TIME SYNCHRONIZATION AND LOW COMPLEXITY

1、TIME SYNCHRONIZATION AND LOW COMPLEXITY DETECTION FOR HIGH SPEED WIRELESS LOCAL AREA NETWORK Timing synchronization in IEEE 802.11n systemsPresentation OutlineAbstractIEEE 802.11n standard, goals and its challengesReview of IEEE 802.11a preamble and its usage802.11n operating modes and frame formats

2、Timing synchronizationLiterature surveyProposed coarse timing estimationProposed fine timing estimationSimulation setup and results discussionConclusion AbstractA low complexity timing synchronization method for the systems leased on the MIMO-OFDM1 based 802.11n standard is proposedTwo high throughp

3、ut operating modes in IEEE 802.11n: Mixed mode where 802.11a/g legacy systems and 802.11n based MIMO-OFDM systems shall co-existGreenfield mode where only 802.11n enabled MIMO-OFDM systems existsFor timing synchronization purposes,Mixed mode : short training field (STF) and long training field (LTF)

4、 in preambleGreenfield mode : Only short training field in preambleEssentially, two time sync algorithms are needed for MIMO modesProposed algorithm uses only STF for timing synchronization and achieves same performance as LTF based algorithmThe STF structure is same on both the modes, so a single t

5、ime sync algorithm can be implemented for all the high throughput modes.1MIMO-OFDM Multiple input multiple output Orthogonal frequency division multiplexingWLAN standardsWi-Fi standards- IEEE 802.11 standard, 1997; 2 Mbps, 2.4GHz, CSMA/CA- IEEE 802.11b std, 1999; 11 Mbps, 2.4GHz, CSMA/CA- IEEE 802.1

6、1a std, 1999; 54 Mbps, 5GHz, CSMA/CA- IEEE 802.11g std, 2003; 11 Mbps & 54 Mbps, 2.4 GHz, CSMA/CA- IEEE 802.11n draft, 2006; 500 Mbps, 2.4 GHz, CSMA/CA802.11n standard Goals and its challengesAchieve higher data rates (around 500 Mbps)Use of MIMO-OFDM technologySupports 20MHz and 40 MHz bandwidth op

7、erationInteroperable with 802.11a/g legacy systemsIncreased complexityMultiple radio frequency (RF) and baseband (BB) chains requiredSpatial detection techniquesBackward compatibilityMIMO-OFDM system should be able to decode the legacy packetsLegacy system should atleast know about the MIMO-OFDM tra

8、nsmission to avoid collisionDesign of preamble impacts on initial receiver tasksReview of IEEE802.11a frameShort training fieldLong training fieldSSSSSSGILS1LS2SIGSignal FieldDataShort symbols1. Start of packet (SOP) detection2. Automatic gain control (AGC)3. Coarse timing estimation4. Coarse freque

9、ncy offset estimationLong symbols5. Fine timing estimation6. Fine frequency offset estimation7. Channel estimation8. Data detectionReceiver tasksInitial receiver tasksAGC &Synchro. ModeCh. Estimation ModeCorrection & Tracking modeStartofpacketAcquisition modePacket detectedTime & frequencyAcquiredCh

10、annel estimatedOffsetupdateData detectionEnd of packet802.11n frame formatsNon-High Throughput frame formatShort training fieldLong training fieldSIGDATASSSSLSLSCPSS Used in the legacy network where only the 802.11a/g enabled devices are present Content is identical to the frame defined in the IEEE

11、802.11a standard STF Short training field LTF Long training field802.11n frame formats contd.DATALegacy format PreambleHigh throughput PreambleL-STFL-LTFL-SIGHT-SIGHT-STFHT-LTFHT-LTFnHigh throughput mixed frame formatHigh throughput stations and legacy stations shall co-existsMIMO stations should tr

12、ansmit and receive the legacy frames and HT framesFor compatibility reasons, Initial preamble part is provided with the first three fields of non-HT preambleHT-SIG, HT-STF and HT-LTFs are used decoding the MIMO packetsIf the tranmission is intended for MIMO_OFDM system, then based on the number of T

13、X antennas cyclic shift is applied as shown in table1802.11n frame formats contd.H-STFHigh throughput PreambleH-LTFH-SIGHT-LTFHT-LTFnDATAHigh throughput Greenfield frame formatOnly HT MIMO-OFDM stations can exist All the training fields specific to MIMO-OFDM systems HT-STF is identical to the L-STF

14、field of mixed mode and is used for timing acquisition, AGC and frequency acquisition For TH-SIG demodulation, channel estimates are obtained from first HT_LTF fieldsRemaining HT-LTFs are used for estimating the channels across multiple transmit and receive antennasFrames in different TX antennas ar

15、e cyclically shifted based on table2 before transmissionCyclic shift for HT frame transmissionNumber of Transmit chainCyclic shift for Tx chain1 ( )Cyclic shift for Tx chain2 ( )Cyclic shift for Tx chain3 ( )Cyclic shift for Tx chain4 ( )1020-20030-100-20040-50-100-150Table1. Cyclic shift for the no

16、n-HT portion of the packet Table2. Cyclic shift for the HT portion of the packet Number of Transmit chainCyclic shift for Tx chain1 ( )Cyclic shift for Tx chain2 ( )Cyclic shift for Tx chain3 ( )Cyclic shift for Tx chain4 ( )1020-40030-400-20040-400-200-600For Backward compatibility802.11nAccess poi

