Smart Spectrum for Future Wireless World

Fujii, Takeo; Umebayashi, Kenta

doi:10.1587/transcom.2016pfi0014

Cited by 24 publications

(14 citation statements)

References 45 publications

(71 reference statements)

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“…Radio map, on the other hand, provides much higher spatial and temporal resolution of spectrum utilization, which allows one to zoom in to examine the detailed PSD distribution at any particular time and location. Besides, radio map also differs from the spectrum database [2], which records relatively static information about radio environment (e.g., registered transmitters' positions, PSD, on-off schedule, and channel prorogation models) and measurement data. In comparison, radio map is dynamically constructed from real-time measurements and constantly updated, thus being able to provide accurate characterization of PSD distribution at different dimensions and scales.…”

Section: Radio Mapmentioning

confidence: 99%

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Engineering Radio Maps for Wireless Resource Management

Lyu

Ding

et al. 2019

IEEE Wireless Commun.

122

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Radio map in general refers to the geographical signal power spectrum density, formed by the superposition of concurrent wireless transmissions, as a function of location, frequency and time. It contains rich and useful information regarding the spectral activities and propagation channels in wireless networks. Such information can be leveraged to not only improve the performance of existing wireless services (e.g., via proactive resource provisioning) but also enable new applications, such as wireless spectrum surveillance.However, practical implementation of radio maps in wireless communication is often difficult due to the heterogeneity of modern wireless networks and the sheer amount of wireless data to be collected, stored, and processed. In this article, we provide an overview on the state-of-the-art techniques for constructing radio maps as well as applying them to achieve efficient and secure resource management in wireless networks. Some key research challenges are highlighted to motivate future investigation. I. INTRODUCTIONModern wireless communication infrastructure consists of densely deployed and heterogeneous wireless networks. Their radio frequency radiations superimpose and cause irregular variations of signal power spectrum density (PSD) over different locations, frequencies and time. One systematic characterization of such variation of PSD over different dimensions is by using radio map, which contains rich and useful information of the spectral activities and propagation channels of wireless networks in a given geographical area. To distinguish from the well-known spectrum map in cognitive radio network, we compare a snapshot of radio map at a given frequency band with a TV spectrum map [1] in Fig. 1. S. Bi is with the College

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Section: Radio Mapmentioning

confidence: 99%

“…Interested readers may refer to[2] for a review on joint frequency-time radio map estimation methods.December 19, 2018 DRAFT…”

mentioning

confidence: 99%

Engineering Radio Maps for Wireless Resource Management

Lyu

Ding

et al. 2019

IEEE Wireless Commun.

122

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“…y Not only to resolve the issue of spectrum sensing but also to provide other benefits to DSA, advanced DSA, known as smart spectrum access (SSA), has been investigated [4], [5]. SSA exploits useful statistical information in terms of PU's spectrum usage such as channel occupancy rate (COR).…”

Section: Introductionmentioning

confidence: 99%

Time and Frequency Varying Noise Floor Estimation for Spectrum Usage Measurement

Iwata

Umebayashi

Al-Tahmeesschi

et al. 2019

2019 IEEE Wireless Communications and Networking Conference Workshop (WCNCW)

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We investigate noise floor (NF) estimation for FFT-ED (Energy Detection based on FFT)-based spectrum usage measurement in the context of smart spectrum access (SSA), in which spectrum usage information of primary users (PUs), such as channel occupancy rate (COR), will be exploited by secondary users (SUs). In FFT-ED, the NF has to be estimated to set a decision threshold for ED appropriately. In general, the NF is frequency-dependent and its level changes with time leading to the need of estimating the NF regularly while performing the spectrum usage measurement. In this paper, we propose an NF estimation method which exploits prior information regarding the shape of NF and forward consecutive mean excision (FCME) algorithm. Numerical and experimental evaluations show the proposed method enables an accurate NF estimation considering the time and frequency dependencies of the NF. Moreover, we show the proposed method can obtain the almost desired detection performance, but can not the comparative method (the original FCME method).

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“…Not only to resolve the issue of spectrum sensing but also to provide other benefits to DSA, advanced DSA, known as smart spectrum access (SSA), has been considered [4]- [6]. SSA is kind of DSA and SSA exploits useful prior information, such as statistical information of spectrum utilization by PUs (DC: duty cycle), to achieve efficient spectrum sharing smartly.…”

Section: Introductionmentioning

confidence: 99%

“…For this issue, two-layer architecture for SSA was proposed, where the first layer corresponds to a DSA system consisting of PUs and SUs, and the second layer is a spectrum awareness system (SAS) [4]- [6]. For PU, any specific wireless system is not assumed, but general wireless system is assumed.…”

Section: Introductionmentioning

confidence: 99%

Welch FFT Segment Size Selection Method for FFT Based Wide Band Spectrum Measurement

Iwata

Umebayashi

Lehtomäki

et al. 2018

IEICE Trans. Commun.

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We introduce a Welch FFT segment size selection method for FFT-based wide band spectrum measurement in the context of smart spectrum access (SSA), in which statistical spectrum usage information of primary users (PUs), such as duty cycle (DC), will be exploited by secondary users (SUs). Energy detectors (EDs) based on Welch FFT can detect the presence of PU signals in a broadband environment efficiently, and DC can be estimated properly if a Welch FFT segment size is set suitably. There is a trade-off between detection performance and frequency resolution in terms of the Welch FFT segment size. The optimum segment size depends on signal-to-noise ratio (SNR) which makes practical and optimum segment size setting difficult. For this issue, we previously proposed a segment size selection method employing a relationship between noise floor (NF) estimation output and the segment size without SNR information. It can achieve accurate spectrum awareness at the expense of relatively high computational complexity since it employs exhaustive search to select a proper segment size. In this paper, we propose a segment size selection method that offers reasonable spectrum awareness performance with low computational complexity since limited search is used. Numerical evaluations show that the proposed method can match the spectrum awareness performance of the conventional method with 70% lower complexity or less.

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Smart Spectrum for Future Wireless World

Cited by 24 publications

References 45 publications

Engineering Radio Maps for Wireless Resource Management

Engineering Radio Maps for Wireless Resource Management

Time and Frequency Varying Noise Floor Estimation for Spectrum Usage Measurement

Welch FFT Segment Size Selection Method for FFT Based Wide Band Spectrum Measurement

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