Artificial intelligence implication on energy sustainability in Internet of Things: A survey
The massive number of Internet of Things (IoT) devices connected to the Internet is
continuously increasing. The operations of these devices rely on consuming huge amounts …
continuously increasing. The operations of these devices rely on consuming huge amounts …
Decentralized deep reinforcement learning meets mobility load balancing
Mobility load balancing (MLB) aims to solve the problem of uneven resource utilization in
cellular networks. Since network dynamics are usually complicated and non-stationary …
cellular networks. Since network dynamics are usually complicated and non-stationary …
Traffic Priority-Aware Multi-User Distributed Dynamic Spectrum Access: A Multi-Agent Deep RL Approach
Real-time information exchange on traffic and channel selection results among users in
dynamic spectrum access (DSA) system consumes scarce spectrum resources. However, it …
dynamic spectrum access (DSA) system consumes scarce spectrum resources. However, it …
A mobility-resilient spectrum sharing framework for operating wireless UAVs in the 6 GHz band
J Hu, SK Moorthy, A Harindranath… - IEEE/ACM …, 2023 - ieeexplore.ieee.org
To mitigate the long-term spectrum crunch problem, the FCC recently opened up the 6 GHz
frequency band for unlicensed use. However, the existing spectrum sharing strategies …
frequency band for unlicensed use. However, the existing spectrum sharing strategies …
UAV networks against multiple maneuvering smart jamming with knowledge-based reinforcement learning
The unmanned aerial vehicles (UAVs) networks are very vulnerable to smart jammers that
can choose their jamming strategy based on the ongoing channel state accordingly …
can choose their jamming strategy based on the ongoing channel state accordingly …
Model-based transfer reinforcement learning based on graphical model representations
Reinforcement learning (RL) plays an essential role in the field of artificial intelligence but
suffers from data inefficiency and model-shift issues. One possible solution to deal with such …
suffers from data inefficiency and model-shift issues. One possible solution to deal with such …
Federated multi-agent deep reinforcement learning (fed-madrl) for dynamic spectrum access
Dynamic spectrum access (DSA) has been introduced as a promising technology that allows
a secondary system to access the licensed spectrum of the primary system to improve …
a secondary system to access the licensed spectrum of the primary system to improve …
Multi-channel opportunistic access for heterogeneous networks based on deep reinforcement learning
This paper investigates a new medium access control (MAC) protocol for multi-channel
heterogeneous networks (HetNets) based on deep reinforcement learning (DRL), referred to …
heterogeneous networks (HetNets) based on deep reinforcement learning (DRL), referred to …
SwarmShare: Mobility-resilient spectrum sharing for swarm UAV networking in the 6 GHz band
J Hu, SK Moorthy, A Harindranath… - 2021 18th Annual …, 2021 - ieeexplore.ieee.org
To mitigate the long-term spectrum crunch problem, the FCC recently opened up the 6 GHz
frequency band for unlicensed use. However, the existing spectrum sharing strategies …
frequency band for unlicensed use. However, the existing spectrum sharing strategies …
Federated and online dynamic spectrum access for mobile secondary users
Users in dynamic spectrum access (DSA) with federated reinforcement learning (FRL)
autonomously access channels, avoiding centralized coordination and protecting users' …
autonomously access channels, avoiding centralized coordination and protecting users' …