Intelligent Radio: When Artificial Intelligence Meets the Radio Network

Tao Chen, Hsiao-Hwa Chen, Zheng Chang, Shiwen Mao
2020 IEEE wireless communications  
Intelligent Radio: When Artificial Intelligence Meets the Radio Network T he advances in wireless communications have continuously been pushing the limit of radio technologies. Nowadays, radio networks can provide extremely high data rate, ultra-low latency, and high reliability to serve communication needs of sectors that could not be imagined before. However, radio technologies have become highly complex and call for new solutions. The recent advances in artificial intelligence (AI),
more » ... machine learning (ML), data mining, and big data analysis, bring significant promise for addressing hard problems in radio networks. It has been the increasing trend to move the intelligence beyond the spectrum access, which is primarily targeted by cognitive radio, to address various challenges in radio networks, including, but not limited to, channel modeling, modulation, beamforming, radio resource allocation, and network management. Radio technologies are on the way evolving to the intelligent radio, in which AI/ML frameworks and algorithms are applied to learn from environments and explore hidden characteristics of networks for new capacity, performance, and services. We believe the intelligent radio will be the prominent feature of next generation wireless networks. It calls for interdisciplinary research to integrate the advances in AI/ML, communications, computing, and cloud technologies. Both theoretical and applied breakthroughs are expected in this new area. This Special Issue aims to provide a comprehensive overview on the recent development of the intelligent radio. Sixteen articles have been selected from the rigorous peer review process among 60 submissions. These articles cover topics on spectrum access, mobile edge computing, radio network modeling, mobility prediction, and new wireless applications. The first two articles provide a broad survey on intelligent radio and network technologies. The article "Pathway to Intelligent Radio," by Z. Qin et al., introduces recent advances in ML in wireless communications. It briefly introduces deep learning applied for physical layer communications and resource allocation. The pros and cons of data-driven or model-driven approaches in deep learning are discussed. It then addresses two classical problems (i.e., spectrum sensing and spectrum access) in cognitive radio. The generative
doi:10.1109/mwc.2020.9023916 fatcat:5o2yvxnb3zhy5hd6af7g7kb3ly