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**POWERLINE COMMUNICATIONS FOR ENABLING SMART GRID APPLICATIONS**

Task ID: Prof. Brian L. Evans Wireless Networking and Communications Group Cockrell School of Engineering The University of Texas at Austin May 3, 2013

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**Principal Investigator: **

Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion Task Description: Improve powerline communication (PLC) bit rates for monitoring/controlling applications for residential and commercial energy uses Anticipated Results: Adaptive methods and real-time prototypes to increase bit rates in PLC networks Principal Investigator: Prof. Brian L. Evans, The University of Texas at Austin Current Students (with expected graduation dates): Ms. Jing Lin Ph.D. (May 2014) Summer 2013 intern at TI Mr. Yousof Mortazavi Ph.D. (Dec. 2013) Mr. Marcel Nassar Ph.D. (Aug. 2013) Defended PhD April 15, 2013 Mr. Karl Nieman Ph.D. (May 2015) Summer 2013 intern at Freescale Industrial Liaisons: Dr. Anuj Batra (TI), Dr. Anand Dabak (TI), Mr. Leo Dehner (Freescale), Mr. Michael Dow (Freescale), Dr. Il Han Kim (TI), Mr. Frank Liu (IBM), Dr. Tarkesh Pande (TI) and Dr. Khurram Waheed (Freescale) Starting Date: August 2010

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**Task Deliverables Date Tasks Dec 2010**

Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion Task Deliverables Date Tasks Dec 2010 Uncoordinated interference in narrowband PLC: measurements, modeling, and mitigation May 2011 Testbed #1 based on TI PLC modems to investigate receiver improvements Dec 2011 Narrowband PLC channel/noise: measurements/modeling May 2012 Standard-compliant receiver methods (3x bit rate increase) Dec 2012 Testbed #2 based on Freescale PLC modems to investigate transmitter improvements (2x bit rate increase) On-going Testbed #3 based on NI equipment to map noise mitigation algorithms onto FPGAs Testbed #4 for two-transmitter two-receiver (2x2) systems based on TI PLC modems to investigate scalability

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**Recent Project Highlights**

Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion Recent Project Highlights Paper in Smart Grid Special Issue (Sep. 2012) IEEE Signal Processing Magazine (impact factor 4.066) Paper on channel impairments, noise, and standards Co-authored with Dr. Anand Dabak (TI) and Dr. Il Han Kim (TI) Channel Model Adopted (Oct. 2012) Reference model for IEEE Standard for Low Frequency Narrow Band Power Line Communications for Smart Grid App. Mr. Marcel Nassar, Dr. Anand Dabak (TI), Dr. Il Han Kim (TI), et al. SRC Technical Transfer Talk (Dec. 2012) Best Paper Award (Mar. 2013) 2013 IEEE Int. Symp. On Power Line Comm. and Its Applications Co-authored with Dr. Khurram Waheed (Freescale)

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**Smart Grid Wind farm Central power plant HV-MV Transformer**

Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion Smart Grid Wind farm Central power plant HV-MV Transformer Grid status monitoring Utility control center Smart meters Integrating distributed energy resources Houses Offices Traditional power grids have been coupled with communication networks today, leading to so-called smart grids. A smart grid enables information flow among various components of the grid, ranging from power plants to distributed energy resources, and from local utilities to residential and commercial customers. The purpose is to better monitor and control power generation and consumption. For example, distributed energy resources, such as solar power, fuel power and other personally-owned energy storage, can be integrated to the grid to provide low-cost or standby energy during peak hours. Transducers deployed over the grids are used to collect measurement data for grid status estimation and outage detection. Smart meters can be used for time-dependent pricing, which motivates customers to scale back their energy usage during peak hours, and also device-specific billing, so that the house owner doesn’t have be pay electric bills for charging his friend’s electric vehicle. In addition, smart buildings and smart homes can be energy efficient with automated lights, air conditioners and other smart appliances. Device-specific billing Automated control for smart appliances Medium Voltage (MV) 1 kV – 33 kV High Voltage (HV) 33 kV – 765 kV Industrial plant

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**Smart Grid Goals Accommodate all generation types**

Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion Smart Grid Goals Accommodate all generation types Renewable energy sources Energy storage options Improve operating efficiencies Scale voltage with energy demand Reduce peak demand Analyze customer load profiles and system load snapshots Improve system reliability Power quality monitoring Remote disconnect/reconnect Outage/restoration event notification Inform customer ISTOCKPHOTO.COM/© SIGAL SUHLER MORAN Enabled by smart meter communications Source: Jerry Melcher, IEEE Smart Grid Short Course, 22 Oct. 2011, Austin TX USA

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**Smart Meter Communications**

Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion Smart Meter Communications Local utility MV-LV transformer Smart meters Data concentrator Communication backhaul carries traffic between concentrator and utility on wired or wireless links Smart meter communications between smart meters and data concentrator via powerline or wireless links On the distribution side of the grids, the communications between a local utility control center and its customers plays an important role in local utility applications that I just mentioned. The local utility communicates with a number of data concentrators located at the medium-voltage lines in the US via communication backhaul. Each data concentrator is in charge of a few houses and can talk to them via the last mile communications to the smart meters. The smart meter serves as a gateway in the home area sensor networks that interconnect transducers on appliances and indoor power lines. Distances from Smart Meter to Data Concentrator: m in Europe/China/India and 3-4 km in US 3-4 Low voltage (LV) under 1 kV Home area data networks connect appliances, EV charger and smart meter via powerline or wireless links

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**Powerline Communications (PLC)**

Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion Powerline Communications (PLC) Use orthogonal frequency division multiplexing (OFDM) Communication challenges Channel distortion Non-Gaussian noise Categories Band Bit Rates Coverage Enables Standards Narrowband 3-500 kHz ~500 kbps Multi-kilometer Smart meter communication (ITU) PRIME, G3 ITU-T G.hnem IEEE P1901.2 Broadband MHz ~200 Mbps <1500 m Home area data networks HomePlug ITU-T G.hn IEEE P1901 The smart grid communications are supported by a heterogeneous set of network technologies, ranging from wireless to wireline solutions. Among the wireline alternatives, powerline communications, or PLC, have been deployed outdoor for last mile communications and indoor for home area networks. Narrowband PLC operating in the kHz band to deliver a few hundred kbps has been used for last mile communications over MV and LV lines. For home area networks, broadband PLC can provide several hundred Mbps in the MHz band. These PLC systems adopt multicarrier communications, or OFDM. (Introduce OFDM here)

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**OFDM Systems in Impulsive Noise**

Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion OFDM Systems in Impulsive Noise FFT in receiver spreads impulsive energy over all tones Signal-to-noise ratio (SNR) in each subchannel decreases Narrowband PLC systems operate -5 dB to 5 dB in SNR Data subchannels carry same number of bits (1-4) in current standards Each 3 dB increase in SNR on data subchannels could give extra bit

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**Narrowband PLC Systems**

Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion Narrowband PLC Systems Problem: Non-Gaussian impulsive noise is #1 limitation to communication performance yet traditional communication system design assumes additive noise is Gaussian Goal: Improve comm. performance in impulsive noise Approach: Statistical modeling of impulsive noise Solution #1: Receiver design (standard compliant) Solution #2: Joint transmitter-receiver design Parametric Methods Nonparametric Methods Listen to environment No training necessary Find model parameters Learn statistical model from communication signal structure Use model to mitigate noise Exploit sparsity to mitigate noise

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**Narrowband PLC Impulsive Noise**

Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion Narrowband PLC Impulsive Noise Cyclostationary Noise Asynchronous Noise Example: rectified power supplies Example: uncoordinated interference Rx Receiver Dominant in outdoor PLC Increases with widespread deployment

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**Non-Parametric Mitigation Methods**

Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion Non-Parametric Mitigation Methods Exploit sparsity of impulsive noise in time domain Build statistical model each OFDM symbol using sparse Bayesian learning (SBL) At receiver, null tones contain only Gaussian + impulsive noise SNR gain vs. conventional OFDM systems at symbol error rate 10-4 Complex, 128-point FFT, QPSK, data tones , rate ½ conv. code Asynchronous Gaussian mixture model and Middleton Class A noise time System Noise SBL w/ null tones SBL w/ all tones SBL w/ decision feedback Uncoded GMM 8 dB 10 dB - MCA 6 dB 7 dB Coded 2 dB 9 dB 1.75 dB 6.75 dB 8.75 dB

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**Time Domain Interleaving**

Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion Time Domain Interleaving Bursts span consecutive OFDM symbols Coded performance in cyclostationary noise Interleave Complex OFDM, 128-point FFT, QPSK, data tones , rate ½ conv. code Bursts spread over many OFDM symbols

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**Time-Domain Interleaving**

Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion Time-Domain Interleaving Coded performance in cyclostationary noise Burst duty cycle 10% Burst duty cycle 30% Time-domain interleaving over an AC cycle Current PLC standards use frequency-domain interleaving (FDI)

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**Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion**

Adaptive signal processing algorithms for bit loading and interference mitigation Hardware Software NI x86 controllers stream data NI cards generates/receives analog signals TI front end couples to power line Transceiver algorithms in C on x86 Desktop LabVIEW configures system and visualizes results 1x1 Testbed

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**Testbed #2: Noise Playback/Analysis**

Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion Testbed #2: Noise Playback/Analysis G3 link using two Freescale PLC modems Freescale software tools allow frame-by-frame analysis Test setup allows synchronous noise injection into power line Freescale PLC G3-OFDM Modem One modem to sample powerline noise in field Collected 16k 16-bit 400 kS/s at each location Freescale PLC Testbed

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**Testbed #2: Cyclic Power Line Noise**

Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion Testbed #2: Cyclic Power Line Noise Analyzed cyclic properties of PLC noise measurements Developed cyclic bit loading method for transmitter Receiver measures noise power over half AC cycle Feedback modulation map to transmitter Allocate more bits in higher SNR subchannels 2x increase in bit rate Won Best Paper Award at ISPLC

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**Testbed #3: FPGA Implementation**

Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion Testbed #3: FPGA Implementation Built NI/LabVIEW testbed with real-time link (G3 settings) Redesigned parametric impulsive noise mitigation algorithm Converted matrix operations to distributed calculations on scalars Based on approximate message passing (AMP) framework Mapped transceiver to fixed-point data/math using Matlab Synthesis: LabVIEW DSP Diagram to Xilinx Vertex 5 FPGAs Received QPSK constellation at equalizer output Utilization Trans. Rec. AMP+Eq FPGA 1 2 3 total slices 32.6% 64.0% 94.2% slice reg. 15.8% 39.3% 59.0% slice LUTs 17.6% 42.4% 71.4% DSP48s 2.0% 7.3% 27.3% blockRAMs 7.8% 18.4% 29.1% conventional receiver with AMP

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**Testbed #4: 2x2 PLC (On-Going)**

Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion Testbed #4: 2x2 PLC (On-Going) Goal: Improve communication performance by another 2x One phase, neutral, ground for 2x2 differential signaling Crosstalk between two channels due to energy coupling Frequency response of a direct channel Crosstalk highly correlated with direct channel response

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**Conclusion PLC systems are interference limited**

Task Summary | Background | Noise Modeling and Mitigation | Testbeds | Conclusion Conclusion PLC systems are interference limited Statistical models for interference Cyclostationary model for impulsive noise synchronous to AC cycle Gaussian mixture model for asynchronous impulsive noise Interference management Cyclic bit loading to double bit rates in cyclostationary noise Time-domain interleaving to mitigate cyclostationary noise followed by receiver impulsive noise mitigation Mapping impulsive noise mitigation algorithms to FPGAs Poor: Non-parametric sparse Bayesian learning algorithms Good: Parametric distributed approximate message algorithms

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**Our Publications Tutorial/Survey Article**

M. Nassar, J. Lin, Y. Mortazavi, A. Dabak, I. H. Kim and B. L. Evans, “Local Utility Powerline Communications in the kHz Band: Channel Impairments, Noise, and Standards”, IEEE Signal Processing Magazine, Special Issue on Signal Processing Techniques for the Smart Grid, Sep Impact Factor Journal Paper 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, Special Issue on Smart Grid Communications, Jul Impact Factor Conference Publications (more on next slide) J. Lin and B. L. Evans, “Non-parametric Mitigation of Periodic Impulsive Noise in Narrowband Powerline Communications”, Proc. IEEE Global Communications Conference, Dec. 2013, Atlanta, GA USA, submitted.

