Computational complexity of a distance‐based active search algorithm

2006 ◽  
Vol 120 (5) ◽  
pp. 3045-3045
Author(s):  
Masahide Sugiyama
Keyword(s):  
Computational Complexity ◽  
Search Algorithm ◽  
Active Search

Nearby Phase Search Algorithm with Reduced Complexity for Higher Order QAM Formats

2013 ◽  
Vol 760-762 ◽  
pp. 1869-1873
Author(s):  
Li Min Xia ◽  
Xian Zhou ◽  
Dong Yan ◽  
Na Na Zhang ◽  
Xiao Yun Wu
Keyword(s):  
Computational Complexity ◽  
Bit Error Rate ◽  
High Speed ◽  
Optical Transmission ◽  
Search Algorithm ◽  
Estimation Algorithm ◽  
Coherent Receivers ◽  
Multi Level ◽  
Reduced Complexity ◽  
Laser Line Width

This paper proposes a nearby phase search (NPS) algorithm based on BPS estimation algorithm in optical coherent receivers. And its suitable for arbitrary multi-level modulation. Making use of the continuity of phase change, the proposed NPS algorithm is applied to process nearby symbols by taking the pre-estimation phase of each symbol block as reference point. Compared to the traditional blind phase search (BPS) algorithm and its improved two-stage BPS algorithm, the performance of the proposed NPS algorithm is greatly improved in ultra-high speed coherent optical transmission system. By the simulation, the effectiveness and feasibility of the proposed algorithm are demonstrated in 28GBaud 16-QAM and 64-QAM system. Its shown that the computational complexity of the NPS algorithm greatly reduces in the guarantee of laser line width tolerance and bit error rate.

scholarly journals The Active Search Algorithm to Automate the Adjustment of the Control Parameters and Its Applications

2016 ◽  
Vol 29 (2) ◽  
pp. 65-75 ◽  
Author(s):  
Akio Noda ◽  
Hikaru Nagano ◽  
Yukiyasu Domae ◽  
Tatsuya Nagatani ◽  
Ken-ichi Tanaka
Keyword(s):  
Search Algorithm ◽  
Control Parameters ◽  
Active Search

scholarly journals Block-segment search of local extrema of images based on analysis of brightnesses of related pixels and areas

2019 ◽  
pp. 4-9
Author(s):  
A. T. Nguyen ◽  
V. Yu. Tsviatkou
Keyword(s):  
Computational Complexity ◽  
Search Algorithm ◽  
Local Maximum ◽  
High Accuracy ◽  
Search Algorithms ◽  
Low Computational Complexity ◽  
Local Extrema ◽  
Single Pixel ◽  
Maximum Minimum ◽  
High Computational Complexity

The aim of the work is to develop an algorithm for extracting local extremes of images with low computational complexity and high accuracy. The known algorithms for block search for local extrema have low computational complexity, but only strict maxima and minima are distinguished without errors. The morphological search gives accurate results, highlighting the extreme areas formed by non-severe extremes, however, it has high computational complexity. The paper proposes a block-segment search algorithm for local extremums of images based on an analysis of the brightness of adjacent pixels and regions. The essence of the algorithm is to search for single-pixel local extremes and regions of uniform brightness, comparing the values of their boundary pixels with the values of the corresponding pixels of adjacent regions: the region is a local maximum (minimum) if the values of all its boundary pixels are larger (smaller) or equal to the values of all adjacent pixels. The developed algorithm, as well as the morphological search algorithm, allows detecting all single-pixel local extremes, as well as extreme areas, which exceeds the block search algorithms. At the same time, the developed algorithm in comparison with the morphological search algorithm requires much less time and RAM.

Joint design of plane-dependent FM screens sets using DBS algorithm

2019 ◽  
Vol 2019 (1) ◽  
pp. 55-61
Author(s):  
Yi Yang ◽  
Jan P. Allebach
Keyword(s):  
Computational Complexity ◽  
Search Algorithm ◽  
Binary Search ◽  
High Quality ◽  
Joint Design ◽  
Halftone Image ◽  
Direct Binary Search ◽  
Continuous Tone ◽  
Binary Search Algorithm ◽  
Halftone Images

Color Halftoning is a technique for generating a halftone image by using a limited number of colorants to simulate a continuous-tone image as perceived by a human viewer. This paper describes an algorithm to jointly design three screens for Cyan, Magenta and Yellow colorants using the Direct Binary Search algorithm. The results show that high-quality color halftone images can be obtained using the screens sets, and the computational complexity will be greatly reduced.

scholarly journals Rate Distortion Performance of Motion Estimation for High Efficiency Video Coding

2019 ◽  
Vol 8 (2) ◽  
pp. 2855-2860
Keyword(s):  
Computational Complexity ◽  
Motion Estimation ◽  
Video Coding ◽  
High Efficiency ◽  
Search Algorithm ◽  
Rate Distortion ◽  
Objective Evaluation ◽  
Video Sequences ◽  
High Efficiency Video Coding ◽  
Different Types

