REDUCED-COMPLEXITY DECODING ALGORITHMS FOR UNITARY SPACE-TIME CODES

基于校验和更新的低复杂度LDPC码硬判决译码算法吴伊蒙;石志东;邓斌【期刊名称】《上海大学学报（自然科学版）》【年(卷),期】2017(023)004【摘要】To reduce computational complexity of hard-decision decoding algorithms of low-density parity-check (LDPC) codes and improve decoding performance, a check-sums algorithm is proposed. It requires less computation, and is applicable to all existing hard-decision algorithms. The algorithm is applied to a multi-threshold bit flipping (MTBF) algorithm whose computation complexity is similar to the bit flipping (BF) algorithm, and further improvement is made. The results show that the proposed algorithm can achieve a 0.15 dB performance gain after 5 iterations with lower computational complexity and bet-ter decoding performance.%为使低密度奇偶校验(low-density parity-check,LDPC)码的硬判决译码算法具有更低的计算复杂度和更高的译码性能,提出了一种新的校验和计算算法,具有较低的计算量,可应用于现有的所有硬判决译码算法.结合该算法对一种计算量近似于比特翻转(bit flipping,BF)算法的多阈值比特翻转(multi-threshold BF,MTBF)算法进行了进一步改进,获得了更低的译码复杂度和更好的译码性能,在迭代5次时获得了0.15 dB的性能增益.【总页数】7页(P510-516)【作者】吴伊蒙;石志东;邓斌【作者单位】上海大学通信与信息工程学院, 上海200444;上海大学通信与信息工程学院, 上海200444;上海大学通信与信息工程学院, 上海200444【正文语种】中文【中图分类】TN919.3+2【相关文献】1.基于MSF的低复杂度chase型RS码软判决译码算法 [J], 张卫;陈亦卉;王琳;曾吉文2.一种改进的LDPC码硬判决译码算法 [J], 詹尹;李宏伟3.基于可靠性更新的低复杂度BP译码算法 [J], 陈昕;门爱东4.基于联合判决消息传递机制的LDPC码译码算法研究 [J], 雷菁;文磊;高永强5.基于校验和的LDPC码硬判决解码算法的研究 [J], 彭立;朱光喜因版权原因，仅展示原文概要，查看原文内容请购买。

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Digital Signal Processing Digital Signal Processing (DSP) is a crucial aspect of modern technology, playing a vital role in various fields such as telecommunications, audio processing, image processing, and many more. It involves the manipulation of digital signals using various algorithms and techniques to extract useful information or to enhance the quality of the signal. However, DSP also presents several challenges and issues that need to be addressed to ensure its effectiveness and efficiency. One of the primary problems in digital signal processing is the issue of noise and interference. Digital signals are susceptible to various types of noise and interference, which can degrade the quality of the signal and affect the accuracy of the processing. This can be particularly problematic in applications such as wireless communications, where the signals are transmitted over long distances and are vulnerable to external interference. Engineers and researchers in the field of DSP are constantly working on developing advanced algorithms and techniques to mitigate the effects of noise and interference, such as adaptive filters and noise cancellation methods. Another significant challenge in DSP is the trade-off between accuracy and processing speed. In many real-time applications, such as audio and video processing, there is a need for high-speed processing to ensure seamless and responsive performance. However, achieving high processing speed often comes at the cost of reduced accuracy, as complex algorithms and computations may need to be simplified or approximated. This trade-off requires careful consideration and optimization to strike the right balance between accuracy and speed, which is a constant area of research and development in the field of DSP. Furthermore, the implementation of DSP algorithms on hardware platforms presents its own set of challenges. While modern digital signal processors and application-specific integrated circuits (ASICs) offer high performance and efficiency, the design and optimization of hardware for specific DSP algorithms can be complex and time-consuming. Additionally, the rapid evolution of technology and the increasing demand for low-power and high-performance devices further complicates the hardware implementation of DSP algorithms. Engineers and designers need to continuously innovate and develop new hardware architectures and platforms to meet the growing demands ofDSP applications. Moreover, the increasing complexity of DSP algorithms and the need for real-time processing pose significant challenges in terms of software development and programming. Developing and optimizing DSP algorithms require a deep understanding of mathematical concepts, signal processing techniques, and programming languages. Additionally, ensuring the real-time performance of DSP algorithms demands efficient coding practices and algorithm optimization, which can be a daunting task for software developers. As a result, there is a continuous need for skilled and knowledgeable professionals in the field of DSP who can develop and optimize algorithms for various applications. In addition totechnical challenges, ethical and social considerations also play a crucial role in the development and deployment of DSP technology. The use of DSP in surveillance, security, and data processing raises concerns about privacy, data security, and potential misuse of the technology. As DSP technology becomes more pervasive in everyday life, it is essential to address these ethical and social implications to ensure that the technology is used responsibly and for the benefit of society. This requires collaboration between technologists, policymakers, and ethicists to establish guidelines and regulations for the ethical use of DSP technology. In conclusion, digital signal processing is a complex and multifaceted field that presents various challenges and issues, ranging from technical and engineering obstacles to ethical and social considerations. Addressing these challenges requires continuous research, innovation, and collaboration across different disciplines. By overcoming these challenges, we can unlock the full potential of DSP technology and harness its benefits for the advancement of society.。

