• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.6
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• pp.700-705
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• 2012

This paper suggests the detecting method in which it uses the candidate symbol obtained through PI-SDR, the little computational complexity is required. By using the candidate symbol matrices obtained through PI-SDR, ZF and MMSE method was applied and the received signal was detected. The linear detecting method using PI-SDR candidate symbol is out of the performance than ML detecting method but the complexity is low. Because of using the symbol come close to the solution of ML, the proposed method's performance is better than the existing ZF and MMSE method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.390-395
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• 2009

The robustness of audio fingerprinting system in a noisy environment is a principal challenge in the area of content-based audio retrieval. The selected feature for the audio fingerprints must be robust in a noisy environment and the computational complexity of the searching algorithm must be low enough to be executed in real-time. The audio fingerprint proposed by Philips uses expanded hash table lookup to compensate errors introduced by noise. The expanded hash table lookup increases the searching complexity by a factor of 33 times the degree of expansion defined by the hamming distance. We propose a new method to improve noise robustness of audio fingerprinting in noise environment using predominant pitch which reduces the bit error of created hash values. The sub-fingerprint of our approach method is computed in each time frames of audio. The time frame is transformed into the frequency domain using FFT. The obtained audio spectrum is divided into 33 critical bands. Finally, the 32-bit hash value is computed by difference of each bands of energy. And only store bits near predominant pitch. Predominant pitches are extracted in each time frames of audio. The extraction process consists of harmonic enhancement, harmonic summation and selecting a band among critical bands.

• Journal of Broadcast Engineering
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• v.2 no.1
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• pp.65-73
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• 1997

This paper propose a new triangular mesh approximation method using wavelet coefficients for large terrain data. Using spatio-freguency localization characteristics of wavelet coefficients, we determine the complexity of terrain data and approximate the data according to the complexity. This proposed algorithm is simple and requires low computational cost due to its top-down approach. Because of the similarity between the mesh approximation and data compression procedures based on wavelet transform, we combine the mesh approximation scheme with the Embedded Zerotree Wavelet (EZW) coding scheme for the effective management of large terrain data. Computer simulation results demonstrate that the proposed algorithm is very prospective for the 3-D visualization of terrain data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.5
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• pp.691-708
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• 2010

An important issue of supporting multi-users with diverse quality-of-service (QoS) requirements over wireless networks is how to optimize the systematic scheduling by intelligently utilizing the available network resource while, at the same time, to meet each communication service QoS requirement. In this work, we study the problem of a variety of communication services over multi-media heterogeneous cognitive OFDM system. We first divide the communication services into two parts. Multimedia applications such as broadband voice transmission and real-time video streaming are very delay-sensitive (DS) and need guaranteed throughput. On the other side, services like file transmission and email service are relatively delay tolerant (DT) so varying-rate transmission is acceptable. Then, we formulate the scheduling as a convex optimization problem, and propose low complexity distributed solutions by jointly considering channel assignment, bit allocation, and power allocation. Unlike prior works that do not care computational complexity. Furthermore, we propose the FAASA (Fairness Assured Adaptive Sub-carrier Allocation) algorithm for both DS and DT users, which is a dynamic sub-carrier allocation algorithm in order to maximize throughput while taking into account fairness. We provide extensive simulation results which demonstrate the effectiveness of our proposed schemes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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• pp.1514-1531
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• 2014

Femtocell provides better coverage and higher spectrum efficiency in areas rarely covered by macrocells. However, serious two-tier interference emerging from randomly deploying femtocells may create dead zones where the service is unavailable for macro-users. In this paper, we present adopting cognitive radio spectrum overlay to avoid intra-tier interference and incorporating spectrum underlay and overlay to coordinate cross-tier interference. It is a novel centralized control strategy appropriate for both uplink and downlink transmission. We introduce the application of proper spectrum sharing strategy plus optimal power allocation to address the issue of OFDM-based femtocells interference-limited downlink transmission, along with, a low-complexity suboptimal solution proposed. Simulation results illustrate the proposed optimal scheme achieves the highest transmission rate on successfully avoiding two-tier interference, and outperforms the traditional spectrum underlay or spectrum overlay, via maximizing the opportunity to transmit. Moreover, the strength of our proposed schemes is further demonstrated by comparison with previous classic power allocation methods, in terms of transmission rate, computational complexity and signal peak-to-average power ratio.

