IEEE TG13 Partial Evaluation of PM-PHY using TG7r1 Channels

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Presentation transcript:

IEEE 802.15 TG13 Partial Evaluation of PM-PHY using TG7r1 Channels doc.: IEEE 802.11-15/0496r1 May 2015 May 2018 IEEE 802.15 TG13 Partial Evaluation of PM-PHY using TG7r1 Channels Date: 2018-05-09 Author: Daniel Chew (JHU/APL) Edward Au (Marvell Semiconductor)

May 2018 Background In the IEEE 802.15 TG13 March 2018 meeting (15-18-0084-01-0013), TG13 requested full or partial evaluation results using PM-PHY through either simplified or TG7r1 channel models [1] [2]. This presentation will show the setup and results of a simulation of the Pulse Modulation through the specified channel environment. 2-PAM (OOK) modulation Quantify Bit Error Rate using the TG7r1 channel models Quantify costs and benefits of Pulse Shaping The current draft standard does not specify any pulse shaping. What are the consequences of that? Different pulse shapes will be used: Square Root-Raised Cosine Daniel Chew (JHU/APL)

May 2018 2-PAM PHY As defined in the draft 15-18-0003-03-0013 Text Proposal for Pulsed Modulation PHY Data Rates from 4.7 Mbit/s to 150 Mbit/s Channel estimation sequences from 32 bits to 1024 bits Optional HCM For this analysis, HCM(1,1) is used Pulse shaping is not defined Daniel Chew (JHU/APL)

May 2018 Simulation This presentation will cover 4 out of 8 of the test points in scenario 3 from TG7r1. The simulation is agnostic to sample rate The data is transmitted at different oversample rates (OSR) This allows the results to be relative to the sample rate of the channel data in TG7r1. A lower OSR would relate to a higher data rate If the sample rate were 1 GSPS, and the OSR were 5, then the data rate for 2-PAM would be 1 Mbit/s. The simulation will run over a range of oversample rates. Daniel Chew (JHU/APL)

Simulation The simulation is as hardware agnostic as possible. May 2018 Simulation The simulation is as hardware agnostic as possible. The simulation will run of a range of Eb/N0, not SNR or transmit power. Eb/N0 does not take propagation into account. Converting transmit power to received SNR to Eb/N0 is specific to the hardware. All signals will be real and positive valued There will be no negative values through the channel. The simulation will not cover clipping at maximum amplitudes. Saturation at the LED is a function of that particular LED. Daniel Chew (JHU/APL)

Symbol Timing and Decimation May 2018 The Receiver Matched Filter Symbol Timing and Decimation Equalizer Hard Decision * Channel Daniel Chew (JHU/APL)

Synchronization Barker code used to confirm sampling phase. Samples May 2018 Synchronization Barker code used to confirm sampling phase. Samples Daniel Chew (JHU/APL)

RRC Parameters Beta = 0.5 Span = 6 May 2018 RRC Parameters Beta = 0.5 Span = 6 RRC has overshoot that will cause negative values. Negative values must be prevented. Clip DC Offset Slide 8 Daniel Chew (JHU/APL)

RRC Negative Value Solutions May 2018 RRC Negative Value Solutions Pulse Shape Filter Matched Filter + DC Offset * TX RX Offset Channel Clipping Negative Values Pulse Shape Filter Matched Filter * TX RX Channel Daniel Chew (JHU/APL)

Baseline Performance in Ideal Channel with No Corrections to RRC PAM May 2018 Baseline Performance in Ideal Channel with No Corrections to RRC PAM Both square and RRC pulses meet theoretical performance in impulse channel. No corrections were applied to the negative values of RRC PAM for this baseline curve. Pe Eb/N0 Daniel Chew (JHU/APL)

CCDF without Corrections to RRC PAM May 2018 CCDF without Corrections to RRC PAM The CCDF for uncorrected PAM RRC extends to 4.38 dB This is 1.35 dB above PAM Daniel Chew (JHU/APL)

Ideal Channel with Offset and Clipping May 2018 Ideal Channel with Offset and Clipping Clipping the small negative values yields a better BER than expending power on a DC Offset. Pe Eb/N0 Daniel Chew (JHU/APL)

CCDF with Offset and Clipping May 2018 CCDF with Offset and Clipping The CCDF for Offset RRC PAM is the same as uncorrected RRC PAM at 4.38 dB, 1.35 dB above PAM. The CCDF for Clipped RRC PAM extends to 4.87 dB. 1.84 dB above PAM Daniel Chew (JHU/APL)

Situation 3: Home Use 8 Locations (D1-D8) May 2018 Image from [1] Daniel Chew (JHU/APL)

May 2018 Situation 3: Home Use Image from [1] Daniel Chew (JHU/APL)

Channel Response – D1 Samples D1 May 2018 Image from [1] Daniel Chew (JHU/APL)

May 2018 Channel Response – D1 Daniel Chew (JHU/APL)

May 2018 Equalization Process Linear Equalization, no decision directed feedback 21 Coefficient Channel Estimate Equalization runs after matched filter and decimation No Noise in training sequence Placing an upper bound on linear equalizer performance Daniel Chew (JHU/APL)

May 2018 Equalization – D1 Daniel Chew (JHU/APL)

May 2018 D1, OSR = 50 Pe Eb/N0 Daniel Chew (JHU/APL)

May 2018 D1, OSR = 7 Pe Eb/N0 Daniel Chew (JHU/APL)

Channel Response – D2 Samples D2 May 2018 Image from [1] Daniel Chew (JHU/APL)

May 2018 Channel Response – D2 Daniel Chew (JHU/APL)

May 2018 D2, OSR = 7 Pe Eb/N0 Daniel Chew (JHU/APL)

Channel Response – D4 Samples D4 May 2018 Image from [1] Daniel Chew (JHU/APL)

May 2018 Channel Response – D4 Daniel Chew (JHU/APL)

May 2018 D4, OSR = 7 Pe Eb/N0 Daniel Chew (JHU/APL)

Channel Response – D5 Samples D5 May 2018 Image from [1] Daniel Chew (JHU/APL)

May 2018 Channel Response – D5 Daniel Chew (JHU/APL)

May 2018 D5, OSR = 7 Pe Eb/N0 Daniel Chew (JHU/APL)

May 2018 Results Most data points in the scenario benefit more from spectral efficiency than the cost in PAPR when there is no equalization. D2 and D3 are the exceptions Pulse Shaping Leads to Higher Spectral Efficiency Higher Spectral Efficiency demonstrated greater resilience to limited channel bandwidth. The system in situation 3 clearly benefits from spectral efficiency. Daniel Chew (JHU/APL)

May 2018 References [1] Uysal, Murat, Tuncer Baykas, Farshad Miramirkhani, Nikola Serafimovskim, and Volker Jungnickel. IEEE P802.15-15-0746-01-007a Technical Report, September 2015. [2] Uysal, Murat, Farshad Miramirkhani, Omer Narmanlioglu, Tuncer Baykas, and Erdal Panayirci. "IEEE 802.15. 7r1 reference channel models for visible light communications." IEEE Communications Magazine 55, no. 1 (2017): 212-217. Daniel Chew (JHU/APL)