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Time Domain Synchronous OFDM Based on Simultaneous Multi-Channel Reconstruction. Linglong Dai, Jintao Wang, Zhaocheng Wang Paschalis Tsiaflakis, Marc Moonen. Tsinghua University & KU Leuven. 2013-06-11. Contents. 1. Technical Background. 2. Proposed Solution. 3. Performance Analysis.
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Time Domain Synchronous OFDM Based on Simultaneous Multi-Channel Reconstruction Linglong Dai, Jintao Wang, Zhaocheng Wang Paschalis Tsiaflakis, Marc Moonen Tsinghua University & KU Leuven 2013-06-11
Contents 1 Technical Background 2 Proposed Solution 3 Performance Analysis 4 Simulation Results 5 Conclusions
CP/ZP-OFDM Symbol OFDM Transmission Technologies CP/ZP Data + Pilots CP/ZP Data + Pilots CP/ZP-OFDM: TDS-OFDM Symbol • Cyclic Prefix OFDM (CP-OFDM) and Zero Padding OFDM (ZP-OFDM) • Time Domain Synchronous OFDM (TDS-OFDM) • High spectral efficiency (increased by about 10% due to no pilot) • Fast and reliable synchronization TDS-OFDM: PN Data (No Pilot) PN Data (No Pilot) (a) Comparison in the time domain Data Pilots CP/ZP-OFDM: Data (b) Comparison in the frequency domain TDS-OFDM:
Application of TDS-OFDM TDS-OFDM is the key technology of the first-generation DTV broadcasting standard DTMB IPR-owned: proposed by China (Tsinghua University) in 2006 Better performance than other standards: DVB-T (EU) , ATSC (USA), ISDB-T (Japan) Widely deployed: China (Hongkong, Macau), Cuba, Cambodia ITU approval: approved by ITU as the fourth international DTV broadcasting standard in 2011
Challenges of TDS-OFDM Requirements of next-generation DTV standard 64QAM vs. 256QAM 30% higher spectrum efficiency Challenges of TDS-OFDM Mutual interferences Difficult to support 256 QAM in static long-delay channels
Theory of Compressive Sensing (CS) A new sampling theory against Shannon-Nyquist theory Key point: sparse signal recovery at a rate far lower than traditional Nyquist rate (2x of the signal bandwidth) Structured CS
Contents 1 Technical Background 2 Proposed Solution 3 Performance Analysis 4 Simulation Results 5 Conclusions
Two Properties of Wireless Channels Sparsity and Inter-Channel Correlation Wireless channel is sparse in nature Path delays vary much slower than the path gains Not considered in conventional TDS-OFDM systems L: Channel length S: Sparsity level S << L Path delay set:
TDS-OFDM Based on Simultaneous Multi-Channel Reconstruction Received TS: interference M N (a) Conventional TDS-OFDM IBI-free region: (b) Dual PN padding TDS-OFDM (DPN-OFDM) G (c) Proposed scheme
Mathematical problem of structured CS Mathematical problem of structured CS Problem: Solution: Proposed simultaneous multi-channel reconstruction scheme Based on classical algorithm called simultaneous orthogonal matching pursuit (SOMP) Key idea: the specific technical feature of TDS-OFDM is exploited to obtain partial priori of the channel to reduce the complexity
Simultaneous Multi-Channel Reconstruction Based on Adaptive SOMP Step 1: Correlation-Based Channel Priori Acquisition Noise and interference path delays vs. path gains
Simultaneous Multi-Channel Reconstruction Based on Adaptive SOMP Step 2: A-SOMP Based Joint Sparsity Pattern Recovery Priori is exploited to reduce the complexity Adaptive to variable channel conditions (channel length, sparsity level, etc.) Key difference with SOMP: (S-S0) instead of S iterations Step 3: ML-based path gain estimation Adaptive SOMP
Contents 1 Technical Background 2 Proposed Solution 3 Performance Analysis 4 Simulation Results 5 Conclusions
Performance Analysis (1) Cramer-Rao lower bound (CRLB) Conditional PDF Fisher information matrix CRLB Final result : noise level S < Gmeans improved accuracy
Performance Analysis (2) Spectral Efficiency 10% higher than standard CP-OFDM 30% higher than conventional TDS-OFDM (64QAM vs. 256WAM)
Contents 1 Technical Background 2 Proposed Solution 3 Performance Analysis 4 Simulation Results 5 Conclusions
Simulation results Simulation setup Setup is configured according to the typical wireless broadcasting systems Signal bandwidth: 7.56 MHz Central radio frequency: 770 MHz FFT size: N=4096 Guard interval length: M=256 Modulation schemes: 256QAM Channel coding: LDPC code with length of 64800 bits and rate 0.6 Channel model: 3GPP six-tap Vehicular B multipath channel (max. delay of 20 us)
Simulation Results (1) • MSE performance comparison in static multipath channel • The proposal outperforms the conventional TDS-OFDM by >5 dB • The actual MSE performance approaches the theoretical CRLB when SNR becomes high
Simulation Results (2) • Comparison between A-SOMP and SOMP • A-SOMP requires fewer measurements than SOMP • The MSE approaches the theoretical CRLB when G becomes large Size of IBI-free region G
Simulation Results (3) • 256QAM supporting in static long-delay channel • Unlike conventional TDS-OFDM, the proposal can support 256QAM • The proposed scheme has superior BER performance than DPN-OFDM and CP-OFDM
Contents 1 Technical Background 2 Proposed Solution 3 Performance Analysis 4 Simulation Results 5 Conclusions
Conclusions • We propose a TDS-OFDM scheme with an improved spectrum efficiency of about 30% for next-generation DTV broadcasting standard • The sparse nature and inter-channel correlation of wireless channels are jointly exploited • The simultaneous multi-channel reconstruction method utilizes multiple IBI-free regions of very small sizeto reconstruct the wireless channel of high dimension under the newly emerging theory of structured compressive sensing • Not only the obviously improved channel reconstruction accuracy could be achieved, but also the mutually conditional time-domain channel estimation and frequency-domain data detection in conventional TDS-OFDM could be decoupled • The proposed scheme could support 256 QAM in static channel with long delays with a LDPC coded BER performance close to the ideal CSI case • The proposed scheme is directly applicable for unique word single carrier (UW-SC) systems