[go: up one dir, main page]

WO2008153798A2 - Inférence/prédiction d'état de voie utilisant des variables observables - Google Patents

Inférence/prédiction d'état de voie utilisant des variables observables Download PDF

Info

Publication number
WO2008153798A2
WO2008153798A2 PCT/US2008/006745 US2008006745W WO2008153798A2 WO 2008153798 A2 WO2008153798 A2 WO 2008153798A2 US 2008006745 W US2008006745 W US 2008006745W WO 2008153798 A2 WO2008153798 A2 WO 2008153798A2
Authority
WO
WIPO (PCT)
Prior art keywords
data packet
error rate
bit error
data packets
given
Prior art date
Application number
PCT/US2008/006745
Other languages
English (en)
Other versions
WO2008153798A3 (fr
Inventor
Shirish S. Karande
Syed Ali Khayam
Yongju Cho
Hayder Radha
Jaegon Kim
Jin-Woo Hong
Original Assignee
Board Of Trustees Of Michigan State University
Electronics And Telecommunications Research Institute
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Board Of Trustees Of Michigan State University, Electronics And Telecommunications Research Institute filed Critical Board Of Trustees Of Michigan State University
Publication of WO2008153798A2 publication Critical patent/WO2008153798A2/fr
Publication of WO2008153798A3 publication Critical patent/WO2008153798A3/fr

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00—Arrangements for detecting or preventing errors in the information received
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00—Arrangements for detecting or preventing errors in the information received
    • H04L1/20—Arrangements for detecting or preventing errors in the information received using signal quality detector