Turbo码及均衡技术【摘要】均衡与编码技术都是用于无线通信系统中提高通信质量、减小误码率的有效措施。

在论文中，主要针对非线性均衡器，如判决反馈均衡器、最大似然序列估计均衡器进行介绍。

Turbo码作为具有接近Shannon极限的纠错编码，是一种优秀的前向纠错码，其纠错能力在高斯信道中接近信道极限。

Turbo码之所以表现出接近香农理论极限的优异性能，主要是由于它采用了迭代译码思想，迭代译码思想也为解决其它通信技术问题提供了新的思路。

论文介绍了Turbo码的基本概念与理论，编码译码结构。

关键词：Turbo码；均衡；编码；非线性均衡器Turbo Code And Equalization[Abstract] Equalization and channel coding are the main techniques that improves the performance of wireless communication system. In this dissertation the equalizers widely used, such as DFE, MLSE, are introduced. As a near Shannon error correction code, the turbo code has received great attention due to its wonderful performance, and has been a research hot spot since it was proposed. Turbo codes are a class of forward error correction codes that offer energy efficiencies to the limits predicted by information theory. The near Shannon performance of the turbo code is mainly due to its iterative decoding, and its iterative principle also provides a new idea to solve some other technique problems in communication systems. The dissertation introduces the fundamentals of turbo codes.Keywords: Turbo code; Equalization; Coding; Equalizer11引言Turbo码作为具有接近Shannon极限的纠错编码，由于其优异的性能引起国内外学者的广泛关注，是一段时期研究的热点课题。

