International audience—Reinforcement algorithms refer to the schemes where the results of the previous trials and a reward-punishment rule are used for parameter setting in the next steps. In this paper, we use the concept of reinforcement algorithms to develop different data transmission models in wireless networks. Considering temporally-correlated fading channels, the results are presented for the cases with partial channel state information at the transmitter (CSIT). As demonstrated, the implemen-tation of reinforcement algorithms improves the performance of communication setups remarkably, with the same feedback load/complexity as in the state-of-the-art schemes
Abstract—In many wireless systems, the channel state infor-mation at the transmitter (CSIT) can not ...
This article briefly surveys a connected body work of the author and his collaborators on opportunis...
This dissertation explores one of the key techniques for future wireless networks, namely feedback e...
International audience—Reinforcement algorithms refer to the schemes where the results of the previo...
Reinforcement-based data transmission in temporally-correlated fading channels: Partial CSIT scenari...
This paper studies the problem of feedback subsampling in temporally-correlated wireless networks ut...
Novel communication techniques such as adaptive resource allocation are useful tools to increase the...
Wireless links adapt the data transmission parameters to the dynamic channel state -- this is called...
The constant increase in wireless handheld devices and the prospect of billion of connected machines...
The number of deployed wireless communication systems has grown rapidly in the last years. Their pop...
Cognitive radio (CR) enables unlicensed users to exploit the underutilized spectrum in licensed spec...
International audienceIn this chapter, we will give comprehensive examples of applying RL in optimiz...
This paper examines the application of reinforcement learning to a wireless communication problem. ...
Abstract—In many wireless networks, link strengths are af-fected by many topological factors such as...
We examine the problem of transmission control, i.e., when to transmit, in distributed wireless comm...
Abstract—In many wireless systems, the channel state infor-mation at the transmitter (CSIT) can not ...
This article briefly surveys a connected body work of the author and his collaborators on opportunis...
This dissertation explores one of the key techniques for future wireless networks, namely feedback e...
International audience—Reinforcement algorithms refer to the schemes where the results of the previo...
Reinforcement-based data transmission in temporally-correlated fading channels: Partial CSIT scenari...
This paper studies the problem of feedback subsampling in temporally-correlated wireless networks ut...
Novel communication techniques such as adaptive resource allocation are useful tools to increase the...
Wireless links adapt the data transmission parameters to the dynamic channel state -- this is called...
The constant increase in wireless handheld devices and the prospect of billion of connected machines...
The number of deployed wireless communication systems has grown rapidly in the last years. Their pop...
Cognitive radio (CR) enables unlicensed users to exploit the underutilized spectrum in licensed spec...
International audienceIn this chapter, we will give comprehensive examples of applying RL in optimiz...
This paper examines the application of reinforcement learning to a wireless communication problem. ...
Abstract—In many wireless networks, link strengths are af-fected by many topological factors such as...
We examine the problem of transmission control, i.e., when to transmit, in distributed wireless comm...
Abstract—In many wireless systems, the channel state infor-mation at the transmitter (CSIT) can not ...
This article briefly surveys a connected body work of the author and his collaborators on opportunis...
This dissertation explores one of the key techniques for future wireless networks, namely feedback e...