CD3-OFDM a new channel estimation method下载

weixin_39821620 2021-01-30 10:30:59

CD3-OFDM a new channel estimation method to improve the spectrum efficiency in digital terrestrial television system
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CD3-OFDM a new channel estimation method to improve the spectrum efficiency in digital terrestrial television system

This paper proposes a simple and efficient method for MIMO-OFDM channel estimation using parameters similar to HIPERLAN/2. Both preamble and pilot structures are compared in a 2 transmit-2 receive Space Frequency Trellis Coded system and the Mean Squared Error is used as a metric for comparing the results.

作者: Yong Soo Cho 目录 Preface. Limits of Liability and Disclaimer of Warranty of Software. 1 The Wireless Channel: Propagation and Fading. 1.1 Large-Scale Fading. 1.1.1 General Path Loss Model. 1.1.2 Okumura/Hata Model. 1.1.3 IEEE 802.16d Model. 1.2 Small-Scale Fading. 1.2.1 Parameters for Small-Scale Fading. 1.2.2 Time-Dispersive vs. Frequency-Dispersive Fading. 1.2.3 Statistical Characterization and Generation of Fading Channel. 2 SISO Channel Models. 2.1 Indoor Channel Models. 2.1.1 General Indoor Channel Models. 2.1.2 IEEE 802.11 Channel Model. 2.1.3 Saleh-Valenzuela (S-V) Channel Model. 2.1.4 UWB Channel Model. 2.2 Outdoor Channel Models. 2.2.1 FWGN Model. 2.2.2 Jakes Model. 2.2.3 Ray-Based Channel Model. 2.2.4 Frequency-Selective Fading Channel Model. 2.2.5 SUI Channel Model. 3 MIMO Channel Models. 3.1 Statistical MIMO Model. 3.1.1 Spatial Correlation. 3.1.2 PAS Model. 3.2 I-METRA MIMO Channel Model. 3.2.1 Statistical Model of Correlated MIMO Fading Channel. 3.2.2 Generation of Correlated MIMO Channel Coefficients. 3.2.3 I-METRA MIMO Channel Model. 3.2.4 3GPP MIMO Channel Model. 3.3 SCM MIMO Channel Model. 3.3.1 SCM Link-Level Channel Parameters. 3.3.2 SCM Link-Level Channel Modeling. 3.3.3 Spatial Correlation of Ray-Based Channel Model. 4 Introduction to OFDM. 4.1 Single-Carrier vs. Multi-Carrier Transmission. 4.1.1 Single-Carrier Transmission. 4.1.2 Multi-Carrier Transmission. 4.1.3 Single-Carrier vs. Multi-Carrier Transmission. 4.2 Basic Principle of OFDM. 4.2.1 OFDM Modulation and Demodulation. 4.2.2 OFDM Guard Interval. 4.2.3 OFDM Guard Band. 4.2.4 BER of OFDM Scheme. 4.2.5 Water-Filling Algorithm for Frequency-Domain Link Adaptation. 4.3 Coded OFDM. 4.4 OFDMA: Multiple Access Extensions of OFDM. 4.4.1 Resource Allocation – Subchannel Allocation Types. 4.4.2 Resource Allocation – Subchannelization. 4.5 Duplexing. 5 Synchronization for OFDM. 5.1 Effect of STO. 5.2 Effect of CFO. 5.2.1 Effect of Integer Carrier Frequency Offset (IFO). 5.2.2 Effect of Fractional Carrier Frequency Offset (FFO). 5.3 Estimation Techniques for STO. 5.3.1 Time-Domain Estimation Techniques for STO. 5.3.2 Frequency-Domain Estimation Techniques for STO. 5.4 Estimation Techniques for CFO. 5.4.1 Time-Domain Estimation Techniques for CFO. 5.4.2 Frequency-Domain Estimation Techniques for CFO. 5.5 Effect of Sampling Clock Offset. 5.5.1 Effect of Phase Offset in Sampling Clocks. 5.5.2 Effect of Frequency Offset in Sampling Clocks. 5.6 Compensation for Sampling Clock Offset. 5.7 Synchronization in Cellular Systems. 5.7.1 Downlink Synchronization. 5.7.2 Uplink Synchronization. 6 Channel Estimation. 6.1 Pilot Structure. 6.1.1 Block Type. 6.1.2 Comb Type. 6.1.3 Lattice Type. 6.2 Training Symbol-Based Channel Estimation. 6.2.1 LS Channel Estimation. 6.2.2 MMSE Channel Estimation. 6.3 DFT-Based Channel Estimation. 6.4 Decision-Directed Channel Estimation. 6.5 Advanced Channel Estimation Techniques. 6.5.1 Channel Estimation Using a Superimposed Signal. 6.5.2 Channel Estimation in Fast Time-Varying Channels. 6.5.3 EM Algorithm-Based Channel Estimation. 6.5.4 Blind Channel Estimation. 7 PAPR Reduction. 7.1 Introduction to PAPR. 7.1.1 Definition of PAPR. 7.1.2 Distribution of OFDM Signal. 7.1.3 PAPR and Oversampling. 7.1.4 Clipping and SQNR. 7.2 PAPR Reduction Techniques. 7.2.1 Clipping and Filtering. 