Energy-Efficient Signal Processing Techniques For Smart Grid Heterogeneous Communication Networks Task ID: Prof. Naofal Al-Dhahir, Univ. of Texas at Dallas Prof. Brian L. Evans, Univ. of Texas at Austin

Task Summary  Task Description: 1) Low-complexity interference cancellation methods that exploit PLC interference characteristics 2) Efficient wireless coexistence mechanisms in the unlicensed MHz frequency band 3) Improve reliability and energy efficiency of two-way wireless and power line communications (PLC) between smart meters & data concentrators  Anticipated Results:  Signal processing algorithms and real-time prototypes to demonstrate enhanced performance of wireless and PLC transceivers for smart grid applications 2

Task Deliverables Date Task June Algorithms/software for low-complexity interference cancellation methods that exploit interference characteristics to reduce bit error rate by 10x (delivered) August Architecture/algorithm for PLC-wireless diversity combining method with at least 2x improvement in energy efficiency over state of the art (delivered) January Efficient wireless coexistence mechanisms in the unlicensed MHz frequency band January Demonstrations on UT Austin wireless and PLC test beds

Students & Liaisons  Graduated Students and Current Affiliation  Dr. Jing Lin, Qualcomm, May 2014  Dr. Karl Nieman, National Instruments, Dec  Current Students and Anticipated Graduation Date  Mostafa Ibrahim, December 2017  Ghadi Sebaali, May 2020  Internships  Mostafa Ibrahim, TI, Summer’14 and Summer’15  Ghadi Sebaali, Freescale, Summer’15  Liaisons  Dr. Il-Han Kim (TI), Dr. Anand Dabak (TI), Dr. Wenxun Qiu (TI), and Dr. Khurram Waheed (Freescale) 4

Smart Grid Goals  Accommodate all generation types  Improve operating efficiencies Scale voltage with energy demand Bill customer using real-time rates Reduce peak demand (duty cycling) Analyze customer load profiles Analyze system load snapshots  Improve system reliability Monitor power quality D isconnect/reconnect remotely Notify outage/restoration event  I nform customer Source: Jerry Melcher, IEEE Smart Grid Short Course, Oct. 2011, Austin TX USA Enabled by two-way smart meter communications ISTOCKPHOTO.COM/© SIGAL SUHLER MORAN 5

Smart Meter Communications 6 Unlicensed 900 MHz Wireless Communications Narrowband Powerline Communications (3-500 kHz) Power loss vs. distance d d – /2 propagation constant e –  (f) d plus attenuation from transformers PropagationDynamicStatic (fixed grid topology) Additive noise/ interference model Gaussian mixtureCyclostationary interference and Gaussian mixture Asynchronous interference Uncoordinated users and electronic emissions Power electronics and uncoordinated users Multi-Input Multi- Output (MIMO) Enhance data rate (spatial multiplexing) & reliability (diversity); Wi-Fi standards Enhance data rate (spatial multiplexing) using single or three phase; not much diversity Noise and interference will be used interchangeably.

Research Summary  Accomplishments during the past year  PLC-wireless diversity combining scheme with at least 2x improvement in energy efficiency over using one link  Organized ISPLC in March 2015 in Austin, Texas  Future directions  Efficient wireless coexistence mechanisms in the sub-1GHz unlicensed MHz frequency band between the IEEE g and the IEEE ah standards  Demonstrations of the achieved PLC/Wireless combining performance gains on UT Austin wireless and PLC testbeds  Technology transfer & industrial interaction  Regular conference calls are being held with TI and Freescale to discuss the work progress and get feedback 7

Task #1: PLC Interference Mitigation  Cyclostationary interference  Period is half AC power cycle  Spectrum varies with time  Modeled as Gaussian noise feeding three different filters  Receiver-based methods  Sparse Bayesian learning  Highly parallel algorithms  Transmitter-receiver methods  Adapt modulation to match cyclostationary noise  Exploit time-frequency sparsity 8

Task #1: PLC Interference Mitigation  Sparse Bayesian Learning (SBL)  Model impulsive noise as a sparse vector in time domain  Estimate and mitigate interference without training  Parallel approximate message passing algorithm  Real-time implementation fills one Xilinx Vertex-5 FPGA 9 SystemNoiseSBL w/ null tones SBL w/ all tones SBL w/ dec. feedback UncodedGaussian Mixture8 dB10 dB-- Middleton Class A6 dB7 dB-- CodedGaussian Mixture2 dB7 dB9 dB Middleton Class A1.5 dB6.3 dB9.3 dB Periodic0.8 dB4.8 dB6.8 dB

Task #1: PLC Interference Mitigation  Time-frequency modulation diversity  Allocate codeword to time-frequency slots  Estimate interference bandwidth and duration  Listen between transmissions to model noise states  Refine during transmission by estimating noise power 10 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

Task #1: PLC Interference Mitigation 11 Parameters Values Sampling Rate400 kHz FFT Size256 CP Length30 # Data Tones72 Convolutional Code Rate 1/2, length 7 Interleaver Size72 bits Packet Size256 Bytes >100x >2dB Length-2 code Length-3 code Subcarriers OFDM symbols … … … … Subcarriers … … … … …

