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Due to the ever-increasing demands on wireless communications and limited spectrum resources, spectrum sharing (SS) is being developed as a key solution to alleviate the spectrum scarcity problem in the current and next generation (NG) communication systems. Major notable SS systems include the 5G New Radio Unlicensed (NR-U), unlicensed LTE or License Assisted Access (LAA), Internet of Things (IoT), CBRS 3-tier access, LTE-WLAN Aggregation (LWA), Multefire, and others. They have used various unlicensed or license-assisted bands such as the ISM (2.4 GHz and 5 GHz) bands, 6 GHz RF band, 3.5 GHz CBRS band, mmWave bands at 60 GHz, and others.
The 5G NR-U and the forthcoming 6G systems involve deployment of small cells or femtocells with ultra-dense configurations and traffic density, in coexistence with incumbent services and other unlicensed networks, such as WLAN. Due to the huge number of small cells which have been or to be deployed, proper design of SS policies and protocols and accurate evaluation of their impacts can save enormous amount of capital expenditures. To enhance coverage, the 5G and pre-6G systems will include Non-Terrestrial Network (NTN) besides land mobile networks. The relevant system coexistence and interference problems deserve an in-depth research. In April 2020 FCC approved the 6 GHz band of 1.2 GHz bandwidth for unlicensed spectrum access. This brings a huge potential for commercial and scientific SS utilization. To efficiently utilize the 60 GHz mmWave band of about 12.96 GHz bandwidth, coexistence of IEEE 802.11ad/ay systems in a multi-operator environment as well as coexistence of 5G NR-U with IEEE 802.11ad/ay have become important research topics. Besides traditional measurement science and optimization techniques, artificial intelligence (AI) and machine learning (ML) have found wide-spread applications in wireless SS systems. Yet, available AI/ML methods often have restrictions such ..
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