scholarly journals A Power-Efficient Soft-Output Detector for Spatial-Multiplexing MIMO Communications

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Hsiao-Chi Wang ◽  
Tung-Lin Liu ◽  
Yuan-Wei Wu ◽  
Hsi-Pin Ma
Keyword(s):  
Power Efficiency ◽  
Search Algorithm ◽  
Power Saving ◽  
Spatial Multiplexing ◽  
Cmos Process ◽  
Tree Search ◽  
Timing Error ◽  
Power Efficient ◽  
Sphere Decoder ◽  
Tree Search Algorithm

VLSI implementation of a configurable power-efficient MIMO detector is proposed to support4×4spatial multiplexing and modulation from QPSK to 64-QAM. A novel tree search algorithm is proposed to enable the detector to provide soft outputs and to be implemented in parallel and pipelined hardware architecture. The frame error rate (FER) of the detector approaches the quasi-optimal sphere decoder, with 0.5-dB degradation. Moreover, the proposed detector can operate at the optimal voltage under different configurations and detect/recover timing error at run time by a novel adaptive voltage scaling technique with double sampling circuitry. The proposed detector, using TSMC 0.18 μm single-poly six-metal CMOS process with a core area of1.17×1.17 mm2, provides fixed throughput of 45 Mbps in 64-QAM configuration, 120 Mbps in 16-QAM configuration, and 60 Mbps in QPSK configuration. The normalized power efficiency of the design for 64-QAM and 16-QAM configurations is 1.56 Mbps/mW and 2.53 Mbps/mW, respectively. Compared with the conservative margin-based design, the proposed design achieves a 48.8% power saving.

scholarly journals Incorporation of Potential Fields and Motion Primitives for the Collision Avoidance of Unmanned Aircraft

2021 ◽  
Vol 11 (7) ◽  
pp. 3103
Author(s):  
Kyuman Lee ◽  
Daegyun Choi ◽  
Donghoon Kim
Keyword(s):  
Collision Avoidance ◽  
Search Algorithm ◽  
Unmanned Aircraft ◽  
Artificial Potential Field ◽  
Tree Search ◽  
Local Minima ◽  
Collision Risk ◽  
Motion Primitives ◽  
Multiple Uavs ◽  
Tree Search Algorithm

Collision avoidance (CA) using the artificial potential field (APF) usually faces several known issues such as local minima and dynamically infeasible problems, so unmanned aerial vehicles’ (UAVs) paths planned based on the APF are safe only in a certain environment. This research proposes a CA approach that combines the APF and motion primitives (MPs) to tackle the known problems associated with the APF. Since MPs solve for a locally optimal trajectory with respect to allocated time, the trajectory obtained by the MPs is verified as dynamically feasible. When a collision checker based on the k-d tree search algorithm detects collision risk on extracted sample points from the planned trajectory, generating re-planned path candidates to avoid obstacles is performed. After rejecting unsafe route candidates, one applies the APF to select the best route among the remaining safe-path candidates. To validate the proposed approach, we simulated two meaningful scenario cases—the presence of static obstacles situation with local minima and dynamic environments with multiple UAVs present. The simulation results show that the proposed approach provides smooth, efficient, and dynamically feasible pathing compared to the APF.

scholarly journals Enhanced Reinforcement Learning Method Combining One-Hot Encoding-Based Vectors for CNN-Based Alternative High-Level Decisions

2021 ◽  
Vol 11 (3) ◽  
pp. 1291
Author(s):  
Bonwoo Gu ◽  
Yunsick Sung
Keyword(s):  
Reinforcement Learning ◽  
Search Algorithm ◽  
Classification Criteria ◽  
Tree Search ◽  
Learning Method ◽  
Board Game ◽  
Ancient China ◽  
Monte Carlo Tree Search ◽  
High Level ◽  
Tree Search Algorithm

Gomoku is a two-player board game that originated in ancient China. There are various cases of developing Gomoku using artificial intelligence, such as a genetic algorithm and a tree search algorithm. Alpha-Gomoku, Gomoku AI built with Alpha-Go’s algorithm, defines all possible situations in the Gomoku board using Monte-Carlo tree search (MCTS), and minimizes the probability of learning other correct answers in the duplicated Gomoku board situation. However, in the tree search algorithm, the accuracy drops, because the classification criteria are manually set. In this paper, we propose an improved reinforcement learning-based high-level decision approach using convolutional neural networks (CNN). The proposed algorithm expresses each state as One-Hot Encoding based vectors and determines the state of the Gomoku board by combining the similar state of One-Hot Encoding based vectors. Thus, in a case where a stone that is determined by CNN has already been placed or cannot be placed, we suggest a method for selecting an alternative. We verify the proposed method of Gomoku AI in GuPyEngine, a Python-based 3D simulation platform.

scholarly journals CASA: An Efficient Automated Assignment of Protein Mainchain NMR Data Using an Ordered Tree Search Algorithm

2005 ◽  
Vol 33 (4) ◽  
pp. 261-279 ◽  
Author(s):  
Jianyong Wang ◽  
Tianzhi Wang ◽  
Erik R. P. Zuiderweg ◽  
Gordon M. Crippen
Keyword(s):  
Search Algorithm ◽  
Automated Assignment ◽  
Tree Search ◽  
Nmr Data ◽  
Ordered Tree ◽  
Tree Search Algorithm

Highly efficient tree search algorithm for irreducible site-occupancy configurations

2022 ◽  
Vol 105 (1) ◽  
Author(s):  
Ji-Chun Lian ◽  
Yuan Si ◽  
Tao Huang ◽  
Wei-Qing Huang ◽  
Wangyu Hu ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Site Occupancy ◽  
Tree Search ◽  
Highly Efficient ◽  
Tree Search Algorithm
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