decodingDecoding: An In-depth Look at the Process and ChallengesAbstract:In the realm of computer science and information theory, decoding is the process of converting encoded information into its original, human-readable form. Whether it is decrypting a secret message or decoding a binary sequence, the process of decoding plays a crucial role in various fields, including cryptography, communication systems, and data transmission. This document aims to provide a comprehensive overview of decoding, exploring its fundamentals, popular decoding algorithms, and challenges encountered in the process.1. IntroductionDecoding, in its simplest sense, refers to the conversion of encoded information into a readable format. It is an essential aspect of many applications, allowing the retrieval of the intended message or data. Understanding the decoding process is vital in fields such as cryptography, where it ensures the confidentiality and integrity of sensitive information, as well as communication systems liketelecommunication and data transmission, where it facilitates error detection and correction.2. Fundamentals of Decoding2.1 Encoding and Decoding BasicsTo comprehend decoding, one must first understand encoding. Encoding refers to the process of transforming information into a coded representation. This transformation could involve converting text into binary, encoding audio signals, or encrypting sensitive data. Decoding, on the other hand, is the reverse process, where the encoded data is converted back into its original form.2.2 Types of Encoding and Decoding TechniquesDifferent encoding techniques are used in various applications, ranging from simple methods like ASCII encoding for text to more complex algorithms like encryption. Similarly, decoding techniques vary depending on the encoding scheme used. This section explores popular encoding and decoding techniques such as binary decoding, base64 decoding, and cryptographic decoding.3. Decoding Algorithms3.1 Binary DecodingBinary decoding is one of the most common decoding algorithms. It involves converting binary code, which represents information using only two digits, 0 and 1, into a readable format. This algorithm is used extensively in computer systems and digital communication.3.2 Base64 DecodingBase64 decoding is commonly used when transferring binary data over text-based protocols such as email or HTML. It allows encoding binary data as ASCII characters, making it compatible with most systems that can handle text.3.3 Cryptographic DecodingIn cryptography, where information security is crucial, cryptographic decoding techniques are employed. These techniques involve the use of cryptographic algorithms, such as symmetric key encryption or public-key encryption, to encrypt and subsequently decrypt sensitive data.4. Challenges in Decoding4.1 Noise and ErrorsOne of the primary challenges in decoding is dealing with noise and errors that may occur during the transmission of encoded data. Errors can be caused by various factors likesignal interference, hardware malfunctions, or even deliberate attempts to tamper with the encoded data. Error detection and correction techniques, such as parity checks and forward error correction codes, play a crucial role in mitigating these challenges.4.2 Complexity of Decoding AlgorithmsSome decoding algorithms, especially those used in cryptography, can be computationally intensive and time-consuming. As technology continues to advance, powerful encryption algorithms are being developed, imposing a greater computational burden on the decoding process. Efficient decoding algorithms and hardware acceleration techniques are continuously being explored to address this challenge.4.3 Security and PrivacyThe process of decoding becomes even more challenging when dealing with encrypted data. Cryptographic decoding requires not only time and computational resources but also the correct decryption key. As such, protecting the encryption key becomes paramount to ensuring the security and privacy of the decoded information.5. ConclusionDecoding plays a significant role in various aspects of computer science and information theory. Understanding the fundamentals, different decoding techniques, and challenges associated with decoding is vital in fields such as cryptography, communication systems, and data transmission. As technology evolves, it is imperative to continue exploring and developing efficient decoding algorithms and techniques to address emerging challenges and ensure the confidentiality, integrity, and privacy of the decoded information.。

降次求值的基本手法English Answer:Dimensional reduction is a fundamental technique in mathematics and physics that involves reducing the number of variables or dimensions in a system while preserving as much information as possible. It is widely used in various fields, including optimization, machine learning, and data analysis. There are several basic techniques for performing dimensional reduction, including:1. Projection: Projection involves finding a lower-dimensional subspace that captures the most important information in the original data. This can be achieved through methods such as principal component analysis (PCA) or singular value decomposition (SVD).2. Linear Transformation: Linear transformations can be used to reduce the dimensionality of a system by applying a linear map that transforms the original variables into anew set of variables with a reduced number of dimensions.3. Sampling: Sampling involves selecting a subset of the original data that is representative of the entire dataset. This can be used to reduce the dimensionality of the data while preserving the essential characteristics.4. Clustering: Clustering algorithms can be used to group similar data points together, which can then be represented by a single representative point. This can lead to a reduction in dimensionality while preserving the underlying structure of the data.5. Manifold Learning: Manifold learning techniques assume that the data lies on a lower-dimensional manifold embedded in a higher-dimensional space. By identifying this manifold, the dimensionality of the data can be effectively reduced while preserving its intrinsic properties.The choice of the appropriate dimensional reduction technique depends on the specific problem and the desired outcome. By carefully selecting and applying thesetechniques, it is possible to reduce the complexity of complex systems, improve computational efficiency, and gain new insights into the underlying data.中文回答：降维求值是数学和物理学中的一种基本技术，它涉及在保持尽可能多信息的情况下减少系统中的变量或维数。

