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1
Further Study on Non-linear Precoding with Guaranteed Gain over Linear Precoding
Document Number:IEEE S802.16m-08-925
Date Submitted:15th Sept 2008
Source: Tsuguhide Aoki, Yong Sun (Toshiba) E-mail: [email protected], [email protected] Laurent Marc de Courville, Fred Vook (Motorola)E-mail: [email protected], [email protected] Ron Porat (Nextwave)E-mail: [email protected] Isamu Yoshii, Takaaki Kishigami (Panasonic)E-mail: [email protected], [email protected]*<http://standards.ieee.org/faqs/affiliationFAQ.html>
Venue:Kobe, Japan
Base Contribution:C80216m-08_925.doc
Purpose:Enabling non-linear predocing in SDD document.
Notice:This document does not represent the agreed views of the IEEE 802.16 Working Group or any of its subgroups. It represents only the views of the participants listed in the “Source(s)” field above. It is offered as a basis for discussion. It is not binding on the contributor(s), who reserve(s) the right to add, amend or withdraw material contained herein.
Release:The contributor grants a free, irrevocable license to the IEEE to incorporate material contained in this contribution, and any modifications thereof, in the creation of an IEEE Standards publication; to copyright in the IEEE’s name any IEEE Standards publication even though it may include portions of this contribution; and at the IEEE’s sole discretion to permit others to reproduce in whole or in part the resulting IEEE Standards publication. The contributor also acknowledges and accepts that
this contribution may be made public by IEEE 802.16.
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<http://standards.ieee.org/guides/bylaws/sect6-7.html#6> and <http://standards.ieee.org/guides/opman/sect6.html#6.3>.
Further information is located at <http://standards.ieee.org/board/pat/pat-material.html> and <http://standards.ieee.org/board/pat >.
2
Non linear precoding (NLP)
• We’ve already submitted contributions on LNP, which gives a significant gain over linear precoding.– [4] IEEE C802.16m-08/842r1, – [5] IEEE C802.16m-08/366, – [6] IEEE C802.16m-08/205r2, – [7] IEEE C802.16m-08/058r1,
• We therefore show our further study on NLP with simulation results which is fully much the EMD.
3
MU-MIMO precoding is similar to MIMO detection
• MIMO detection– Linear detection
• ZF and MMSE• Suffer from noise
enhancement– Non-linear detection
• Ordered successive interference
• ML (Sphere, Lattice reduction
• MU-MIMO precoding– Linear precoding
• ZF and MMSE• Suffer from power
penalty– Non-linear precoding
• Tomlinson-Harashima Precoding (THP)
• Vector Precoding.
Non-linear precoding has advantageous like non-linear detection on MIMO decoding
4
Link-level simulation parametersParameters Values
Bandwidth 10MHz
FFT size 1024
Carrier Frequency 2.5GHz
Subframe structure 16m
Channel Model SCME
Radio environment Urban Micro
Mobile speed 0km/h
Linear precoding schemes ZF, MMSE
Non-linear precoding schemes
Tomlinson-Harashima Precoding (ZF, MMSE)Vector Precoding (ZF, MMSE)
CQI feedback frequency, error, and delay
Assume perfect CSI
Antenna configuration 4x4
BS antenna spacing 10
Receiver algorithm Single antenna and 2 antenna-MMSE
Channelization Localized
Resource allocation size 3RB per user
Channel Coding 16e CTC
Modulation QPSK
Code rate 1/2
Channel Estimation Perfect channel estimation
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Frame Error Rate performance
• Significant gain can be achieved in EMD environment
• The simple THP with MMSE gives almost the same performance of VP.
s
n
Normalized SNR=
;transmit power, ;noise powers n
5 10 15 20 25 3010- 3
10- 2
10- 1
100
Normalized SNR
FER
SCME, QPSK 4x4 CTC(R=1/ 2) 10MHz, 3RU per user, MS has 2 antenna
ZFTHP(ZF)VP(ZF)MMSETHP(MMSE)VP(MMSE)
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NLP has no complexity problem
• THP– Need QR or cholesky decomposition to get up
per triangle matrix.
• VP– Need channel inversion and search algorithm
to search the optimum transmit sequence
• THP is fair complexity compared with SVD based linear precoding
• VP is higher complexity, however, but it is vender dependent.
7
NLP is not affected by the scheduling
• Linear precoding could have gain by choose semi-orthogonal users through user scheduling.
• However, the gain is not significant because these users don’t always require connections or transmissions.
• Moreover, the user scheduling bring system complexity.
• NLP is not affected by the scheduling or user allocation.
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NLP is more robust
• The simulation results (842r1) show that NLP is robust for CSI errors at Tx
• Simulation parameters– SCME urban micro channel model @ 2.5GHz, 10
MHz bandwidth 10MHz (FFT size is 1024), 3kmph– Packet composed of 10 PUSC subchannels
• 2 frequency subchannels and 5 time slots– R=1/2 Conv. Code (o133, o171)– 10 OFDM symbol delay between UL CSI estimatio
n and DL, 2D MMSE channel estimator– Pilot pattern: 16e PUSC– 4TX(BS), 4 1RX(MS) served– Normalized energy at TX(BS): not per user(MS)
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Summary
• Non-linear precoding provides high performance gain as we showed in further study.
• SDD should support non-linear precoding.
10
Text proposal
• 11.8.2.2.1 Precoding technique
• Non-linear precoding is FFS supported.
• In the non-linear precoding, the value shows the processed transmit signal to reduce the transmit power. The receiver may need Modulo operation.
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