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**Our Publications Conference Publications (more on next slide)**

M. Nassar, P. Schniter and B. L. Evans, “Message-Passing OFDM Receivers for Impulsive Noise Channels”, Proc. Asilomar Conf. on Signals, Systems, and Computers, Nov. 2013, Pacific Grove, CA, submitted. K. F. Nieman, M. Nassar, J. Lin and B. L. Evans, “FPGA Implementation of a Message-Passing OFDM Receiver for Impulsive Noise Channels”, Proc. Asilomar Conf. on Signals, Systems, and Computers, Nov. 2013, Pacific Grove, CA, submitted. K. Nieman, J. Lin, M. Nassar, K. Waheed, and B. L. Evans, “Cyclic Spectral Analysis of Power Line Noise in the kHz Band”, Proc. IEEE Int. Sym. on Power Line Comm. and Its App., Mar. 2012, Johannesburg, South Africa. Best Paper Award. J. Lin and B. L. Evans, “Cyclostationary Noise Mitigation in Narrowband Powerline Communications”, Proc. APSIPA Annual Summit and Conf., invited paper, Dec. 2012, Hollywood, CA USA. M. Nassar, A. Dabak, I. H. Kim, T. Pande and B. L. Evans, “Cyclostationary Noise Modeling In Narrowband Powerline Communication For Smart Grid Applications”, Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Proc., Mar. 2012, Kyoto, Japan

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**Our Publications Conference Publications (more on next slide)**

M. Nassar, K. Gulati, Y. Mortazavi, and B. L. Evans, “Statistical Modeling of Asynchronous Impulsive Noise in Powerline Communication Networks”, Proc. IEEE Int. Global Communications Conf., Dec. 2011, Houston, TX USA. J. Lin, M. Nassar and B. L. Evans, “Non-Parametric Impulsive Noise Mitigation in OFDM Systems Using Sparse Bayesian Learning”, Proc. IEEE Int. Global Communications Conf., Dec. 2011, Houston, TX USA. Standards Contribution A. Dabak, B. Varadrajan, I. H. Kim, M. Nassar, and G. Gregg, “Appendix for noise channel modeling for IEEE P1901.2”, IEEE P Std., June 2011, doc: 2wg PHM5. Adopted as reference noise model in Oct ballot.

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**Thank you for your attention…**

Questions?

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Backup Slides

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**Today’s Power Grids in USA**

7 large-scale power grids each managed by a regional utility company 700 GW generation capacity in total for long-haul high-voltage power transmission Synchronized independently, and exchange power via DC transfer 130+ medium-scale power grids each managed by a local utility Local power distribution to residential, commercial and industrial customers Heavy penalties in US for blackouts (2003 legislation) Utilities generate expected energy demand plus 12% Energy demand correlated with time of day Effect of plug-in electric vehicles (EVs) on energy demand uncertain Generation cost 30x higher during peak times vs. normal load Traditional ways to increase capacity to meet peak demand increase Build new large-scale power generation plant at cost of $1-10B if permit issued Build new transmission line at $0.6M/km which will take 5-10 years to complete Source: Jerry Melcher, IEEE Smart Grid Short Course, 22 Oct. 2011, Austin TX USA

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**Comparison of Wireless & PLC Systems**

Wireless Communications Narrowband PLC (3-500 kHz) Time selectivity Time-selective fading and Doppler shift (cellular) Periodic with period of half AC main freq. plus lognormal time-selective fading Power loss vs. distance d d –n/2 where n is propagation constant e – a(f) d plus additional attenuation when passing through transformers Propagation Dynamically changing Determinism from fixed grid topology Synchronization Varies AC main power frequency Additive noise/ interference Assumed stationary and Gaussian Gaussian plus non-Gaussian noise dominated by cyclostationary component Asynchronous interference Uncoordinated users in Wi-Fi bands; Frequency reuse in cellular Due to power electronics and uncoordinated users using other standards MIMO Standardized for Wi-Fi and cellular Number of wires minus 1; G.9964 standard for broadband PLC

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**Cyclostationary Noise Asynchronous Impulsive Noise**

PLC Noise Scenarios Background Noise Cyclostationary Noise Asynchronous Impulsive Noise Spectrally shaped noise Decreases with frequency Superposition of lower-intensity sources Includes narrowband interference Cylostationary in time and frequency Synchronous and asynchronous to AC main frequency Comes from rectified and switched power supplies (synchronous), and electrical motors (asynchronous) Dominant in narrowband PLC Impulse duration from micro to millisecond Random inter-arrival time 50dB above background noise Caused by switching transients and uncoordinated interference Present in narrowband and broadband PLC time

