Distributed Computing

Augmented reality (AR)/virtual reality (VR) over wireless is becoming a key application driver for the future in various use cases including interactive story worlds, immersive communication and panoramic video content. Technical approaches for mobile edge, cloud and fog computing will bring intelligence from centralised cloud services closer to end users and applications. These developments together call for intelligent distributed computing and data analytics solutions that spread across the network to meet the stringent requirements arising from future AR/VR applications.

Distributed Computing

The true challenge facing the delivery of AR/VR over wireless is how to efficiently distribute the required computation and data analytics across the network elements and locations while fulfilling the heterogeneous service/latency requirements and backhaul/fronthaul limitations.


Our goal in 6Genesis research is to develop novel AI enhanced cooperative arrangements of dynamically connected nodes that can opportunistically their computing resources. We will develop mechanisms to optimize the split of signal-processing functions among cloud, edge servers and radio heads in the case of cloud RAN-type architectures.


Our practical design solutions will consider human psychology and physiology for AR/VR applications to develop algorithms that consider human perceptions. They will be beneficial in environments where data are dynamically changing and latency requirements for data exchange are very stringent.


We will also develop new distributed learning mechanisms to allow algorithms to run at BSs, user terminals and other devices with limited data while providing strong robustness in device and link failures. Our practical design solutions include adaptive and distributed protocols including distributed communications, routing, load balancing and data caching.

Key Publications

Ultrareliable and Low-Latency Wireless Communication

Mehdi Bennis ; Mérouane Debbah ; H. Vincent Poor 2018 Proceedings of the IEEE ( Volume: 106 , Issue: 10 , Oct. 2018 )
Distributed Computing

Sparse projections matrix binary descriptors for face recognition

Fan, Chunxiao; Tian, Lei; Ming, Yue; Hong, Xiaopeng; Zhao, Guoying; Pietikäinen, Matti 2018 Neurocomputing Volume 297, 5 July 2018, Pages 8-21
Distributed Computing

ADC-Assisted Random Sampler Architecture for Efficient Sparse Signal Acquisition

Safarpour, Mehdi; Inanlou, Reza; Charmi, Mostafa; Shoaei, Omid; Silvén, Olli IEEE Transactions on Very Large Scale Integration (VLSI) Systems ( Volume: 26 , Issue: 8 , Aug. 2018 )
Distributed Computing
Key Researchers
29.8.2018 Researcher

Olli Silven

21.11.2017 Researcher

Madhusanka Liyanage