• Title/Summary/Keyword: Additive signal dependent Gaussian noise

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On Additive Signal Dependent Gaussian Noise Channel Capacity for NOMA in 5G Mobile Communication

  • Chung, Kyuhyuk
    • International Journal of Internet, Broadcasting and Communication
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    • v.12 no.2
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    • pp.37-44
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    • 2020
  • The fifth generation (5G) mobile communication has been commercialized and the 5G applications, such as the artificial intelligence (AI) and the internet of things (IoT), are deployed all over the world. The 5G new radio (NR) wireless networks are characterized by 100 times more traffic, 1000 times higher system capacity, and 1 ms latency. One of the promising 5G technologies is non-orthogonal multiple access (NOMA). In order for the NOMA performance to be improved, sometimes the additive signal-dependent Gaussian noise (ASDGN) channel model is required. However, the channel capacity calculation of such channels is so difficult, that only lower and upper bounds on the capacity of ASDGN channels have been presented. Such difficulties are due to the specific constraints on the dependency. Herein, we provide the capacity of ASDGN channels, by removing the constraints except the dependency. Then we obtain the ASDGN channel capacity, not lower and upper bounds, so that the clear impact of ASDGN can be clarified, compared to additive white Gaussian noise (AWGN). It is shown that the ASDGN channel capacity is greater than the AWGN channel capacity, for the high signal-to-noise ratio (SNR). We also apply the analytical results to the NOMA scheme to verify the superiority of ASDGN channels.

Accuracy Improvement Scheme for Location Awareness based on UWB system (UWB 기반 위치인식 정확도 향상 기법)

  • Choi, Young-Hoon;Bae, Jung-Nam;Kim, Jin-Young
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.11 no.2
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    • pp.231-236
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    • 2011
  • In recent years, LBS(Location Based Service) is applied in many different fields. Therefore, various location-aware schemes have been studied. In the location awareness system using time dependent algorithm, TOA(Time of Arrival) or TDOA(Time Difference of Arrival) algorithm, distortion of a signal by AWGN(Additive White Gaussian Noise) and multi-path effects cause the degradation of location awareness performance. In this paper, the unexpected noise is eliminated by averaging multiple pulses in order to overcome the degradation of performance. Also, we research the technique for improving the performance of the location awareness by detecting direct-path signal with adjusting threshold.

Gaussian Weighted CFCM for Blind Equalization of Linear/Nonlinear Channel

  • Han, Soo-Whan
    • Journal of the Institute of Convergence Signal Processing
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    • v.14 no.3
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    • pp.169-180
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    • 2013
  • The modification of conditional Fuzzy C-Means (CFCM) with Gaussian weights (CFCM_GW) is accomplished for blind equalization of channels in this paper. The proposed CFCM_GW can deal with both of linear and nonlinear channels, because it searches for the optimal desired states of an unknown channel in a direct manner, which is not dependent on the type of channel structure. In the search procedure of CFCM_GW, the Bayesian likelihood fitness function, the Gaussian weighted partition matrix and the conditional constraint are exploited. Especially, in contrast to the common Euclidean distance in conventional Fuzzy C-Means(FCM), the Gaussian weighted partition matrix and the conditional constraint in the proposed CFCM_GW make it more robust to the heavy noise communication environment. The selected channel states by CFCM_GW are always close to the optimal set of a channel even when the additive white Gaussian noise (AWGN) is heavily corrupted. These given channel states are utilized as the input of the Bayesian equalizer to reconstruct transmitted symbols. The simulation studies demonstrate that the performance of the proposed method is relatively superior to those of the existing conventional FCM based approaches in terms of accuracy and speed.

Speckle Removal of SAR Imagery Using a Point-Jacobian Iteration MAP Estimation

  • Lee, Sang-Hoon
    • Korean Journal of Remote Sensing
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    • v.23 no.1
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    • pp.33-42
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    • 2007
  • In this paper, an iterative MAP approach using a Bayesian model based on the lognormal distribution for image intensity and a GRF for image texture is proposed for despeckling the SAR images that are corrupted by multiplicative speckle noise. When the image intensity is logarithmically transformed, the speckle noise is approximately Gaussian additive noise, and it tends to a normal probability much faster than the intensity distribution. MRFs have been used to model spatially correlated and signal-dependent phenomena for SAR speckled images. The MRF is incorporated into digital image analysis by viewing pixel types as slates of molecules in a lattice-like physical system defined on a GRF Because of the MRF-SRF equivalence, the assignment of an energy function to the physical system determines its Gibbs measure, which is used to model molecular interactions. The proposed Point-Jacobian Iterative MAP estimation method was first evaluated using simulation data generated by the Monte Carlo method. The methodology was then applied to data acquired by the ESA's ERS satellite on Nonsan area of Korean Peninsula. In the extensive experiments of this study, The proposed method demonstrated the capability to relax speckle noise and estimate noise-free intensity.