Spectrum Intelligent Radio: Technology, Development, and Future Trends

Abstract

The advent of Industry 4.0 with massive connectivity places significant strains on the current spectrum resources, and challenges the industry and regulators to respond promptly with new disruptive spectrum management strategies. The current radio development, with certain elements of intelligence, is nowhere near showing an agile response to the complex radio environments. Following the line of intelligence, we propose to classify spectrum intelligent radio into three streams: classical signal processing, machine learning (ML), and contextual adaptation. We focus on the ML approach, and propose a new intelligent radio architecture with three hierarchical forms: perception, understanding, and reasoning. The proposed perception method achieves fully blind multi-level spectrum sensing. The understanding method accurately predicts the primary users' coverage across a large area, and the reasoning method performs a near-optimal idle channel selection. Opportunities, challenges, and future visions are also discussed for the realization of a fully intelligent radio.

A Passive-Mixer-First Acoustic-Filtering Superheterodyne RF Front-End

“…14). 3 A differential architecture is utilized to reduce LO clock leakage and harmonic responses around clock even harmonics similar to many mixer-first receivers and N-path filters [5], [8], [11], [21]. A wideband 1:1 off-the-shelf transformer is served as a balun at the RF input for single-ended to differential conversion.…”

“…A possible solution is to employ widely tunable and compact RF filters to replace numerous fixed-frequency acoustic filters in a mobile device. Also, widely tunable and compact RF filters are essential for future high-performance software-defined and intelligent radios operating in a congested EM environment [3]. Hence, the development of such tunable RF filters has long been an important research topic.…”

Applications of Intelligent Radio Technologies in Unlicensed Cellular Networks - A Survey

“…Intelligent radio (IR) or smart radio (SR) based on AI technologies has been viewed as a vast unexplored frontier for futuristic wireless communications. Several works had employed AI technologies and ML algorithms in wireless networks, such as intelligent UE and intelligent BS design in IR [18], heterogeneous CR based spectrum resource allocation [19] and the envisioning structure of IR [8]. Among those fantastic AI technologies, Q-Learning (QL) algorithms, which is one of the most commonly used Reinforcement Learning (RL) algorithms, have widely been employed in IR-based LTE-LAA/NR-U networks.…”

“…Featured by massive connectivity with densely deployed UEs and small cell base stations (SBSs), 5G promises to serve more than one million devices per square kilometer to support emerging machine-type communication (MTC), which again requires a large amount of spectrum resources. Obviously, a fundamental and critical issue arises on how to accomplish such great amount of wireless access in the case of limited spectrum resources [8]. As 5G and B5G networks need to support an enormous number of devices and connections in an extremely complex RF environment, a lot of challenging issues, such as different QoS requirements, fast-changing traffic dynamics, and spectrum heterogeneity across networks, need to be taken into account.…”

“…Traditional cognitive radio users detect and sense channels through licensed spectrum to make sure whether it is occupied or not and then make decisions in accordance with the sensing results. However, the conventional dynamic spectrum management (DSM) mechanism is not applicable to the diverse RF environments in 5G networks [8]. In other words, CR users should be more intelligently assisted with AI or machine-learning (ML) technologies to use the licensed and unlicensed spectra opportunistically.…”