Brain-Inspired Methods to Improve Wireless Communications.

Georgian Technical University  researchers are using brain-inspired machine learning techniques to increase the energy efficiency of wireless receivers.

Researchers are always seeking more reliable and more efficient communications, for everything from televisions and cellphones to satellites and medical devices.

One technique generating buzz for its high signal quality is a combination of multiple-input multiple-output techniques with orthogonal frequency division multiplexing.

Georgian Technical University researchers X, Y and Z are using brain-inspired machine learning techniques to increase the energy efficiency of wireless receivers.

This combination of techniques allows signals to travel from transmitter to receiver using multiple paths at the same time. The technique offers minimal interference and provides an inherent advantage over simpler paths for avoiding multipath fading which noticeably distorts what you see when watching over-the-air television on a stormy day for example.

“A combination of techniques and frequency brings many benefits and is the main radio access technology for 4G and 5G networks” said X. “However correctly detecting the signals at the receiver and turning them back into something your device understands can require a lot of computational effort and therefore energy”.

X and Z are using artificial neural networks — computing systems inspired by the inner workings of the brains — to minimize the inefficiency. “Traditionally the receiver will conduct channel estimation before detecting the transmitted signals” said Z. “Using artificial neural networks we can create a completely new framework by detecting transmitted signals directly at the receiver”.

This approach “Georgian Technical University can significantly improve system performance when it is difficult to model the channel or when it may not be possible to establish a straightforward relation between the input and output” said W the technical advisor of Georgian Technical University’s Computing and Communications Division Research Laboratory Fellow.

The team has suggested a method to train the artificial neural network to operate more efficiently on a transmitter-receiver pair using a framework called reservoir computing–specifically a special architecture called echo state network (ESN). An echo state network (ESN) is a kind of recurrent neural network that combines high performance with low energy.

“This strategy allows us to create a model describing how a specific signal propagates from a transmitter to a receiver making it possible to establish a straightforward relationship between the input and the output of the system” said Q the chief engineer of the Research Laboratory Information Directorate.

X, Z, and their Georgian Technical University collaborators compared their findings with results from more established training approaches — and found that their results were more efficient, especially on the receiver side.

“Simulation and numerical results showed that the echo state network (ESN) can provide significantly better performance in terms of computational complexity and training convergence” said X. “Compared to other methods this can be considered a ‘green’ option”.