The contemporary coding standard for video is High Efficiency Video Coding Standard (HEVC). It’s introduced by ITU-T (International Telegraph Union) and Joint Collaborative Team on Video Coding (JCT-VC). HEVC attains the requirement of video storage and transmission with high resolution. Although it requires the high amount of computational complexity. Motion Vectors are determined with motion estimation analysis; it is implemented with different types of algorithm. In this paper, Motion Estimation Process is implementing with the content split block search algorithm. It improves Peak Signal Noise Ratio (PSNR) than to the existing algorithms. The Objective evaluation has been performed with various video sequences such as BQ Terrace and also improved PSNR.

scholarly journals NOMA resource allocation method in IoV based on prioritized DQN-DDPG network

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Mengli He ◽  
Yue Li ◽  
Xiaofei Wang ◽  
Zelong Liu
Keyword(s):  
Resource Allocation ◽  
Computational Complexity ◽  
Power Allocation ◽  
Optimization Algorithm ◽  
Power Optimization ◽  
Search Algorithm ◽  
Optimization Methods ◽  
Exhaustive Search ◽  
User Grouping ◽  
Sum Rate

AbstractTo meet the demands of massive connections in the Internet-of-vehicle communications, non-orthogonal multiple access (NOMA) is utilized in the local wireless networks. In NOMA technique, various optimization methods have been proposed to provide optimal resource allocation, but they are limited by computational complexity. Recently, the deep reinforcement learning network is utilized for resource optimization in NOMA system, where a uniform sampled experience replay algorithm is used to reduce the correlation between samples. However, the uniform sampling ignores the importance of sample. To this point, this paper proposes a joint prioritized DQN user grouping and DDPG power allocation algorithm to maximize the system sum rate. At the user grouping stage, a prioritized sampling method based on TD-error (temporal-difference error) is proposed. At the power allocation stage, to deal with the problem that DQN cannot process continuous tasks and needs to quantify power into discrete form, a DDPG network is utilized. Simulation results show that the proposed algorithm with prioritized sampling can increase the learning rate and perform a more stable training process. Compared with the previous DQN algorithm, the proposed method improves the sum rate of the system by 2% and reaches 94% and 93% of the exhaustive search algorithm and optimal iterative power optimization algorithm, respectively. Although the sum rate is improved by only 2%, the computational complexity is reduced by 43% and 64% compared to the exhaustive search algorithm and the optimal iterative power optimization algorithm, respectively.

scholarly journals NOMA Resource Allocation Method in IoV Based on Prioritized DQN-DDPG Network

2021 ◽  
Author(s):  
Mengli He ◽  
Yue Li ◽  
Xiaofei Wang ◽  
Zelong Liu
Keyword(s):  
Resource Allocation ◽  
Computational Complexity ◽  
Power Allocation ◽  
Optimization Algorithm ◽  
Power Optimization ◽  
Search Algorithm ◽  
Exhaustive Search ◽  
System Capacity ◽  
User Grouping ◽  
Sum Rate

Abstract To meet the demands of massive connections in the Internet-of-vehicle (IoV) communications, non-orthogonal multiple access (NOMA) is utilized in the local wireless networks. In NOMA technique, power multiplexing and successive interference cancellation techniques are utilized at the transmitter and the receiver respectively to increase system capacity, and user grouping and power allocation are two key issues to ensure the performance enhancement. Various optimization methods have been proposed to provide optimal resource allocation, but they are limited by computational complexity. Recently, the deep reinforcement learning (DRL) network is utilized to solve the resource allocation problem. In a DRL network, an experience replay algorithm is used to reduce the correlation between samples. However, the uniform sampling ignores the importance of sample. Different from conventional methods, this paper proposes a joint prioritized DQN user grouping and DDPG power allocation algorithm to maximize the sum rate of the NOMA system. At the user grouping stage, a prioritized sampling method based on TD-error (temporal-difference error) is proposed to solve the problem of random sampling, where TD-error is used to represent the priority of sample, and the DQN takes samples according to their priorities. In addition, sum tree is used to store the priority to speed up the searching process. At the power allocation stage, to deal with the problem that DQN cannot process continuous tasks and needs to quantify power into discrete form, a DDPG network is utilized to complete power allocation tasks for each user. Simulation results show that the proposed algorithm with prioritized sampling can increase the learning rate and perform a more stable training process. Compared with the previous DQN algorithm, the proposed method improves the sum rate of the system by 2% and reaches 94% and 93% of the exhaustive search algorithm and optimal iterative power optimization algorithm, respectively. While the computational complexity is reduced by 43% and 64% compared with the exhaustive search algorithm and optimal iterative power optimization algorithm, respectively.

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