Clipping Noise Cancellation Based on Compressed Sensing for Visible Light Communication

Transcription

1 Clipping Noise Cancellation Based on Compressed Sensing for Visible Light Communication Presented by Jian Song Tsinghua University, China 1

2 Contents 1 Technical Background 2 System Model 3 Proposed Solutions 4 Simulation Results 5 Conclusions 2

3 Contents 1 Technical Background 2 System Model 3 Proposed Solutions 4 Simulation Results 5 Conclusions 3

4 Technical Background Asymmetrically clipped optical OFDM (ACO-OFDM) Hermitian symmetry (real-valued) Only the odd subcarriers in the frequency domain are occupied (non-negative) Clipping noise nonlinear transfer characteristics of LEDs generate the self-interference deteriorates the performance 4

5 Technical Background Proposed scheme to reconstruct clipping noise compressed sensing Taking advantage of the time-domain sparsity of the clipping noise Using sparsity adaptive matching pursuit (SAMP) greedy algorithm partially aware support a coarse estimation of the clipping noise location improve the accuracy and robustness, complexity is also lower 5

6 Contents 1 Technical Background 2 System Model 3 Proposed Solutions 4 Simulation Results 5 Conclusions 6

7 System Model The transmitter block diagram of the OFDM systems Mapping Serial to Parallel IFFT Add Cyclic Prefix Parallel to Serial Clipping Operation D/A Converter The transmitted symbol The ACO-OFDM signal X = (0, X1,0, X2, XN/2 1,0, XN/2 1,,0, X1 ) xx AAAAAA,nn = xx nn, xx nn 0, 0, xx nn < 0. NN 1 xx nn = XX kk exp kk=0 jj2ππππππ NN XX kk = 2XX AAAAAA,kk 7

8 System Model The transmitter block diagram of the OFDM systems Mapping Serial to Parallel IFFT Add Cyclic Prefix Parallel to Serial Clipping Operation D/A Converter The clipped signal xx AAAAAA,nn = xx AAAAAA,nn, xx AAAAAA,nn AA ttt, AA ttt, xx AAAAAA,nn > AA ttt, xx AAAAAA,nn = xx AAAAAA,nn + cc nn XX AAAAAA,kk = XX AAAAAA,kk + CC kk 8

9 System Model The proposed receiver block diagram of the OFDM systems A/D Converter y Y Maximum FFT Likelihood X Estimation + Reliable Observation CS Reconstruc -tion c C FFT + Maximum Likelihood Estimation The received symbol Compressed Sensing Model Y = X + Z = X + C + Z k ACO, k k ACO, k k k The initial decision Xˆ k = arg min 2 Yk s, s χ Xˆ Xˆ Xˆ Y = X + C+ Z = C+ ( X + Z) The final decision Xˆ = arg min 2 ( Y Cˆ ) s, s χ k k k 9

10 Contents 1 Technical Background 2 System Model 3 Proposed Solutions 4 Simulation Results 5 Conclusions 10

11 Proposed Solutions Compressed Sensing Model Measurement vector Sensing matrix φ unknown vector c = Xˆ Xˆ Y = C+ ( X + Z) = C+ θ 2 2 Y = S( Y Xˆ / 2) = SC + Sθ = SFc + Sθ =Φ c + η Selection matrix S y = SCˆ φ = S F select a series of reliable tones 11

12 Proposed Solutions Compressed Sensing Model Y = SFc + Sθ =Φ c + η Measurement vector Sensing matrix φ unknown vector c RIP (restricted isometry property) = N kn, N 2 j π π k( n+ ) j kn N 2 N F = e = e = F kn, Φ mn, = Φ N RIP doesn t hold mn+, 2 y = SCˆ φ = S F needs to be reconsidered! 12

13 Proposed Solutions The Transformation of CS Problem Φ = [A, A], c = [c ;c ] 1 2 Y = SFc + Sθ =Φ c + RIP η c, 1 Y = Φc + η Y = [A, A] + η = Ac + η c= c1 c2 c 2 c1,n = 0,c2,n = c, if c > 0, c1,n = c, c2,n = 0, if c 0. the clipping noise c 0 13

14 Proposed Solutions Problem Y =Φ c+ η Solution CS method clipping noise is variable and unknown SAMP (sparsity adaptive matching pursuit) not require the sparsity level to be known partially aware support PAS-SAMP 14

15 Proposed Solutions priori information 1.2 partial support { n y 2 } n λt (0) Π = > 0.6 Facilitate the CS recovery process

16 Proposed Solutions The priori information initial support set Complexity the testing sparsity level (0) T K + j s T j s Adaptivity 16

17 Contents 1 Technical Background 2 System Model 3 Proposed Solutions 4 Simulation Results 5 Conclusions 17

18 Simulation Results 16-QAM,N=256,Ath=1.5 Sparse level K =10 At the target BER=10-3 PAS-SAMP outperforms SAMP 0.2dB the gap to worst case is 1.5dB 18

