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Found 9 papers, 0 papers with code
Deep Learning for Joint Design of Pilot, Channel Feedback, and Hybrid Beamforming in FDD Massive MIMO-OFDM Systems
This letter considers the transceiver design in frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems for high-quality data transmission.
Hardware Acceleration for Open Radio Access Networks: A Contemporary Overview
Radio access networks (RAN) are going through a paradigm shift towards interoperable, intelligent, software-defined, and cloud-native open RAN solutions.
Deep Reinforcement Learning for Interference Management in UAV-based 3D Networks: Potentials and Challenges
In this paper, we propose leveraging deep reinforcement learning for interference management to tackle this shortcoming.
Artificial Intelligence in 3GPP 5G-Advanced: A Survey
Industries worldwide are being transformed by artificial intelligence (AI), and the telecom industry is no different.
Autonomous Navigation and Configuration of Integrated Access Backhauling for UAV Base Station Using Reinforcement Learning
A deep reinforcement learning algorithm is designed to jointly optimize the access and backhaul antenna tilt as well as the three-dimensional location of the UAV-BS in order to best serve the on-ground MC users while maintaining a good backhaul connection.
A Primer on HIBS -- High Altitude Platform Stations as IMT Base Stations
Mobile communication via high-altitude platforms operating in the stratosphere is an idea that has been on the table for decades.
Networking and Internet Architecture
Throughput and Capacity Evaluation of 5G New Radio Non-Terrestrial Networks with LEO Satellites
For a LEO NTN similar to the Kuiper project proposed by Amazon, we find that, due to the large cell sizes in the LEO NTN, the area capacity density is moderate: 1-10 kbps/km$^2$ in the S band downlink and 14-120 kbps/km$^2$ in the Ka band downlink depending on latitude.
Networking and Internet Architecture
A Deep Reinforcement Learning Approach to Efficient Drone Mobility Support
In this paper, we propose a novel handover framework for providing efficient mobility support and reliable wireless connectivity to drones served by a terrestrial cellular network.
Efficient Drone Mobility Support Using Reinforcement Learning
Flying drones can be used in a wide range of applications and services from surveillance to package delivery.
