Spectrum Occupancy Measurements and Analysis in 2.4 GHz WLAN

Cheema, Adnan Ahmad; Salous, Sana

doi:10.3390/electronics8091011

Cited by 12 publications

(11 citation statements)

References 27 publications

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“…LTE based), they can provide an initiative for a UAV enabled 3D channel models [93,94]. Furthermore, the placement of UAV BS for effective energy utilization with maximum coverage [95] while also taking into account the overall network delay [96], interference management from adjacent cells [97,98], dynamic spectrum access for UAV enabled networks [99], and 3D positing control [100] are vital research areas for UAV enabled network and required further investigation.…”

Section: Uav Based Heterogeneous 3d Wireless Networkmentioning

confidence: 99%

A survey of radio propagation channel modelling for low altitude flying base stations

2020

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The increased utilization of unmanned aerial vehicles (UAVs) in the commercial market and military on account of their agility, nonpiloted and easy manoeuvering leads their applications in the telecommunication sector as well. It is expected that UAVs will play a vital role in 5G and Beyond 5G (B5G) networks as flying base stations (BSs) and/or relays. Recently, they are also proposed to assist the existing terrestrial communication infrastructure in forthcoming 5G/B5G to provide improved wireless network coverage particularly to the areas difficult to reach, the scenarios demanding high data rate and low latency on emergency needs, transceiving sensors data from field to the ground servers and providing wireless network coverage in a disaster where existing terrestrial communication infrastructure gets partially/severely damaged. However, it is of an utmost challenge to model the radio propagation channel from a UAV (low altitude platforms) to existing terrestrial BSs, the receiver on ground and with other flying UAVs in a network. This paper provides a survey of both measurement and simulation based radio propagation channel modelling investigations for a low altitude UAV enabled wireless network. Furthermore, the potential open research gaps and use cases are highlighted which will be key to define the role of UAVs in future wireless networks for various applications.

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Section: Uav Based Heterogeneous 3d Wireless Networkmentioning

confidence: 99%

A survey of radio propagation channel modelling for low altitude flying base stations

2020

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“…In [19] exploits the unused spectrum and determines the maximum permitted transmitting power on the basis of the radio environment maps. In addition, high time resolution spectrum occupancy measurements and analysis are presented in [2] for 2.4 GHz WLAN signals. The authors propose a custom-designed wideband sensing engine to record the received power of signals, and its performance is presented to select the decision threshold required to define the channel state (busy/idle).…”

Section: Related Workmentioning

confidence: 99%

“…On the basis of the Intel Core i7-8750H processor and RTX 2060 graphics card, the PRF and PRF-NFE need 6.7 s and 2.5 s per iteration, respectively. We prefer the PRF algorithm for 3 reasons: (1) the PRF algorithm has a better estimation performance than PRF-NFE, as verified by the following subsection; (2) The training data are limited in most cases. As for the REMs estimation task, a neural network with stronger information extracting ability is important because of the limited and incomplete training images; (3) A computer with a more powerful calculation ability can solve the time-consuming problem for the training process of the PRF algorithm.…”

Section: Settings Of the Prf Algorithmmentioning

confidence: 99%

“…Cognitive radio network (CRN) is a promising technology to optimize the utilization of the spectrum resources [1]. In the cognitive radio network, unlicensed users can access the spectrum holes in time, frequency and space or any of their combinations, provided they cause no interference [2]. The users of the CRNs change their transmitting parameters intelligently to solve the spectrum scarcity problem [3,4].…”

Section: Introductionmentioning

confidence: 99%

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A Radio Environment Maps Estimation Algorithm based on the Pixel Regression Framework for Underlay Cognitive Radio Networks Using Incomplete Training Data

Xue

et al. 2020

Sensors

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In the underlay cognitive radio networks, the radio environment maps (REMs) estimation is the main challenge in sensing the idle wireless spectrum resources. Traditional deep learning-based algorithms estimate the REMs on the basis of the high-quality, large-scale complete training images. However, collecting the complete radio environment images is time-consuming and requires a numerous number of power spectrum sensing nodes. For this reason, we propose a generative adversarial networks-based pixel regression framework (PRF) for underlay cognitive radio networks. The PRF algorithm relaxes the requirement of the complete training images, and estimates the radio environment maps only on the basis of the incomplete REMs images, which are easier to be collected. First, we transform the radio environment maps estimation task into a pixel regression task through the color mapping progress. Then, to extract helpful information from the incomplete training data, we design a feature enhancing module for the PRF algorithm, which intelligently learns and emphasizes the important features from the training images. Finally, we use the trained pixel regression framework to reconstruct the radio environment maps in the target area. The proposed algorithm learns accurate radio environment characteristics from the incomplete training data rather than making direct biased or imprecise radio propagation assumptions as in the traditional methods. Thus, the PRF algorithm has a better REMs reconstruction performance than the traditional methods, as verified by simulations.Sensors 2020, 20, 2245 2 of 17 use the licensed band if the interference does not exceed the thresholds of the primary users because of the power attenuation in the wireless radio propagation path [8]. The above radio resources allocation scheme improves the spectrum efficiency by utilizing the wireless "white space", which is about the idle licensed spectrum resource in the space and frequency domains. The underlay CRN has strict requirements of the working power of the SUs, which can significantly improve the utilization of the radio resources based on the efficient and intelligent spectrum management.In the underlay cognitive radio network, we adopt the radio environment maps (REMs) to display the primary users' power spectrum (PS) in the CRN area. It is a visible map of the wireless environment, which adds the PS information into the spatial map. Based on the estimation of the REMs, we can handle the conflict between the SUs and PUs and maximize the utilization of the spectrum resources in a particular CRN region.Estimating and utilizing the radio environment maps is extraordinarily helpful in wide-area CRN [9]. To reduce the interference to PUs, the SUs intelligently change their transmitting power on the basis of the REMs, allowing the remote users to dynamically utilize the idle radio resources [10,11].As shown in Figure 1, for estimating the radio environment maps, the general setting includes several transmitting PUs and receiving SUs. We suppose that they ...

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“…Cheema and Salous [6] have conducted various high time resolution spectrum occupancy measurements and analysis for 2.4 GHz WLAN signals. To this end, it was found that distributions such as Gamma and lognormal can be used to model the idle state of a 2.4 GHz WLAN channel along with the generalized Pareto distribution.…”

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confidence: 99%

Antennas and Propagation Aspects for Emerging Wireless Communication Technologies

2021

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Spectrum Occupancy Measurements and Analysis in 2.4 GHz WLAN

Cited by 12 publications

References 27 publications

A survey of radio propagation channel modelling for low altitude flying base stations

A survey of radio propagation channel modelling for low altitude flying base stations

A Radio Environment Maps Estimation Algorithm based on the Pixel Regression Framework for Underlay Cognitive Radio Networks Using Incomplete Training Data

Antennas and Propagation Aspects for Emerging Wireless Communication Technologies

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