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Powerline communications - impulse noise-

Powerline communications - impulse noise-. A.J. Han Vinck Vijay Bhargava 60 September 2008. content. Introduction Models attenuation, noise Impulsive noise - Middleton Class A - Capacity Modulation and Coding (touch) OFDM; FSK; Reed Solomon Codes; Permutation Codes.

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Powerline communications - impulse noise-

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  1. Powerline communications- impulse noise- A.J. Han Vinck Vijay Bhargava 60 September 2008

  2. content Introduction Models attenuation, noise Impulsive noise - Middleton Class A - Capacity Modulation and Coding (touch) OFDM; FSK; Reed Solomon Codes; Permutation Codes Han Vinck Victoria, 2008

  3. Powerline communication model Information coupling Information coupling Han Vinck Victoria, 2008

  4. Powerline communications access (narrowband) indoor (broadband) Meter reading internet, telephony (2000) indoor (plug-play) load control, -management(1920 London) the electrical power utility in London used PLC to remotely control some of its equipment on the grid (such as high-voltage switches) in the 1920s. This technique is still employed by several utilities that use analog or digital devices to transfer 9.6 Kbits/s over many miles of electrical cable. Han Vinck Victoria, 2008

  5. Do you need technical problems? • capacity: it is not AWGN • noise F(frequency), Phase (10°) • interference Services, Radio amateurs • impulse noise man made ( > 30dB + ) • narrow band monitor, broadcasters, etc • attenuation 40-100dB/Km • multipath indoor (30 MHz) • regulations modulation - EMC Han Vinck Victoria, 2008

  6. Impulse noise (Japanese measurements) Who is vacuum cleaning? Han Vinck Victoria, 2008

  7. Middleton Class-A noise model Han Vinck Victoria, 2008

  8. Example: A = 0.2 2G/2I = 0.01 impulsive m = 0 e-A  0.8G2 m = 1 0.2 e-A 501 G2 m = 2 0.02 e-A 1001G2 small A Han Vinck Victoria, 2008

  9. Q: Capacity when side information available ? σm2 encode modulator channel detection decode average power S difference in capacity or error rate ? Han Vinck Victoria, 2008

  10. Capacity for Additive White Gaussian Noise Noise Input X Output Y W is (single sided) bandwidth Input X is Gaussian with power spectral density (psd) ≤S/2W; Noise is Gaussian with psd = 2noise Output Y is Gaussian with psd = y2 = S/2W + 2noise For Gaussian Channels: y2 =x2 +noise2 Han Vinck Victoria, 2008

  11. Noise X Y X Y Han Vinck Victoria, 2008

  12. Channel state - +: known at receiver - -: unknown at receiver T = G2 / I2 Han Vinck Victoria, 2008

  13. Channel state + + : known at receiver + -: unknown at receiver T = G2 / I2 conclusion: inform receiver Han Vinck Victoria, 2008

  14. RESULT C b/Hz from a capacity point of view, spreading destroys information! 15 10 A = T = 0.01 Gain -10log10T dB 5 -10 0 10 20 SNR Han Vinck Victoria, 2008

  15. How to gain back the loss? -Let me think- Han Vinck Victoria, 2008

  16. How to gain back the loss? freq time freq. F- F Info Info impulsive noise + N symbols narrowband noise + Diversity! Han Vinck Victoria, 2008

  17. Iterative Impulsive noise suppression! (ISPLC2000) received IF-1 + n I‘ IF-1 + n– n* I + (n-n*)F - F detect n* n‘F n‘ estimate F-1 Simple version - detect: minimum (Euclidean) distance - estimate: n* = 0 for n‘ < Threshold Jürgen Häring Han Vinck Victoria, 2008

  18. More sophisticated (complicated) receiver • Remarks: • n > 128 necessary for convergence • convergence after 2 steps • for SNR  0 dB not stable • (from impulsive to Gaussian) • - Better estimators give better SER N = 256, A = 0.1, T = 0.001 Performance: SER against SNR of the algorithm after iterating Han Vinck Victoria, 2008

  19. Expected behavior2 state approximation AWGN, σ20 out in AWGN, σ21 good bad In the low SNR region the noise can be detected In the middle region: A dominates the Pe, i.e. an error floor In the High SNR region noise is amplified by a factor of 1/AT Han Vinck Victoria, 2008

  20. coded behavior Gain 200 m ! At 100 dB/Km Han Vinck Victoria, 2008

  21. Expected uncoded behavior good => <=bad Han Vinck Victoria, 2008

  22. Vijay Wake-up! Han Vinck Victoria, 2008

  23. Another approach Transmit messages as:  sequences (code words) of length M where all M symbols are different minimum distance (# of differences) Dp Example: M = 3 Dp = 2 Code: 123 312 231 132 321 213 f time Han Vinck Victoria, 2008

  24. Frequency Hopping Codes: Effect of noise (simplified) Transmitted Background-insertion Background-deletion Impulsive-broadband narrowband-jammer frequency selective fading Han Vinck Victoria, 2008

  25. Performance: conform • Permutation codewords: • M slots; M different symbols; • minimum distance Dp • Correct decoding if # error events ( impulse + narrow + background + fading ) < Dp • cardinality of the code Ian Blake Han Vinck Victoria, 2008