Task #2: Coexistence in 900MHz Band  Goal: Coexistence of IEEE ah & g smart utility networks in unlicensed 900 MHz band  Interference avoidance and/or management  Our simulation results show 13m physical separation needed between interferers and victims (not practical)  Receiver: channel sensing  Exploit signal waveform properties  Transmitter: dynamic spectrum management  Adjust transmit power/BW to reduce mutual interference  Long successful track record in DSL and other standards 12

Task # 3 : PLC/Wireless Diversity Simultaneous PLC/wireless transmissions using low-voltage power lines in kHz band and unlicensed MHz wireless band  Goal : Improve reliability of smart grid communications using PLC/wireless receive diversity combining methods

Task #3 :Symmetric Diversity Combining  Same channel, noise, and interference statistics  Same Average SNR Old: Combining of two wireless links

Task # 3 : Asymmetric Diversity Combining  Different channel, noise and interference statistics  PLC and wireless might have different average SNR ! New : PLC/Wireless combining for Smart Grid Comm.

Task # 3 : Noise Models Gaussian mixture Noise R1R2R3

Task # 3 : Applying Conventional MRC 17

Task # 3 : Impulsive Noise in PLC and Wireless Noise power over frequency sub-channels across multiple OFDM blocks PLC PAR = 21 dB AWGN PAR = 10 dB Wireless PAR = 14 dB

Task # 3 : Proposed PLC/Wireless Combining 19

Task # 3 : Proposed PLC/Wireless Combining PSD Combining Instantaneous SNR Combining

Task # 3 : Combining Metrics Comparison 21 Noise Power Ratio = PLC Noise Power/ Wireless Noise Power One OFDM Block 36 Active Sub-Channels out of 256 Noise power over frequency sub-channels across multiple OFDM blocks

Task # 3 : Simulation Parameters 22 Region 1Region 2Region 3 Time Percentage60 %30 %10 % Power (dB)

Task # 3 : Performance Results  Average BER vs Eb/No of both links (equal Eb/No) - Fading channels Average SNR Combining 4 dB PSD Combining 5.5 dB Instantaneous SNR Combining 7 dB

Task # 3 : Performance Results  Average BER vs Eb/No of the PLC link at Eb/No = 2 dB for the wireless link - Fading channels Average SNR Combining 4.5 dB PSD Combining 8 dB Instantaneous SNR Combining 10 dB

Task # 3 Conclusions  PSD combining provides the best performance /complexity tradeoff - better performance than average-SNR combining at lower complexity than instantaneous-SNR combining  Our proposed PSD estimation method does not require pilot overhead while instantaneous-SNR combining requires high pilot overhead (resulting in data rate loss) 25

Back-up Slides 26

Task #1: Interference Mitigation  Contribution  Model impulsive noise as a sparse vector in time domain  Apply sparse Bayesian learning (SBL) methods for estimation and mitigation without training  Three SBL algorithms proposed  Estimate and subtract the noise impulses by using the noise projection onto null and pilot tones  Perform joint noise estimation and OFDM detection using information in the date tones  Embed the algorithm into a decision feedback structure 27

Task #1: SBL System Overview 28 A time-domain interleaved OFDM system  Demodulated OFDM:  Assumptions:  Select interleaver size such that e_π is a sparse vector  Perfect channel estimation  New decision metric:

Task #1: Noise Estimation Methods  How to estimate  Apply Sparse Bayesian Learning  the global optimum is always the sparsest solution  all local optimal solutions are sparse  the number of local optima is the smallest  Propose three non-parametric algorithms:  Estimation Using Null and Pilot Tones  Estimation Using All Tones  Decision Feedback Estimation 29

Task #1: SBL Using Null & Pilot Tones  Apply SBL technique to the impulsive noise estimation using null and pilot tones  Use EM algorithm to obtain the MAP estimate of the time-domain impulsive noise  Transform to the frequency domain and subtract it from the received signal 30

Task #1: Estimation Using All Tones  Motivation:  High number of non-data tones is a tradeoff between improved performance and reduced throughput  Goal:  Exploit information available in all tones to estimate the impulsive noise given limited number of non-data tones  Apply EM algorithm:  Three hyperparameters 31

Task #1: SBL w/ Decision Feedback  Goal:  Exploit redundancy in the coded data tones as side information to provide a second estimate of eˆ′.  Transfer information back-and-forth between  impulsive noise estimator using non-data tones  the decoder using data tones 32

Task #1: Low Complexity Algorithms  Sequential SBL: performs a sequential addition and deletion of candidate basis functions 33 EstimatorOperationComplexity Using null and pilot tonesMatrix MultiplyO(N 2 M) Matrix InversionO(M 3 ) Using all tonesMatrix MultiplyO(N 3 ) Matrix InversionO(N 3 ) Sequential SBL w/ unknown background noise power Matrix MultiplyO(N 2 K) Matrix InversionO(K 3 ) Sequential SBL w/ known background noise power Matrix MultiplyO(N 2 K)

