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20192022
most citedLearning the Wireless V2I Channels Using Deep Neural Networks

6 citations · 11 across the 5 of their papers we have counts for

collaborators

5 papers

cs.NI20222 cited

Deep Q-Learning Based Resource Allocation in Interference Systems With Outage Constraint

Saniul Alam, Sadia Islam, Muhammad R. A. Khandaker +3

This correspondence considers the resource allocation problem in wireless interference channel (IC) under link outage constraints. Since the optimization problem is non-convex in n…

cs.CR2021

Thinking Out of the Blocks: Holochain for Distributed Security in IoT Healthcare

Shakila Zaman, Muhammad R. A. Khandaker, Risala T. Khan +2

The Internet-of-Things (IoT) is an emerging and cognitive technology which connects a massive number of smart physical devices with virtual objects operating in diverse platforms t…

cs.CY2020

An Automated Contact Tracing Approach for Controlling Covid-19 Spread Based on Geolocation Data from Mobile Cellular Networks

Md. Tanvir Rahman, Risala T. Khan, Muhammad R. A. Khandaker +1

The coronavirus (COVID-19) has appeared as the greatest challenge due to its continuous structural evolution as well as the absence of proper antidotes for this particular virus. T…

eess.SP20196 cited

Learning the Wireless V2I Channels Using Deep Neural Networks

Tian-Hao Li, Muhammad R. A. Khandaker, Faisal Tariq +2

For high data rate wireless communication systems, developing an efficient channel estimation approach is extremely vital for channel detection and signal recovery. With the trend…

eess.SP20193 cited

Deep Neural Network Based Resource Allocation for V2X Communications

Jin Gao, Muhammad R. A. Khandaker, Faisal Tariq +2

This paper focuses on optimal transmit power allocation to maximize the overall system throughput in a vehicle-to-everything (V2X) communication system. We propose two methods for…