Performance analysis of ED over air-to-ground and ground-to-ground fading channels: A unified and exact solution

Chauhan, Puspraj Singh; Kumar, Sandeep; Upaddhyay, Vipin Kumar; Mishra, Ravi Shankar; Kumar, Brijesh; Soni, Sanjay Kumar

doi:10.1016/j.aeue.2021.153839

Cited by 6 publications

(14 citation statements)

References 38 publications

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“…We describe additive Gaussian white noise and time-varying multi-path fading of the channel impulse response. Our channel models contribute dilation, translation, uncertain scale, and impulsive noise to our synthetic signal sets (19) . The trained CNN network detects the following eight forms of digital modulation and three types of analogue modulation viz.…”

Section: Dataset Generationmentioning

confidence: 99%

Over-the-Air Modulation Classification using Deep Learning in Fading Channels for Cognitive Radio

Arjun¹,

Surekha²

2021

IJST

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Background/Objectives: The ability to recognize the type of modulation is a critical function of Cognitive Radio. The objective of this study is to increase the modulation classification efficiency in Over-The-Air (OTA) signals by utilizing channel characteristics that are strong. Methods: In this work, we demonstrate how to classify Over-The-Air modulation using a deep learning technique under various fading channels simulating real-time data. The network recognizes eight different digital modulation schemes and three different analogue modulation methods. Each modulation scheme will consist of 10,000 frames with 1024 samples per frame and a sampling rate of 200 kHz. Each sample will pass through fading channels prior to training, with 80% of samples are for training, 10% for validation, and 10% for testing. Six convolutional layers and one fully linked layer comprise our network. The final convolution layer is followed by a batch normalization layer, an activation layer utilizing rectified linear units (ReLUs), and a maximum pooling layer. As a result, the final convolution layer contains soft-max activation instead of the maximum pooling layer. Findings: Modulation categorization OTA is done with two separate ADALM-PLUTO SDRs working in various channel configurations. Network-I has a forecast accuracy of 91.4 percent using 12 Mini-Batch Size and 256 Epochs, whereas Network-II has a prediction accuracy of 95.3 percent using 24 Mini-Batch Size and 128 Epochs. There are a number of ways in which SDR technology can aid to make computer-generated data more realistic, such as adopting alternative channel models. Novelty: Using Software Defined Radio hardware; the same network was used to analyze various fading situations, such as Rayleigh, Rician or Lognormal distributions, and to optimize the network topology by adjusting hyper-parameters to increase accuracy.

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Section: Dataset Generationmentioning

confidence: 99%

Over-the-Air Modulation Classification using Deep Learning in Fading Channels for Cognitive Radio

Arjun¹,

Surekha²

2021

IJST

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“…Various researchers have proposed novel frame-works for analysing the performance of wireless systems relying on Fox's H-fading [1], [2], [6]- [10]. In this regard, Yilmaz et al [6] has presented a methodology for deriving the closed-form expressions of the average BEP and average capacity for transmission over the Hyper Fox's H-fading model expressed in terms of Fox's H-function.…”

Section: Introductionmentioning

confidence: 99%

“…The results presented in [9] subsumed the results presented in previous treatises. Furthermore, El Ayadi [2] and Chauhan et al [10] derived cognitive radio performance metrics for transmission over Fox's H-fading under different propagation environments. In [1], [2], [6]- [10] the performance metrics relied either on univariate, bivariate, or multivariate Fox's H-functions.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, El Ayadi [2] and Chauhan et al [10] derived cognitive radio performance metrics for transmission over Fox's H-fading under different propagation environments. In [1], [2], [6]- [10] the performance metrics relied either on univariate, bivariate, or multivariate Fox's H-functions. However, the earlier analytics may limit the operating range due to instabilities, while evaluating these expressions by numerical integration.…”

Section: Introductionmentioning

confidence: 99%

“…It also helps in comparing the performance of various modulation schemes. This approach has been frequently invoked [1], [2], [10]- [14]. Specifically, the asymptotic results derived for various metrics are discussed both for SISO and SIMO scenarios [1], [2], [10].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Asymptotic Framework for Fox's H-Fading Channel With Application to Diversity-Combining Receivers

Chauhan

Kumar

Jain

et al. 2023

IEEE Open J. Veh. Technol.

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Unified statistics are valuable in the performance analysis of communication systems. In this context, Fox's Hfunction has been shown to be eminently suitable for diverse scenarios. Another pivotal requirement is to have a low computational complexity, which is often hard to achieve for generalized models. Given this motivation, in this article we have presented high-power, low-complexity solutions for the outage probability (OP) and the average symbol error probability (SEP). Additionally, diversity techniques are harnessed for mitigating the effect of fading, which are then analysed based on the results derived. The entire methodology is governed by the origin probability density function rather than approximating the performance metrics under high-power. All the presented mathematical expressions are compared and validated through computer simulations (Monte-Carlo) to verify the accuracy of the proposed framework.

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High-Power Analysis for Outage Probability and Average Symbol Error Probability over Non-identical $$\kappa $$-$$\mu $$ Double Shadowed Fading

Chauhan

Kumar²,

Jain

et al. 2023

Lecture Notes in Networks and Systems

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Performance analysis of ED over air-to-ground and ground-to-ground fading channels: A unified and exact solution

Cited by 6 publications

References 38 publications

Over-the-Air Modulation Classification using Deep Learning in Fading Channels for Cognitive Radio

Over-the-Air Modulation Classification using Deep Learning in Fading Channels for Cognitive Radio

An Asymptotic Framework for Fox's H-Fading Channel With Application to Diversity-Combining Receivers

High-Power Analysis for Outage Probability and Average Symbol Error Probability over Non-identical $$\kappa $$-$$\mu $$ Double Shadowed Fading

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