mechanism that allows Long Term Evolution to operate in the 5-GHz unlicensed band (LTE-U/LAA) and fairly coexist with 802.11 Wireless Local Area Networks (WLANs) is proposed.
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Performance analysis of IEEE 802.11ac wireless backhaul networks in saturated conditions

TL;DR: This paper evaluates the new features of IEEE 802.11ac's physical and medium access control (MAC) features in a fully connected wireless mesh network using an analytic model and simulations, and proposes an extended request-to-send/clear- to-send scheme that integrates the explicit compressed feedback (ECFB) mechanism for the channel sounding.
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ORLA/OLAA: Orthogonal Coexistence of LAA and WiFi in Unlicensed Spectrum

TL;DR: In this article, an orthogonal (collision-free) listen-before-talk coexistence paradigm is proposed to enable fair coexistence with incumbent WiFi networks, which provides a substantial improvement in performance, yet imposes no penalty on existing WiFi networks.
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Proportional Fair MU-MIMO in 802.11 WLANs

TL;DR: This work characterise, for the first time, the proportional fair allocation of MU-MIMO spatial streams and station transmission opportunities in an 802.11 WLAN that supports multi-user MIMO (MU-M IMO) transmission by one or more stations.
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LaSR: A Supple Multi-Connectivity Scheduler for Multi-RAT OFDMA Systems

TL;DR: LaSR is proposed, a practical multi-connectivity scheduler for OFDMA-based multi-RAT systems that makes optimal discrete control actions by solving a sequence of simple optimization problems that do not require prior information of traffic patterns.