Recent Submissions

Now showing 1 - 5 of 33
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    Expansion of Cell Range with Geometric Information of Pico Cell for Maximum Sum Rate in Heterogeneous Networks
    (Společnost pro radioelektronické inženýrství, 2018-06) Jung, Taesung; Song, Iickho; Lee, Seungwon; Jung, Seungjae; Yoon, Seokho; Kang, Joonhyuk
    In this paper, taking the positions of pico-cell base stations (PBSs) into consideration, a scheme of cell range expansion (CRE) for maximum sum rate is addressed in heterogeneous multi-input multi-output multi-user wireless networks. The optimal CRE bias obtained numerically by the proposed CRE scheme with inter-cell interference coordination (ICIC) allows us to maximize the sum rate while successfully maintaining the load balance between the macrocell base station and PBSs. Numerical results confirm that the proposed CRE scheme with ICIC can provide higher sum rate than conventional schemes and balanced load.
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    Image Super-Resolution via Wavelet Feature Extraction and Sparse Representation
    (Společnost pro radioelektronické inženýrství, 2018-06) Alvarez-Ramos, Valentin; Ponomaryov, Volodymyr; Sadovnychiy, Sergiy
    This paper proposes a novel Super-Resolution (SR) technique based on wavelet feature extraction and sparse representation. First, the Low-Resolution (LR) image is interpolated employing the Lanczos operation. Then, the image is decomposed into sub-bands (LL, LH, HL and HH) via Discrete Wavelet Transform (DWT). Next, the LH, HL and HH sub-bands are interpolated employing the Lanczos interpolator. Principal Component Analysis (PCA) is used to reduce and to obtain the most relevant features information from the set of interpolated sub-bands. Overlapping patches are taken from the features obtained via PCA. For each patch, the sparse representation is computed using the Orthogonal Matching Pursuit (OMP) algorithm and the LR dictionary. Subsequently, this sparse representation is used to reconstruct a High-Resolution (HR) patch employing the HR dictionary and it is added to the LR image. By applying the quality objective criteria PSNR and SSIM, the novel technique has been evaluated demonstrating the superiority of the novel framework against state-of-the-art techniques.
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    Tensor-based Match Pursuit Algorithm for MIMO Radar Imaging
    (Společnost pro radioelektronické inženýrství, 2018-06) Huang, Ping; Li, Xin; Wang, Hui
    In MIMO radar, existing sparse imaging algorithms commonly vectorize the receiving data, which will destroy the multi-dimension structure of signal and cause the algorithm performance decline. In this paper, the sparsity characteristic and multi-dimension characteristic of signals are considered simultaneously and a new compressive sensing imaging algorithm named tensor-based match pursuit(TMP) is proposed. In the proposed method, MIMO radar tensor signal model is established to eliminate “dimension disaster” at first. Then, exploiting tensor decomposition to process tensor data sets, tensor-based match pursuit is formulated for multi-dimension sparse signal recovery, in which atom vectors orthogonality selection strategy and basis-signal reevaluation are used to eliminate the wrong indices and enhance resolution respectively. Simulation results validates that the proposed method can complete high-resolution imaging correctly compared with conventional greedy sparse recovery algorithms. Additionally, under fewer snapshots condition, RMSE of proposed method is far lower than other sparse recovery algorithms.
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    GNSS Signals Acquisition and Tracking in Unfavorable Environment
    (Společnost pro radioelektronické inženýrství, 2018-06) Chebir, Saifeddine; Aidel, Salih; Rouabah, Khaled; Atia, Salim; Flissi, Mustapha
    In this paper, we propose a method based on applying specific transformations to the Global Navigation Satellite System (GNSS) signals received in unfavorable environment. As a result, one simple classical receiver including these adjustments becomes sensitive to several Multi-Constellation and Multi-Frequency (MC/MF) GNSS signals and achieves efficiently their collective acquisition. The proposed method consists of three variants each dedicated to a particular type of Binary Offset Carrier (BOC) family signals; the primary is based on undersampling process, the second is founded on time expansion and the last one permits the acquisition of more than five different GNSS signals by a single local Composite Binary Coded Symbols (CBCS) waveform replica. Hence, the proposed scheme, by avoiding the use of multiple demodulators in the baseband, allows less receiver complexity and accordingly better realization cost. The simulation results showed that the proposed method presents an effective solution for the reception of MC/MF signals in unfavorable environments.
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    A Distributed Compressed Sensing-based Algorithm for the Joint Recovery of Signal Ensemble
    (Společnost pro radioelektronické inženýrství, 2018-06) Jahanshahi, Javad Afshar; Danyali, Habibollah; Helfroush, Mohammad Sadegh
    This paper considers sparsity-aware adaptive compressed sensing acquisition and the joint reconstruction of intra- and inter-correlated signals in the wireless sensor networks via distributed compressed sensing. textcolor{red}{ Due to the different sparsity order of the finite-length signals, we develop an adaptive sensing framework based on the sparsity order, in which sensor readings are sampled according to its own sparsity order measure.} On the decoder side, utilizing a distributed compressive sensing scheme, a joint reconstruction method is proposed to recover signal ensemble even in imperfect data communication. textcolor{red}{Moreover, we explore that by adapting the sampling rate of the sensed signals, not only the whole required number of measurements is reduced, but also the reconstruction performance is significantly improved. Numerical experiments verify that our proposed algorithm achieves higher reconstruction accuracy with a smaller number of required transmission, and with lower complexity as compared to those of the state of the art CS methods.