Automatic modulation classification for cognitive radios using cyclic feature detection

Ramkumar, Barathram

doi:10.1109/mcas.2008.931739

Cited by 201 publications

(91 citation statements)

References 78 publications

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“…All possible paths from source to destination node and their corresponding weights depending on the channel parameters of each modulation technique is determined [12]. All possible clusters with their corresponding weights and the cluster that is also a shortest path to the destination is identified and the best cluster is chosen depending on the weight.…”

Section: Data Transmissionmentioning

confidence: 99%

Optimizing Network Layer with Adaptive Modulation for Time Varying Channel

Navaneethan

Prabha²

2016

Wireless Pers Commun

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A finite group of nodes placed in the network layer for inter process communication has to guarantee specified performance measures like high throughput, low jitter, avoid contention, etc. At present, the data security is taken care by the upper layer i.e. Transport (TCP/IP) to some extent and the network layer has to simply deliver the packets in an optimal manner. The security now offered by the TCP/IP layer is in the form of retransmission for the corrupt packet(s). If the channel conditions are not good, then repeated retransmission requests, can lower the throughput, increase the jitter and render the network less useful. Hence, to ensure uniform SNR in each of the link, it is necessary to place critical design issues intrinsically in the individual nodes. This includes, for example, that, each node can autonomously detect the modulation and demodulate among a group set, and thereby support dynamic/adaptive modulation capability. This can enable the node to alter the modulation to maintain a uniform SNR and reduce the outage probability. Additionally, the communication strategy need to look for the shortest path, identify the channel support in that path and choose the best modulation based on shortest path algorithm. In this context, in this paper, each node is provided with the capability to perform (a) Autonomous Modulation (to maintain uniform SNR) (b) Switch to the corresponding demodulator and get the packet (reduces contention) and (c) route the packets to the next intended node through the shortest path (reduces jitter). This process is recursively done at every node as and when they receive the data. The metrics studied include the network layer metrics, wireless channel related metrics, modulation based aspects etc.

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Section: Data Transmissionmentioning

confidence: 99%

Optimizing Network Layer with Adaptive Modulation for Time Varying Channel

Navaneethan

Prabha²

2016

Wireless Pers Commun

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“…The recognition parameter of first-order cyclic stationary applied in this paper mainly includes: first-order cyclic series(CFS), first-order cyclic mean value (CM) and the absolute value of first-order cyclic mean value [10][11][12]. the algorithm used in this paper consists of the following:…”

Section: A the Algorithm Of First-order Cyclic Mean Valuementioning

confidence: 99%

A Novel Method for VHF Signal Modulation Classification Based on Algorithm of First-Order Cyclic Moment

Yang¹,

Luo²,

Li³

et al. 2015

Proceedings of the International Conference on Computer Information Systems and Industrial Applications

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Abstract--In view of the problem of modulation recognition algorithm existing about low recognition rate under environment condition of low SNR, the algorithm of first-order cyclic moment are first presented used in band signal of VHF modulation classification recognition in this paper and the recognition rate increased significantly. The first-order cycle frequencies at which estimated first-order cyclic moment magnitudes exceed the cutoff value were selected as candidate cycle frequencies based on estimation first-order cyclic moment at first-order cycle frequency series, then decided to choose the number of candidate cycle frequency by a cyclostationarity test to realize signal modulation classification recognition, the classification recognition rate can reach more than 98% under condition of SNR is greater than -6 dB.

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“…With the rapid development of modern communication technology, in the civil, modulation recognition of communication signals is mainly applied to software radio [1], spectrum monitoring and management, cognitive radio and so on [2][3]. In the increasingly complicated signal environment, some of the illegal users interfere and utilize the wireless spectrum, affecting the normal communication of legitimate users seriously.…”

Section: Introductionmentioning

confidence: 99%

MQAM Modulation Recognition Based on AP Clustering Method

Shen

Wang

2016

MATEC Web of Conferences

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Abstract. For signal analysis in the management and monitoring of the radio spectrum, it is need to automatically identify the modulated signal. This paper performs clustering algorithm on signal constellation diagram after equalization through AP clustering method, and proposes a clustering method for limited problem of modulation recognition type. After normalizing the shape of the constellation diagram, the size of the constellation diagram, and signal AP clustering, it realizes the matching recognition of the constellation diagram. The simulation results show that the AP clustering has efficient recognition ability. a Corresponding author: Liqiang_fly@163.com

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Automatic modulation classification for cognitive radios using cyclic feature detection

Cited by 201 publications

References 78 publications

Optimizing Network Layer with Adaptive Modulation for Time Varying Channel

Optimizing Network Layer with Adaptive Modulation for Time Varying Channel

A Novel Method for VHF Signal Modulation Classification Based on Algorithm of First-Order Cyclic Moment

MQAM Modulation Recognition Based on AP Clustering Method

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