• ETRI Journal
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• v.37 no.3
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• pp.491-501
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• 2015

Spectrum sensing is an elementary function in cognitive radio designed to monitor the existence of a primary user (PU). To achieve a high rate of detection, most techniques rely on knowledge of prior spectrum patterns, with a trade-off between high computational complexity and long sensing time. On the other hand, blind techniques ignore pattern matching processes to reduce processing time, but their accuracy degrades greatly at low signal-to-noise ratios. To achieve both a high rate of detection and short sensing time, we propose fast spectrum sensing with coordinate system (FSC) - a novel technique that decomposes a spectrum with high complexity into a new coordinate system of salient features and that uses these features in its PU detection process. Not only is the space of a buffer that is used to store information about a PU reduced, but also the sensing process is fast. The performance of FSC is evaluated according to its accuracy and sensing time against six other well-known conventional techniques through a wireless microphone signal based on the IEEE 802.22 standard. FSC gives the best performance overall.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.1C
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• pp.1-8
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• 2007

LMS(Least Mean Square) algorithm using steepest descent way in adaptive signal processing requires simple equation and is used widely because of the less complexity. But eigenvalues change by width of input signals in time domain, so the rate of convergence becomes low. In this paper, we propose a new fast running FIR filter structure that improves the convergence speed of adaptive signal processing and the same performance as the existing fast wavelet transform algorithm with less computational complexity. The proposed filter structure is applied to wavelet based adaptive algorithm. Simulation results show a better performance than the existing one.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.2
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• pp.99-105
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• 2012

The focus of this paper is a proposal for a computationally efficient dynamic subcarrier allocation (DSA) algorithm for orthogonal frequency-division multiple access (OFDMA) systems. The proposed DSA algorithm considerably reduces the computational complexity and the amount of channel quality information (CQI) compared to amplitude craving greedy (ACG) algorithms, which use full CQI. At the same time, the performance of the proposed algorithm closely appear to ACG algorithms. Moreover, the authors present a new bandwidth-assignment algorithm produced by modifying bandwidth assignment based on the signal-to-noise ratio (BABS). This modified BABS algorithm enables the proposed DSA algorithm to produce a strong outage performance gain over the conventional scheme.

• ETRI Journal
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• v.45 no.6
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• pp.963-973
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• 2023

Non-orthogonal multiple access (NOMA) is considered a key candidate technology for next-generation wireless communication systems due to its high spectral efficiency and massive connectivity. Incorporating the concepts of multiple-input-multiple-output (MIMO) into NOMA can further improve the system efficiency, but the hardware complexity increases. This study develops an energy-efficient (EE) subchannel assignment framework for MIMO-NOMA systems under the quality-of-service and interference constraints. This framework handles an energy-efficient co-training-based semi-supervised learning (EE-CSL) algorithm, which utilizes a small portion of existing labeled data generated by numerical iterative algorithms for training. To improve the learning performance of the proposed EE-CSL, initial assignment is performed by a many-to-one matching (MOM) algorithm. The MOM algorithm helps achieve a low complex solution. Simulation results illustrate that a lower computational complexity of the EE-CSL algorithm helps significantly minimize the energy consumption in a network. Furthermore, the sum rate of NOMA outperforms conventional orthogonal multiple access.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.1
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• pp.112-127
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• 2002

A useful pattern is a pattern that contributes much to learning. For a classification problem those patterns near the class boundary surfaces carry more information to the classifier. For a regression problem the ones near the estimated surface carry more information. In both cases, the usefulness is defined only for those patterns either without error or with negligible error. Using only the useful patterns gives several benefits. First, computational complexity in memory and time for learning is decreased. Second, overfitting is avoided even when the learner is over-sized. Third, learning results in more stable learners. In this paper, we propose a pattern 'utility index' that measures the utility of an individual pattern. The utility index is based on the bias and variance of a pattern trained by a network ensemble. In classification, the pattern with a low bias and a high variance gets a high score. In regression, on the other hand, the one with a low bias and a low variance gets a high score. Based on the distribution of the utility index, the original training set is divided into a high-score group and a low-score group. Only the high-score group is then used for training. The proposed method is tested on synthetic and real-world benchmark datasets. The proposed approach gives a better or at least similar performance.