Deep learning for 5G and 6G

Deep learning (DL) is a promising technology for enhancing the development of fifth generation (5G) and sixth generation (6G) mobile networks, as it can improve their capabilities, security, and performance. However, there are still significant challenges to be addressed in the implementation of DL techniques in these networks. To address these challenges, we conducted a systematic review of the literature on DL techniques in 5G and 6G applications following the PRISMA guidelines. The review was conducted in three stages: data collection, analysis, and reporting of primary findings. After evaluating and reviewing the databases, we found that hybrid DL and ensemble techniques show promise in optimizing 5G and 6G networks, given proper implementation. Finally, we discussed the open issues and challenges in this field. This review provides important insights into the potential of DL techniques in improving 5G and 6G networks, and it highlights the need for further research to overcome the remaining challenges. The results of this primary communication will be further developed and extended into a journal article.

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