Spectrum Knowledge and Real-Time Observing Enabled Smart Spectrum Management

Abstract

Spectrum is the indispensable resource for 5G wireless systems and beyond. The dynamic spectrum management (DSM) framework with spectrum sharing among different kinds of users is essential to satisfy the sustainable rapid increase of the bandwidth requirement. Based on the analysis of the DSM as well as the latent applications of the machine learning and artificial intelligence (AI) in the spectrum access, this paper will address the problem of incorporating intelligence into the spectrum management. Firstly, the spectrum opportunity (SOP) related information is layered into the spectrum data, the spectrum information and the spectrum knowledge based on the processing and abstractive level. The spectrum knowledge is formally defined as the extendible and scalable information to reason and predict the SOP usability as well as the outcome of the SOP occupancy. Then the smart spectrum model (SSM) is proposed with the spectrum knowledge as the key enabler and the coupled SOP exploration and exploitation as the core feature. Under the SSM framework, the spectrum knowledge and real-time observing (SKRO) enabled SOP exploration scheme is designed, which can make use of both the historical information and the real-time sensing information for smart SOP exploration. Extensive simulations are provided which demonstrate that the SKRO enabled SSM can achieve much better SOP utilization while satisfying the required legacy assurance. Specifically, in the low signal to noise environment, at least 16.55% gains on the SOP utilization ratio can be obtained compared with the DSM.

To mitigate primary user emulation attack trajectory using cognitive single carrier frequency division multiple access approaches: Towards next generation green IoT

“…Vehicle identification, tracking, and classification are among the applications of spectrum sensing in the time‐frequency plane. The threshold is calculated using the PSD distribution of WGN adjusted with the Chi‐square distribution 27–29,31,64 . The challenge of spectrum sensing for NC signals is managed in CR networks with uncelebrated multiple antennas.…”

“…25,26 For 5G wireless systems and beyond 5G, the spectrum is an essential resource to meet the sustained rapid increase in bandwidth demand. To familiarize with the vital facts for green CRN research a Dynamic Spectrum Management (DSM) system with spectrum sharing among distinct types of users is required 27 for improving communication capacity in the design of next-generation green wireless networks. Such approaches will enhance the energy efficiency of the developed IoT devices with convincing glitches in everyday life which eventually make the environment safe and healthy.…”

An Interference Management System for a Shared Spectrum Access Network

“…Nevertheless, artificial intelligence algorithms, have led to advanced spectrum sensing techniques. As a result, the future of spectrum management is a hybrid or completely intelligent autonomous system [15], [16]. Camelo et al implemented an intelligent CBRS system in [17], where spectral-sensed…”

Multi‐agent spectrum access with sensing skipping based on reinforcement learning

“…Compared with the upper bound of 5G performance, the spectrum efficiency of 6G will need to be increased by 10 times 6 . Therefore, dynamic and intelligent spectrum sharing based on flexible user equipment and intelligent spectrum access capability has been regarded as one of the fundamental ways to solve the spectrum scarcity problem and to support the significant improvement of delay, power consumption, and rate performance of 5G and future 6G mobile communication systems 7 …”