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Deep learning-driven MIMO: data encoding and processing mechanism

PHYSICAL COMMUNICATION 2023年第1期57卷

作者： Song, Zhendong Ma, Jinping Shenzhen Polytech Shenzhen 518055 Peoples R China

Large-scale Multiple-Input Multiple Output (MIMO) is the key technology of 5G communication. However, dealing with physical channels is a complex process. Machine learning techniques have not been utilized commercially because of the limited learning capabilities of traditional machine learning algorithms. We design a deep learning hybrid precoding scheme based on the attention mechanism. The method mainly includes channel modeling and deep learning encoding two modules. The channel modeling module mainly describes the problem formally, which is convenient for the subsequent method design and processing. The model design module introduces the design framework, details, and main training process of the model. We utilize the attention layer to extract the eigenvalues of the interference between multiple users through the output attention distribution matrix. Then, according to the characteristics of inter-user interference, the loss minimization function is used to study the optimal precoder to achieve the maximum reachable rate of the system. Under the same condition, we compare our proposed method with the traditional unsupervised learning-based hybrid precoding algorithm, the TTD-based (True-Time-Delay, TTD) phase correction hybrid precoding algorithm, and the deep learning-based method. Additionally, we verify the role of attention mechanism in the model. Extensive simulation results demonstrate the effectiveness of the proposed method. The results of this research prove that deep learning technology can play a driving role in the encoding and processing of MIMO with its unique feature extraction and modeling capabilities. In addition, this research also provides a good reference for the application of deep learning in MIMO data processing problems.(c) 2023 Elsevier B.V. All rights reserved.

关键词： MIMO 5G Deep learning data encoding

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data encoding patterns for quantum computing 20

Data encoding patterns for quantum computing

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Proceedings of the 27th Conference on Pattern Languages of Programs

作者： Manuela Weigold Johanna Barzen Frank Leymann Marie Salm University of Stuttgart

ISBN: (纸本)9781941652169

Quantum computers have the potential to solve certain problems faster than classical computers. However, loading data into a quantum computer is not trivial. To load the data, it must be encoded in quantum bits (qubits). There are several ways how qubits can represent the data and, thus, multiple data encodings are possible. Both the data itself and the chosen encoding influence the runtime of the loading process. In the worst case, loading requires exponential time. This is critical because quantum algorithms that promise a speed-up assume that loading data can be done faster, in logarithmic or linear time. To outline abstract knowledge about encodings and the consequences of choosing a particular data encoding, we present three common encodings as patterns. Especially in complex domains like quantum computing, patterns can contribute to making this new technology and its broad potential accessible to users with different backgrounds. In particular, they facilitate the development of quantum applications for software developers.

关键词： speed-up quantum computing quantum algorithms patterns data encoding

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An Efficient Dual-Parameter Full Waveform Inversion for GPR data Using data encoding

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷

作者： Feng, Deshan Li, Bingchao Wang, Xun Ding, Siyuan Tai, Xiaoyong Cai, Liqiong Su, Xuan Cent South Univ Sch Geosci & Infophys Changsha 410083 Peoples R China Fuzhou City Construct Design & Res Inst Co Ltd Fuzhou 350001 Peoples R China

Ground penetrating radar (GPR) is an important shallow electromagnetic nondestructive detection technology. The full waveform inversion (FWI) of GPR data utilizes all information, including dynamics and kinematics, theoretically has the highest imaging accuracy, and meets the increasingly sophisticated needs of engineering exploration imaging. However, the bottleneck restricting the FWI is the low calculation efficiency, which cannot meet the requirements of rapid reconstruction of underground medium in actual engineering. In order to improve the calculation efficiency, we introduce the data encoding into the GPR dual-parameter FWI. data encoding often brings crosstalk noise, and the noise is closely related to the encoding methods and data types. For this reason, we select the encoding of the crosshole data, wide-angle reflection and refraction data, and common-offset data for inversion. The experiments show that data encoding can effectively reduce computing time, and three different GPR data require different encoding methods due to their different redundancies. Total variation (TV) regularization can suppress the noise caused by data encoding. Although it will slightly increase the calculation time, it can significantly improve the inversion quality.

