Probabilistic Spectrum Sensing Based on Feature Detection for 6G Cognitive Radio: A Survey

Abstract

With the advent of Sixth Generation (6G) telecommunication systems already envisioned, increased effort is made to further develop current communication technologies, so they can be incorporated together with the novel ones, to deliver uninterrupted and satisfactory service for any application in every location on the ground, underwater, in the air, or in space. One such technology is Cognitive Radio (CR) which has received much attention due to its potential for increase of utilization, especially in the bands below 6 GHz. The main enabler for CR is spectrum sensing because it provides the opportunity for dynamic assessment of the radio environment to identify unused channels. This functionality has been the object of many research works for that very reason. In spite of this, the provision of accurate and fast spectrum characterization in time, frequency and space has proven to be a non-trivial task. This paper presents a detailed review of probabilistic spectrum sensing methods classified by the feature they extract from the received signal samples, to provide accurate detection of the primary user (PU) signal. The main design characteristics (such as probability of detection, robustness to noise and fading, signal/noise model assumptions, and computational complexity), strengths and weaknesses for each type are also summarized. Based on current concepts for 6G networks and applications, a framework for human-centric cognition-based wireless access is presented, which specifies the role of spectrum sensing-based CR in future networks.INDEX TERMS 6G, cognitive radio, feature detection, human-centric wireless access, Internet of Things, probabilistic spectrum sensing, wireless communications.

Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications

“…The CR devices take advantage of SDR-empowered transceivers that enable DSA for mutually beneficial SSh between themselves and the primary users (PUs) of the spectrum, i.e., those which are incumbent to it by a license. Protecting their communications while providing better spectrum utilization in time, frequency, and space, is the fundamental problem of CR networks (their nodes are referred to as secondary users, or SUs) [ 74 ]. The SSh between primary and CR networks is achieved either by an autonomous operation (interweave mode) of the latter from the former, or through cooperation (underlay, overlay, and hybrid modes) for their mutual benefit.…”

“…Therefore, the graph-based solution may include UL/DL decoupling on different modes (DL on interweave, featuring non-graph based optimization, UL on underlay), with preliminary training of a GSP-based DRL method for specific deployment scenarios. The UA and RA of SUs/CAPs should be designed with consideration of whether the CR nodes are stationary, airborne (on UAVs), ground-based vehicular, marine, or carried/worn by humans [ 74 ]. They are situated in different locations/heights and diverse radio propagation conditions, so the complexity/capability of their functionalities for spectrum sensing, clustering, UA, and RA should reflect the environment’s severity.…”

“…The UA and RA of SUs/CAPs should be designed with consideration of whether the CR nodes are stationary, airborne (on UAVs), ground-based vehicular, marine, or carried/worn by humans [ 74 ]. They are situated in different locations/heights and diverse radio propagation conditions, so the complexity/capability of their functionalities for spectrum sensing, clustering, UA, and RA should reflect the environment’s severity.…”

Blockchain-Envisioned Unmanned Aerial Vehicle Communications in Space-Air-Ground Integrated Network: A Review

“…Through AI and Machine Learning (ML), the physical world and the digital world can be connected in real-time, and people can capture, retrieve, and access a large volume of information and knowledge in real-time as a step forward to the intelligent and fully connected era [10] . At the same time, Integrated Sensing And Communication (ISAC) [11,12] , distributed computing, Mobile Edge Computing (MEC) [13,14] , short-range wireless communication technology [15] , and advanced Space-Air-Ground Integrated Network (SAGIN) [7,16,17] will lay a foundation for the future intelligent mobile communication.…”

Power of Error Correcting Codes for SFBC-OFDM Classification Over Unknown Channels

“…Historically, SCL has been utilized in the military to detect and analyze unknown signals originating from unfriendly transmissions. The advent of intelligent radios has heightened further curiosity in SCL within the framework of civilian implications [2].…”