Download presentation

Presentation is loading. Please wait.

Published byHector Powers Modified over 8 years ago

1
Using Probability Factor for MS feedback in E-MBS Document Number: IEEE C802.16m-09/0071 Date Submitted: 2009-01-05 Source: Hua Xu, Steven Xu, Suresh Kalyanasundaram, Vinod RamachandranVoice: +1 847 632 2176 Motorola E-mail: hua.xu; steven.xu; suresh.kalyanasundaram; vinodkumar@motorola.comhua.xu; steven.xu; @motorola.com Venue: IEEE 802.16m-08/052: Call for Contributions and Comments on Project 802.16m System Description Document (SDD) Contributions on topic: “PHY aspects of enhanced MBS” Base Contribution: Purpose: For 802.16m discussion, adoption into the SDD and eventual adoption for standardization 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
Introduction Using common feedback is agreed in current E-MBS harmonized text Dynamic MCS adaptation is needed UL feedback overhead can still be huge if without any optimization –In addition, UL interference to other cells will also be large in case a large number of users use the same shared feedback channel It is unnecessary that all MSs need to send the feedback when MS failed to decode MBS burst. Significant increase can still be achieved in downlink MBS data rate (capacity) by ignoring a small percentage of users

3
General Principle Probability factor applies only to mobiles failed to use current MCS Feedback scheme designed towards maximizing capacity while ignoring only the tolerable fraction of mobiles, and while not exceeding the desired UL feedback load Able to better estimate the mobiles not able to operate at current MCS

4
Procedure The BS broadcasts a probability factor. Mobiles which are unable to use current MCS (due to poor channel conditions) will send feedback with this probability factor. –mobiles that can receive current MBS data burst do not send feedback. The BS estimates number of mobiles failed to use current MCS based on the number of feedbacks received. The BS chooses MCS which is the largest possible MCS than can be supported by a certain fraction, for example, 95% of all users. The BS adjust the probability factor such that the number of users sending feedback is smaller than the chosen threshold

5
Based on the estimate, if the fraction of ignored users is smaller than desired threshold less a small value, then increase MCS by one step If fraction of ignored users is larger than desired threshold, reduce MCS by one step Probability factor is updated as follows, where N is the desired number of mobiles that should report One sample algorithm for determining the MCS and probability factor to use is detailed below. Other variants are possible. Let n be the actual number of mobiles failed to use current MCS, and a be the IIR filter coefficient for estimating number of mobiles failed to use current MCS We estimate the number of mobiles whose channel condition is poorer to use current MCS as follows: Detailed procedure

6
Benefits In case a number of orthogonal codes are used for send ing shared feedback –We can reduce the number of orthogonal codes needed –Reduced intra-cell interference Reduced inter-cell interference by reducing the number of users that send feedback Improved ability to control the actual percentage of dropped mobiles (i.e. makes it easier to obtain the data rate advantage) Battery savings at mobile

7
Summary Probability Factor can significantly reduce feedback overhead and interference while can still achieve increase in MBS throughput –Will still be able to accurately estimate the number/percentage of users unable to decode the chosen MCS BS may dynamically adjust the probability factor to achieve better performance

8
Proposed SDD Text Section 11.9.1.6 E-MBS Feedback E-MBS may employ a common uplink channel which is used by MSs to transmit feedback. E-MBS feedback transmission through a dedicated channel is FFS. If a predefined feedback condition is met, a NACK is transmitted through a common E-MBS feedback channel with probability factor. The feedback condition and probability factor may be configured by either the BS or the network. During E-MBS service initiation, a common feedback channel per E-MBS service may be allocated. The allocation of more than one common E-MBS feedback channel per E-MBS service is FFS. The allocation of the common E- MBS feedback channel may be configured by the BS. The allocation of the common E-MBS feedback channel configured by the network is FFS. Probability factor should be used to reduce E-MBS feedback overhead. BS may dynamically change the probability factor. Other methods for reducing E-MBS feedback overhead are FFS, e.g. probabilistic transmission. The use of the feedback channels for other purposes, (e.g., counting) is FFS.

Similar presentations

© 2024 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google