We introduce a new semantic communication mechanism, whose key idea is to preserve the semantic information instead of strictly securing the bit-level precision.
With the development of deep learning (DL), natural language processing (NLP) makes it possible for us to analyze and understand a large amount of language texts.
Online reinforcement learning (RL) has been widely applied in information processing scenarios, which usually exhibit much uncertainty due to the intrinsic randomness of channels and service demands.
Since the deep neural network models in federated learning are trained locally and aggregated iteratively through a central server, frequent information exchange incurs a large amount of communication overheads.
In this paper, we propose a novel network training mechanism called "dynamic channel propagation" to prune the neural networks during the training period.
Knowledge graphs (KGs) have become the preferred technology for representing, sharing and adding knowledge to modern AI applications.
With the rapid evolution of wireless mobile devices, there emerges an increased need to design effective collaboration mechanisms between intelligent agents, so as to gradually approach the final collective objective through continuously learning from the environment based on their individual observations.
Furthermore, as DPGD only works in continuous action space, we embed a k-nearest neighbor algorithm into DQL to quickly find a valid action in the discrete space nearest to the DPGD output.
Moreover, we further develop Dueling GAN-DDQN, which uses a specially designed dueling generator, to learn the action-value distribution by estimating the state-value distribution and the action advantage function.
Afterwards, we highlight the potential huge impact of CI on both communications and intelligence.
Collective intelligence is manifested when multiple agents coherently work in observation, interaction, decision-making and action.
Stigmergy has proved its great superiority in terms of distributed control, robustness and adaptability, thus being regarded as an ideal solution for large-scale swarm control problems.
Time series prediction can be generalized as a process that extracts useful information from historical records and then determines future values.
Software Defined Internet of Things (SD-IoT) Networks profits from centralized management and interactive resource sharing which enhances the efficiency and scalability of IoT applications.
Network slicing is born as an emerging business to operators, by allowing them to sell the customized slices to various tenants at different prices.
So, the RCLSTM, with certain intrinsic sparsity, have many neural connections absent (distinguished from the full connectivity) and which leads to the reduction of the parameters to be trained and the computational cost.
As an inevitable trend of future 5G networks, Software Defined architecture has many advantages in providing central- ized control and flexible resource management.
Afterwards, with the aid of the traffic "big data", we make a comprehensive study over the modeling and prediction framework of cellular network traffic.
Recent works have validated the possibility of improving energy efficiency in radio access networks (RANs), achieved by dynamically turning on/off some base stations (BSs).