Dynamic Spectrum Sensing with Automatic Modulation Classification for a Cognitive Radio Enabled NomadicBTS

Olaloye, Folarin; Adetiba, Emmanuel

doi:10.1088/1742-6596/1378/4/042092

Cited by 5 publications

(5 citation statements)

References 9 publications

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“…The optimal AMR model obtained in this study is based on the compact FOS features that will form a CR component for the real-time deployment of NomadicBTS architecture to achieve dynamic spectrum sensing [26]. Similar efforts on the use of statistical features and CR for spectrum sensing have also been reported in the literature [27]- [29].…”

Section: Resultsmentioning

confidence: 69%

“…The NomadicBTS architecture has two vital sub-modules with the front-end housing the SDR hardware while the SDR software operates on a personal computer (PC) at the back-end [26]. The architecture was extended in our study reported in [27] by incorporating CR capability with the AMR-based spectrum sensing model in the NomadicBTS architecture, where four (4) modulation schemes were considered and employed, namely, amplitude modulation (AM), Gaussian minimum shift key (GMSK), frequency modulation (FM) and (iv) noise (no-modulation).…”

Section: Methodsmentioning

confidence: 99%

“…This setup allows the USRP to effectively communicate with the host computer as shown in Figure 3. The technical and operating parameters of the USRP B200 are detailed in [27], while the parameter configurations for the different modulation schemes in this study are presented in Table 2. Each class had 50 real-time signals gathered, resulting in a total of 350 samples.…”

Section: Real-time Rf Data Acquisitionmentioning

confidence: 99%

“…As seen in Figure 1, the model in this paper is classified into seven categories (modulation plus no-modulation). With the implementation of this model in the NomadicBTS architecture [26], [27] for practical deployment, it will be able to differentiate between occupied and vacant spectrum bands. It will also indicate the type of modulation scheme for an appropriate choice of demodulation algorithm, which enables adaptability across wireless standards in an SDR scenario.…”

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confidence: 99%

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Compact automatic modulation recognition using over-the-air signals and FOS features

et al. 2022

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The recent deployment of automatic modulation recognition (AMR) for cognitive radio (CR) systems has significantly enhanced spectrum sensing capabilities. The utilization of real-time over-the-air digital radio frequency (RF) data for the development of a digital spectrum sensing model based on the automatic modulation classification (AMC) is presented in this study as a step for incorporating opportunistic spectrum sensing onto the NomadicBTS architecture. Some digital modulation techniques were studied for second-generation (2G) through fourth-generation (4G) technology. The raw RF signal dataset was digitized and curated, while non-complex first-order statistical (FOS) features were used with algorithms based on the Scaled conjugate gradient (SCG) and Levenberg-Marquardt (LM) to find the best learning algorithm for the generated AMR model. The results show that the developed AMR model has a very high likelihood of correctly classifying signals, with distinct patterns for each of the features of FOS. The results are compared to reveal a least mean square error (MSE) of 0.0131 with a maximum accuracy of 93.5 percent when the model was trained with seventy (70) neurons in the hidden layer using the LM method. The best model's accuracy will allow for the most precise identification of spectrum holes in the bands under consideration.

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Section: Resultsmentioning

confidence: 69%

Section: Methodsmentioning

confidence: 99%

Section: Real-time Rf Data Acquisitionmentioning

confidence: 99%

mentioning

confidence: 99%

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Compact automatic modulation recognition using over-the-air signals and FOS features

et al. 2022

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“…An approach to incorporate opportunistic spectrum sensing into the Nomadic Base Transceiver Station (BTS) architecture, a hybrid Automatic Modulation Classification (AMC) based spectrum sensing model was developed in [27]. Selected analogue and second generation (2G) digital modulation methods were evaluated, and the accuracy of the best model obtained will allow for the most accurate detection of spectrum holes within the bands under consideration].…”

Section: Related Workmentioning

confidence: 99%

Optimized Deep Learning Model for Effective Spectrum Sensing in Dynamic SNR Scenario

Arunachalam¹,

SureshKumar²

2023

Computer Systems Science and Engineering

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The main components of Cognitive Radio networks are Primary Users (PU) and Secondary Users (SU). The most essential method used in Cognitive networks is Spectrum Sensing, which detects the spectrum band and opportunistically accesses the free white areas for different users. Exploiting the free spaces helps to increase the spectrum efficiency. But the existing spectrum sensing techniques such as energy detectors, cyclo-stationary detectors suffer from various problems such as complexity, non-responsive behaviors under low Signal to Noise Ratio (SNR) and computational overhead, which affects the performance of the sensing accuracy. Many algorithms such as Long-Short Term Memory (LSTM), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) play an important role in designing intelligent spectrum sensing techniques due to the excellent learning ability of deep learning frameworks, but still require improvisation in terms of sensing accuracy under dynamic environmental conditions. This paper, we propose the novel and hybrid CNN-Cuttle-Fish Optimized Long Short Term Memory (COLSTM), an improved version of LSTM that is well suited for the dynamic changes of environmental SNR with less computational overhead and complexity. The proposed COLSTM based spectrum sensing technique exploits the various statistical features from spectrum data of PU to improve the sensing efficiency. Furthermore, the addition of shuttle-fish optimization in LSTM has reduced the computational overhead and complexity which in turn enhanced the sensing performances. The proposed methodology is validated on spectrum data acquired using RaspberryPi-RTLSDR experimental test-beds. The proposed spectrum sensing technique and the existing classical spectrum sensing techniques are compared. Experimental results show that the proposed scheme has shown the brighter enhancement of performance under different SNR environments. Further, the improvised performance has been achieved at low complexity and low computational overhead when compared with the other existing LSTM networks.

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Radio Environment Map Construction: A Mini-Review

Dare,

Okokpujie,

Adetiba

2023

2023 2nd International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS)

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Dynamic Spectrum Sensing with Automatic Modulation Classification for a Cognitive Radio Enabled NomadicBTS

Cited by 5 publications

References 9 publications

Compact automatic modulation recognition using over-the-air signals and FOS features

Compact automatic modulation recognition using over-the-air signals and FOS features

Optimized Deep Learning Model for Effective Spectrum Sensing in Dynamic SNR Scenario

Radio Environment Map Construction: A Mini-Review

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