Context Design of NOMA and SIC Software Defined Radio Conclusion and Perspectives
Non-Orthogonal Multiple Access for Vehicular Networks based Software-Defined Radio Rafik Zitouni ‡ ?
‡ ?
and Samir Tohme
VEDECOM Institute, Versailles, France SIC laboratory-ECE Paris, Paris, France
[email protected]
Limassol, Cyprus June 28, 2018
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Rafik Zitouni
IWCMC2018
‡
Context Design of NOMA and SIC Software Defined Radio Conclusion and Perspectives
About VEDECOM Mobility Program V2X communications, IoT, Smart cities, Big data etc Autonomous car level 4/5
Vehicular Program Electrification, Inductive battery charging, etc
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Rafik Zitouni
IWCMC2018
Context Design of NOMA and SIC Software Defined Radio Conclusion and Perspectives
Summary
1
Context IEEE 802.11p standard NOMA and SIC Software Defined Radio: GNU Radio flow graphs
2
Design of NOMA and SIC Software Defined Radio Flow graphs Simulations Results
3
Conclusion and Perspectives
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Rafik Zitouni
IWCMC2018
Context Design of NOMA and SIC Software Defined Radio Conclusion and Perspectives
IEEE 802.11p standard NOMA and SIC Software Defined Radio: GNU Radio flow graphs
Context Wireless Vehicular Ad-hoc NETworks (VANETs) allow cars to exchange information about their perception of the surrounding environment in order to avoid emergency situations The direct V2V communication is required through the de-facto standard IEEE 802.11p for VANETs.
Limited radio channel capacity
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Rafik Zitouni
IWCMC2018
Context Design of NOMA and SIC Software Defined Radio Conclusion and Perspectives
IEEE 802.11p standard NOMA and SIC Software Defined Radio: GNU Radio flow graphs
IEEE 802.11p standard The standard defines CSMA/CA and Orthogonal Frequency Division Multiplexing (OFDM) for MAC and PHY layer, respectively IEEE 802.11a/g (WiFi) BPSK, QPSK, 16QAM, 64QAM 6; 9; 12; 18; 24; 36; 48; 54 2.4 GHz and 5 GHz 20
Number of subcarriers Symbol duration
IEEE 802.11p (ITS-G5) BPSK, QPSK, 16QAM, 64QAM 3; 3.5; 6; 9; 12; 18; 24; 27 5.9 GHz 5, 10 and 20 5 channels for ITS-G5 and 7 channels for DSRC 52 (48 for data and 4 pilots) 8 µs
Exchanged packets
CAM, DENM, etc
Beacons, DATA, ACK, etc
Modulations Data rate (Mb/s) Frequency band Channel bandwidth (MHz) Channels
14 channels, 189 channels 52 (48 for data and 4 pilots) 4 µs
How to increase the throughput or channel capacity of the network without changing standard’s specifications? How to virtualize the IEEE 802.11p radio devices for high reconfigurability? 5 / 27
Rafik Zitouni
IWCMC2018
Context Design of NOMA and SIC Software Defined Radio Conclusion and Perspectives
IEEE 802.11p standard NOMA and SIC Software Defined Radio: GNU Radio flow graphs
IEEE 802.11p standard Brief review of OFDM LTE, DVB, WiFi, DAB etc Complex modulations symbols (BPSK, QPSK, 16QAM, etc) Subcarriers: Discrete frequencies on which data are transmitted Pilot symbols: Special symbols known a-priori
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Rafik Zitouni
IWCMC2018
Context Design of NOMA and SIC Software Defined Radio Conclusion and Perspectives
IEEE 802.11p standard NOMA and SIC Software Defined Radio: GNU Radio flow graphs
NOMA and SIC Non Orthogonal Multiple Access (NOMA) is a 5G radio access technology candidate promising the frequency reuse
x=
√
p1 s1 +
√
p2 s2
p1 , p2 are output powers of superoposed signals s1 and s2 , respectively
We superpose two signals with NOMA, and we extract them through Successive Interference Cancellation (SIC) Downlink: One vehicle superposes two signals and receivers perform SIC Uplink: Two vehicles send signals on the same frequency, and Road Side Unit (RSU) extracts them via SIC 7 / 27
Rafik Zitouni
IWCMC2018
Context Design of NOMA and SIC Software Defined Radio Conclusion and Perspectives
IEEE 802.11p standard NOMA and SIC Software Defined Radio: GNU Radio flow graphs
NOMA and SIC Downlink NOMA is able to double the channel capacity (b/s/Hz) of OMA for two users (vehicles) OMA
NOMA
p1 |h1 |2 ) Cu1 = β log2 (1 + βN0,1 p2 |h2 |2 Cu2 = (1 − β) log2 (1 + ) (1 − β)N0,2
p1 |h1 |2 ) Cu1 = log2 (1 + N0,1 2 p |h 2 2| Cu2 = log2 (1 + ) p1 |h2 |2 + N0,2
12 OMA NOMA
10
β channel bandwidth in (Hz) pi output power of signal si N0,i power spectrum density or Gaussian noise on signal si hi complex channel coefficient between vehicles i
