scholarly journals Optimal Sharpening of Compensated Comb Decimation Filters: Analysis and Design

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
David Ernesto Troncoso Romero ◽  
Massimiliano Laddomada ◽  
Gordana Jovanovic Dolecek
Keyword(s):  
Computational Complexity ◽  
Performance Improvement ◽  
Low Complexity ◽  
Lower Degree ◽  
Magnitude Response ◽  
Scheme Design ◽  
Analysis And Design ◽  
Decimation Filters ◽  
Comb Filters ◽  
Stopband Attenuation

Comb filters are a class of low-complexity filters especially useful for multistage decimation processes. However, the magnitude response of comb filters presents a droop in the passband region and low stopband attenuation, which is undesirable in many applications. In this work, it is shown that, for stringent magnitude specifications, sharpening compensated comb filters requires a lower-degree sharpening polynomial compared to sharpening comb filters without compensation, resulting in a solution with lower computational complexity. Using a simple three-addition compensator and an optimization-based derivation of sharpening polynomials, we introduce an effective low-complexity filtering scheme. Design examples are presented in order to show the performance improvement in terms of passband distortion and selectivity compared to other methods based on the traditional Kaiser-Hamming sharpening and the Chebyshev sharpening techniques recently introduced in the literature.

Approaches to Comparison and Their Influence on Social Comparison

2019 ◽  
pp. 430-442
Author(s):  
Arthur B. Markman
Keyword(s):  
Cognitive Psychology ◽  
Computational Complexity ◽  
Social Comparison ◽  
Mental Representation ◽  
Spatial Models ◽  
Structural Models ◽  
Low Complexity ◽  
Comparison Process ◽  
Social Settings ◽  
Computationally Intensive

Cognitive psychology identifies different assumptions about the mental representations that form the basis of theories of comparison. Each representation requires a different process to generate a comparison, and both the computational complexity and the output of the different processes differ. Spatial models require a low-complexity process but only reveal the distance between points representing individuals. Featural models are more intensive than spatial comparisons but provide access to particular commonalities and differences. Structural models are more computationally intensive but support a distinction between alignable and nonalignable differences. Social comparison theories make assumptions about how knowledge is represented, but they are rarely explicit about the type of comparison process that is likely to be involved. The merging of work on social comparison with more explicit cognitive science theories of comparison science has the potential to both identify gaps in the literature and expand our knowledge about how comparison operates in social settings. This chapter first discusses the concept of mental representation and then addresses spatial models of comparison, featured models of comparison, structural models of comparison, transformation models. The chapter concludes with a discussion of similarity models and social comparison.

scholarly journals Wi-Fi Backscatter System with Tag Sensors Using Multi-Antennas for Increased Data Rate and Reliability

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1314
Author(s):  
Taeoh Kim ◽  
Hyobeen Park ◽  
Yunho Jung ◽  
Seongjoo Lee
Keyword(s):  
Computational Complexity ◽  
Euclidean Distance ◽  
Multiple Antennas ◽  
Power Level ◽  
Low Complexity ◽  
Data Rate ◽  
Threshold Method ◽  
Channel State ◽  
Distance Method ◽  
Ber Performance

In this paper, we propose tag sensor using multi-antennas in a Wi-Fi backscatter system, which results in an improved data rate or reliability of the signal transmitted from a tag sensor to a reader. The existing power level modulation method, which is proposed to improve data rate in a Wi-Fi backscatter system, has low reliability due to the reduced distance between symbols. To address this problem, we propose a Wi-Fi backscatter system that obtains channel diversity by applying multiple antennas. Two backscatter methods are described for improving the data rate or reliability in the proposed system. In addition, we propose three low complexity demodulation methods to address the high computational complexity problem caused by multiple antennas: (1) SET (subcarrier energy-based threshold) method, (2) TCST (tag’s channel state-based threshold) method, and (3) SED (similar Euclidean distance) method. In order to verify the performance of the proposed backscatter method and low complexity demodulation schemes, the 802.11 TGn (task group n) channel model was utilized in simulation. In this paper, the proposed tag sensor structure was compared with existing methods using only sub-channels with a large difference in received CSI (channel state information) values or adopting power-level modulation. The proposed scheme showed about 10 dB better bit error rate (BER) performance and throughput. Also, proposed low complexity demodulation schemes were similar in BER performance with a difference of up to 1 dB and the computational complexity was reduced by up to 60% compared to the existing Euclidean distance method.

scholarly journals A Novel Iterative Discrete Estimation Algorithm for Low-Complexity Signal Detection in Uplink Massive MIMO Systems

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 980 ◽  
Author(s):  
Hui Feng ◽  
Xiaoqing Zhao ◽  
Zhengquan Li ◽  
Song Xing
Keyword(s):  
Computational Complexity ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Estimation Algorithm ◽  
Low Complexity ◽  
Detection Algorithm ◽  
Base Station ◽  
Damping Factor ◽  
Mimo Detection ◽  
Discrete Estimation

In this paper, a novel iterative discrete estimation (IDE) algorithm, which is called the modified IDE (MIDE), is proposed to reduce the computational complexity in MIMO detection in uplink massive MIMO systems. MIDE is a revision of the alternating direction method of multipliers (ADMM)-based algorithm, in which a self-updating method is designed with the damping factor estimated and updated at each iteration based on the Euclidean distance between the iterative solutions of the IDE-based algorithm in order to accelerate the algorithm’s convergence. Compared to the existing ADMM-based detection algorithm, the overall computational complexity of the proposed MIDE algorithm is reduced from O N t 3 + O N r N t 2 to O N t 2 + O N r N t in terms of the number of complex-valued multiplications, where Ntand Nr are the number of users and the number of receiving antennas at the base station (BS), respectively. Simulation results show that the proposed MIDE algorithm performs better in terms of the bit error rate (BER) than some recently-proposed approximation algorithms in MIMO detection of uplink massive MIMO systems.