Definitions

  • FIELD [0001] The present disclosure relates generally to channel state inference and/or prediction using observable variables.
  • Figures 1A and 1 B are diagrams of exemplary topologies for wireless trace collection
  • Figures 2A-2F are charts illustrating average statistics deduced from error traces
  • Figure 3 is a graph showing the average value of BER over corrupted packets as a function of SSR;
  • Figure 4 is a flowchart illustrating a method for deriving models for estimating BER of a data packet;
  • Figure 5A and 5B are charts showing the concentration gains for two exemplary traces
  • Figure 6 is a chart illustrating the temporal correlation in BER between data packets
  • Figure 7 is a flowchart illustrating a method for predicting the BER of a data packet.
  • Figures 8A and 8B are graphs showing the concentration loss for two exemplary traces.
  • the drawings described herein are for illustration purposes only and are not intended to limit the scope of the present disclosure in any way. DETAILED DESCRIPTION
  • FIG. 1A A measurement based study of 802.11b wireless LANs is presented that analyzes the utility of observable variables in channel state inference and prediction.
  • Figures 1A and 1 B Two wireless setups for collecting error traces as shown in Figures 1A and 1 B.
  • Figure 1A five wireless receivers were used to simultaneously collect error traces on an 802.1 1 b WLAN.
  • One receiver was placed within clear line-of-sight (LoS) of the access point (AP), while the remaining four receivers were placed at different locations in a room across the hallway (referred to herein as setup A).
  • LiS line-of-sight
  • setup B six receivers were used to simultaneously collect error traces (referred to herein as setup B).
  • a wired sender was used to send multicast packets with a predetermined payload on the wireless LAN; multicasting disabled MAC layer retransmissions.
  • Each experiment comprised of one million packets with a payload of 1,000 bytes each.
  • the auto rate selection feature of the AP was disabled and for each experiment the AP was forced to transmit at a fixed data rate.
  • Each trace collection experiment was repeated for 5.5 and 11 Mbps physical layer (PHY) data rates. For each PHY data rate and for each SETUP, we collected traces for two distinct packet transmission rates.
  • PHY physical layer
  • the transmission rate is controlled by adjusting the time interval * between packets.
  • setup A we collected traces at 500 Kbps and 1024 Kbps, while in Setup B we collected traces at 750 Kbps and 900 Kbps.
  • Setup B we collected traces at 750 Kbps and 900 Kbps.
  • Table I describes the numbering we shall use for the remainder of the work.
  • the receivers' MAC layer device drivers were modified to pass corrupted packets to higher layers.
  • packet dissectors were implemented inside the device drivers. These packet dissectors ensured that only packets pertinent to our wireless experiment are processed, while all other packets are simply dropped.
  • three additional parameters were logged at the receivers:
  • Background traffic A four byte number representing the total number of background packets observed between two trace packets;
  • FIGS. 2A-2F show some of a set of average statistics deduced from the error traces we consider in this work. These average statistics should provide a good representation of the wireless environment in which we have conducted our experiments.
  • Figure 2A and 2D show ⁇ the proportion of packets that were corrupted
  • Figure 2B and 2E show the average value of SSR
  • Figure 2C and 2F show the average value of BT intensity p.
  • trace 3 and 20 which correspond to the LOS client have a very good link quality and rarely see any packet corruptions. Thus we often exclude these traces from our analysis.
  • the value of p was less than 50 packets/s for most traces but for certain traces the intensity can be well above 200 packets/s. Comparing the plots for 5.5MBps and 11 MBps it can be seen that the long-term average value of SSR for a specific link does not vary significantly, however the same cannot be said about the BT. Additionally, note that as expected the packet corruption ratio is lower when the PHY data rate is 5.5Mbps as compared with the 11 Mbps traces.
  • CSI we specifically refer to a problem where we want to estimate the BER in a packet that has already been received. Accuracy of such estimates plays an important role in soft-decoding algorithms and can also be important for a variety of other reactive protocols.
  • Figure 3 shows the average value of BER ( ⁇ ), over all the corrupted packets as a function of SSR, for the cases when there is no BT and when there is heavy BT.
  • BER in the packet is ⁇ .
  • * is made up of binary symbols and the probability distribution on these symbols is completely defined by the parameters . All the discussion in this section is strictly for the case of binary symbols and often we shall use ⁇ to actually represent the probability distribution on the binary symbols.
  • f( ⁇ , ⁇ can be interpreted as a "loss in capacity" on account of assuming that the binary process ⁇ x is governed by the Bernoulli parameter ⁇ when it is actually governed by ⁇ .
  • This "capacity loss” interpretation can occur, for example, if additional information needs to be transmitted to compensate for the distance o( ⁇
  • a data set A which is a set of packets, enforces a probability distribution / > A (-) on the types ⁇ ⁇ or on the parameter ⁇ . / > A ( ⁇ ) represents the frequency with which a packet of type ⁇ ⁇ is observed in a data set A .
  • Our approach is based on choosing a representative type r. for a set A so that the average cost of mis-representing members of A by type T- is minimized.
  • Models are derived from a plurality of data packets which serve as training data and are indicative of the data link over which data packets traverse.
  • Each data packet in the training data is labeled at 41 with at least one observable parameter, such as SSR and/or BT, and actual bit error rate for the packet. It is understood that this technique may be extended to other types of observable parameters associated with a data packet.
  • Data packets having similar observable parameters are grouped together at 42 to form groups of data packets.
