Methods of detecting of unknown signals in cognitive radio networks

Ivanenko, Stanislav; Bezruk, Valeriy

doi:10.1109/infocommst.2016.7905380

Cited by 7 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A resource that is very precious in wireless communication is the radio spectrum [1] which is regulated by the Federal Communication Commission (FCC). The severe demands for high data rates due to multimedia applications [2] and the growth in new wireless technologies [3] has led to a noticeable shortage in radio spectrum when using conventional wireless communication paradigms [2]. Most of the spectrum is underutilised because of the spectrum utilisation is dependent upon the time and place of the wireless terminal(s) [4].…”

Section: Introductionmentioning

confidence: 99%

Robust underlay cognitive network download beamforming in multiple users, multiple groups multicell scenario

et al. 2020

View full text Add to dashboard Cite

Achieving high quality of service (QOS) at the end‐users while maintaining the low interference power at the primary users is the main goal in underlay cognitive radio networks. This goal becomes a more difficult task in the designing of beamforming vectors where all channels state information (CSIs) in the secondary network, as well as the interference CSIs, are uncertain. This task is addressed in this study using an iterative optimization technique. In this technique, the original CSI problem, which is difficult to solve as a single optimization problem, is instead separated into two sub‐problems. The first subproblem is the interference power problem, which can be solved either sub‐optimally or optimally using Lagrange duality. The second sub‐problem is the QOS problem, which can be solved either sub‐optimally or robustly using non‐monotone spectral projected gradient method. The two sub‐problem solutions are then recombined into a single problem to extract beamforming vectors. Two methods are invoked to extract the beamforming vectors: either the successive convex approximation (SCA) method or the bisection search method. Theoretical analysis and simulation results indicate that the two methods can offer a tradeoff between better QOS (using bisection search method) or less computational complexity (using SCA method)

show abstract

Section: Introductionmentioning

confidence: 99%

Robust underlay cognitive network download beamforming in multiple users, multiple groups multicell scenario

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Although the terahertz frequency spectrum is relatively pure, with the development of information technology, it is necessary to develop terahertz frequency signal monitoring system. Compared with traditional signal detection systems, which are often working at millimeter wave, terahertz signal detection system has the characteristics of large bandwidth over 10 GHz.…”

Section: Introductionmentioning

confidence: 99%

The research on 220‐GHz signal detecting system

Niu

Zhang

Yang

2019

Int J Numerical Modelling

View full text Add to dashboard Cite

This paper presents our investigation into a 220-GHz signal monitoring system.The detectable frequency band of the proposed system is 220 to 230 GHz with the ability to distinguish mirror frequency. The detectable signal ranging from one tone signal to 1 GHz and the frequency accuracy is 1 KHz. More importantly, the terahertz monitoring system can accurately estimate the center frequencies of monotone signals and common modulation signals. The modulation methods including BPSK, QPSK, 8PSK, 16QAM, 32QAM, and 64QAM.The front end of the receiver consists of a 220-GHz subharmonic mixer and a 110-GHz tripler that are based on Schottky diodes. Both the mixer and the tripler show great performance and guarantees the well operation of the monitoring system. In addition, the baseband algorithm is modified to improve efficiency and reduce computation. The experiment of signal detection shows the system can identify the frequency, power, bandwidth, and the modulation mode of the unknown signal during the operation band.

show abstract

“…In wireless networks with the presence of fading, MUD by definition allows various users to know diverse conditions of fading channels and provide feedback to the base station of the individual channel states. The MUA system addressing this issue is discussed in [8][9][10] and this proposed system needed prior information on channel status between the PU and the SU. Generally, the SU has no knowledge of the PU channel; for this reason, we perform a new scheme with a suboptimal response to this question while applying the MUD with the quantization weights of the antennas.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of New Spectrum Sensing Scheme Using Multiantennas with Multiuser Diversity in Cognitive Radio Networks

Yawada

Dong

2018

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

The spectrum detection (SD) is a critical step in cognitive radio (CR) to identify the available spectrum and avoid interference and harm to primary users (PUs). Nonetheless, the practical detection process is often compromised with problems of receptors uncertainty, shadows, and multipath fading. To overcome these problems, a cooperative SD scheme is proposed by exploiting the system multiantenna (MUA) with the multiuser diversity (MUD). The SD gives unlicensed secondary users (SUs) the opportunity to use the licensed spectral band until the PU makes its appearance. By allowing the cooperation between the SUs of the same spectral band, the time of the detection is reduced and increases the general dexterity, which is the strong point of cooperative SD. In this paper, we proposed an SD scheme using a new MUA design. The scheme proposed is a clear difference compared to other systems because in this design weight of quantification is multiplied by each receiving antenna. The scheme has the ability to detect low-level signals to facilitate sharing between the SU and PU system. The numerical results are made to prove the performance of the proposed system compared to the conventional system. The results obtained confirm the efficiency of the proposed scheme.

show abstract

Methods of detecting of unknown signals in cognitive radio networks

Cited by 7 publications

References 0 publications

Robust underlay cognitive network download beamforming in multiple users, multiple groups multicell scenario

Robust underlay cognitive network download beamforming in multiple users, multiple groups multicell scenario

The research on 220‐GHz signal detecting system

Performance Analysis of New Spectrum Sensing Scheme Using Multiantennas with Multiuser Diversity in Cognitive Radio Networks

Contact Info

Product

Resources

About