Capacity (bit/s/Hz)
Cui channel capacity of user i 8
6
4
2
0 2
4
6
8
10
12
SNR(dB)
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Rafik Zitouni
IWCMC2018
14
16
18
20
Context Design of NOMA and SIC Software Defined Radio Conclusion and Perspectives
IEEE 802.11p standard NOMA and SIC Software Defined Radio: GNU Radio flow graphs
NOMA and SIC
Successive Interference Cancellation 1
Symbol level SIC receiver
2
Codeword-level SIC s1+s2+noise Decode s2
-
s2
Encode s2
s1+noise
Decode s1
s1
Re-encoding technique would increase the probability of successful recovery of signal at closest receiver
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Rafik Zitouni
IWCMC2018
Context Design of NOMA and SIC Software Defined Radio Conclusion and Perspectives
IEEE 802.11p standard NOMA and SIC Software Defined Radio: GNU Radio flow graphs
NOMA and SIC If the transmitter superposes two Quadrature Phase Shift Keying (QPSK) for each subcarrier, the receiver decodes 16 Quadrature Amplitude Modulation (16QAM)
=
+
QPSK
QPSK
16QAM
α is the ratio between the two output powers p1 and p2 is where 1 α = p1p+p , α ∈ [0, 1] 2 We implemented a proof of this concept on Software Defined Radio (SDR) 10 / 27
Rafik Zitouni
IWCMC2018
Context Design of NOMA and SIC Software Defined Radio Conclusion and Perspectives
IEEE 802.11p standard NOMA and SIC Software Defined Radio: GNU Radio flow graphs
Software Defined Radio: GNU Radio flow graphs Software Defined Radio (SDR) offers reconfigurable solutions Antenna Front End Intermediate Frequency(IF)
ADC
DAC
Software
Baseband Processing
SDR is more reconfigurable compared to hardware transceivers GNU Radio toolkit allows designers to prototype an SDR of IEEE 802.11p PHY 1 We proposed to modify this prototype and introduce NOMA and SIC SDR 1 Bloessl, B., Segata, M., Sommer, C., and Dressler, F. (2017). Performance Assessment of IEEE 802.11p with an Open Source SDR-based Prototype. IEEE Transactions on Mobile Computing, pages 1–1 11 / 27
Rafik Zitouni
IWCMC2018
Context Design of NOMA and SIC Software Defined Radio Conclusion and Perspectives
IEEE 802.11p standard NOMA and SIC Software Defined Radio: GNU Radio flow graphs
Software Defined Radio: GNU Radio flow graphs Flow graphs or software radio programs are the result of connecting signal and data processing blocks Developed in C++ and glued through Python scripts
Data or samples are buffered between software processing blocks of a flow graph Example of a flow graph or a Flowgraph Additional latency Write
Read
Processing Block 1
Write
Read Processing Block 3
Processing Block 2 Data Buffer
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Additional latency
Data Buffer
Rafik Zitouni
IWCMC2018
Context Design of NOMA and SIC Software Defined Radio Conclusion and Perspectives
IEEE 802.11p standard NOMA and SIC Software Defined Radio: GNU Radio flow graphs
Software Defined Radio: GNU Radio flow graphs 802.11p Tx flow graph
2
2 Bloessl, B., Segata, M., Sommer, C., and Dressler, F. (2017). Performance Assessment of IEEE 802.11p with an Open Source SDR-based Prototype. IEEE Transactions on Mobile Computing, pages 1–1 13 / 27
Rafik Zitouni
IWCMC2018
Context Design of NOMA and SIC Software Defined Radio Conclusion and Perspectives
IEEE 802.11p standard NOMA and SIC Software Defined Radio: GNU Radio flow graphs
Software Defined Radio: GNU Radio flow graphs 802.11p Rx flow graph
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Rafik Zitouni
IWCMC2018
Context Design of NOMA and SIC Software Defined Radio Conclusion and Perspectives
IEEE 802.11p standard NOMA and SIC Software Defined Radio: GNU Radio flow graphs
Software Defined Radio: GNU Radio flow graphs Interoperabilty between OBU-201 Autotalks transceiver and USRP B210 driven by GNU Radio receiver
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Rafik Zitouni
IWCMC2018
Context Design of NOMA and SIC Software Defined Radio Conclusion and Perspectives
Flow graphs Simulations Results
Design of NOMA and SIC Software Defined Radio Objective Implement NOMA at the transmitter and SIC at the receiver for each subcarrier as in NOMA cellular downlink NOMA transmitter Message Source
MAC
User 1
Addresses
Mapper Channel Encoder
BPSK, QPSK
SIGNAL Field
OFDM Carrier
Encoding
Allocator
Message Source
MAC
User 2
Addresses
Mapper Channel Encoder
Ampli er
BPSK, QPSK
We use two QPSK subcarriers in our simulations 16 / 27
Rafik Zitouni
IWCMC2018
IFFT
OFDM Cyclic Pre xer
Packet Pad
