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Manoj A

dblp:198/0581 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2018
0000-0003-1568-1139ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Physical-layer communications · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Physical-layer communications
channel estimation
0.312018
Channel Estimation Strategies for Multi-User mm Wave Systems · IEEE Trans. Commun. 2018
Physical-layer communications › signal processing for communications
compressive sensing
0.312018
Channel Estimation Strategies for Multi-User mm Wave Systems · IEEE Trans. Commun. 2018
Physical-layer communications › MIMO
millimeter wave MIMO
0.312018
Channel Estimation Strategies for Multi-User mm Wave Systems · IEEE Trans. Commun. 2018
Physical-layer communications
beamforming
0.112018
Channel Estimation Strategies for Multi-User mm Wave Systems · IEEE Trans. Commun. 2018
Physical-layer communications
pilot training
0.112018
Channel Estimation Strategies for Multi-User mm Wave Systems · IEEE Trans. Commun. 2018

Methods — techniques the papers use, named apart from their topics

orthogonal matching pursuit · 0.3compressive sensing · 0.3block sparsity · 0.3
YearPublicationVenuePosition
2018 Channel Estimation Strategies for Multi-User mm Wave Systems
abstract
In a multi-user millimeter (mm) wave communication system, we consider the problem of estimating the channel response between the base station (BS) and each of the user equipments (UEs). We propose three different pilot training schemes/strategies, namely: 1) estimating mm wave channels separately at the UE's; 2) joint estimation of mm wave channels of all the UE's simultaneously at the BS; and 3) two stage process involving estimation at both the UE's and the BS. We propose two random constructions for the pilot/training signals/beamforming weights and analytically characterize the mutual coherence parameter of the resulting sensing matrices for one of the constructions. By exploiting the structure in mm wave channels, we develop a generalized block orthogonal matching pursuit (G-BOMP) algorithm for channel estimation in all the three strategies. Furthermore, we establish sufficient conditions for exact support recovery by the proposed G-BOMP algorithm and derive an upper bound on the norm of the channel estimation error. We also study the performance of our G-BOMP algorithm in terms of the average beamforming gain and average spectral efficiency achieved via simulations. Our results show that the proposed G-BOMP algorithm outperforms the conventional OMP algorithm and other existing multiuser mm wave channel estimation techniques. G-BOMP also performs comparable to the computationally expensive compressive estimation using Newton 's refinement technique, for the first pilot training strategy in which the UE's estimate the channels separately.
Manoj A, Arun Pachai Kannu
IEEE Trans. Commun.1