Page 12 - ITU Journal Future and evolving technologies Volume 2 (2021), Issue 4 – AI and machine learning solutions in 5G and future networks

ITU Journal on Future and Evolving Technologies, Volume 2 (2021), Issue 4

Enhanced shared experiences in heterogeneous network with generative AI

Pages 27–46
Neeraj Kumar, Ankur Narang, Brejesh Lall, Nitish Kumar Singh

COVID-19 has made the immersive experiences such as video conferencing, virtual reality/augmented
reality, the most important modes of exchanging information. Despite much advancement in the
network bandwidth and codec techniques, the current system still suffers from glitches, lags and poor
video quality, especially under unreliable network conditions. In this paper, we propose the method of
a video streaming pipeline to provide better video quality under erratic network conditions. We propose
an environment where the participants can interact with each other through video conferencing by only
sending the audio in the network. We propose a Multimodal Adaptive Normalization (MAN)-based
architecture to synthesize a talking person video of arbitrary length using as input: an audio signal and
a single image of a person. The architecture uses multimodal adaptive normalization, keypoint heatmap
predictor, optical flow predictor and class activation map-based layers to learn movements of expressive
facial components and hence generates a highly expressive talking-head video of the given person. We
demonstrate the effectiveness of proposed streaming that dynamically controls the Quality of
Experience (QoE) as per the requirements.

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A dynamic Q-learning beamforming method for inter-cell interference mitigation
in 5G massive MIMO networks

Pages 47–55
Aidong Yang, Xinlang Yue, Mohan Wu, Ye Ouyang

Beamforming is an essential technology in 5G Massive Multiple-Input Multiple-Output (MMIMO)
communications, which are subject to many impairments due to the nature of wireless transmission
channel. The Inter-Cell Interference (ICI) is one of the main obstacles faced by 5G communications
due to frequency-reuse technologies. However, finding the optimal beamforming parameter to
minimize the ICI requires infeasible prior network or channel information. In this paper, we propose a
dynamic Q-learning beamforming method for ICI mitigation in the 5G downlink that does not require
prior network or channel knowledge. Compared with a traditional beamforming method and other
industrial Reinforcement Learning (RL) methods, the proposed method has lower computational
complexity and better convergence efficiency. Performance analysis shows the quality of service
improvement in terms of Signal-to-Interference-plus-Noise-Ratio (SINR) and the robustness towards
different environments.
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