  • groups are empirically formed. However, it is envisioned that other techniques for grouping the data packet may be employed.
  • a cost function for estimating bit error rate is defined at 43 in the manner described above. For each group of data packets, the cost function is then minimized at 44, thereby determining an estimated bit error rate for data packets having the observable parameter associated with the given group of data packets.
  • a corresponding model is selected based on its observable parameters. This technique may be used to develop models for estimating other types of unobservable parameters of a data packet from freely observable parameters associated with the data packet.
  • N simply represents the total number of packets in each trace.
  • SSR SSR
  • p side-information to obtain the estimate S, .
  • S 1 S(SSR 1 )
  • S 1 S(P 1 )
  • S,(I) S(SSR,,P, ) . Since we are concentrating all our analysis on corrupted packets it is implicit that Z is always being used as side-information.
  • Figure 5A and 5B show the concentration gains for 5.5Mbps and 11 Mbps, respectively. It can be clearly seen that both BT and SSR can provide concentration gains. Note a gain of 3dB implies an improvement in concentration by a factor of 2. Thus for 14/22 traces collected at 11 Mbps and for 10/22 traces collected at 5.5MBps, utilizing SSR as side-information can improve the accuracy of estimating BER by at least a factor of 2. It can be clearly seen that on some traces the improvement is in excess of 24 dB which corresponds to an improvement in concentration by a factor greater than 250. Thus clearly the improvement in CSI by just utilizing SSR, despite the presence of BT, can be significant in many practical scenarios.
  • the gains provided by BT are modest. However, there still exist a few traces, e.g. 6, 1 1 , 14, 19, 21 at 11 Mbps and 11 , 12, 16 at 5.5Mbps where the gain is close to or above 3dB. In certain cases it is possible to combine SSR and BT, to jointly use them as side-information, to achieve further gains. For example, see traces 11 and 14 for 11 Mbps, and traces 11 , 12 and 16 for 5.5Mbps.
  • An accurate estimate for BER of a data packet has many uses.
  • One exemplary use is to improve the error recovery process for corrupt data packets received in a wireless communication system.
  • the BER is estimated for each individual data packet received at a receiver.
  • the BER is estimated at a data link layer of the receiver or some other layer receiver below an application layer as defined by an Open System Interconnection (OSI) model.
  • OSI Open System Interconnection
  • the BER for each data packet is then passed to an application layer of the receiver and an error recovery operation is performed in relation to a corrupt data packet using the BER associated with the corrupt data packet. More specifically, the BER is translated to a probability that a given bit in the data packet is in error and this probability is in turn used to decode each bit within the corrupt data packet.
  • CSP channel state prediction
  • the CSP can be employed or communicated to the transmitter with the help of feedback.
  • robust CSP can be used for controlling the rate of source and channel codes.
  • Figure 3 shows the correlation coefficients calculated on the basis of the BER process.
  • the coefficient can be calculated simply as r_ g t ⁇ g
  • Figure 6 clearly exhibits the existence of temporal correlation (at times significant).
  • Temporal correlation in BER between two adjacent packets can be be taken advantage of to predict the BER in the future packet. This can be done in a simplistic manner by utilizing the BER in the current packet as an estimate for the BER in the next packet:
  • the model may be a Markov model.
  • Other types of statistical models such as Hidden Markow models, hierarchical Markov models or multifractal models, are contemplated by this disclosure.
  • Model based predictor that obtained from equation (8) as SSR based predictor and that that obtained from (9) as SSR + Model based predictor. It is readily understood that other type of observable parameters, including B, may be used as side-information.
  • this method for predicting the BER of a given data packet may be summarized as follows. Another data packet temporally correlated to the given data packet is received 71 at a receiver. For instance, the another data packet immediately precedes the given data packet.
  • a bit error rate for the another data packet is estimated at 72 using variable freely observable in the manner described above. Other techniques for estimating the bit error rate of the another data packet are also within the broader aspects of this disclosure.
  • the bit error rate for the given data packet is then determined at 73 using a model that predicts the bit error rate based on the estimated bit error rate of the temporally correlated data packet.
  • Equation (10) we limit the analysis for our predictors to only predicting errors in the corrupted packets. However, our methods can be easily generalized by developing a packet level model for predicting the event of a packet being corrupted. Also note that in equation (10) N n just merely represents the number of times we see two consecutive corrupted packets in trace
  • Model specifically refers to the predictor g A ( ⁇ .) obtained by training on trace ⁇ , while Me[SSR, SSR+ Model ⁇ . It is important to note that the SSR models are obtained by training on all the traces.
  • Figure 8A and 8B show the concentration loss 5.5Mbps and 1 1 Mbps respectively. Ideally we would like to limit the concentration loss to less than 3db, at worst to less than 5dB (which represents a loss of concentration by a factor of 3). It can be seen that when we use the SSR based predictor the concentration loss, for 14/20 traces at 11 Mbps and for 12/20 traces at 5.5Mbps, is less than 5dB. These numbers drop to 9 and 6 when we want to limit the concentration loss to approx. 3db or less. Thus even though SSR based predictor can often provide satisfactory prediction, we would desire to have mechanism that provides robust prediction more consistently. Hence, to achieve additional gains a link-specific model can be utilized in conjunction with SSR.