7.2.2 PAPR Reduction Code. 7.2.3 Selective Mapping. 7.2.4 Partial Transmit Sequence. 7.2.5 Tone Reservation. 7.2.6 Tone Injection. 7.2.7 DFT Spreading. 8 Inter-Cell Interference Mitigation Techniques. 8.1 Inter-Cell Interference Coordination Technique. 8.1.1 Fractional Frequency Reuse. 8.1.2 Soft Frequency Reuse. 8.1.3 Flexible Fractional Frequency Reuse. 8.1.4 Dynamic Channel Allocation. 8.2 Inter-Cell Interference Randomization Technique. 8.2.1 Cell-Specific Scrambling. 8.2.2 Cell-Specific Interleaving. 8.2.3 Frequency-Hopping OFDMA. 8.2.4 Random Subcarrier Allocation. 8.3 Inter-Cell Interference Cancellation Technique. 8.3.1 Interference Rejection Combining Technique. 8.3.2 IDMA Multiuser Detection. 9 MIMO: Channel Capacity. 9.1 Useful Matrix Theory. 9.2 Deterministic MIMO Channel Capacity. 9.2.1 Channel Capacity when CSI is Known to the Transmitter Side. 9.2.2 Channel Capacity when CSI is Not Available at the Transmitter Side. 9.2.3 Channel Capacity of SIMO and MISO Channels. 9.3 Channel Capacity of Random MIMO Channels. 10 Antenna Diversity and Space-Time Coding Techniques. 10.1 Antenna Diversity. 10.1.1 Receive Diversity. 10.1.2 Transmit Diversity. 10.2 Space-Time Coding (STC): Overview. 10.2.1 System Model. 10.2.2 Pairwise Error Probability. 10.2.3 Space-Time Code Design. 10.3 Space-Time Block Code (STBC). 10.3.1 Alamouti Space-Time Code. 10.3.2 Generalization of Space-Time Block Coding. 10.3.3 Decoding for Space-Time Block Codes. 10.3.4 Space-Time Trellis Code. 11 Signal Detection for Spatially Multiplexed MIMO Systems. 11.1 Linear Signal Detection. 11.1.1 ZF Signal Detection. 11.1.2 MMSE Signal Detection. 11.2 OSIC Signal Detection. 11.3 ML Signal Detection. 11.4 Sphere Decoding Method. 11.5 QRM-MLD Method. 11.6 Lattice Reduction-Aided Detection. 11.6.1 Lenstra-Lenstra-Lovasz (LLL) Algorithm. 11.6.2 Application of Lattice Reduction. 11.7 Soft Decision for MIMO Systems. 11.7.1 Log-Likelihood-Ratio (LLR) for SISO Systems. 11.7.2 LLR for Linear Detector-Based MIMO System. 11.7.3 LLR for MIMO System with a Candidate Vector Set. 11.7.4 LLR for MIMO System Using a Limited Candidate Vector Set. Appendix 11.A Derivation of Equation (11.23). 12 Exploiting Channel State Information at the Transmitter Side. 12.1 Channel Estimation on the Transmitter Side. 12.1.1 Using Channel Reciprocity. 12.1.2 CSI Feedback. 12.2 Precoded OSTBC. 12.3 Precoded Spatial-Multiplexing System. 12.4 Antenna Selection Techniques. 12.4.1 Optimum Antenna Selection Technique. 12.4.2 Complexity-Reduced Antenna Selection. 12.4.3 Antenna Selection for OSTBC. 13 Multi-User MIMO. 13.1 Mathematical Model for Multi-User MIMO System. 13.2 Channel Capacity of Multi-User MIMO System. 13.2.1 Capacity of MAC. 13.2.2 Capacity of BC. 13.3 Transmission Methods for Broadcast Channel. 13.3.1 Channel Inversion. 13.3.2 Block Diagonalization. 13.3.3 Dirty Paper Coding (DPC). 13.3.4 Tomlinson-Harashima Precoding. References. Index.

OFDMA, Orthogonal Frequency Division Multiple Access is a broad band communication method. Here channel estimation is more crucial. Based on the FFT size used for OFDM transmission method that many number of channel co-efficients are to be estimated and received signals are to be equalized using this co-efficients. Various methods trying to improve the performance and fastness are proposed. As part of master thesis various methods have be studied and evaluated using the WiMAX 802.16 system.

In orthogonal frequency-division multiplexing (OFDM)-based cognitive radio (CR) systems, the subcarriers already occupied by the primary users cannot be used by the secondary users. This leads to possibly non-contiguous positions of the available subcarriers for the secondary users. The conventional pilot design methods are no longer effective for such systems. In this paper, we propose a new practical pilot design method for OFDM-based CR systems. We first formulate the pilot design as a new op

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