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**Cyclostationary Noise**

Noise Sources Noise Trace

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**Uncoordinated Interference Results**

Homogeneous PLC Network General PLC Network

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**Cyclostationary Noise Modeling**

Measurement data from UT/TI field trial Cyclostationary Gaussian Model [Katayama06] Proposed model uses three filters [Nassar12] Demux Period is one half of an AC cycle s[k] is zero-mean Gaussian noise Adopted by IEEE P narrowband PLC standard

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**Asynchronous Noise Modeling**

Dominant Interference Source Ex. Rural areas, industrial areas w/ heavy machinery Middleton Class A Distribution [Nassar11] Impulse rate l Impulse duration m Homogeneous PLC Network li = l, mi = m, g(di) = g0 Ex. Semi-urban areas, apartment complexes Middleton Class A Distribution [Nassar11] General PLC Network li, mi, g(di) = gi Ex. Dense urban and commercial settings Gaussian Mixture Model [Nassar11] Middleton Class A is a special case of the Gaussian Mixture Model.

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**Parametric vs. Nonparametric Methods**

Must build a statistical model of the noise Yes No Requires training data to compute model parameters Degrades in performance due to model mismatch Has high complexity when receiving message data Prior receiver methods to cope with non-Gaussian noise can be categorized as parametric and nonparametric approaches. Parametric methods assumes a statistical noise model, estimates model parameters during a training stage, and use that to mitigate noise during data transmissions. These methods generally allow low-complexity implementations during the transmission stage. However, in time-varying noise statistics, re-training is needed which introduces extra overhead. Otherwise performance degradation can be expected due to model mismatch. Nonparametric methods, on the other hand, require no additional training since they don’t assume any noise models. Instead they denoise the received signal by exploiting certain sparsity structure of the noise. These methods are more robust in various noise environments since it doesn’t rely on statistical models but the sparse structure of the noise. However, the denoising algorithms are generally of high computational complexity.

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**Asynchronous Noise Sparse in time domain Learn statistical model**

Use sparse Bayesian learning (SBL) Exploit sparsity in time domain [Lin11] SNR gain of 6-10 dB Increases 2-3 bits per tone for same error rate - OR - Decreases bit error rate by x for same SNR time ~10dB ~6dB Transmission places 0-3 bits at each tone (frequency). At receiver, null tone carries 0 bits and only contains impulsive noise.

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**Performance w/o Error Correction**

NSI Gaussian mixture model noise Non-parametric methods in blue Parametric methods in red Proposed CS+LS: [Caire08] MMSE: [Haring02] SBL: [Lin11]

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**Performance w/ Error Correction**

NSI Proposed Non-parametric methods in blue Parametric methods in red NSI Gaussian mixture model noise

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**Power Line Noise at Residential Site**

frequency sweep f = 170 kHz narrowband f = 140 kHz complex spectrum f = kHz

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**Analysis of Residential Noise**

though spectrally complex, many components have strong stationarity at 120 Hz

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**Testbed #1 Quantify application performance vs. complexity tradeoffs**

Extend our real-time DSL testbed (deployed in field) Integrate ideas from multiple narrowband PLC standards Provide suite of user-configurable algorithms and system settings Display statistics of communication performance Investigate Adaptive signal processing algorithms Improved communication performance 2-3x

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**Message-Passing OFDM Receiver**

RT controller LabVIEW RT data symbol generation FlexRIO FPGA Module 1 (G3TX) LabVIEW DSP Design Module data and reference symbol interleave reference symbol LUT 43.2 kSps 8.6 kSps zero padding (null tones) generate complex conjugate pair 103.6 kSps 256 IFFT w/ 22 CP insertion 368.3 kSps NI 5781 16-bit DAC 10 MSps BER/SNR calculation w/ and w/o AMP FlexRIO FPGA Module 2 (G3RX) 14-bit ADC sample rate conversion 400 kSps time and frequency offset correction 256 FFT w/ 22 CP removal FlexRIO FPGA Module 3 (AMPEQ) null tone and active tone separation 184.2 kSps 51.8 kSps ZF channel estimation/ equalization AMP noise estimate Subtract noise estimate from active tones data and reference symbol de- interleave Host Computer LabVIEW 43.1 kSps 256 FFT, tone select testbench control/data visualization differential MCX pair Example Input Noise Resource Utilization

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