关于医学创新的英语作文Medical innovation has been a driving force in the advancement of healthcare and the improvement of human wellbeing. From the development of life-saving vaccines to the creation of groundbreaking surgical techniques, the field of medicine has witnessed remarkable progress that has transformed the way we approach and manage various health conditions. In this essay, we will explore the significance of medical innovation, the factors that drive it, and the challenges that innovators face in bringing their ideas to fruition.At the heart of medical innovation lies the relentless pursuit of knowledge and the desire to alleviate human suffering. Researchers, clinicians, and entrepreneurs alike are constantly seeking new ways to diagnose, treat, and prevent diseases more effectively. This pursuit is fueled by a deep understanding of the complexities of the human body and the intricate mechanisms that govern its functioning. Through rigorous scientific research, innovative thinkers are able to identify unmet needs and develop novel solutions that can have a profound impact on the lives of patients.One of the key drivers of medical innovation is the rapid advancement of technology. The integration of cutting-edge technologies, such as artificial intelligence, robotics, and nanotechnology, has opened up new frontiers in healthcare. For instance, the use of AI-powered algorithms in medical imaging has revolutionized the way we detect and diagnose various conditions, enabling earlier and more accurate diagnoses. Similarly, the development of minimally invasive surgical techniques, facilitated by robotic systems, has led to faster recovery times and reduced patient discomfort.Another crucial factor that propels medical innovation is the collaborative nature of the field. Researchers, clinicians, and industry partners often work together, pooling their expertise and resources to tackle complex healthcare challenges. This interdisciplinary approach allows for the cross-pollination of ideas and the integration of diverse perspectives, ultimately leading to more comprehensive and effective solutions. Collaborative efforts also foster the sharing of knowledge, enabling the rapid dissemination of new discoveries and innovations, which can have a far-reaching impact on global health.Despite the immense potential of medical innovation, the path to bringing new ideas to market is often fraught with challenges. Oneof the primary obstacles is the stringent regulatory framework that governs the development and approval of new medical products and treatments. Innovators must navigate a complex maze of safety and efficacy requirements, ensuring that their innovations meet the highest standards of quality and safety. This process can be time-consuming and resource-intensive, often delaying the introduction of potentially life-saving technologies.Another significant challenge is the need for substantial financial investment. Developing new medical solutions, from research and development to clinical trials and commercialization, requires significant capital. Securing funding from sources such as government grants, venture capital, or pharmaceutical companies can be a daunting task, particularly for small startups and individual researchers. The high costs associated with medical innovation can deter some individuals and organizations from pursuing their ideas, limiting the overall pace of progress in the field.Furthermore, the inherent complexity of the human body and the diverse range of health conditions present unique challenges for medical innovators. Developing solutions that are both effective and safe requires a deep understanding of the underlying biological mechanisms and the ability to anticipate potential unintended consequences. This complexity can make the innovation process more challenging, requiring extensive testing and refinement beforenew technologies or treatments can be safely introduced.Despite these obstacles, the drive for medical innovation remains strong, fueled by the potential to save lives and improve the quality of life for individuals around the world. Innovative thinkers continue to push the boundaries of what is possible, leveraging the power of science, technology, and collaboration to address pressing healthcare needs.One promising area of medical innovation is the field of personalized medicine, where treatments are tailored to the unique genetic and physiological characteristics of individual patients. By leveraging advances in genomics, proteomics, and bioinformatics, healthcare providers can develop more targeted and effective therapies, reducing the risk of adverse reactions and improving patient outcomes. This personalized approach has the potential to transform the way we manage a wide range of diseases, from cancer to neurological disorders.Another exciting area of medical innovation is the development of regenerative therapies, which aim to harness the body's natural ability to heal and restore damaged tissues and organs. Stem cell research, tissue engineering, and gene therapy are at the forefront of this field, offering the promise of new treatments for conditions such as spinal cord injuries, organ failure, and degenerative diseases.These innovative approaches hold the potential to revolutionize the way we address some of the most challenging and debilitating health conditions.In conclusion, medical innovation is a vital component of the ongoing quest to improve human health and wellbeing. By harnessing the power of technology, fostering collaborative efforts, and overcoming the challenges that innovators face, we can unlock new frontiers in healthcare and transform the lives of patients around the world. As we continue to push the boundaries of what is possible in the field of medicine, we can look forward to a future where innovative solutions become the norm, rather than the exception, and where the pursuit of better health becomes a shared global endeavor.。

