Wireless networks and Artificial Intelligence

In the near future, control and optimization tasks in many areas of technology will become so complex that conventional optimization approaches will no longer be sufficient. This applies in particular when real-time processing and high adaptability to constantly changing frameworks are required. These challenges can be addressed with Artificial Intelligence or Machine Learning.

The new mobile standard 5G and its potential successors provide a broad field of application. Thus, Machine Learning is one of the main research areas in the department of Wireless Networks. Innovative solutions are being pursued, by introducing new research approaches and combining previous research results with Machine Learning.

In Machine Learning, the largest possible amounts of data are evaluated using special methods enabling the researchers to draw the necessary conclusions for future decisions. The methods for these procedures are very diverse, but generally, those who have large amounts of data at their disposal are at an advantage. The reason that Artificial Intelligence is only now applied increasingly is due to the enormous computing power it requires. For many Artificial Intelligence applications, even today's computer processors (CPUs) are too slow. However, this can often be compensated by the use of very powerful graphics-based processors (GPUs).

The work on the wireless channel, which can be improved or solved with the help of Machine Learning, is divided into three areas here. First, there is the optimization of the wireless network, which due to its size and the resulting flood of data is particularly suitable for solving this task efficiently with methods of Machine Learning. Furthermore, there are methods for optimizing the data transfer to the mobile user, which are preferably executed in the network layer, but which can also be executed in the physical layer if required by the processing speed.

In addition, general considerations and further approaches are being developed, researched and investigated. Thus, among other things, interferences were evaluated to be used for learning processes [1], [2], so-called deep neural networks (see figure) were designed specifically for different problems with the help of the Laplace technique[3], [4], and distributed learning was intensively investigated with regard to various criteria [5], [6], [7], [8].

 

 

The current focus is on research activities relating to time reversal. With the time reversal, the reception pattern resulting from the fading is used in time reversal as radiation pattern, so that all portions arrive at the other receiver at the same time. With sufficient fading, it is possible to achieve a very power-efficient transmission, which can furthermore be realized with relatively simple hardware. However, in order to be able to retrieve the potential, a fast acquisition of the fading profile is of essential importance and therefore takes place in the physical layer.

References

[1] S. Limmer, J. Mohammadi, S. Stanczak, “A Simple Algorithm for Approximation by Nomographic Functions”, 53rd Annual Allerton Conference on Communication, Control, and Computing, 2015

[2] K. Ralinovski, M. Goldenbaum and S. Stanczak, Energy-efficient Classification for Anomaly Deteciton: The Wireless Channel as a Helper, IEEE ICC, 2016

[3] S. Limmer and S. Stanczak, "Optimal deep neural networks for sparse recovery via Laplace techniques," arXiv:1709.01112, Sep. 2017

[4] S. Limmer and S. Stanczak. A neural architecture for Bayesian compressive sensing over the simplex via Laplace techniques. IEEE Trans. on Signal Processing, 66(22):6002–6015, Nov. 2018

[5] R. L. G. Cavalcante, S. Stanczak, and I. Yamada, Cooperative Cognitive Radios with Diffusion Networks. IN: Mechanisms and Games for Dynamic Spectrum Allocation. Cambridge University Press, UK, 2014

[6] R. L. G. Cavalcante and S. Stanczak, "A distributed subgradient method for dynamic convex optimization problems under noisy information exchange,"IEEE Journal of Selected Topics in Signal Processing, vol. 7, no. 2, pp. 243-256, April 2013

[7] R. L. G. Cavalcante, A. Rogers, N. R. Jennings, and I. Yamada, "Distributed Asymptotic Minimization of Sequences of Convex Functions by a Broadcast Adaptive Subgradient Method," IEEE Journal of Selected Topics in Signal Processing, vol. 5, no. 4, pp. 739-753, Aug. 2011

[8] R. L. G. Cavalcante, I. Yamada, and B. Mulgrew, "An Adaptive Projected Subgradient Approach to Learning in Diffusion Networks,” IEEE Trans. Signal Processing,vol. 57, no. 7, pp. 2762-2774, July 2009