• Title/Summary/Keyword: additive white Gaussian noise channel

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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.

Blind linear/nonlinear equalization for heavy noise-corrupted channels

  • Han, Soo- Whan;Park, Sung-Dae
    • Journal of information and communication convergence engineering
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    • v.7 no.3
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    • pp.383-391
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    • 2009
  • In this paper, blind equalization using a modified Fuzzy C-Means algorithm with Gaussian Weights (MFCM_GW) is attempted to the heavy noise-corrupted channels. The proposed algorithm can deal with both of linear and nonlinear channels, because it searches for the optimal channel output states of a channel instead of estimating the channel parameters in a direct manner. In contrast to the common Euclidean distance in Fuzzy C-Means (FCM), the use of the Bayesian likelihood fitness function and the Gaussian weighted partition matrix is exploited in its search procedure. The selected channel states by MFCM_GW are always close to the optimal set of a channel even the additive white Gaussian noise (AWGN) is heavily corrupted in it. Simulation studies demonstrate that the performance of the proposed method is relatively superior to existing genetic algorithm (GA) and conventional FCM based methods in terms of accuracy and speed.

Improved Iterative Decoding of Parallel and Serially Concatenated Trellis Coded Modulation (병렬 및 직렬적으로 연접된 트렐리스 부호화 변조 기법을 위한 향상된 반복적 복호 기법)

  • You, Cheol-Woo;Seo, Dong-Sun
    • Journal of IKEEE
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    • v.11 no.4
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    • pp.198-204
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    • 2007
  • For parallel and serially concatenated trellis coded modulation (TCM), improved iterative decoding schemes with a simple mechanism are proposed and their performances are compared with those of conventional decoding schemes. Simulation results have shown that the proposed schemes have provided a considerable decoding gain in additive white Gaussian noise (AWGN) channels and Rayleigh fading channels, even if they can be implemented by a simple modification of conventional decoding algorithms.

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Channel Capacity for NOMA Weak Channel User and Capacity Region for NOMA with Gaussian Mixture Interference

  • Chung, Kyuhyuk
    • Journal of IKEEE
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    • v.23 no.1
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    • pp.302-305
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    • 2019
  • Non-orthogonal multiple access (NOMA) has been considered for the fifth generation (5G) mobile networks to provide high system capacity and low latency. We calculate the channel capacity for the weak channel user in NOMA and the channel capacity region for NOMA. In this paper, Gaussian mixture channel is compared to the additive white Gaussian noise (AWGN) channel. Gaussian mixture channel is modeled when we assume the practical signal modulation for the inter user interference, such as the binary phase shift keying (BPSK) modulation. It is shown that the channel capacity with BPSK inter user interference is better than that with Gaussian inter user interference. We also show that the channel capacity region with BPSK inter user interference is larger than that with Gaussian inter user interference. As a result, NOMA could perform better in the practical environments.

Pilot-Aided Channel Estimation for OFDM System Using Wavelet Transform and Interpolation (웨이블릿 변환과 보간법을 이용한 OFDM 파일럿 지원 채널 추정기술)

  • Kong Hyung-Yun;Khuong Ho Van;Nam Doo-Hee
    • The KIPS Transactions:PartC
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    • v.12C no.5 s.101
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    • pp.665-672
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    • 2005
  • We present a novel pilot-aided channel estimation method for OFDM (Orthogonal Frequency Division Muitiplexing) system using WT(Wavelet transform) and interpolation. Due to excellent AWGN (Additive White Gaussian Noise) cancellation capability of n, pilot channels are estimated quite exactly and then, Dey are used in 2-degree polynomial interpolating the other remaining data symbol channels. The simulation results for Short WATM (Wireless Asynchronous Transfer Mode) channel show that the degradation in BER (Bit Error Ratio) performance of OFDM system iか this estimator is negligible compared to the case of perfect knowledge of CSI (Channel State Information).

De-noising in Power Line Communication Using Noise Modeling Based on Deep Learning (딥 러닝 기반의 잡음 모델링을 이용한 전력선 통신에서의 잡음 제거)

  • Sun, Young-Ghyu;Hwang, Yu-Min;Sim, Issac;Kim, Jin-Young
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.18 no.4
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    • pp.55-60
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    • 2018
  • This paper shows the initial results of a study applying deep learning technology in power line communication. In this paper, we propose a system that effectively removes noise by applying a deep learning technique to eliminate noise, which is a cause of reduced power line communication performance, by adding a deep learning model at the receive part. To train the deep learning model, it is necessary to store the data. Therefore, it is assumed that the existing data is stored, and the proposed system is simulated. we compare the theoretical result of the additive white Gaussian noise channel with the bit error rate and confirm that the proposed system model improves the communication performance by removing the noise.

Blind Channel Estimator based on the RLS algorithm (RLS 알고리즘에 기반을 둔 블라인드 채널 추정)

  • 서우정;하판봉;윤태성
    • Proceedings of the IEEK Conference
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    • 1999.11a
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    • pp.655-658
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    • 1999
  • In this study, We derived Recursive Least Squares(RLS) algorithm with adaptive maximum -likelihood channel estimate for digital pulse amplitude modulated sequence in the presence of intersymbol interference and additive white Gaussian noise. RLS algorithms have better convergence characteristics than conventional algorithms, LMS Least Mean Squares) algorithms.

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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.

Correlative Encoded Frequency Shift Keying (CEFSK) Modulation Technique

  • Lee, Kee-Hoon;Seo, Jong-Soo
    • Journal of Communications and Networks
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    • v.6 no.1
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    • pp.35-37
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    • 2004
  • A new power and bandwidth efficient modem technique-Correlative Encoded FSK (CEFSK) is proposed. CEFSK has a spectral efficiency comparable to Gaussian filtered FSK (GFSK), and it achieves 0.7db Eb/N0 improvement at bit error rate (BER) of 1 * 10 -4 over GFSK in an additive white Gaussian noise (AWGN) channel and 3.0dB improvement in a Rayleigh fading channel

Error Probability Evaluation of a Novel Cooperative Communications Signaling Strategy in CDMA Systems

  • Khuong Ho-Van;Kong Hyung-Yun
    • Journal of Communications and Networks
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    • v.8 no.3
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    • pp.257-266
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    • 2006
  • The powerful benefits of multi-antenna systems can be obtained by cooperative communications among users in multiple access environments without the need for physical arrays. This paper studies a novel cooperative signaling strategy that achieves high performance and low implementation complexity for synchronous code division multiple access (CDMA) wireless mobile networks. The validity of the proposed strategy under slow flat Rayleigh fading channel plus additive white Gaussian noise (AWGN) is verified through closed-form error probability expressions and MonteCarlo simulations. A variety of analytical results reveal that the new cooperative strategy significantly outperforms direct transmission subject to the same spectral efficiency and transmit power constraint.