19 Simulation Results 64-QAM,N=1024,Ath=1.8 Sparse level K =20 At the target BER=10-3 PAS-SAMP outperforms SAMP 0.3dB the gap to worst case is 1.6dB 19

20 Contents 1 Technical Background 2 System Model 3 Proposed Solutions 4 Simulation Results 5 Conclusions 20

21 Conclusions Clipping noise cancellation for ACO-OFDM systems based on compressed sensing with partially aware support Apply CS to clipping noise cancellation in ACO-OFDM systems Solves the RIP problem that the sensing matrix for ACO-OFDM systems Improve the accuracy and robustness of the proposed scheme Computational complexity is lower 21

22 References 1. E. B. Al-Safadi and T. Y. Al-Naffouri. Peak reduction and clipping mitigation in OFDM by augmented compressive sensing. IEEE Trans. Signal Process., 60(7): , July J. Armstrong. OFDM for optical communications. J. Lightw. Technol., 27(3): , Feb J. Armstrong and A. J. Lowery. Power efficient optical OFDM. Electron. Lett., 42(6): , Mar S. Arnon. Visible Light Communications. Cambridge University Press, E. J. Candes, J. K. Romberg, and T. Tao. Stable signal recovery from incomplete and inaccurate measurements. Commun. Pure Appl. Math, 59(8): , Aug E. J. Candes and T. Tao. Near-optimal signal recovery from random projections: Universal encoding strategies? IEEE Trans. Inf. Theory, 52(12): , Dec. 7. H. Chen and A. M. Haimovich. Iterative estimation and cancellation of clipping noise for OFDM signals. IEEE Comm. Lett., 7(7): , July J. Dang, Z. Zhang, and L. Wu. A novel receiver for ACO-OFDM in visible light communication. IEEE Commun. Lett., 17(12): , Dec S. Dimitrov, S. Sinanovic, and H. Haas. Clipping noise in OFDM-based optical wireless communication systems. IEEE Trans. Commun., 60(4): , Apr W. Ding, Y. Lu, F. Yang, W. Dai, P. Li, S. Liu, and J. Song. Spectrally efficient CSI acquisition for power line communications: A Bayesian compressive sensing perspective. IEEE Journal on Selected Areas in Communications, 34(7): , W. Ding, F. Yang, C. Pan, L. Dai, and J. Song. Compressive sensing based channel estimation for OFDM systems under long delay channels. IEEE Trans. Broadcast., 60(2): , June S. D. Dissanayake, K. Panta, and J. Armstrong. A novel technique to simultaneously transmit ACO-OFDM and DCO-OFDM in IM/DD systems. In Proc. IEEE GLOBECOM Workshops, Dec T. T. Do, L. Gan, N. Nguyen, and T. D. Tran. Sparsity adaptive matching pursuit for practical compressed sensing. In Asilomar Conf. on Signals, Systems, and Computers, Oct

23 References (cont d) 14. D. L. Donoho. Compressed sensing. IEEE Trans. Inf. Theory, 52(4): , Apr H. Elgala, R. Mesleh, and H. Haas. Non-linearity effects predistortion in optical OFDM wireless transmission using LEDs. International Journal of Ultra Wideband Communications and Systems, 1(2): , Aug A. Jovicic, J. Li, and T. Richardson. Visible light communication: opportunities, challenges and the path to market. IEEE Commun. Mag., 51(12):26 32, Dec D. Kim and G. L. Stuber. Clipping noise mitigation for OFDM by decision-aided reconstruction. IEEE Comm. Lett., 3(1):4 6, Jan K. H. Kim, H. Park, J. S. No, and H. C. D. Shin. Clipping noise cancelation for OFDM systems using reliable observations based on compressed sensing. IEEE Trans. Broadcast., 61(1): , Mar S. Liu, F. Yang, W. Ding, and J. Song. Double kill: Compressive-sensing-based narrow-band interference and impulsive noise mitigation for vehicular communications. IEEE Trans. Veh. Technol., 65(7): , July S. Liu, F. Yang, and J. Song. Narrowband interference cancelation based on priori aided compressive sensing for DTMB systems. IEEE Trans. Broadcast., 61(1):66 74, Mar S. Liu, F. Yang, C. Zhang, and J. Song. Narrowband interference mitigation based on compressive sensing for OFDM systems. IEICE Trans. Funda., E98-A(3), Mar M. Peng, Y. Li, Z. Zhao, and C. Wang. System architecture and key technologies for 5G heterogeneous cloud radio access networks. IEEE Network, 29(2):6 14, Mar F. Yang, J. Gao, and S. Liu. Novel visible light communication approach based on hybrid OOK and ACO-OFDM. IEEE Photon. Technol. 23

24 Jian Song Tsinghua University, China