Machine learning for 6G wireless networks: Carrying forward enhanced bandwidth, massive access, and ultrareliable/low-latency service
To satisfy the expected plethora of demanding services, the future generation of wireless
networks (6G) has been mandated as a revolutionary paradigm to carry forward the …
networks (6G) has been mandated as a revolutionary paradigm to carry forward the …
Deep reinforcement learning for 5G networks: Joint beamforming, power control, and interference coordination
The fifth generation of wireless communications (5G) promises massive increases in traffic
volume and data rates, as well as improved reliability in voice calls. Jointly optimizing …
volume and data rates, as well as improved reliability in voice calls. Jointly optimizing …
Distributed power control for large energy harvesting networks: A multi-agent deep reinforcement learning approach
In this paper, we develop a multi-agent reinforcement learning (MARL) framework to obtain
online power control policies for a large energy harvesting (EH) multiple access channel …
online power control policies for a large energy harvesting (EH) multiple access channel …
Deep learning for SWIPT: Optimization of transmit-harvest-respond in wireless-powered interference channel
In this paper, we consider a wireless-powered two-way communication, called transmit-
harvest-respond, with co-channel interference. The two-way communication considered …
harvest-respond, with co-channel interference. The two-way communication considered …
Deep learning–based energy beamforming with transmit power control in wireless powered communication networks
In this paper, we propose deep learning–based energy beamforming in a multi-antennae
wireless powered communication network (WPCN). We consider a WPCN where a hybrid …
wireless powered communication network (WPCN). We consider a WPCN where a hybrid …
Stealthy inference attack on dnn via cache-based side-channel attacks
The advancement of deep neural networks (DNNs) motivates the deployment in various
domains, including image classification, disease diagnoses, voice recognition, etc. Since …
domains, including image classification, disease diagnoses, voice recognition, etc. Since …
AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks
Integrating autonomous unmanned aerial vehicles (UAVs) with fifth-generation (5G)
networks presents a significant challenge due to network interference. UAVs' high altitude …
networks presents a significant challenge due to network interference. UAVs' high altitude …
Low‐complexity detection for uplink massive MIMO SCMA systems
This paper presents a sparse code multiple access (SCMA) system with massive antennas
at the base station. This system is referred to as M‐SCMA system. A spectrally‐efficient and …
at the base station. This system is referred to as M‐SCMA system. A spectrally‐efficient and …
Multi-agent deep reinforcement learning based power control for large energy harvesting networks
The goal in this work is to design online power control policies for large energy harvesting
(EH) networks where, due to large energy overhead involved in the exchange of state …
(EH) networks where, due to large energy overhead involved in the exchange of state …
Optimal selective transmission policy for energy-harvesting wireless sensors via monotone neural networks
K Wu, F Li, C Tellambura… - IEEE Internet of Things …, 2019 - ieeexplore.ieee.org
We investigate the optimal transmission policy for an energy-harvesting wireless sensor
node. The node must decide whether an arrived packet should be transmitted or dropped …
node. The node must decide whether an arrived packet should be transmitted or dropped …