activity
20092026
most citedOn-Off Random Access Channels: A Compressed Sensing Framework

73 citations · 162 across the 36 of their papers we have counts for

collaborators
Showing 2020Show all

10 papers · 1 filter

eess.SP2020

Generative Neural Network Channel Modeling for Millimeter-Wave UAV Communication

William Xia, Sundeep Rangan, Marco Mezzavillla +4

The millimeter wave bands are being increasingly considered for wireless communication to unmanned aerial vehicles (UAVs). Critical to this undertaking are statistical channel mode…

eess.SP2020

Millimeter Wave Channel Modeling via Generative Neural Networks

William Xia, Sundeep Rangan, Marco Mezzavilla +4

Statistical channel models are instrumental to design and evaluate wireless communication systems. In the millimeter wave bands, such models become acutely challenging; they must c…

cs.RO2020

Enabling Remote Whole-Body Control with 5G Edge Computing

Huaijiang Zhu, Manali Sharma, Kai Pfeiffer +4

Real-world applications require light-weight, energy-efficient, fully autonomous robots. Yet, increasing autonomy is oftentimes synonymous with escalating computational requirement…

eess.SP202026 cited

Millimeter Wave Remove UAV Control and Communications for Public Safety Scenarios

William Xia, Michele Polese, Marco Mezzavilla +3

Communication and video capture from unmanned aerial vehicles (UAVs) offer significant potential for assisting first responders in remote public safety settings. In such uses, mill…

q-bio.NC2020

Low-Rank Nonlinear Decoding of -ECoG from the Primary Auditory Cortex

Melikasadat Emami, Mojtaba Sahraee-Ardakan, Parthe Pandit +6

This paper considers the problem of neural decoding from parallel neural measurements systems such as micro-electrocorticography (-ECoG). In systems with large numbers of array…

cs.LG20202 cited

Generalization Error of Generalized Linear Models in High Dimensions

Melikasadat Emami, Mojtaba Sahraee-Ardakan, Parthe Pandit +2

At the heart of machine learning lies the question of generalizability of learned rules over previously unseen data. While over-parameterized models based on neural networks are no…