关于ai优势和劣势的英语作文Artificial intelligence (AI) has become a rapidly growing and increasingly prevalent technology in our modern world. As AI systems continue to advance and become more sophisticated, it is important to consider both the advantages and disadvantages that this technology presents. On one hand, AI offers numerous benefits that can enhance our lives and improve various industries. However, there are also significant drawbacks and challenges that must be addressed as AI becomes more widely adopted.One of the primary advantages of AI is its ability to automate and streamline many tasks and processes. AI-powered systems can perform repetitive or tedious work with greater speed, accuracy, and efficiency than human counterparts. This can lead to increased productivity, reduced errors, and cost savings for businesses and organizations. For example, AI-powered chatbots and virtual assistants can handle customer service inquiries and provide quick responses, freeing up human employees to focus on more complex or strategic tasks.Additionally, AI can be invaluable in fields that require the processing and analysis of large amounts of data. AI algorithms can quickly identify patterns, trends, and insights that would be nearly impossible for humans to detect manually. This can be particularly beneficial in industries such as healthcare, finance, and scientific research, where data-driven decision-making is crucial. AI-powered diagnostic tools, for instance, can analyze medical images and patient data to assist healthcare professionals in making more accurate and timely diagnoses.Moreover, AI has the potential to enhance human capabilities in various ways. AI-powered assistive technologies can help individuals with disabilities or cognitive impairments to overcome barriers and participate more fully in daily activities. Autonomous vehicles, powered by AI, can potentially improve transportation safety and accessibility, especially for those who are unable to drive. Additionally, AI can be used to augment human creativity and problem-solving abilities, allowing for the development of innovative solutions to complex challenges.However, the widespread adoption of AI also comes with significant drawbacks and challenges. One of the primary concerns is the potential for job displacement as AI systems become more capable of performing tasks traditionally done by human workers. As AI-powered automation becomes more prevalent, there is a risk ofwidespread job losses, particularly in industries that rely on manual labor or repetitive tasks. This can lead to economic disruption and social upheaval, as individuals and communities struggle to adapt to the changing job market.Another significant concern is the potential for AI systems to perpetuate or exacerbate existing biases and inequalities. AI algorithms are trained on data that may reflect societal biases, and if not carefully designed and monitored, these biases can be reflected in the decisions and outputs of AI systems. This can lead to unfair or discriminatory treatment of individuals or groups, further entrenching systemic inequalities.Furthermore, the increasing reliance on AI raises significant ethical and privacy concerns. As AI systems collect and process vast amounts of personal data, there are concerns about the protection of individual privacy and the potential for misuse or abuse of this data. Additionally, the development and deployment of AI systems must be accompanied by robust ethical frameworks and governance mechanisms to ensure that these technologies are used in a responsible and accountable manner.Another challenge posed by AI is the potential for the technology to be used for malicious purposes, such as the development of autonomous weapons or the spread of misinformation anddisinformation. As AI becomes more advanced, it is crucial to implement robust security measures and regulatory frameworks to mitigate these risks and ensure that AI is used for the benefit of humanity rather than to cause harm.Finally, the rapid pace of AI development and the complexity of these systems can present significant challenges in terms of transparency and accountability. It can be difficult to understand the inner workings of AI algorithms and to hold developers and deployers accountable for the decisions and outcomes of these systems. This lack of transparency and accountability can erode public trust in AI and hinder its widespread adoption.In conclusion, the advantages and disadvantages of AI are multifaceted and complex. While AI offers numerous benefits in terms of automation, data analysis, and the enhancement of human capabilities, it also presents significant challenges related to job displacement, bias, privacy, security, and transparency. As the development and deployment of AI continue to accelerate, it is crucial that we carefully consider these issues and work to address them through thoughtful policy, regulation, and ethical frameworks. By doing so, we can harness the power of AI to improve our lives while mitigating the potential risks and drawbacks of this transformative technology.。