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

OFDM Transmission Technologies 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 CP/ZP-OFDM Symbol CP/ZP-OFDM: TDS-OFDM: TDS-OFDM Symbol CP/ZP PN Data + Pilots Data (No Pilot) (a) Comparison in the time domain Pilots Data (b) Comparison in the frequency domain

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 (a) Original megapixel image with pixel values in the range [0,255] and (b) its wavelet transform coefficients (arranged in random order for enhanced visibility). Relatively few wavelet coefficients capture most of the signal energy; many such images are highly compressible. (c) The reconstruction obtained by zeroing out all the coefficients in the wavelet expansion but the 25,000 largest (pixel values are thresholded to the range [0,255]). The difference with the original picture is hardly noticeable. More precisely, CS exploits the fact that many natural signals are sparse or compressible in the sense that they have concise representations when expressed in the proper basis

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 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 < G means 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 size to 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

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