关键词： encoding TV Mathematical models Crosstalk Linear programming data models Conductivity data encoding dual parameter full waveform inversion (FWI) ground penetrating radar (GPR) total variation (TV) regularization

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Polarization assisted fast data encoding and transmission using coherence based spectral anomalies

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JOURNAL OF OPTICS 2013年第5期15卷 055407-055407页

作者： Kanseri, Bhaskar Max Planck Inst Sci Light D-91058 Erlangen Germany

Two methods for fast information encoding and free space communication are proposed, which are based on the rapid transitions in coherence-based (spatial and temporal) spectral anomalies called 'spectral switches'. The information (data bits) could be encoded in terms of red and blue shifts in the source spectrum. The encoding process itself could be made fast by polarization assisted switching of spectral anomalies using a polarization selective device such as an electro-optic modulator. The advantages and limitations of this polarization based data processing mechanism are also discussed.

关键词： spectral switch data encoding

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Area Efficient Soft Error Tolerant RISC Pipeline: Leveraging data encoding and Inherent ALU Redundancy 60

Area Efficient Soft Error Tolerant RISC Pipeline: Leveraging...

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60th IEEE International Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Hasan, Syed Rafay Tangellapalli, Phani Tennessee Technol Univ Dept Elect & Comp Engn Cookeville TN 38505 USA

ISBN: (纸本)9781509063895

This paper proposes novel soft error detection and mitigation technique in reduced instruction set computer (RISC) based pipeline processors. We leveraged the data encoding techniques (re-computing with rotated operands (RERO)) in conjunction with back pressure controlling mechanism in pipeline architecture. In order to alleviate the performance degradation due to potential stalling, we exploited the inherent ALU redundancy in conjunction with data encoding. Synthesis results on Stratix II FPGA from Altera suggested that if 5% of the instructions are subject to soft error then for 2.25 additional stall cycles (on average) our proposed ALU-redundancy based solution in conjunction with data encoding provides better execution time even if the clock cycle time increases up to 11% because of additional hardware. Area wise our technique requires up to 3 times lesser area compared to other contemporary techniques and provides soft error tolerance in combinational blocks along with intermediate registers.

关键词： Soft Error data encoding RISC pipeline

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Quantum Approach to Image data encoding and Compression

IEEE SENSORS LETTERS

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IEEE SENSORS LETTERS 2023年第2期7卷 1页

作者： Majji, Sathwik Reddy Chalumuri, Avinash Manoj, B. S. Indian Space Res Org Vikram Sarabhai Space Ctr Thiruvananthapuram 695022 India Gayatri Vidya Parishad Coll Engn Visakhapatnam 530048 India Indian Inst Space Sci & Technol Thiruvananthapuram 695547 India

High-resolution images are being generated due to the rapid development of image sensors. Various image processing algorithms require images of reduced sizes, given the computational constraints. Hence, preprocessing the images to reduce their size is crucial for many applications. This letter proposes a novel approach to compress images using quantum computing. A comparative study on different standard data encoding techniques used in quantum computing is undertaken. We propose four quantum compression techniques by extending the unitary operations of amplitude embedding for compressing images. The proposed methods provide exponential scaling as amplitude embedding is used, where 2(n) classical data values are encoded into n qubits. Compression performance, visual evaluation, and objective evaluation are carried out to assess the proposed compression techniques. Our experimental results show that the crucial patterns in images are retained in the compressed images even at 75% compression. The compressed images can be used for postprocessing tasks using classical or quantum computing algorithms.

关键词： Image coding Qubit Satellites Wires encoding Computers Optical imaging Sensor signal processing data compression data encoding image processing quantum circuit quantum computing

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Vlsi Design Of Low Power data encoding Techniques For Network-On-Chip

Vlsi Design Of Low Power Data Encoding Techniques For Networ...

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International Conference on Signal Processing, Communication, Power and Embedded System

作者： G. Sravya M. Sailaja M. Tech Scholar Department of Electronics and Communication Engineering JNTUK

ISBN: (纸本)9781509046218

As technology improves, the power dissipated by the links of a network-on-chip (NoC) starts to contend with the power dissipated by the additional elements of the correspond ion subsystem, namely, the routers and the network interfaces (NIs). Here, we present a set of data encoding schemes to diminish the power dissipated by the links of an NoC. In this paper, the encoder in LDPC is replaced with our data encoding schemes in order to reduce the power consumption in Low Density Parity Check Techniques. Experiments carried out on both synthetic and real traffic scenarios show the effectiveness of the proposed schemes.

