Deep Learning Adoption for Wireless Capacity

Deep Learning Adoption for Wireless Capacity

Enterprise Networking Mag | Tuesday, September 21, 2021

5G networks can grow smarter through predictive analytics and sophisticated software that adjusts to dynamic network demands.

FREMONT, CA: Much has been written about the technology that will power 5G, particularly how those technologies would improve users' connected experiences. Much has also been said about how current technological advancements will usher in a new generation of network-aware applications. This article will look at one of the most important aspects of 5G technology and how it will affect the growth of wireless network capacity.

Another key reason for the convergence of cloud computing and wireless communications is that it makes sense. This is one of the more essential yet frequently overlooked aspects of the evolution of wireless communication. To put it another way, the software can address many of the difficult problems involved with 5G wireless networks, eliminating the need for expensive, time-consuming, and often slow-to-evolve hardware that has been employed in the past.

Deep Learning for Wireless Capacity

5G is developing toward open architecture, with numerous options for network optimization. While this strategy adds to the complexity, deep learning techniques can be utilized to address these problems, which are normally beyond human capabilities. 5G networks can grow smarter through predictive analytics and sophisticated software that adjusts to dynamic network demands.

Microsoft has made significant investments in machine learning and artificial intelligence and supported the work of world-renowned professionals in these fields. Firms are also focusing on deep learning techniques that incorporate domain knowledge to improve communications networks. In addition, businesses are looking into how deep learning techniques could be used to adjust transmission power to reduce interference and boost capacity.

The telecoms industry benefits significantly from continuous machine learning (driven by flexible edge computing to simulate the dynamic radio frequency environment and user movement patterns) and controls the signal processing pipeline. This huge step forward allows for the rapid integration of new research breakthroughs into the system, not just to increase wireless capacity but also to improve the overall operational efficiency of 5G networks.

See Also: Top Wireless Solution Companies

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