• Journal of applied mathematics & informatics
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• v.20 no.1_2
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• pp.637-645
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• 2006

An adaptive algorithm for reducing the hardware complexity is presented. This paper proposes a simplified LMS algorithm for the adaptive system and analyzes its convergence characteristics mathematically. An objective of the proposed algorithm is to reduce the hardware complexity. In order to test the performances, it is applied to the echo canceller, and a program is described. The results from simulations show that the echo canceller adopting the proposed algorithm achieves almost the same performances as one adopting the NLMS algorithm. If an echo canceller is implemented with this algorithm, its computation quantities are reduced to the half as many as the one that is implemented with the LMS algorithm, without so much degradation of performances.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3433-3446
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• 2019

Aiming at the high complexity of traditional single-channel demodulation algorithm for PCMA signals, a new demodulation algorithm based on neural network is proposed to reduce the complexity of demodulation in the system of non-cooperative PCMA communication. The demodulation network is trained in this paper, which combines the preprocessing module and decision module. Firstly, the preprocessing module is used to estimate the initial parameters, and the auxiliary signals are obtained by using the information of frequency offset estimation. Then, the time-frequency characteristic data of auxiliary signals are obtained, which is taken as the input data of the neural network to be trained. Finally, the decision module is used to output the demodulated bit sequence. Compared with traditional single-channel demodulation algorithms, the proposed algorithm does not need to go through all the possible values of transmit symbol pairs, which greatly reduces the complexity of demodulation. The simulation results show that the trained neural network can greatly extract the time-frequency characteristics of PCMA signals. The performance of the proposed algorithm is similar to that of PSP algorithm, but the complexity of demodulation can be greatly reduced through the proposed algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3772-3790
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• 2016

It is well known that the constant modulus algorithm (CMA) presents a large steady-state mean-square error (MSE) for high-order quadrature amplitude modulation (QAM) signals. In this paper, we propose a low-complexity hybrid adaptive blind equalization algorithm, which augments the CMA error function with a novel constellation matched error (CME) term. The most attractive advantage of the proposed algorithm is that it is computationally simpler than concurrent CMA and soft decision-directed (SDD) scheme (CMA+SDD), and modified CMA (MCMA), while the approximation of steady-state MSE of the proposed algorithm is same with CMA+SDD, and lower than MCMA. Extensive simulations demonstrate the performance of the proposed algorithm.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.1
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• pp.95-102
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• 2013

This paper covers nulling algorithm. In this algorithm, we assume that nulling points are already known. In general, nulling algorithm using matrix equation was utilized. But, this algorithm is pointed out that computational complexity is disadvantage. So, we choose gradient method to reduce the computational complexity. In order to further reduce the computational complexity, we propose approximate gradient method using characteristic of trigonometric functions. The proposed method has same performance compared with conventional method while having half the amount of computation when the number of antenna and nulling point are 20 and 1, respectively. In addition, we could virtually eliminate the trigonometric functions arithmetic. Trigonometric functions arithmetic cause a big problem in actual implementation like FPGA processor(Field Programmable gate array). By utilizing the above algorithm in a multi-cell environment, beamforming gain can be obtained and interference can be reduced at same time. By the above results, the algorithm can show excellent performance in the cell boundary.

• ETRI Journal
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• v.44 no.3
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• pp.400-412
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• 2022

In cognitive radio networks (CRNs), the computational complexity of resource allocation algorithms is a significant problem that must be addressed. However, the high computational complexity of the optimal solution for tackling resource allocation in CRNs makes it inappropriate for use in practical applications. Therefore, this study proposes a power-based pricing algorithm (PPA) primarily to reduce the computational complexity in downlink CRN scenarios while restricting the interference to primary users to permissible levels. A two-stage approach reduces the computational complexity of the proposed mathematical model. Stage 1 assigns subcarriers to the CRN's users, while the utility function in Stage 2 incorporates a pricing method to provide a power algorithm with enhanced reliability. The PPA's performance is simulated and tested for orthogonal frequency-division multiplexing-based CRNs. The results confirm that the proposed algorithm's performance is close to that of the optimal algorithm, albeit with lower computational complexity of O(M log(M)).

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.11-19
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• 2014

In this paper, a low complexity parallel sphere decoder algorithm is proposed for high-order MIMO system. It reduces the computational complexity compared to the fixed-complexity sphere decoder (FSD) algorithm by static tree-pruning and dynamic tree-pruning using scalable node operators, and offers near-maximum likelihood decoding performance. Moreover, it also offers hardware-friendly node operation algorithm through fixing the variable computational complexity caused by the sequential nature of the conventional SD algorithm. A Monte Carlo simulation shows our proposed algorithm decreases the average number of expanded nodes by 55% with only 6.3% increase of the normalized decoding time compared to a full parallelized FSD algorithm for high-order MIMO communication system with 16 QAM modulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.8C
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• pp.801-805
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• 2006

A normalization approach to coefficient adaptation in the escalator(ESC) filter structure that conventionally employs least mean square(LMS) algorithm is introduced. Using Taylor's expansion of the local error signal, a normalized form of the ESC-LMS algorithm is derived. Compared with the computational complexity of the conventional ESC-LMS algorithm employs input power estimation for time-varying convergence coefficient using a single-pole low-pass filter, the computational complexity of the proposed method can be reduced by 50% without performance degradation.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.173-184
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• 2001

We present an algorithm for the complexity reduction of a general Gaussian mixture model by using a penalized likelihood method. One of our important assumptions is that we begin with an overfitted model in terms of the number of components. So our main goal is to eliminate redundant components in the overfitted model. As shown in the section of simulation results, the algorithm works well with the selected densities.

• Journal of Korea Multimedia Society
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• v.10 no.12
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• pp.1551-1558
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• 2007

The standard of evaluating function of speech coder for mobile telecommunication can be shown in channel capacity, noise immunity, encryption, complexity and encoding delay largely. This study is an algorithm to reduce complexity applying to CDMA(Code Division Multiple Access) mobile telecommunication system, which has a benefit of keeping the existing advantage of telecommunication quality and low transmission rate. This paper has an objective to reduce the computing complexity by controlling the frequency band nonuniform during the changing process of LSP(Line Spectrum Pairs) parameters from LPC(Line Predictive Coding) coefficients used for EVRC(Enhanced Variable-Rate Coder, IS-127) speech coders. Its experimental result showed that when comparing the speech coder applied by the proposed algorithm with the existing EVRC speech coder, it's decreased by 45% at average. Also, the values of LSP parameters, Synthetic speech signal and Spectrogram test result were obtained same as the existing method.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.05
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• pp.281-284
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• 1995

This paper describes a complexity measure algorithm based on nonlinear dynamics(chaos theory). In order to quantify complexity or regularity of biomedical signal, this paper proposed fractal dimension-1 and fractal dimension-2 algorithm with digital filter. Approximate entropy algorithm which measure a system regularity are also compared. In this paper investigate what we quantify of biomedical signal. These quantified complexity measure may be a useful information about human physiology.