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Quality & Reliability (AREA)
  • Detection And Prevention Of Errors In Transmission (AREA)
  • Data Exchanges In Wide-Area Networks (AREA)

Abstract

L'invention concerne la possibilité d'utiliser des variables observables dans l'inférence et la prédiction de l'état d'une voie. Par exemple, des mesures du rapport signal/bruit et du trafic d'arrière-plan peuvent être utilisées pour améliorer la précision de l'inférence de l'état de voie. Un modèle invariant par rapport à la liaison permettant d'estimer le taux d'erreur sur les bits d'un paquet de données est calculé au moyen de paramètres pouvant être observés librement, tels que le rapport signal/bruit et/ou le bruit de fond. On peut aussi obtenir une prédiction en tenant compte de l'estimation, pour un paquet de données en cours, comme prédiction pour un futur paquet de données ou en utilisant un modèle spécifique de liaison qui saisit la corrélation, dans le taux d'erreur sur les bits, de paquets temporellement adjacents.
PCT/US2008/006745 2007-05-30 2008-05-28 Inférence/prédiction d'état de voie utilisant des variables observables WO2008153798A2 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US93229207P 2007-05-30 2007-05-30
US60/932,292 2007-05-30

Publications (2)

Publication Number Publication Date
WO2008153798A2 true WO2008153798A2 (fr) 2008-12-18
WO2008153798A3 WO2008153798A3 (fr) 2009-02-26

Family

ID=39791311

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2008/006745 WO2008153798A2 (fr) 2007-05-30 2008-05-28 Inférence/prédiction d'état de voie utilisant des variables observables

Country Status (2)

Country Link
KR (1) KR101006394B1 (fr)
WO (1) WO2008153798A2 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11159279B2 (en) 2017-05-23 2021-10-26 Samsung Electronics Co., Ltd. Electronic device, and method for Bluetooth-based data transmission in electronic device

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012125008A2 (fr) * 2011-03-16 2012-09-20 한국전자통신연구원 Procédé et appareil servant à estimer l'état d'un canal sans fil au moyen d'informations supplémentaires, et procédé destiné à ajuster le rendement de codage dans un réseau sans fil au moyen de ce procédé et de cet appareil
KR102480856B1 (ko) 2016-06-17 2022-12-23 삼성전자주식회사 블루투스 기반의 무선 통신 시스템에서 스트리밍 데이터의 통신 방법 및 장치

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20010056560A1 (en) 1998-10-08 2001-12-27 Farooq Khan Method and system for measurement based automatic retransmission request in a radiocommunication system
JP2001333050A (ja) 2000-05-19 2001-11-30 Fujitsu Ltd Ｂｅｒ推定装置及びその方法
KR100493079B1 (ko) * 2001-11-02 2005-06-02 삼성전자주식회사 고속 순방향 패킷 접속 방식을 사용하는 광대역 부호 분할다중 접속 통신 시스템에서 순방향 채널 품질을 보고하는장치 및 방법
US7929563B2 (en) 2005-01-20 2011-04-19 New Jersey Institute Of Technology System and/or method for channel estimation in communication systems

