output
20162026
most citedTurbo Autoencoder: Deep learning based channel codes for point-to-point communication channels

84 citations

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5 papers · 1 filter

eess.SP20201 cited

The Final Frontier: Deep Learning in Space

Vivek Kothari, Edgar Liberis, Nicholas D. Lane

Machine learning, particularly deep learning, is being increasing utilised in space applications, mirroring the groundbreaking success in many earthbound problems. Deploying a spac…

eess.SP2019

Mobility and Coverage Evaluation of New Radio PDCCH for Point-to-Multipoint Scenario

Hongzhi Chen, De Mi, Belkacem Mouhouche +2

This paper provides the mobility and coverage evaluation of New Radio (NR) Physical Downlink Control Channel (PDCCH) for Point-to-Multipoint (PTM) use cases, e.g., eMBMS (evolved M…

eess.SP2019

Evaluation of Low Complexity Massive MIMO Techniques Under Realistic Channel Conditions

Manijeh Bashar, Alister G. Burr, Katsuyuki Haneda +4

A low complexity massive multiple-input multiple-output (MIMO) technique is studied with a geometry-based stochastic channel model, called COST 2100 model. We propose to exploit th…

eess.SP20197 cited

Techno-economic analyses for vertical use cases in the 5G domain

Sandrine Roblot, Mythri Hunukumbure, Nadège Varsier +5

This paper provides techno-economic analyses on the network deployments to cover 4 key verticals, under 5G-NR. These verticals, namely Automotive, Smart city, Long range connectivi…

eess.SP2019

MIND: Model Independent Neural Decoder

Yihan Jiang, Hyeji Kim, Himanshu Asnani +1

Standard decoding approaches rely on model-based channel estimation methods to compensate for varying channel effects, which degrade in performance whenever there is a model mismat…