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WIRELESS QUANTUM NETWORKS: Intelligent Continuous Variable Technology

As a part of the solution to the constant demand for higher data rates, wireless communications are moving towards higher and higher frequencies including mmWave and THz bands. At the same time quantum physics is experimenting with quantum state transmission over sub-optical, THz and even much lower bands. In the anticipation of the development of quantum computer networks and quantum key distribution QKD over wireless networks, there is a need for design tools that will enable optimization of the heterogeneous networks that will seamlessly merge these two technologies as much as possible.

At the same time, these networks will relay more and more on artificial intelligence so that further research is needed to integrate classical and quantum machine learning algorithms.

In general quantum technology can use either discrete (dv) or continuous (cv) variable information processing, where variables are modeled in the space of finite or infinite dimensions respectively. While the former option, used in our recent book, is used for systematic introduction to the field of quantum computing the latter is more feasible for practical implementation and for this reason is in the focus of this book.

Here, we make an effort to provide a summary of an impressive work done so far by the quantum physics, computer science and artificial intelligence researchers and elaborate why and how it should serve as a basis for coming up with the solutions for integrated heterogeneous networks as defined above. We believe that 7G wireless networks will be based on this concept although the step-by-step application of this technology is already being proposed for 5G and will be seen in 6G as well.

When it comes to using the book for undergrade and postgraduate courses we incorporate a number of DESIGN EXAMPLES to replace the classical concept of using “problems and solutions” addendums at the end of the chapters/book. This enables using more sophisticated assignments for the teamwork of the students. Our students have shown great enthusiasm for such approach.

In addition to universities the professionals in research, industry and regulatory institutions should benefit from the comprehensive coverage of the book.

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COURSES

 

Future Wireless Networks: 5G/6G Technology

Softerization and Network Function Virtualization introduced by 5G technology has enabled unprecedented opportunities for introducing new dynamic networking paradigms that will enable further increase in network capacity, improved network latency, significant network densification with improved energy efficiency.
Participants with background in either Networks Design or Networks/Internet Economics should benefit from participation.
The course discusses these new incoming technologies with emphasis for the need of joint optimization of technology and business models, including new monetary systems, with significant involvement of Artificial Intelligence.

Future Wireless Networks: 5G/6G Technology

Artificial Intelligence in Wireless Networks

By increasing the density and number of different functionalities in wireless networks there is more and more need for the use of artificial intelligence for planning the network deployment, running their optimization and dynamically controlling their operation.

Machine learning algorithms are used for the prediction of traffic and network state in order to timely reserve resources for smooth communication with high reliability and low latency. Big data mining is used to predict customer behavior and timely pre-distribute (cashing) the information content across the network so that it can be efficiently delivered as soon as requested.

Intelligent agents can search the internet on behalf of the customer in order to find the best options when it comes to buying any product on line

The course covers a review of AI based learning algorithms with a number of case studies supported by Python and R programs.

It provides a discussion of the learning algorithms used in decision making based on game theory and a number of specific applications in wireless networks such as channel, network state and traffic prediction

Artificial Intelligence in Wireless Networks

Quantum Computing

When moving from 5G to 6G/7G communication networks there will be need for more sophisticated network optimization tools with respect to both, computational speed and efficiency of optimization algorithms.

It looks like that Quantum Computing (QC) technology offers significant improvements in both of these aspects. Due to inherent parallelism in the processing of quantum information, unpresented increases in computation speed are anticipated.  Similarly a number of algorithms developed within the umbrella of quantum technology, like quantum search algorithms (QSA) developed for big data analysis, could be used to help us  with more efficient optimization of communication networks. In the past, most of the time we would end up with a formulation of the optimization problem that would need exhaustive search through all combination of the system parameters. This would be unacceptable solution when it comes to complexity and as a consequence we would be looking for simplifications that would on the other hand reduce the performance. By combining the advantageousness of both, unprecedented increase of computational speed and efficiency of QSA we will be able to significantly improve the optimization algorithms for network, deployment and optimization.

For these reasons, the focus of the course will be on discussing the principles of QC and detailed presentations of possibilities of using QSA in communication networks.

Quantum Computing in Wireless Networks

INS Institute for Networking Sciences
Telecommunication industry, Energy Sector Economics/Banking systems, Transportation, Bio Networks Artificial Intelligence, NS Networking Sciences