声明
摘要
Abstract
Contents
List of Figures
List of Tables
List of Symbols
List of Acronyms
Chapter 1 Introduction
1．1 Internet of Things
1．2 Evolution of Narrowband Internet of Things
1．3 Channel Estimation Issues in NB-IoT Systems
1．4 Channel Equalization Issues in NB-IoT Systems
1．5 Motivation
1．6 Contributions of the Research
1．7 Organization of the Dissertation
1．8 Summary
Chapter 2 Literature Review and Physical Layer Aspects of NB-IoT Systems
2．1 Introduction
2．2．2 Reduced Power Consumption
2．2．3 Reduced System Complexity
2．2．4 Dense Deployment of IoT Devices
2．3．1 Enhancements of Release-14
2．4 NB-IoT Transmission Systems and Numerology
2．4．1 NB-IoT Deployment Options
2．4．2 NB-IoT Frame Structures
2．4．3 Resource Unit
2．5 NB-IoT Uplink Transport Channels
2．5．1 Uplink Shared Channel
2．6 NB-IoT Physical Channels and Signals
2．7 Modulation Schemes
2．8 Propagation Channel Models for NB-IoT Systems
2．9 Summary
Chapter 3 Channel Estimation Using Modified LS and MMSE Algorithms
3．1 Introduction
3．2 Background of the LS and MMSE Channel Estimation in NB-IoT Systems
3．3 NB-IoT Uplin k System Model
3．3．1 Uplink NDMRS Sequence Generation and RU Mapping
3．3．2 Analytical Uplink NB-IoT Signal Model
3．4 Channel Estimation in Uplink NB-IoT Systems
3．4．1 The Proposed LS and MMSE Algorithms
3．5 Simulation Results and Computational Complexity Analysis
3．5．1 Simulation Assumptions and Parameter Settings
3．5．2 System BER Performance Analysis
3．5．3 Computational Complexity Analysis
3．6 Merits and Demerits of the Proposed LS and MMSE Algorithms
3．7 Summary
Chapter 4 Improved DFT-Based Low-Complexity Transform Domain Channel Estimation
4．1 Introduction
4．2 Background of the DFT-Based Channel Estimation
4．3 NB-IoT Uplink Signal Model
4．4 DFT-Based Channel Estimation in Uplink NB-IoT Systems
4．4．1 The Initial Channel Estimation with LS Algorithm
4．4．2 Proposed Random Sorting LS (RS-LS) Algorithm
4．4．3 Proposed De-noising LS (D-LS) Algorithm
4．4．4 Proposed MMSE-Approximation (MMSE-A) Estimator
4．5 Receiver Signal Processing
4．5．1 Time Dimensional Linear Interpolation
4．5．2 Frequency Domain Channel Equalization
4．6 Numerical Results and Computational Complexity
4．6．1 System Parameters and Simulation Setup
4．6．2 Mean Square Error (MSE) Analysis
4．6．3 Block Error Rate (BLER) Analysis
4．6．4 Computational Complexity Analysis
4．7 Advantages and Drawbacks of Proposed DFT-Based Algorithms
4．8 Summary
Chapter 5 Efficient DCT Type-Ⅰ Based Low-Complexity Transform Domain Channel Estimation
5．1 Introduction
5．2 Background of the DCT-I Based Channel Estimation
5．3 NB-IoT Uplin k Received Signal Model
5．4 Channel Estimation in Uplink NB-IoT Systems
5．4．1 The Initial Channel Estimation with Classical LS Algorithm
5．4．2 Conventional DFT-Based Channel Estimation
5．4．3 Classical DCT-I Based ChannelEstimation
5．4．4 The Proposed Modified DCT-I (MDCT-I) Algorithm
5．5 Numerical Results and Computational Complexity Analysis
5．5．1 Simulation Parameters
5．5．2 Mean Square Error (MSE) Analysis
5．5．3 System Bit Error Rate (BER) Analysis
5．5．4 Computational Complexity Analysis
5．6 Benefits and Limitations of the Proposed MDCT-I Estimators
5．7 Summary
Chapter 6 Frequency Domain Channel Equalization for Uplink NB-IoT Systems
6．1 Introduction
6．2 The Literature on Frequency Domain Channel Equalization
6．3 Signal Model of Uplink NB-IoT Systems
6．4 Channel Equalization in Uplink NB-IoT Systems
6．4．1 Channel Equalization with ZF and MMSE Techniques
6．4．2 The Proposed Channel Equalization Algorithm and Analysis
6．5 Numerical Simulations
6．5．1 Simulation Setup and Parameters Setting
6．5．2 Symbol Error Rate (SER) Performance
6．5．3 Throughput Performance Analysis
6．6 Advantages and Disadvantages of the Proposed Equalizer
6．7 Summary
Chapter 7 Conclusions and Future Works
7．1 Conclusions
7．2 Future Works
References
List of Publications
Acknowledgments
中国科学技术大学;
