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H

H. Vincent Poor

Researcher at Princeton University

Publications -  2363
Citations -  92436

H. Vincent Poor is an academic researcher from Princeton University. The author has contributed to research in topics: Computer science & Communication channel. The author has an hindex of 109, co-authored 2116 publications receiving 67723 citations. Previous affiliations of H. Vincent Poor include Beihang University & Peking University.

Papers
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Book

An Introduction to Signal Detection and Estimation

TL;DR: Signal Detection in Discrete Time and Signal Estimation in Continuous Time: Elements of Hypothesis Testing and Elements of Parameter Estimation.
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Channel Coding Rate in the Finite Blocklength Regime

TL;DR: It is shown analytically that the maximal rate achievable with error probability ¿ isclosely approximated by C - ¿(V/n) Q-1(¿) where C is the capacity, V is a characteristic of the channel referred to as channel dispersion, and Q is the complementary Gaussian cumulative distribution function.
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On the Performance of Non-Orthogonal Multiple Access in 5G Systems with Randomly Deployed Users

TL;DR: In this letter, the performance of non-orthogonal multiple access (NOMA) is investigated in a cellular downlink scenario with randomly deployed users and developed analytical results show that NOMA can achieve superior performance in terms of ergodic sum rates; however, the outage performance of N OMA depends critically on the choices of the users' targeted data rates and allocated power.
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Application of Non-Orthogonal Multiple Access in LTE and 5G Networks

TL;DR: A systematic treatment of non-orthogonal multiple access, from its combination with MIMO technologies to cooperative NOMA, as well as the interplay between N OMA and cognitive radio is provided.
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Impact of User Pairing on 5G Nonorthogonal Multiple-Access Downlink Transmissions

TL;DR: Both analytical and numerical results are provided to demonstrate that F-NOMA can offer a larger sum rate than orthogonal MA, and the performance gain of F- NOMA over conventional MA can be further enlarged by selecting users whose channel conditions are more distinctive.