Task #1: Performance Analysis  SNR gains in asynchronous impulsive noise  Up to 9 dB in coded systems  Up to 10 dB in uncoded systems  SNR gains in periodic impulsive noise  Up to 6 dB in coded systems 34 SystemNoiseSBL w/ null tones SBL w/ all tones SBL w/DF UncodedGM8 dB10 dB-- MCA6 dB7 dB-- CodedGM2 dB7 dB9 dB MCA1.5 dB6.3 dB9.3 dB Periodic0.8 dB4.8 dB6.8 dB

Task #1: Time-frequency Modulation  Allocate components of a codeword to time- frequency slots  Require partial noise information  Narrowband interference width  Burst duration 35 Time-domain noise Subcarriers OFDM symbols … … … …

Task #1: Noise Power Estimation  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 36 Time Offline Semi- online Transmission Workload at the noise power estimator Low Med High

Task #1: Semi Online Approach  Measure noise using cyclic prefix  Formulate a compressed sensing problem  Define  Collect multiple measurements in the same stationary interval 37 Cyclic Prefix OFDM symbol + - Noise NBI AWGN

Task #1: Proposed OFDM PLC System 38  Using new noise model, add: 1.Impulsive noise mitigation 2.Cyclic adaptive modulation and coding

Task #1: Implementation on FPGA 39 Determine static schedule, map to fixed-point data and arithmetic Translate to hardware Floating- point algorithm

Task #1: Real-Time Implementation  Up to 8 dB of impulsive noise mitigated in real-time testbed 40 uncoded bit-error-rate (BER) signal-to-noise ratio (SNR) [dB] target BER = dB gain for 20 dB impulse power 8 dB gain for 30 dB impulse power

Task #1: Adaptive Modulation/Coding  Motivation: spectral and temporal variations of noise power over an AC cycle and the cyclic nature of the noise  G3-PLC currently supports static modulation with tone mask over an OFDM frame but only allows for a fixed group of six subcarriers to be masked over the duration of the OFDM frame   Extended mechanisms to a cyclic adaptive modulation and coding scheme (MCS) (changes to the green traditional system are shown in red in the above). 41

Task #1: Results for Adaptive Mod. Throughput of NB-OFDM PLC system employing cyclic adaptive MCS scheme over the G3-PLC CENELEC-A band is boosted 2× versus the conventional G3-PLC operation using the next-best rate-optimal choice DQPSK with tone map. 42

Task # 3 : Proposed PLC/Wireless Combining

Task # 3 : Instantaneous Noise Power Estimation  As a simple technique to estimate the instantaneous noise power, we employ comb-type pilots inserted periodically within the data symbols  We estimate the noise power in the pilot locations followed by linear interpolation to compute estimates over all symbols

Task # 3 : Noise PSD Estimation  The noise PSD can be estimated by averaging the received signal power  Estimated PSD and actual PSD vs the active sub-channel indices (36 sub-channels in the CENELEC A band [35-91]kHz  Averaging is performed over 512 OFDM Symbols

Task #4: PLC Testbed Problem Noise, interference, frequency selectivity, fading, and cross-talk Goal Increase communication rate and reliability Solution System-level design exploration tool 46 Quantify communication performance vs. complexity tradeoffs Determine achievable communication delay, throughput and reliability Broadband powerline communication transceivers

Task #4: PLC Testbed Some of the algorithms:  Power/Bit Loading  Echo/Near End Cancellation (NEXT)  FEQ and FEXT Cancellation  Time-domain Equalizer (TEQ) 47 HardwareSoftware NI PXI 1045 Embedded ControllerGraphical User Interface (GUI) in LabVIEW NI PXI-5122 for analog-to-digital (A/D) conversion Real-time target in LabVIEW RT NI PXI-5421 for digital-to-analog (D/A) conversion C++ Dynamically Linked Library (DLL)

References  Jing Lin; Pande, T.; Il Han Kim; Batra, A.; Evans, B.L., "Robust transceiver to combat periodic impulsive noise in narrowband powerline communications," in Communications (ICC), 2015 IEEE International Conference on, vol., no., pp , 8-12 June doi: /ICC  J. Lin, M. Nassar and B. L. Evans, "Impulsive Noise Mitigation in Powerline Communications using Sparse Bayesian Learning", IEEE Journal on Selected Areas in Communications, vol. 31, no. 7, Jul. 2013, pp  Karl Neiman, “Space-Time-Frequency Methods for Interference-Limited Communication Systems “, PhD. Dissertaition,The University of Texas at Austin,  K.F. Nieman, J. Lin, M. Nassar, K. Waheed, and B.L. Evans, "Cyclic spectral analysis of power line noise in the kHz band," Proc. IEEE ISPLC, Won best paper award  K.F. Nieman, M. Nassar, J. Lin, and B.L. Evans, "FPGA implementation of a message-passing OFDM receiver for impulsive noise channels. Proc. IEEE Asilomar Conf. on Signals, Systems, and Computers, Won best student paper Architecture and Implementation Track  K. Waheen, K. F. Nieman, Adaptive cyclic channel coding for orthogonal frequency division multiplexed (OFDM) systems, US patent pending,