A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Unknown Signal Recognition. <s> Energy detection is a favorable mechanism in several applications relating to the identification of deterministic unknown signals such as in radar systems and cognitive radio communications. The present work quantifies the detrimental effects of cascaded multipath fading on energy detection and investigates the corresponding performance capability. A novel analytic solution is firstly derived for a generic integral that involves a product of the Meijer G−function, the Marcum Q−function and arbitrary power terms. This solution is subsequently employed in the derivation of an exact closed-form expression for the average probability of detection of unknown signals over N*Rayleigh channels. The offered results are also extended to the case of square-law selection, which is a relatively simple and effective diversity method. It is shown that the detection performance is considerably degraded by the number of cascaded channels and that these effects can be effectively mitigated by a non-substantial increase of diversity branches. P. C. Sofotasios is with the Department of Electronics and Communications Engineering, Tampere University of Technology, 33101 Tampere, Finland and with the Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece (e-mail: p.sofotasios@ieee.org) L. Mohjazi is with the Centre for Communication Systems Research, Department of Electronic Engineering, University of Surrey, GU2 7XH, Surrey, U.K. (e-mail: l.mohjazi@surrey.ac.uk) S. Muhaidat is with the Department of Electrical and Computer Engineering, Khalifa University, PO Box 127788, Abu Dhabi, UAE and with the Centre for Communication Systems Research, Department of Electronic Engineering, University of Surrey, GU2 7XH, Guildford, U.K. (e-mail: muhaidat@ieee.org) M. Al-Qutayri is with the Department of Electrical and Computer Engineering, Khalifa University, P.O. Box 127788, Abu Dhabi, UAE. (e-mail: mqutayri@kustar.ac.ae) G. K. Karagiannidis is with the Department of Electrical and Computer Engineering, Khalifa University, PO Box 127788 Abu Dhabi, UAE and with the Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece (e-mail: geokarag@ieee.org) <s> BIB001 </s> A Survey on Deep Learning Techniques in Wireless Signal Recognition <s> Unknown Signal Recognition. <s> There is an on-going need for faster, more accurate, and easier to implement threat identification systems for concealed electronics, to thwart terrorism and espionage attempts. Common electronics are used in the design of improvised explosive devices (IEDs), targeting military, and civilian populations, while concealed recording devices steal proprietary/confidential data, compromising both government and industrial resources. This paper discusses a novel, nonintrusive, repeatable, reliable, expandable, and simple-to-implement detection and identification (D&I) system for identifying threats by using unintended radiated emissions (URE). This paper differs from current URE proposals, by avoiding energy radiation. The proposed process also addresses big data problems involved in capturing and building databases of URE characteristics. Issue interpretation is utilized with URE data to distinguish between threat/nonthreat devices using multiple criteria decision analysis and other decision-making techniques to determine model and mode of hidden devices. System optimization, verified by testing, is used to improve the speed and accuracy of the identification algorithm. The system is developed with data from 130 devices and validated with 33 separate devices, which are representative of IED and espionage threats. Due to slow progress in the URE D&I field, the properties and potential of a more effective D&I system, compared to current methods, will be of interest to the explosive ordnance disposal, security services, electronic systems manufacturing, automated inventory, mobile application development communities, and potentially others as well. For a field that is not well documented, this paper provides a clear, concise, and detailed model for future research and applications of URE D&I. <s> BIB002
In the present literatures, the type of signal to be recognized is assumed to be within a known set and few literatures are capable of coping with unknown signals. Even if the spectral characteristics and background noise are obtained in practical situations, it is unrealistic to know the set of signal types in advance and unexpected interference may occur at any instant BIB001 BIB002 . Many proposed approaches lack universality for the signals outside of the set. So a comprehensive set of signal types or a more reasonable model design may be needed for the unknown signal recognition.