Efficient TV white space database construction via spectrum sensing and spatial inference

Tang, Mengyun; Zheng, Ze; Ding, Guoru; Xue, Zhen

doi:10.1109/pccc.2015.7410268

Cited by 7 publications

(8 citation statements)

References 16 publications

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“…Note that this scheme is also our proposed algorithm.  A baseline scheme called "Interpolation Completion + Kernel" (IC-Kernel scheme), which uses the incomplete data and processed by an interpolation completion method in [29] for the final position estimation. The kernel learning method is used to find the best point in the matching phase.…”

Section: Effectiveness Analysis Of the Proposed Localization Algorithmmentioning

confidence: 99%

Efficient Kernel Based 3-D Source Localization via Tensor Completion

2019

KSII TIIS

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Source localization in three-dimensional (3-D) wireless sensor networks (WSNs) is becoming a major research focus. Due to the complicated air-ground environments in 3-D positioning, many of the traditional localization methods, such as received signal strength (RSS) may have relatively poor accuracy performance. Benefit from prior learning mechanisms, fingerprinting-based localization methods are less sensitive to complex conditions and can provide relatively accurate localization performance. However, fingerprinting-based methods require training data at each grid point for constructing the fingerprint database, the overhead of which is very high, particularly for 3-D localization. Also, some of measured data may be unavailable due to the interference of a complicated environment. In this paper, we propose an efficient kernel based 3-D localization algorithm via tensor completion. We first exploit the spatial correlation of the RSS data and demonstrate the low rank property of the RSS data matrix. Based on this, a new training scheme is proposed that uses tensor completion to recover the missing data of the fingerprint database. Finally, we propose a kernel based learning technique in the matching phase to improve the sensitivity and accuracy in the final source position estimation. Simulation results show that our new method can effectively eliminate the impairment caused by incomplete sensing data to improve the localization performance.

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Section: Effectiveness Analysis Of the Proposed Localization Algorithmmentioning

confidence: 99%

Efficient Kernel Based 3-D Source Localization via Tensor Completion

2019

KSII TIIS

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“…With the rapid development of wireless communications, the shortage of spectrum resources, the severity of spectrum security, and the intensity of spectrum confrontation are becoming increasingly severe [1][2][3]. As a new paradigm for the spectrum space, the radio environment map (REM) or the spectrum environment map (SEM) can visualize the current state and the future development trend of electromagnetic environments.…”

Section: Introductionmentioning

confidence: 99%

Temporal prediction for spectrum environment maps with moving radiation sources

et al. 2022

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Spectrum resources are becoming harder to come by for wireless communications. The spectrum environment map (SEM), which depicts the electromagnetic environment's current state and future trend, is a valuable technique for managing and allocating spectrum resources. Most SEM construction approaches only take static SEMs into account and cannot forecast time‐domain changes and trends of SEMs in dynamic scenes. In this paper, a brand‐new temporal SEM prediction method for the high dynamic spectrum environment is proposed. This method is based on knowledge of radiation source and the optical flow driven by propagation channel models. First, a novel radiation source localization strategy is designed to obtain the radiation source movement information. Then, the optical flow field of the available SEMs is combined with the information regarding radiation source movement. In order to forecast future SEMs, a propagation model driven reconstruction technique is developed. Simulation findings demonstrate how well the suggested strategy is tailored to capture the spatiotemporal correlation of SEMs. This technique performs better than the state‐of‐the‐art in terms of single‐ and multiple‐step SEM predictions.

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Section: Introductionmentioning

confidence: 99%

“…In the reference of [31], KNN was simply used for data recovery in white space database as a mechanism for majority voting. The classification problem in [31] is also a binary one and the KNN decides a label based on majority labels of the neighboring data points. The proposed spectrum sensing scheme is different from that of [31] in that quantized energy levels are used to train the classifier and then the sensing reports are used to find the class label of the current sensing report by finding the distance between them.…”

Section: Introductionmentioning

confidence: 99%

“…The classification problem in [31] is also a binary one and the KNN decides a label based on majority labels of the neighboring data points. The proposed spectrum sensing scheme is different from that of [31] in that quantized energy levels are used to train the classifier and then the sensing reports are used to find the class label of the current sensing report by finding the distance between them. Instead of majority voting, we have used an efficient distance measuring algorithm, Smith-Waterman algorithm (SWA) to calculate the similarity of the current sensing report and the training reports.…”

Section: Introductionmentioning

confidence: 99%

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Reliable Machine Learning Based Spectrum Sensing in Cognitive Radio Networks

Shah

Koo

2018

Wireless Communications and Mobile Computing

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Spectrum sensing is of crucial importance in cognitive radio (CR) networks. In this paper, a reliable spectrum sensing scheme is proposed, which uses K-nearest neighbor, a machine learning algorithm. In the training phase, each CR user produces a sensing report under varying conditions and, based on a global decision, either transmits or stays silent. In the training phase the local decisions of CR users are combined through a majority voting at the fusion center and a global decision is returned to each CR user. A CR user transmits or stays silent according to the global decision and at each CR user the global decision is compared to the actual primary user activity, which is ascertained through an acknowledgment signal. In the training phase enough information about the surrounding environment, i.e., the activity of PU and the behavior of each CR to that activity, is gathered and sensing classes formed. In the classification phase, each CR user compares its current sensing report to existing sensing classes and distance vectors are calculated. Based on quantitative variables, the posterior probability of each sensing class is calculated and the sensing report is classified into either representing presence or absence of PU. The quantitative variables used for calculating the posterior probability are calculated through K-nearest neighbor algorithm. These local decisions are then combined at the fusion center using a novel decision combination scheme, which takes into account the reliability of each CR user. The CR users then transmit or stay silent according to the global decision. Simulation results show that our proposed scheme outperforms conventional spectrum sensing schemes, both in fading and in nonfading environments, where performance is evaluated using metrics such as the probability of detection, total probability of error, and the ability to exploit data transmission opportunities.

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Efficient TV white space database construction via spectrum sensing and spatial inference

Cited by 7 publications

References 16 publications

Efficient Kernel Based 3-D Source Localization via Tensor Completion

Efficient Kernel Based 3-D Source Localization via Tensor Completion

Temporal prediction for spectrum environment maps with moving radiation sources

Reliable Machine Learning Based Spectrum Sensing in Cognitive Radio Networks

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