2015 IEEE Int. Conf. on Communications Robust Transceiver to Combat Periodic Impulsive Noise In Narrowband Power-line Communications Jing Lin1, Tarkesh Pande2, Il Han Kim2, Anuj Batra2, Brian L. Evans3 1 Qualcomm Inc. 2 Texas Instruments Inc. 3 The University of Texas at Austin 9 June /2015

Outline Introduction Noise in PLC and previous work Contribution Modulation Diversity Noise Estimation using sparse Bayesian techniques Results

Periodically varying and spectrally shaped noise Wideband impulses Narrowband interferences Sub-channel SNR is highly frequency-selective and time-varying

Previous vs. Proposed Transmitter Methods Throughput Reduction Channel/Noise Info at Transmitter Previous Adaptive modulation [Nieman13] ✗ Full Concatenated error correction coding (PLC standards) ✔ None Proposed Time-frequency modulation diversity Partial

Modulation Diversity (I) Sub-channels SNR s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 X Symbols b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13 b14 b15 ✔ X Bits Data rate = 1 bit / channel use [Schober03]

Example: Hochwald/Sweldens Code Map N bits to a length-N codeword consisting of PSK symbols Special case: PSK repetition code Constellation mappings are optimized for channel statistics 000 110 001 010 011 100 101 111 Optimal length-3 code in Rayleigh fading channel [Hochwald00]

Proposed Time-Frequency Mapping (Transmitter) Allocate components of a codeword to time-frequency slots Require partial noise information Narrowband interference width Burst duration Subcarriers Time-domain noise … … … … OFDM symbols

Diversity Demodulation (Receiver) Combine signals received from N sub-channels Estimated sub-channel Diversity Demodulator Received signal Log-likelihood ratio (LLR) Estimated noise power

Noise Power Estimation (II) Offline estimation Utilize silent intervals between transmissions Semi-online estimation Between transmissions: Estimate start/end instances of all stationary intervals In transmissions: Estimate noise power spectrums Time Offline Semi-online Transmission Workload at the noise power estimator Low Med High

Proposed Semi-Online Estimation Measure noise using cyclic prefix Formulate a compressed sensing problem (where ) Collect multiple measurements in the same stationary interval Cyclic Prefix OFDM symbol Noise NBI AWGN + -

Proposed Semi-Online Estimation (Cont.) Apply sparse Bayesian learning algorithm IG - Inverse Gamma dist.; IW - Inverse Wishart dist. EM - Expectation maximization Prior [Zhang11] EM Updates Row sparsity Temporal correlation Diversity Receiver Slicing Error Estimation Hyper-prior

System Parameters Parameters Reference System TFMD System Sampling Frequency 400kHz FFT Size 256 CP Length 30 Data Subcarriers 23:58 (36 tones) Convolutional Code Rate ½ K=7 Reed-Solomon Code 235/251 N/A Interleaver Size (Bits) 4032 (packet) 36 Packet Size (Bytes) 235 Data Rate (kbps) 23.5 25 ΔT symbols (TFMD) 4 ΔK Tones (Nd =2/Nd=3) 18/12 Noise: Model LPTV with three regions [70% 29% 1%] Channel: Flat

Results Coherent mode gains : > 6dB Non-Coherent mode gains: 8dB

Conclusions Modulation diversity as an effective method for improving performance in PLC channels Developed a methodology for estimating noise variance Exploits the cyclic prefix Uses Sparse Bayesian learning techniques