• Journal of Internet Computing and Services
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• v.15 no.2
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• pp.143-147
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• 2014

In this paper, a novel MDI(Multiplex Distribution Interface) analyzer is designed in Ethernet-mode for DRM(Digital Radio Mondiale), and DRM+(Digital Radio Mondiale Plus) systems. The proposed MDI analyzer can reduce the overload of MDI packets by using memory sharing techniques into a common module block. In consequence, it verifies the received MDI packets by composition information of IP(Internet Protocol) and FAC(Fast Access Channel)/SDC(Service Description Channel) in DRM/DRM+ systems for the next generation digital radio broadcasting systems.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.8
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• pp.25-32
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• 2012

In this paper, we propose a method for improving the performance of low complexity LS channel estimation for OFDM in fast time varying channels. The CE-BEM channel model used for the low complexity LS channel estimation has a problem on its own and deteriorates channel estimation performance. In this paper, we first use time domain windowing in order to remove the effect of ICI caused by data symbols. Then samples are taken from the results of the LS channel estimation and the effects of the windowing are removed from them. For resolving the defect of CE-BEM, the channel responses are recovered by interpolating the resultant samples with DPSS employed as basis functions the characteristics of which is well matched to the time variation of the channel. Computer simulations show that the proposed channel estimation method gives rise to performance improvement over conventional methods especially when channel variation is very fast and confirm that not only which type of functions is selected for the basis but how many functions are used for the basis is another key factor to performance improvement.

• Journal of electromagnetic engineering and science
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• v.1 no.2
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• pp.113-119
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• 2001

Conventional channel estimation methods for orthogonal frequency division multiplexing (OFDM) system don't show the good characteristics in terms of fast fading channels. To solve this drawback in conventional methods, we propose the channel estimation method fur OFDM, assisted pilot for improvement and convergence in mobile system (APIM), which has a good performance and computational complexity in consideration of other methods. APIM uses the more developed concept of conventional methods and a block frame structure within a whole channel. This concept prevents overall performance from diverging or showing a poor one. The simulation results demonstrate the APIM outperforms pilot symbol assisted modulation (PSAM) and extended symbol aided estimation (ESAE) in terms of mean square error (MSE) and bit error rate (BER) performance under all Rayleigh fading environment. Considering the simulation performance and computational complexity, we can see APIM shows better characteristics than conventional methods for OFDM and has not any error floor even in a fast Rayleigh fading environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2513-2533
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• 2018

In cognitive radio technology, the overall efficiency of communications systems can be improved without allocating additional bands by allowing a secondary system to utilize the licensed band when the primary system, which has the right to use the band, does not use it. In this paper, we propose a fast and reliable common channel initialization protocol without any exchange of initialization messages between the cluster head and the member nodes in cognitive ad-hoc networks. In the proposed method, the cluster and member nodes perform channel-based spectrum sensing. After sensing, the cluster head transmits a system activation signal through its available channels with a predetermined angle difference pattern. To detect the cluster head's transmission channels and to join the cluster, each member node implements fast Fourier transform (FFT) and computes autocorrelation for the angle difference sequence of the received signal patterns. This is compared to the predetermined reference angle difference pattern. The join-request and channel-decision procedures are presented in this paper. Performance evaluation of the proposed method is presented in the simulation results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.123-145
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• 2017

Due to the increasing demand for spectrum resources, cognitive radio networks and dynamic spectrum access draw a lot of research into efficiently utilizing limited spectrum resources. To set up cluster-based CR ad-hoc common channels, conventional methods require a relatively long time to successfully exchange the initialization messages. In this paper, we propose a fast and reliable common channel initialization protocol for CR ad-hoc networks. In the proposed method, the cluster head sequentially broadcasts a system activation signal through its available channels with a predetermined correlation pattern. To detect the cluster head's broadcasting channels and to join the cluster, each member node implements fast Fourier transform (FFT) and computes autocorrelation of an FFT bin sequence for each available channel of the member node. This is compared to the predetermined reference pattern. The join request and channel decision procedures are also presented in this paper. In a simulation study, the performance of the proposed method is evaluated.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.11 s.353
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• pp.44-53
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• 2006

