Rui Han – författare

Comprehensive reference covering signal detection for random access in IoT systems from the beginner to expert level With a carefully balanced blend of theoretical elements and applications, IoT Signal Detection is an easy-to-follow presentation on signal detection for IoT in terms of device activity detection, sparse signal detection, collided signal detection, round-trip delay estimation, and backscatter signal division, building progressively from basic concepts and important background material up to an advanced understanding of the subject. Various signal detection and estimation techniques are explained, e.g., variational inference algorithm and compressive sensing reconstruction algorithm, and a number of recent research outcomes are included to provide a review of the state of the art in the field. Written by four highly qualified academics, IoT Signal Detection discusses sample topics such as: ML, ZF, and MMSE detection, Markov chain Monte Carlo-based detection, variational inference-based detection, compressive sensing-based detectionSparse signal detection for multiple access, covering Bayesian compressive sensing algorithm and structured subspace pursuit algorithmCollided signal detection for multiple access using automatic modulation classification algorithm, round-trip delay estimation for collided signalsSignal detection for backscatter signals, covering central limited theorem-based detection including detection algorithms, performance analysis, and simulation resultsSignal design for multi-cluster coordination, covering successive interference cancellation design, device grouping and power control, and constructive interference-aided multi-cluster coordinationWith seamless coverage of the subject presented in a linear and easy-to-understand way, IoT Signal Detection is an ideal reference for both graduate students and practicing engineers in wireless communications.

Comprehensive reference covering signal detection for random access in IoT systems from the beginner to expert level

With a carefully balanced blend of theoretical elements and applications, IoT Signal Detection is an easy-to-follow presentation on signal detection for IoT in terms of device activity detection, sparse signal detection, collided signal detection, round-trip delay estimation, and backscatter signal division, building progressively from basic concepts and important background material up to an advanced understanding of the subject. Various signal detection and estimation techniques are explained, e.g., variational inference algorithm and compressive sensing reconstruction algorithm, and a number of recent research outcomes are included to provide a review of the state of the art in the field.

Written by four highly qualified academics, IoT Signal Detection discusses sample topics such as:

ML, ZF, and MMSE detection, Markov chain Monte Carlo-based detection, variational inference-based detection, compressive sensing-based detection Sparse signal detection for multiple access, covering Bayesian compressive sensing algorithm and structured subspace pursuit algorithm Collided signal detection for multiple access using automatic modulation classification algorithm, round-trip delay estimation for collided signals Signal detection for backscatter signals, covering central limited theorem-based detection including detection algorithms, performance analysis, and simulation results Signal design for multi-cluster coordination, covering successive interference cancellation design, device grouping and power control, and constructive interference-aided multi-cluster coordination

With seamless coverage of the subject presented in a linear and easy-to-understand way, IoT Signal Detection is an ideal reference for both graduate students and practicing engineers in wireless communications.

Comprehensive reference covering signal detection for random access in IoT systems from the beginner to expert level

With a carefully balanced blend of theoretical elements and applications, IoT Signal Detection is an easy-to-follow presentation on signal detection for IoT in terms of device activity detection, sparse signal detection, collided signal detection, round-trip delay estimation, and backscatter signal division, building progressively from basic concepts and important background material up to an advanced understanding of the subject. Various signal detection and estimation techniques are explained, e.g., variational inference algorithm and compressive sensing reconstruction algorithm, and a number of recent research outcomes are included to provide a review of the state of the art in the field.

Written by four highly qualified academics, IoT Signal Detection discusses sample topics such as:

ML, ZF, and MMSE detection, Markov chain Monte Carlo-based detection, variational inference-based detection, compressive sensing-based detection Sparse signal detection for multiple access, covering Bayesian compressive sensing algorithm and structured subspace pursuit algorithm Collided signal detection for multiple access using automatic modulation classification algorithm, round-trip delay estimation for collided signals Signal detection for backscatter signals, covering central limited theorem-based detection including detection algorithms, performance analysis, and simulation results Signal design for multi-cluster coordination, covering successive interference cancellation design, device grouping and power control, and constructive interference-aided multi-cluster coordination

With seamless coverage of the subject presented in a linear and easy-to-understand way, IoT Signal Detection is an ideal reference for both graduate students and practicing engineers in wireless communications.

This book constitutes the thoroughly revised selected papers of the 4th and 5th workshops on Big Data Benchmarks, Performance Optimization, and Emerging Hardware, BPOE 4 and BPOE 5, held respectively in Salt Lake City, in March 2014, and in Hangzhou, in September 2014. The 16 papers presented were carefully reviewed and selected from 30 submissions. Both workshops focus on architecture and system support for big data systems, such as benchmarking; workload characterization; performance optimization and evaluation; emerging hardware.

This book constitutes the thoroughly revised selected papers of the 4th and 5th workshops on Big Data Benchmarks, Performance Optimization, and Emerging Hardware, BPOE 4 and BPOE 5, held respectively in Salt Lake City, in March 2014, and in Hangzhou, in September 2014. The 16 papers presented were carefully reviewed and selected from 30 submissions. Both workshops focus on architecture and system support for big data systems, such as benchmarking; workload characterization; performance optimization and evaluation; emerging hardware.

This book constitutes the thoroughly revised selected papers of the 6th workshop on Big Data Benchmarks, Performance Optimization, and Emerging Hardware, BPOE 2015, held in Kohala Coast, HI, USA, in August/September 2015 as satellite event of VLDB 2015, the 41st International Conference on Very Large Data Bases.The 8 papers presented were carefully reviewed and selected from 10 submissions. The workshop focuses on architecture and system support for big data systems, aiming at bringing researchers and practitioners from data management, architecture, and systems research communities together to discuss the research issues at the intersection of these areas. This book also invites three papers from several industrial partners, including two papers describing tools used in system benchmarking and monitoring and one paper discussing principles and methodologies in existing big data benchmarks.

This book constitutes the thoroughly revised selected papers of the 6th workshop on Big Data Benchmarks, Performance Optimization, and Emerging Hardware, BPOE 2015, held in Kohala Coast, HI, USA, in August/September 2015 as satellite event of VLDB 2015, the 41st International Conference on Very Large Data Bases.

The 8 papers presented were carefully reviewed and selected from 10 submissions. The workshop focuses on architecture and system support for big data systems, aiming at bringing researchers and practitioners from data management, architecture, and systems research communities together to discuss the research issues at the intersection of these areas. This book also invites three papers from several industrial partners, including two papers describing tools used in system benchmarking and monitoring and one paper discussing principles and methodologies in existing big data benchmarks.