Transmitter SNR for Maximum Coverage

Bendov, O.

doi:10.1109/tbc.2008.2000567

Cited by 4 publications

(5 citation statements)

References 4 publications

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“…In the case of cooperative spectrum sensing, CRs perform the spectrum sensing and the data is sent to the FC, where trained ASA-NN is employed to make final decision based on received samples 1. SNR: It is considered that the TV transmitter is equipped with omnidirectional antenna and it is transmitting signal with a transmission power ranging from 10 to 50 kW [51]. As the TV transmission is assumed to have coverage up to 2.5 km, so an SU within this coverage can opportunistically utilize the spectrum to enhance its bandwidth.…”

Section: Asa-nn For Channel Predictionmentioning

confidence: 99%

“…

S N R_{T V_{r}} = \frac{T V_{(r v p)}}{B {normalN}_{0}} .

As the distance increases

S N R_{T V_{r}}

decreases. The Federal Communications Commission (FCC) has defined certain minimum threshold level for

S N R_{T V_{r}}

[51, 53, 54] . So the

S N R_{T V_{r}}

samples between its max and min bound are employed as one of the input for training ASA‐based NN.The TV frequency band in U.K. 470–790 MHz (channel 21–60) in any particular location not used by digital terrestrial television (DTT) could be used by low power devices.…”

Section: Asa‐trained Nnmentioning

confidence: 99%

“…

S N R_{T V_{r}} = \frac{T V_{(r v p)}}{B {normalN}_{0}} .

As the distance increases

S N R_{T V_{r}}

decreases. The Federal Communications Commission (FCC) has defined certain minimum threshold level for

S N R_{T V_{r}}

[51, 53, 54] . So the

S N R_{T V_{r}}

samples between its max and min bound are employed as one of the input for training ASA‐based NN.…”

Section: Asa‐trained Nnmentioning

confidence: 99%

See 2 more Smart Citations

Advanced squirrel algorithm‐trained neural network for efficient spectrum sensing in cognitive radio‐based air traffic control application

2021

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In the current scenario, there is a drastic increase in air traffic. The air to ground communication plays a crucial role in the air traffic control system. There is a limited spectrum available for aircraft to establish a connection with the Air Traffic Controller (ATC). With air traffic growth, the available spectrum is getting more congested. This paper proposed an Advanced Squirrel Algorithm (ASA)-trained neural network (NN) for efficient spectrum sensing for cognitive radio-based air traffic control applications. ASA is a novel metaheuristic-based training algorithm for an NN. With the proposed algorithm, it is possible to dynamically allocate the unused spectrum for air to ground communication between aircraft and ATC. The quantitative analysis of the proposed ASA-NN-based spectrum sensing is done by comparing it with the existing metaheuristic-based NN training algorithms, namely, particle swarm optimization Gravitational Search Algorithm (PSOGSA), particle swarm optimization (PSO), gravitational search algorithm (GSA), and artificial bee colony (ABC). Simulation-based evaluation shows that the proposed ASA-NN is capable of efficiently detecting the spectrum holes with high convergence rate as compared to PSOGSA-, PSO-, GSA-, and ABC-based algorithms.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Asa-nn For Channel Predictionmentioning

confidence: 99%

“…

S N R_{T V_{r}} = \frac{T V_{(r v p)}}{B {normalN}_{0}} .

As the distance increases

S N R_{T V_{r}}

decreases. The Federal Communications Commission (FCC) has defined certain minimum threshold level for

S N R_{T V_{r}}

[51, 53, 54] . So the

S N R_{T V_{r}}

Section: Asa‐trained Nnmentioning

confidence: 99%

See 1 more Smart Citation

Advanced squirrel algorithm‐trained neural network for efficient spectrum sensing in cognitive radio‐based air traffic control application

2021

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“…SNR if below a threshold level, than the channel can be considered as vacant. As per the FCC regulations the threshold regulation of actual TV receiver is 15.3 dB [44][45][46]. The ANN model has to detect the presence of analog and digital TV signal and voice signal.…”

Section: Snrmentioning

confidence: 99%

“…The distance "d" is over a range of 100 m to 2000 m. The threshold for SNR is taken as −10 dB. As per FCC [45,46] minimum SNR over a transmitting channel has to be 15.3 dB. Hence the channel capacity has to be 40.99 Mbps and spectral efficiency has to be 5.12 bps/Hz.…”

Section: Bandwidth Efficiencymentioning

confidence: 99%

Artificial Intelligence Based Model for Channel Status Prediction: A New Spectrum Sensing Technique for Cognitive Radio

Pattanayak¹,

Venkateswaran²,

Nandi³

2013

IJCNS

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The recent phenomena of tremendous growth in wireless communication application urge increasing need of radio spectrum, albeit it being a precious but natural resource. The recent technology under development to overview the situation is the concept of Cognitive Radio (CR). Recently the Artificial Intelligence (AI) tools are being considered for the topic. AI is the core of the cognitive engine that examines the external and internal environment parameters that leads to some postulations for QoS improvement. In this article, we propose a new Artificial Neural Network (ANN) model for detection of a spectrum hole. The model is trained with some pertinent features over a channel like SNR, channel capacity, bandwidth efficiency etc. The channel capacity status could be identified in a quantized index form. Some simulation results are presented.

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Cancelling Adjacent Channel Interference for In-Band Full-Duplex Communications

Hong,

Wu,

et al. 2024

2024 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

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Transmitter SNR for Maximum Coverage

Cited by 4 publications

References 4 publications

Advanced squirrel algorithm‐trained neural network for efficient spectrum sensing in cognitive radio‐based air traffic control application

Advanced squirrel algorithm‐trained neural network for efficient spectrum sensing in cognitive radio‐based air traffic control application

Artificial Intelligence Based Model for Channel Status Prediction: A New Spectrum Sensing Technique for Cognitive Radio

Cancelling Adjacent Channel Interference for In-Band Full-Duplex Communications

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