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 as reliance on large training datasets, high computational power, and slow convergence. Searching for efficient AI/ ML techniques is a critical research topic. This workshop provides a venue to bring together standards developers, leading researchers and engineers from government, industry, and academia to present and discuss recent results on shared spectrum technology, and to promote its expedited development. The topics include (but are not limited to):
- Recent policy and standardization progress on unlicensed access or spectrum sharing systems, such as pre-6G, 5G NR-U, IEEE 802.11ay/11ax/11be,CBRS, and others.
- Spectrum sharing issues in new system architectures, such as Non-Terrestrial Network (NTN), Cloud/Fog computing and Multi-access edge computing, related with 5G and pre-6G systems.
- New SS techniques and applications on the 3.5 GHz, 6 GHz,mmWave, and ISM bands.
- Intra- and inter-system spectrum sharing for pre-6G, 5G, 4G, IOT, WLAN and WPAN systems.
- Efficient AI techniques for adaptive measurement and spectrum sharing enhancement.
- Coexistence system modelling, analysis, and optimization, such as Multi-RAT multi-operator IEEE 802.11ad/ay, 5G NR-U with802.11ad/ay, and CBRS PAL/GAA or GAA/GAA coexistence.
- Stochastic geometry, aggregate interference, and traffic models for system planning and optimization.
- Spectrum sensing and signal classification to support wireless coexistence.
- Methods to quantify measurement uncertainties related with SS system evaluation.
- Experiment and metrology for spectrum sharing and electromagnetic compatibility, such as testing results following procedures given by 3GPP, IEEE, ANSI C63.27, and others.
- Evaluation and mitigation of hardware imperfection, receiver susceptibility, interference, and noise, such as distributed techniques for in-field assessments, incumbent protection and receiver susceptibility,adjacent and co-channel interference, and LTE aggregate emission characterization.
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