scholarly journals Low-Complexity Detection Algorithms for Spatial Modulation MIMO Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Xinhe Zhang ◽  
Yuehua Zhang ◽  
Chang Liu ◽  
Hanzhong Jia
Keyword(s):  
Computational Complexity ◽  
Mimo Systems ◽  
Optimal Solution ◽  
Low Complexity ◽  
Search Tree ◽  
Spatial Modulation ◽  
Algorithm Analysis ◽  
Detection Algorithms ◽  
Search Order ◽  
Ber Performance

In this paper, the authors propose three low-complexity detection schemes for spatial modulation (SM) systems based on the modified beam search (MBS) detection. The MBS detector, which splits the search tree into some subtrees, can reduce the computational complexity by decreasing the nodes retained in each layer. However, the MBS detector does not take into account the effect of subtree search order on computational complexity, and it does not consider the effect of layers search order on the bit-error-rate (BER) performance. The ost-MBS detector starts the search from the subtree where the optimal solution is most likely to be located, which can reduce total searches of nodes in the subsequent subtrees. Thus, it can decrease the computational complexity. When the number of the retained nodes is fixed, which nodes are retained is very important. That is, the different search orders of layers have a direct influence on BER. Based on this, we propose the oy-MBS detector. The ost-oy-MBS detector combines the detection order of ost-MBS and oy-MBS together. The algorithm analysis and experimental results show that the proposed detectors outstrip MBS with respect to the BER performance and the computational complexity.

scholarly journals Neural Network Approach to MPPT Control and Irradiance Estimation

2020 ◽  
Vol 10 (15) ◽  
pp. 5051
Author(s):  
Žarko Zečević ◽  
Maja Rolevski
Keyword(s):  
Neural Network ◽  
Computational Complexity ◽  
Low Complexity ◽  
Maximum Power ◽  
Photovoltaic Module ◽  
Neural Network Approach ◽  
Point Tracking ◽  
The Neural Network ◽  
Power Point ◽  
The One

Photovoltaic (PV) modules require maximum power point tracking (MPPT) algorithms to ensure that the amount of power extracted is maximized. In this paper, we propose a low-complexity MPPT algorithm that is based on the neural network (NN) model of the photovoltaic module. Namely, the expression for the output current of the NN model is used to derive the analytical, iterative rules for determining the maximal power point (MPP) voltage and irradiance estimation. In this way, the computational complexity is reduced compared to the other NN-based MPPT methods, in which the optimal voltage is predicted directly from the measurements. The proposed algorithm cannot instantaneously determine the optimal voltage, but it contains a tunable parameter for controlling the trade-off between the tracking speed and computational complexity. Numerical results indicate that the relative error between the actual maximum power and the one obtained by the proposed algorithm is less than 0.1%, which is up to ten times smaller than in the available algorithms.

scholarly journals PCA-Aided Linear Precoding in Massive MIMO Systems with Imperfect CSI

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Van-Khoi Dinh ◽  
Minh-Tuan Le ◽  
Vu-Duc Ngo ◽  
Chi-Hieu Ta
Keyword(s):  
Principal Component Analysis ◽  
Computational Complexity ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Minimum Mean Square Error ◽  
Principal Component ◽  
Low Complexity ◽  
Component Analysis ◽  
Linear Precoding ◽  
Block Diagonalization

In this paper, a low-complexity linear precoding algorithm based on the principal component analysis technique in combination with the conventional linear precoders, called Principal Component Analysis Linear Precoder (PCA-LP), is proposed for massive MIMO systems. The proposed precoder consists of two components: the first one minimizes the interferences among neighboring users and the second one improves the system performance by utilizing the Principal Component Analysis (PCA) technique. Numerical and simulation results show that the proposed precoder has remarkably lower computational complexity than its low-complexity lattice reduction-aided regularized block diagonalization using zero forcing precoding (LC-RBD-LR-ZF) and lower computational complexity than the PCA-aided Minimum Mean Square Error combination with Block Diagonalization (PCA-MMSE-BD) counterparts while its bit error rate (BER) performance is comparable to those of the LC-RBD-LR-ZF and PCA-MMSE-BD ones.

Low-Complexity Multiplication Using Complement and Signed-Digit Recoding Methods

2014 ◽  
Vol 619 ◽  
pp. 342-346
Author(s):  
Te Jen Chang ◽  
Ping Sheng Huang ◽  
Shan Jen Cheng ◽  
Ching Yin Chen ◽  
I Hui Pan
Keyword(s):  
Computational Complexity ◽  
Low Complexity ◽  
Fast Multiplication ◽  
Speed Up ◽  
Overall Performance ◽  
Representation Method ◽  
Computing Method

In this paper, a fast multiplication computing method utilizing the complement representation method and canonical recoding technique is proposed. By performing complements and canonical recoding technique, the number of partial products can be reduced. Based on these techniques, we propose algorithm provides an efficient multiplication method. On average, our proposed algorithm to reduce the number of k-bit additions from (0.25k+logk/k+2.5) to (k/6 +logk/k+2.5), where k is the bit-length of the multiplicand A and multiplier B. We can therefore efficiently speed up the overall performance of the multiplication. Moreover, if we use the new proposes to compute common-multiplicand multiplication, the computational complexity can be reduced from (0.5 k+2 logk/k+5) to (k/3+2 logk/k+5) k-bit additions.

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