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
ANONYMOUS: "Index of /waves/people/kiran_files" INTERNET ARTICLE, [Online] XP002499104 Retrieved from the Internet: URL:http://www.egr.msu.edu/waves/people/kiran_files/> [retrieved on 2008-10-07] *
SHIRISH KARANDE ET AL: "Utilizing SSR Indications for Improved Video Communication in Presence of 802.11B Residue Errors" MULTIMEDIA AND EXPO, 2006 IEEE INTERNATIONAL CONFERENCE ON, IEEE, PI, 1 July 2006 (2006-07-01), pages 1973-1976, XP031033250 ISBN: 978-1-4244-0366-0 *
SHIRISH KARANDE, SYED ALI KHAYAM, YONGJU CHO, KIRAN MISRA, HAYDER RADHA, JAEGON KIM AND JIN-WOO HONG: "On Channel State Inference and Prediction Using Observable Variables in 802.11b Networks"[Online] 13 January 2007 (2007-01-13), XP002498988 Retrieved from the Internet: URL:http://www.egr.msu.edu/waves/people/kiran_files/ICC_CSI_Infer.pdf> [retrieved on 2008-10-07] *
SYED ALI KHAYAM: "Syed Ali Khayam - Resume" INTERNET ARTICLE, [Online] 11 January 2007 (2007-01-11), XP002499105 Retrieved from the Internet: URL:http://www.egr.msu.edu/waves/people/Ali_files/resume.pdf> [retrieved on 2008-10-07] *
YONGJU CHO ET AL: "A Multi-Tier Model for BER Prediction over Wireless Residual Channels" INFORMATION SCIENCES AND SYSTEMS, 2007. CISS '07. 41ST ANNUAL CON FERENCE ON, IEEE, PI, 1 March 2007 (2007-03-01), pages 450-455, XP031131900 ISBN: 978-1-4244-1063-7 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11159279B2 (en) 2017-05-23 2021-10-26 Samsung Electronics Co., Ltd. Electronic device, and method for Bluetooth-based data transmission in electronic device

Also Published As

Publication number Publication date
KR20080106088A (ko) 2008-12-04
WO2008153798A3 (fr) 2009-02-26
KR101006394B1 (ko) 2011-01-10

Similar Documents

Publication Publication Date Title
Vlavianos et al. Assessing link quality in IEEE 802.11 wireless networks: Which is the right metric?
Koksal et al. Quality-aware routing metrics for time-varying wireless mesh networks
US11070301B2 (en) Learning-based wireless transmission parameter adaptation based on client activity detection
CN103716123A (zh) 一种传感器网络中分组丢失和错误的原因识别方法和系统
Rekik et al. Low-power link quality estimation in smart grid environments
CN104780006A (zh) 基于最小错误概率准则的频谱检测器软融合方法
US7436810B2 (en) Determination of wireless link quality for routing as a function of predicted delivery ratio
WO2015168120A1 (fr) Gestion d'interférences en connaissance des interférences par rafales, faisant appel à des longueurs de suites
WO2008153798A2 (fr) Inférence/prédiction d'état de voie utilisant des variables observables
Zhang et al. Models for non-intrusive estimation of wireless link bandwidth
Chen et al. Towards accurate corruption estimation in zigbee under cross-technology interference
Kanto et al. Wireless Link Quality Estimation Using LSTM Model
Huang et al. EasiPLED: Discriminating the causes of packet losses and errors in indoor WSNs
CN110996341B (zh) 一种跨协议干扰下的低功率无线链路质量估计方法
Ilyas et al. Measurement based analysis and modeling of the error process in IEEE 802.15. 4 LR-WPANs
Karande et al. On channel state inference and prediction using observable variables in 802.11 b network
US20240373287A1 (en) Capacity Monitor For Cross-Layer Cellular Protocol
US20150318936A1 (en) Bursty-interference-aware interference management utilizing conditional metric
Cai et al. Non-intrusive, dynamic interference detection for 802.11 networks
Sutton et al. Modelling capture effect for 802.11 DCF under Rayleigh fading
US20080037434A1 (en) Modeling of residue errors using models that adapt on the basis of link quality indicators
Cho et al. A multi-tier model for BER prediction over wireless residual channels
Cao et al. Mrs. Z: Improving ZigBee throughput via multi-rate transmission
Guo et al. Improving packet delivery ratio estimation for indoor ad hoc and wireless sensor networks
CN113364715B (zh) 一种基于信用值投票机制的协作式自动调制分类方法

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 08767910

Country of ref document: EP

Kind code of ref document: A2

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 08767910

Country of ref document: EP

Kind code of ref document: A2