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Potential technologies and applications based on deep learning in the 6G networks
Zheng Z., Wang L., Zhu F., Liu L. Computers and Electrical Engineering95 1-8,2021.Type:Article
Date Reviewed: Apr 4 2023

The sixth generation of cellular networks (6G) still hasn’t reached its peak in terms of hype, and in light of 4G and 5G coordinates and ruling experiences, concerned theorists are working to define 6G’s framework, use cases, potential functionalities, and overall technical aspects according to the requirements. Normally in this era, lots of mature and crude ideas can be found in the related literature. After identifying the primary ideas, like something witnessed in the 5G era, the art of engineering presents itself in the utilization of software, hardware, and firmware capabilities to perform an amazing concert of scheduling, synchronization, and sequencing of bits, like notes of a glorious music in the precision of the nanoseconds among the components to implement the tasks.

With a general review of 5G systems and brief discussion of the probable applications of 6G for the Internet of Things (IoT), massive machine-type communication, and augmented reality/virtual reality (AR/VR), the paper claims the certain involvement of artificial intelligence (AI) and 6G for signal processing, resource allocation, energy efficiency, and signal transmission. The application of deep learning for big data and cloud computing, with possible usage in 6G’s reliability and security, is provisioned. In the next section, a general literature review about deep learning is provided, but its trend and relevance within wireless communications is severely vague. Finally, in the potential application, a very general discussion about the usage of deep learning in the physical, network, and application layers is mentioned.

Unfortunately, this is not a scientific paper with any degree of novelty or significance. It is more like a surveying work. Most of the ideas and topics have been adopted from its references, which are generally represented. The paper cannot be recommended.

Reviewer:  Mohammad Sadegh Kayhani Pirdehi Review #: CR147571 (2306-0075)
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