关键词： data encoding interconnection on chip low density parity check majority logic decoding power analysis

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Wireless Sensor Network data Description and encoding in Heterogeneous Building Systems

Wireless Sensor Network Data Description and Encoding in Het...

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7th IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Conway, Chris Barnes, Matthew Univ Edinburgh Sch Informat Ctr Speckled Comp Edinburgh EH8 9AB Midlothian Scotland

ISBN: (纸本)9781457705137

This paper presents initial results and future directions for data description and payload encodings for heterogeneous networks in IP based building intelligence systems. The efficiency of several open standards for sensor data representation are evaluated along with a consideration of the complexity of implementation and suitability for use in development of 6lowPAN based intelligent building systems. A combination of a simplified generic XML schema for data description combined with Google Protobuffer encoding is suggested as the basis for further development of the architecture as this combines simple, platform independent implementation for developers with efficient compression and payload encoding for limited bandwidth 6lowPAN networks.

关键词： Architecture Buildings encoding Google Protobuffer Internet Internet protocol Payloads Protocols Wireless sensor networks XML XML schema building management systems data description data encoding data handling heterogeneous network intelligent building sy

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LRP: learned robust data partitioning for efficient processing of large dynamic queries

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FRONTIERS OF COMPUTER SCIENCE 2025年第9期19卷 1-18页

作者： Liu, Pengju Cai, Pan Zhong, Kai Li, Cuiping Chen, Hong Minist Educ Engn Res Ctr Database & Business Intelligence Beijing 100872 Peoples R China Renmin Univ China Sch Informat Beijing 100872 Peoples R China

The interconnection between query processing and data partitioning is pivotal for the acceleration of massive data processing during query execution, primarily by minimizing the number of scanned block files. Existing partitioning techniques predominantly focus on query accesses on numeric columns for constructing partitions, often overlooking non-numeric columns and thus limiting optimization potential. Additionally, these techniques, despite creating fine-grained partitions from representative queries to enhance system performance, experience from notable performance declines due to unpredictable fluctuations in future queries. To tackle these issues, we introduce LRP, a learned robust partitioning system for dynamic query processing. LRP first proposes a method for data and query encoding that captures comprehensive column access patterns from historical queries. It then employs Multi-Layer Perceptron and Long Short-Term Memory networks to predict shifts in the distribution of historical queries. To create high-quality, robust partitions based on these predictions, LRP adopts a greedy beam search algorithm for optimal partition division and implements a data redundancy mechanism to share frequently accessed data across partitions. Experimental evaluations reveal that LRP yields partitions with more stable performance under incoming queries and significantly surpasses state-of-the-art partitioning methods.

关键词： data partitioning data encoding query prediction beam search data redundancy

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A Bit-encoding Based New data Structure for Time and Memory Efficient Handling of Spike Times in an Electrophysiological Setup

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NEUROINFORMATICS 2018年第2期16卷 217-229页

作者： Ljungquist, Bengt Petersson, Per Johansson, Anders J. Schouenborg, Jens Garwicz, Martin Lund Univ Neuronano Res Ctr Integrat Neurophysiol & Neurote Scheelevagen 2 S-22381 Lund Sweden Umea Univ Dept Integrat Med Biol Linneus Vag 9 S-90187 Umea Sweden

Recent neuroscientific and technical developments of brain machine interfaces have put increasing demands on neuroinformatic databases and data handling software, especially when managing data in real time from large numbers of neurons. Extrapolating these developments we here set out to construct a scalable software architecture that would enable near-future massive parallel recording, organization and analysis of neurophysiological data on a standard computer. To this end we combined, for the first time in the present context, bit-encoding of spike data with a specific communication format for real time transfer and storage of neuronal data, synchronized by a common time base across all unit sources. We demonstrate that our architecture can simultaneously handle data from more than one million neurons and provide, in real time (< 25 ms), feedback based on analysis of previously recorded data. In addition to managing recordings from very large numbers of neurons in real time, it also has the capacity to handle the extensive periods of recording time necessary in certain scientific and clinical applications. Furthermore, the bit-encoding proposed has the additional advantage of allowing an extremely fast analysis of spatiotemporal spike patterns in a large number of neurons. Thus, we conclude that this architecture is well suited to support current and near-future Brain Machine Interface requirements.

关键词： Electrophysiology databases data encoding Real time

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