  26. May be wireless has more future? 1991 ! Han Vinck Victoria, 2008

  27. conclusion Powerline communication problem offers many boundaries • without boundaries, no challenges, no creativity - we need practical data from modulation and coding schemes We do not claim that our results are directly applicable, but expect that they contribute to the general understanding of PLC Han Vinck Victoria, 2008

  28. PROST! Han Vinck Victoria, 2008

  29. Rest is from another presentation Han Vinck Victoria, 2008

  30. Solution principle: waterfilling ( general) PSD B S f fB Han Vinck Victoria, 2008

  31. ISPLC-1997(Essen)impulsive noise PLC characterization Noise amplitude distribution Noise interval distribution Han Vinck Victoria, 2008

  32. Channel state T-:unknown at transmitter R+:known at receiver - Receiver knows the state and thus the i2 - Modulator uses constant input power S  Y|state is Gaussian distributed with variance S/2W+i2 Han Vinck Victoria, 2008

  33. Channel state T+:known at transmitter R-:unknown at receiver • Sketch of lower bound: Capacity maximizes H(output) – H(Noise) • Choose input power per channel the same as for (T+,R+), i.e. • S1/w + 12 = S2/w + 22= S3/w +32 = S /2W + • this lower bounds H(output) • 2. H(Noise) maximized by using average variance as Gaussian noise Han Vinck Victoria, 2008

  34. Channel state T-:unknown at transmitter R-:unknown at receiver Capacity ≥ lower bound on H(output) – H(Noise) 1. constant input power S per channel 2.H(output) > H(output|state) 3.H(Noise) maximized by using average variance as Gaussian noise Han Vinck Victoria, 2008

  35. ISPLC 2000: Start of Impulsive noise in PLC?Limerick (Ireland) Gen Marubayashi and Tom Coffey Han Vinck Victoria, 2008

  36. Some other (early) references Modeling of Microwave Oven Interference Using Class-A Impulsive Noise and Optimum Reception Katayama, M.; Morinaga, N.Miyamoto, S.; IEICE transactions on communications (1997) pp. 670-677 “Parameter Measurement of Class A Interference on Powerline” K.Yamauchi, N. Takahasi and M. Maeda Tr. on IEICE, E72, 1. , pp. 7-9, Jan 1989 Performance analysis of QAM systems under class A impulsive noise environmentMiyamoto, S.; Katayama, M.; Morinaga, N.IEEE Transactions on electromagnetic Compatibility, May 1995 Page(s):260 - 267 Han Vinck Victoria, 2008

  37. Performance (simple) • Remarks: • n > 128 necessary for convergence • convergence after 2 steps • for SNR  0 dB not stable • (from impulsive to Gaussian) • - Better estimators give better SER Performance: SER against SNR of the simple algorithm after iterating Han Vinck Victoria, 2008

  38. From block to convolutional codeslike coded modulation! Advantages: lower complexity decoding lower decoding error probability Han Vinck Victoria, 2008

  39. Example of the mapping (a) Original code (b) permutation trellis code. Han Vinck Victoria, 2008

  40. Reason why Distance tables conv. code output permutation code word 00 01 10 11 231 213 132 123 00 0 1 1 2 231 0 2 2 3 01 1 0 2 1 213 2 0 3 2 10 1 2 0 1 132 2 3 0 2 11 2 1 1 0 123 3 2 2 0 +1 per branch! Han Vinck Victoria, 2008

  41. Q: Capacity when side information available ? σm2 encode modulator channel detection decode average power S We distinguish 4 CASES: Transmitter (not) informed and Receiver (not) informed Han Vinck Victoria, 2008

  42. Channel state T+:known at Transmitter R+:known at Receiver Han Vinck Victoria, 2008

  43. Principle waterfilling (parallel channels) PSD B note added: i2 <B S1/W 12 S2/W S3/W 22 32 f P1W P2W P3W Same Solution: S1/W + 12 = S2/W + 22= S3/W +32= S/2W+ Han Vinck Victoria, 2008

  44. Channel state summary conclusion: inform receiver Han Vinck Victoria, 2008

  45. Principle waterfilling (parallel channels) PSD B note added: i2 <B S1/W 12 S2/W S3/W 22 32 f P1W P2W P3W Same Solution: S1/W + 12 = S2/W + 22= S3/W +32= S/2W+ Han Vinck Victoria, 2008

  46. Channel state summary conclusion: inform receiver Han Vinck Victoria, 2008

  47. conclusion • from a capacity point of view, spreading destroys information! • Problem: match measurements to model • transmission speed Han Vinck Victoria, 2008

  48. Class A Middleton model properties channel m has probability and variance and average variance System parameters: T = G2 / I2 ; G2 ; A Han Vinck Victoria, 2008

  49. Solution principle: waterfilling (parallel channels) PSD B note added: i2 <B S1/w 12 S2/w S3/w 22 Rob. Gallager 32 w w w f Total signal power S = 2(S1 + S2 + S3) Solution: S1/w+ 12= S2/w+ 22= S3/w+ 32 Han Vinck Victoria, 2008

  50. Another approach: Permutation codes Why? CENELEC: maximum transmit amplitude we use M-ary FSK Bandwidth  100 kHz noise: severe impulsive narrowband disturbances high attenuation (40 – 100 dB/km) Han Vinck Victoria, 2008

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