Dynamic Spectrum Sharing and Cognitive Radio Systems

Dear Colleagues,

Wireless communication networks suffer from capacity bottlenecks because the amount of available spectrum is fixed, while wireless traffic demands keep growing by approximately 50% a year. In addition, as everything will be connected to the network in the forthcoming IoT era, a further spectrum is required to accommodate a large number of wireless devices. This is particularly the case in the lower spectrum bands (<7 GHz) exhibiting the most favorable propagation properties, but mmWave bands are also becoming more crowded, both for terrestrial and satellite communications. Since the early days of wireless communication, a wireless spectrum has been allocated according to a static frequency plan leading to many fixed frequency bands. Most of these bands are licensed for exclusive use by specific services or radio technologies, and the process for changing spectrum allocation is extremely slow (cf. spectrum allocation for 5G taking many years). Fixed, exclusive spectrum allocation is further characterized by severe overprovisioning and underutilization both in time and space, hence leading to a waste of precious resources. Static frequency planning is obviously not a sustainable spectrum allocation model, leaving no room for future wireless services and new wireless actors.

However, as there is a variety of wireless systems such as fixed, mobile, satellite, and drone-based communication and sensing systems such as radar, from long-range to short-range systems, identifying how to use a scarce frequency spectrum as efficiently as possible to accommodate the various abovementioned wireless systems is becoming more complicated issue. To improve spectrum efficiency, significant efforts have been made to enable sharing the spectrum among multiple wireless systems while avoiding harmful interference. Typical examples include LTE-U for spectrum sharing with LTE and WLAN, and DFS for spectrum sharing with WLAN and radar. With the advent of AI and machine learning techniques, more dynamic and smarter spectrum sharing is becoming feasible to further improve spectrum efficiency, taking advantage of the sensing data.

As there is significant progress in cognitive radio technologies as well as spectrum sharing based on AI and machine learning technologies, this Special Issue aims to highlight advances in the research and development of spectrum sharing and cognitive radio system technologies as well as novel spectrum usage and sharing to improve spectrum efficiency in time and space.

Topics include but are not limited to:

• Cognitive radio systems;
• Dynamic spectrum sharing;
• Smart spectrum sharing;
• Spectrum sharing for multiple network operators;
• AI-based spectrum sharing;
• Machine learning for spectrum sharing.

Prof. Dr. Masahiro Umehira
Prof. Dr. Takeo Fujii
Guest Editors

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