3.7k citations
- University of California San DiegoUS25 papers
- The University of Texas at AustinUS14 papers
- Qualcomm (United Kingdom)GB11 papers
- University of California, IrvineUS10 papers
- University of California, Los AngelesUS10 papers
- École Polytechnique Fédérale de LausanneCH9 papers
- Market MattersUS9 papers
- Princeton UniversityUS9 papers
- University of California, BerkeleyUS9 papers
- University of TorontoCA9 papers
- Texas A&M UniversityUS8 papers
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107 papers · 1 filter
6G Takes Shape
Jeffrey G. Andrews, Todd E. Humphreys, Tingfang Ji
The contours of 6G -- its key technical components and driving requirements -- are finally coming into focus. Through twenty questions and answers, this article defines the importa…
Bounds on Covert Capacity with Sub-Exponential Random Slot Selection
Shi-Yuan Wang, Keerthi S. K. Arumugam, Matthieu R. Bloch
We consider the problem of covert communication with random slot selection over binary-input Discrete Memoryless Channels and Additive White Gaussian Noise channels, in which a tra…
Amplitude Constrained Vector Gaussian Wiretap Channel: Properties of the Secrecy-Capacity-Achieving Input Distribution
Antonino Favano, Luca Barletta, Alex Dytso
This paper studies secrecy-capacity of an -dimensional Gaussian wiretap channel under a peak-power constraint. This work determines the largest peak-power constraint $\bar{\math…
Bayesian Channel Estimation for Intelligent Reflecting Surface-Aided mmWave Massive MIMO Systems With Semi-Passive Elements
In-soo Kim, Mehdi Bennis, Jaeky Oh +2
In this paper, we propose a Bayesian channel estimator for intelligent reflecting surface-aided (IRS-aided) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) s…
Coded Demixing for Unsourced Random Access
Jamison R. Ebert, Vamsi K. Amalladinne, Stefano Rini +2
Unsourced random access (URA) is a recently proposed multiple access paradigm tailored to the uplink channel of machine-type communication networks. By exploiting a strong connecti…
A Deep Reinforcement Learning Framework for Contention-Based Spectrum Sharing
Akash Doshi, Srinivas Yerramalli, Lorenzo Ferrari +2
The increasing number of wireless devices operating in unlicensed spectrum motivates the development of intelligent adaptive approaches to spectrum access. We consider decentralize…