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Performance Analysis of Compressed Sensing Given Insufficient Random Measurements
Ahmad M. Rateb,Sharifah Kamilah Syed-Yusof 한국전자통신연구원 2013 ETRI Journal Vol.35 No.2
Most of the literature on compressed sensing has not paid enough attention to scenarios in which the number of acquired measurements is insufficient to satisfy minimal exact reconstruction requirements. In practice, encountering such scenarios is highly likely, either intentionally or unintentionally, that is, due to high sensing cost or to the lack of knowledge of signal properties. We analyze signal reconstruction performance in this setting. The main result is an expression of the reconstruction error as a function of the number of acquired measurements.
Enhanced FCME Thresholding for Wavelet-Based Cognitive UWB over Fading Channels
Haleh Hosseini,Norsheila Fisal,Sharifah Kamilah Syed-Yusof 한국전자통신연구원 2011 ETRI Journal Vol.33 No.6
The cognitive ultra-wideband (UWB) network detects interfering narrowband systems and adapts its configuration accordingly. An inherently adaptive and flexible candidate for cognitive UWB transmission is the wavelet packet multicarrier modulation (WPMCM). In this letter, we use an enhanced forward consecutive mean excision thresholding algorithm to tackle the noise uncertainty in the wavelet-based sensing of WPMCM systems, and mathematical analysis is performed for primary user channel fading. As a benchmark, we compare the proposed system with a conventional fast Fourier transformation-based system, and performance investigation proves significant improvements when primary and secondary links are subjected to multipath fading and noise.
Joshua Abolarinwa,Nurul Mu’azzah Abdul Latiff,Sharifah Kamilah Syed Yusof,Norsheila Fisal 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.2
Cognitive radio-based wireless sensor network is the next-generation sensor network paradigm. Important to this emerging sensor network is the need to reduce energy consumption, paving way for ‘green’ communication among sensor nodes. Therefore, in this paper, we have proposed an energy-efficient, learning-inspired, adaptive and dynamic channel decision and access technique for cognitive radio-based wireless sensor networks. Using intelligent learning technique based on the previous experience, the cognitive radio-based wireless sensor network agent decides which available channel to access based on the energy-efficiency achievable by transmitting using the channel. From simulation results, we found that as the channel packet availability increases, the energy-efficiency of the channel increase. This lends credence to the fact that the proposed learning-inspired algorithm is significantly energy-efficient for cognitive radio-based wireless sensor networks.