Matched filter based spectrum sensing and power level recognition with multiple antennas

Lv, Qing; Gao, Feifei

doi:10.1109/chinasip.2015.7230413

Cited by 29 publications

(20 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During the spectrum sensing, the secondary users identify the state of each frequency channel-free or occupied-using spectrum sensing techniques, such as energy detection [4][5][6][7][8][9], cyclostationary features detection [10][11][12][13][14][15], and matched filter detection [16,17].…”

Section: Introductionmentioning

confidence: 99%

Multi-Attributes, Utility-Based, Channel Quality Ranking Mechanism for Cognitive Radio Networks

2018

View full text Add to dashboard Cite

Abstract:Cognitive radio is an intelligent wireless solution that aims to enhance the access to the radio spectrum. In this technology, secondary users sense the radio spectrum, select the best channel among a pool of free channels, and determine the optimal transmission parameters to meet their quality-of-service requirements while maximizing the spectral efficiency. Over the past decade, several channel-ranking mechanisms have been proposed. However, these mechanisms consider only the remaining idle time of the channel and exclude some crucial parameters. This convincingly demonstrates a strong need for a new channel quality-ranking model that jointly considers several parameters to select the best communication channel for transmission. This paper proposes a utility model that integrates several important parameters for ranking channels. First, we underline the importance of the process of the channel quality ranking. Then, we describe a multi-attributes, utility-based, channel quality-ranking model. Finally, we describe a series of experiments and their results, which show that our model effectively ranks the best communication channels first.

show abstract

Section: Introductionmentioning

confidence: 99%

Multi-Attributes, Utility-Based, Channel Quality Ranking Mechanism for Cognitive Radio Networks

2018

View full text Add to dashboard Cite

show abstract

“…The spectrum sensing is a fundamental component in CR networks. There are many schemes studied in the literature to perform the spectrum sensing for deciding the status (presence/ absence) of primary user in the spectrum band, including energybased detection (ED) 5,6,7 , matched filter detection (MF) 8,9 , cyclostationary feature detection 10,11 , maximum eigen value detection method 12,13 and covariance-based method [14][15][16][17][18][19] . Among all these schemes, the ED-based spectrum sensing technique has gained a lot of attention in the literature because it is very easy to apply and does not need prior knowledge of the licensed PU.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, these methods require an estimate of noise power, which, in practice, is very challenging to obtain 4,18 . Matched filter-based spectrum sensing technique 8,9 needs prior knowledge of licensed user, although it can detect even at low signal to noise ratio and under noise uncertainty environment. Cyclostationary featurebased detection requires the knowledge of the cyclic frequency of the PUs, which in some cases may not be realistic 10,11 .…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Spectrum-Sensing Algorithm for Cognitive Radio Networks based on the Sample Covariance Matrix

Charan

Pandey

2017

Def. Sc. Jl.

View full text Add to dashboard Cite

A novel adaptive threshold spectrum sensing technique based on the covariance matrix of received signal samples is proposed. The adaptive threshold in terms of signal to noise ratio (SNR) and spectrum utilisation ratio of primary user is derived. It considers both the probability of detection and the probability false alarm to minimise the overall decision error probability. The energy-based spectrum sensing scheme shows high vulnerability under noise uncertainty and low SNR. The existing covariance-based spectrum sensing technique overcomes the noise uncertainty problem but its performance deteriorates under low SNR. The proposed covariance-based scheme effectively addresses the low SNR problem. The superior performance of this scheme over the existing covariancebased detection method is confirmed by the simulation results in terms of probability of detection, probability of error, and requirement of samples for reliable detection of spectrum.

show abstract

“…Spectrum sensing methods can be categorized as synchronous detection and non-synchronous detection. The methods of synchronous detection are matched filter detection [4,5] and cyclostationary feature detection [6][7][8], whereas energy detection is a non-synchronous detection method. The practical method for spectrum sensing is energy detection [9][10][11] which functions as a blind detection [12,13].…”

Section: Introductionmentioning

confidence: 99%

Reduction of Reporting Time for Throughput Enhancement in Cooperative Spectrum Sensing Based Cognitive Radio

Sairam¹,

Sivaparvathi²

2018

IJIES

View full text Add to dashboard Cite

Radio spectrum (3 KHz to 300 GHz) is a limited natural resource and efficient utilization of the spectrum can be accomplished by cognitive radio (CR). In CR, spectrum sensing is one of the significant challenging issues. Cooperative spectrum sensing (CSS) based CR is a captivating solution to enhance spectrum sensing. In CSS, multiple secondary users (SUs) report their results to fusion center (FC) and final decision is taken on the status of the primary user (PU) based on the predetermined fusion rule. This cooperative decision in CSS based CR, improves the spectrum sensing performance. Nevertheless, the foremost drawback in CSS is low throughput due to reporting time introduced by the multiple SUs. We propose a novel method to reduce the reporting time so as to enhance the throughput performance for the same number of SUs in CSS based CR. In the proposed scheme k SUs, where k is even, are divided into two groups, each with k/2 users. Two groups report their sensing results to two FCs separately and final decision is taken by the third FC. Consequently, the reporting time is condensed in the proposed scheme. The sensing performance of the proposed CSS (PCSS) for all possible fusion rules is reviewed. Analytical expressions for probability of false alarm, probability of detection and throughput are derived for PCSS. Both simulation results and theoretical analyses validate that PCSS improves throughput significantly when compared with CSS.

show abstract

Matched filter based spectrum sensing and power level recognition with multiple antennas

Cited by 29 publications

References 10 publications

Multi-Attributes, Utility-Based, Channel Quality Ranking Mechanism for Cognitive Radio Networks

Multi-Attributes, Utility-Based, Channel Quality Ranking Mechanism for Cognitive Radio Networks

An Adaptive Spectrum-Sensing Algorithm for Cognitive Radio Networks based on the Sample Covariance Matrix

Reduction of Reporting Time for Throughput Enhancement in Cooperative Spectrum Sensing Based Cognitive Radio

Contact Info

Product

Resources

About