Neuro-fuzzy signal classifier (NFSC) for standard wireless technologies

Ahmad, Kaleem; Shresta, Ganesh; Meier, Uwe; Kwaśnicka, Halina

doi:10.1109/iswcs.2010.5624411

Cited by 15 publications

(6 citation statements)

References 11 publications

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“…The NFSC [8] is an expert system which classifies frequency bands with respect to known wireless technologies. The purpose of the NFSC is similar to the proposed CNN.…”

Section: B Neuro-fuzzy Signal Classifiermentioning

confidence: 99%

Wireless interference identification with convolutional neural networks

Schmidt

Block

Meier

2017

2017 IEEE 15th International Conference on Industrial Informatics (INDIN)

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143

106

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The steadily growing use of license-free frequency bands requires reliable coexistence management for deterministic medium utilization. For interference mitigation, proper wireless interference identification (WII) is essential.In this work we propose the first WII approach based upon deep convolutional neural networks (CNNs). The CNN naively learns its features through self-optimization during an extensive data-driven GPU-based training process. We propose a CNN example which is based upon sensing snapshots with a limited duration of 12.8 µs and an acquisition bandwidth of 10 MHz. The CNN differs between 15 classes. They represent packet transmissions of IEEE 802.11 b/g, IEEE 802.15.4 and IEEE 802.15.1 with overlapping frequency channels within the 2.4 GHz ISM band. We show that the CNN outperforms state-ofthe-art WII approaches and has a classification accuracy greater than 95 % for signal-to-noise ratio of at least -5 dB.

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“…The NFSC [8] is an expert system which classifies frequency bands with respect to known wireless technologies. The purpose of the NFSC is similar to the proposed CNN.…”

Section: B Neuro-fuzzy Signal Classifiermentioning

confidence: 99%

Wireless interference identification with convolutional neural networks

Schmidt

Block

Meier

2017

2017 IEEE 15th International Conference on Industrial Informatics (INDIN)

Self Cite

143

106

View full text Add to dashboard Cite

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“…Hidden layers maybe one layer or multilayer, and each layer consists of several nodes. The [26,37] (ii) KNN [38,91] (iii) SVM [6,27,47,48,92] (iv) Naïve Bayes [39] (v) HMM [46] (vi) Fuzzy classifier [93] (vii) Polynomial classifier [40,94] (i) DNN [24,30,31,61] (ii) DBN [49,63] (iii) CNN [17, 19-21, 54, 64, 65, 70, 73-76, 79, 81, 82, 95, 96] (iv) LSTM [29,69] (v) CRBM [53] (vi) Autoencoder network [50,62] (vii) Generative adversarial networks [66,67] (viii) HDMF [71,72] (ix) NFSC [78] Pros (i) works better on small data (ii) low implementation cost (i) simple pre-processing (ii) high accuracy and efficiency (iii) adaptive to different applications Cons (i) time demanding (ii) complex feature engineering (iii) depends heavily on the representation of the data (iv) prone to curse of dimensionality (i) demanding large amounts of data (ii) high hardware cost node presented in Figure 3 is the basic operational unit, in which the input vector is multiplied by a series of weights and the sum value is fed into the activation function . These operational units contribute to a powerful network, which could realize complex functions such as regression and classification.…”

Section: Definition Of DL Problemmentioning

confidence: 99%

“…Wireless technology recognition Algorithms (i) DNN [24,30,31,61] (ii) DBN [49,63] (iii) CNN [17, 19-21, 54, 64, 65, 70, 95] (iv) LSTM [69] (v) CRBM [53] (vi) Autoencoder network [50,62] (vii) Generative adversarial networks [66,67] (viii) HDMF [71,72] (i) CNN [73-76, 79, 81, 82, 96] (ii) LSTM [29] (iii) NFSC [78] [74], the amplitude and phase difference representation were employed for CNN training procedure. The results indicate that the recognition of radar signals has been realized successfully with the proposed scheme even under the condition of LTE and WLAN signals coexisting at the same time.…”

Section: Modulation Recognitionmentioning

confidence: 99%

“…Results demonstrated that the proposed strategy is efficient for explicit signal features extraction. Likewise, neurofuzzy signal classifier (NFSC) to recognize nanoNET, WLAN, Atmel, and BT signals by utilizing measured PSD information is presented in [78]. Results show that the performance is improved by using wideband and narrowband data acquisition modes in real-time coexistence environments.…”

Section: Frequency Domain Featuresmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Deep Learning Techniques in Wireless Signal Recognition

Dong

Zhou

et al. 2019

Wireless Communications and Mobile Computing

112

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Wireless signal recognition plays an important role in cognitive radio, which promises a broad prospect in spectrum monitoring and management with the coming applications for the 5G and Internet of Things networks. Therefore, a great deal of research and exploration on signal recognition has been done and a series of effective schemes has been developed. In this paper, a brief overview of signal recognition approaches is presented. More specifically, classical methods, emerging machine learning, and deep leaning schemes are extended from modulation recognition to wireless technology recognition with the continuous evolution of wireless communication system. In addition, the opening problems and new challenges in practice are discussed. Finally, a conclusion of existing methods and future trends on signal recognition is given.

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“…A Neuro-fuzzy signal classifier (NFSC), presented in [16][17], is used to decompose the multi-PU coexisting environment without loosing any temporalspectral information of its constituent radio systems as shown in Fig. 2.…”

Section: A Modelling Multi-pu Coexisting Environmentmentioning

confidence: 99%

Target sensing for cognitive radios

Ahmad

Meier

2011

2011 8th International Symposium on Wireless Communication Systems

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Since spectrum sensing provides the foundation of a cognitive radio system, it has to be highly reliable. Most often, the bandwidth of the underlying sensor is much smaller than the bandwidth of interest. Conventional swept sensing methods can easily miss primary user systems with either relatively low duty cycles or fast hopping. A traditional way to improve the detection probability in such situations is to sense and average over an appropriate time interval, but obviously at the expense of precious system resources.We suggest to use predictive modeling of the radio environment and sense only those spectral areas, which are expected to be occupied by a primary system. We name this novel algorithm 'target sensing' as the sensing approach is adapted to the target of interest. In this contribution, we discuss different challenges and issues involved with target sensing. Moreover, we compare its performance with conventional sensing methods. We can state that target sensing performs significantly better, especially when small sensing bandwidth platforms are used.

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Neuro-fuzzy signal classifier (NFSC) for standard wireless technologies

Cited by 15 publications

References 11 publications

Wireless interference identification with convolutional neural networks

Wireless interference identification with convolutional neural networks

A Survey on Deep Learning Techniques in Wireless Signal Recognition

Target sensing for cognitive radios

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