Context Design of NOMA and SIC Software Defined Radio Conclusion and Perspectives
Flow graphs Simulations Results
Design of NOMA and SIC Software Defined Radio SIC receiver Autocorrelation
Frame
Frequency
Symbol
Stream to
Detection
Correction
Alignment
Vector
Equalize
SIGNAL Field
Constellation
Symbols
Frame
Symbols
Decoding
Decoder
User 1
Decoding
FFT
Codeword-level SIC Equalize
Signal Field
Constellation
Symbols
Frame
Symbols
Decoding
Decoder
User 1
Decoding
Constellation
Ampli er
Encoder SUB
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Rafik Zitouni
IWCMC2018
Symbols
Frame
User 2
Decoding
Context Design of NOMA and SIC Software Defined Radio Conclusion and Perspectives
Flow graphs Simulations Results
Design of NOMA and SIC Software Defined Radio
SIC function is a computationally complex due to the re-encoding operation Through simulation, we can calculate the Bit Error Rate (BER) rather than frame error, and we avoid all complex issues of the receiver synchronization Simulate NOMA and SIC with loopback flow graphs on one subcarrier and calculate BER We consider the case of platooning since the distance is fixed and the output power could be estimated
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Rafik Zitouni
IWCMC2018
Context Design of NOMA and SIC Software Defined Radio Conclusion and Perspectives
Flow graphs Simulations Results
Simulations
Parameters of loopbak flowgraphs OS and SDR CPU
Ubuntu 14.04.5 LTS, GNU Radio 2.7.9 Intel Core i5 at 2.4 GHz, 6 family
NOMA superposition Amplifier p1 , p2
Two QPSK 1 α = p p+p , α ∈ [0, 1]
Channel model
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1
2
−
Gaussian Noise PN = 10
Rafik Zitouni
SNR 10 , SNR(dB) ∈ [0, 20]
IWCMC2018
Context Design of NOMA and SIC Software Defined Radio Conclusion and Perspectives
Flow graphs Simulations Results
Design of NOMA and SIC Software Defined Radio GNU Radio Flow graphs NOMA Tx
Rx without SIC Rx with SIC
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Rafik Zitouni
IWCMC2018
Context Design of NOMA and SIC Software Defined Radio Conclusion and Perspectives
Flow graphs Simulations Results
Design of NOMA and SIC Software Defined Radio Superposition of two users
Symbol constellations at SIC receiver
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Rafik Zitouni
IWCMC2018
Context Design of NOMA and SIC Software Defined Radio Conclusion and Perspectives
Flow graphs Simulations Results
Design of NOMA and SIC Software Defined Radio Receiver with and without SIC
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Rafik Zitouni
IWCMC2018
Context Design of NOMA and SIC Software Defined Radio Conclusion and Perspectives
Flow graphs Simulations Results
Results BER function of α for user 1 with SIC function NOMA/SIC with an SNR=10dB 1
User 1 BER without SIC User 1 BER with SIC
Bit Error Rate (BER)
0.8
0.6
0.4
0.2
0 0
0.2
0.4
0.6 α amplifier P1/P
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Rafik Zitouni
IWCMC2018
0.8
1
Context Design of NOMA and SIC Software Defined Radio Conclusion and Perspectives
Flow graphs Simulations Results
Results BER function of α for two users with SIC NOMA/SIC with an SNR=10dB 1
User 1 BER with SIC User 2 BER with SIC
Bit Error Rate (BER)
0.8
0.6
0.4
0.2
0 0
0.2
0.4
0.6 α amplifier P1/P
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Rafik Zitouni
IWCMC2018
0.8
1
Context Design of NOMA and SIC Software Defined Radio Conclusion and Perspectives
Conclusion
We proposed a design of NOMA and SIC SDR for IEEE 802.11p without changing the standard NOMA and SIC have been found useful to increase the channel capacity or spectrum efficiency We illustrated the user fairness through a calculated BER versus the ration (α) between the output powers of superposed signals. For two users: The capcity of channel would be improved and BER decreased by up to 20% The SIC receiver gets BER ' 0 when α = 0.9 or α = 0.1
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Rafik Zitouni
IWCMC2018
Context Design of NOMA and SIC Software Defined Radio Conclusion and Perspectives
Perspectives
What is the impact of mobility and scalability on the channel capacity? Considerate the processing optimization of the SIC computational complexity on SDR
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Rafik Zitouni
IWCMC2018
Context Design of NOMA and SIC Software Defined Radio Conclusion and Perspectives
Thank you for your attention
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Rafik Zitouni
IWCMC2018