In this paper, we propose a new Active Channel Scanning scheme by scanning active channels employed by neighbor APs' with the handover counts and non overlap channel information for fast handover. Under the proposed scheme, the mobile which has finished handover to connect a new APsends the neighbor AP's channel information learned by itself during handover to the new AP. And then, the new AP relays the neighbor channel information to the old AP. It decides a priority by handover counts and non overlap channel information for building a Neighbor Channel Table(NCT), and also sends the table information to its associated mobile nodes, periodically. As a result each mobile can scan only active neighbor APs' channels when performs handover based on the referring to NCT information. Using NS-2 Simulator, we applied to supposed ACS that the result of simulation decides to sort by handover counts and non overlap channel information. we evaluate our proposed ACS scheme based on NCT along with the full scanning scheme and the selective scanning scheme. From simulation results, we show that the proposed scheme is advantageous over the other two schemes in terms of the number of scanning channels and the scanning latency.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.32A no.8
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• pp.11-18
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• 1995

This paper proposes a nonlinear adaptive equalizer which is based on fuzzy rules and fuzzy inference of several affine mapping for the received channel data. The proposed nolonlinear adaptive equalizers with the significantly lower computational complexity. Also it can be applied to the on-line adaptation environments owing to its fast convergence characteristics and the lower computational load. When using the decision feedback vectors, this equaalizer can be easily realized in the form of the DFE structure with out the requirement for the perfect channel knowledge as in the case of the fuzzy adaptive filter.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.1B
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• pp.90-98
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• 2002

To increase system capacity in WCDMA receiver, the coherent detection is an essential technology for recovering transmitted symbols correctly. For coherent detection, an accurate and fast channel estimation is required. In next generation mobile communication system, high frequency carriers are used, and high data rate services are provided for subscribers having high mobility. In this communication environment, however, the rapidly-changing doppler frequency degrades the quality of the transmitted signal a lot. In WCDMA system, the channel estimation is conducted using pilot channel and/or pilot symbols which are known to both transmitter and receiver. Some existing channel estimation algorithms utilize these information quite well, but the performance of channel estimation gets very poor in a complicated fading channel distorted by the doppler effect. In this paper, we propose a new channel estimation algorithm combining an early-late doppler compensator so that the doppler effect and the fading characteristics are compensated at the same time. By computer simulation, the proposed algorithm proved to be a much more efficient channel estimation method in the fast fading channel, even at reasonably low Eb/No.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.11
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• pp.917-924
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• 2003

Recurrent neural networks have been successfully applied to communications channel equalization. Major disadvantages of gradient-based learning algorithms commonly employed to train recurrent neural networks are slow convergence rates and long training sequences required for satisfactory performance. In a high-speed communications system, fast convergence speed and short training symbols are essential. We propose decision feedback equalizers using a recurrent neural network trained with Kalman filtering algorithms. The main features of the proposed recurrent neural equalizers, utilizing extended Kalman filter (EKF) and unscented Kalman filter (UKF), are fast convergence rates and good performance using relatively short training symbols. Experimental results for two time-varying channels are presented to evaluate the performance of the proposed approaches over a conventional recurrent neural equalizer.

• Journal of information and communication convergence engineering
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• v.8 no.3
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• pp.267-272
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• 2010

For the last decade, recurrent neural networks (RNNs) have been commonly applied to communications channel equalization. The major problems of gradient-based learning techniques, employed to train recurrent neural networks are slow convergence rates and long training sequences. In high-speed communications system, short training symbols and fast convergence speed are essentially required. In this paper, the derivative-free Kalman filter, so called the unscented Kalman filter (UKF), for training a fully connected RNN is presented in a state-space formulation of the system. The main features of the proposed recurrent neural equalizer are fast convergence speed and good performance using relatively short training symbols without the derivative computation. Through experiments of nonlinear channel equalization, the performance of the RNN with a derivative-free Kalman filter is evaluated.