1. 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: tsuguhide.aoki@toshiba.co.jp, sun@toshiba-trel.comtsuguhide.aoki@toshiba.co.jpsun@toshiba-trel.com Laurent Marc de Courville, Fred Vook (Motorola) E-mail: marc.de.courville@motorola.com, fred.vook@motorola.commarc.de.courville@motorola.comfred.vook@motorola.com Ron Porat (Nextwave) E-mail: RPorat@nextwave.comRPorat@nextwave.com Isamu Yoshii, Takaaki Kishigami (Panasonic) E-mail: yoshii.isamu@jp.panasonic.com, kishigami.takaaki@jp.panasonic.comyoshii.isamu@jp.panasonic.com * 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. Patent Policy: The contributor is familiar with the IEEE-SA Patent Policy and Procedures: and.http://standards.ieee.org/guides/bylaws/sect6-7.html#6http://standards.ieee.org/guides/opman/sect6.html#6.3 Further information is located at and.http://standards.ieee.org/board/pat/pat-material.htmlhttp://standards.ieee.org/board/pat

2. 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. 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. 4 Link-level simulation parameters ParametersValues Bandwidth10MHz FFT size1024 Carrier Frequency2.5GHz Subframe structure16m Channel ModelSCME Radio environmentUrban Micro Mobile speed0km/h Linear precoding schemesZF, MMSE Non-linear precoding schemes  Tomlinson-Harashima Precoding (ZF, MMSE)  Vector Precoding (ZF, MMSE) CQI feedback frequency, error, and delay Assume perfect CSI Antenna configuration4x4 BS antenna spacing 10 Receiver algorithmSingle antenna and 2 antenna-MMSE ChannelizationLocalized Resource allocation size3RB per user Channel Coding16e CTC ModulationQPSK Code rate1/2 Channel EstimationPerfect channel estimation

5. 5 Frame Error Rate performance Significant gain can be achieved in EMD environment The simple THP with MMSE gives almost the same performance of VP. Normalized SNR= ;transmit power, ;noise power

6. 6 NLP has no complexity problem THP –Need QR or cholesky decomposition to get upper 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. 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.

8. 8 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, 10MHz 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 estimation 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)

9. 9 Summary Non-linear precoding provides high performance gain as we showed in further study. SDD should support non-linear precoding.

10. 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.