17、ntLegacy modeGreen field modeMixed modeOnly frames in legacy formatPreambles that are specificto MIMO-OFDM systems Preamble should be compatible to legacy stations Should work better for MIMO-OFDM systemsTypical 802.11n network802.11nAccess point802.11g802.11g802.11g802.11n802.11n802.11g802.11g802.1

18、1nActive nodeInactive nodeLegacy modeGreen field modeMixed mode802.11nTypical MIMO-OFDM system modelSpatial DemuxOFDM TXSpatial DetectionOFDM TXOFDM RXOFDM RXSpatial MuxchannelTransmitterReceiverNtxNr MIMO-OFDM systemReceived signal modelis the transmitted signal from the TX antenna whereis the impu

19、lse response of the channel between the transmit and receive antennaReceived signal at the receive antenna is the AWGN at the RX antenna with zero mean and variance is the normalized frequency offsetis the channel length and remains static across The total power transmitted is normalized across the

20、transmit antennas and is given asTiming synchronizationTiming synchronizationTo estimate the sampling time of the OFDM symbolThe start of OFDM symbol varies based on the strongest path of the fading channelNon-optimal sampling causes ISI and ICIDone in two stepsCoarse timing offset (CTO) estimationF

21、ine timing offset (FTO) estimationCoarse timing offset estimationRough estimate is obtainedAfter start of packet detection and AGC, timing estimator is triggeredFine timing offset estimationOptimal starting of OFDM symbol is obtainedLiterature surveyIn 4, T. M. Schmidl and D.C. Cox had proposed a ma

22、ximum likelihood (Ml) synchronization timing estimation method for a SISO-OFDM system. An extension of this method for MIMO-OFDM system was proposed in 5 by A. N. Mody and G.L. Stuber, and in 6 by A. Van Zelst and Tim C. W. Schenk. The drawback of these methods is that the preambles assumed in the p

23、apers are not the same as in the 802.11n standards. In 7, Jianhua Liu and Jian Li presented a timing synchronization technique for a preamble that is similar to the one in the 802.11n standard.However, the computational complexity of this method is high due to the cross correlation performed on the

24、LTF for fine timing estimation. Coarse timing offset estimationThe objective of the CTO estimator is to find the rough starting position of any of the short symbolTypically 5-6 blocks of SS is taken for AGC operationCoarse timing estimation can be performed only after AGC convergence.An easy way is

25、to find the end of the STF by using the autocorrelation property of the received signal. Proposed Coarse timing offset estimationProposed coarse timing estimation techniqueA metric is calculated from the instant k at which the AGC is converged This metric is similar to the one in 7 and is given aswh

26、ereandis the value of the cross correlation between the signal and noise terms is the sum of noise energy and value of cross correlation between the signal and noise terms Step1:Proposed CTO estimator contd. with The metric will form the end of the plateau and could be noisy due to AWGN and multipat

27、h fading conditions To have a smooth plateau, the current metric is filtered through a weight filter and is given asWhere is the weight factor given to previous value and is the weight applied to the current metric The value of metric can take different values based on the index.is the sum of the cr

28、oss correlation of the signal and noise terms, and cross correlation between samples from STF and LTF.Since the fields STF and LTF are highly uncorrelated, the parameter decreases withthereby reduction inPlot of metric1Reference for metricMetricThe falling end of plateau is noisy and getting a coars

29、e timingestimates will be erroneousThreshold based detectionMetric forms a Plateau - 2x2 system under the channel D with SNR=10dBProposed CTO estimator contd.The value of metric depends on the instantFor with the metric will be represented as and represents the averaged power of the STF and LTF resp

30、ectively The total averaged power of the difference signal will increase as n increases. This is because of the contributions from LTFA smoothing operation is done on the metric by weighted averaging and is given as A new metric which is the average power of a difference signal over a window of samp

31、les is defined from the instant The metric is given asStep2:Plot of CTO metricsIntersection pointM2Metric plotted for a 2x2 system under the channel D without noiseProposed CTO estimator contd.The metrics and can be used to get a reliable estimate of the CTO Steady increase in metric2 from and stead

32、y decrease in the value of metric1 from The intersection point between these two metrics is estimated as the coarse time The instant should lie within the range , At low SNR, both the metrics will be noisy and fluctuating and this would result in wrong estimate There might more than one intersecting

33、 point due to fluctuations To avoid this a simple condition is proposedLet be the intersecting point then this instant will be chosen as the CTO estimate when the conditions given below are satisfied , , Where is the number of samples used to make sure that the estimate is not a false alarm due to n

34、oise Plot of metricsMetric 1Reference for metric 1Reference for metric 2Metric 22x2 MIMO-OFDM system;Channel model D; SNR=10dBProposed Fine timing offset estimationProposed fine timing estimatorThe objective of the fine timing offset estimator is to find the exact start of the OFDM symbol In multipa

35、th channel conditions this might not be possible because the strongest path could occur at non-zero delays In the proposed FTO estimator, we find an index in the starting of the 9th SS where the sum of channel impulse response energy is maximum between the receive antenna and transmit antennaThis is

36、 achieved by using the correlation property of the STF and the advantages of the cyclic shift Achieved in two steps Proposed FTO estimator contd.Step1:A simple cross correlation is performed between the received signal and the transmit signal The fine timing offset estimation algorithm is triggered

37、from the index The received signal at each receive antenna is correlated with all the transmit signals Then, the cross correlation output between RX antenna and TX antenna is given asLet be the received signal at the RX antenna after coarse frequency offset correction,Since the received signal at ea

38、ch receive antenna contains multiple versions of the transmit signal in cyclically shifted manner, the cross correlation between the received signal and the transmit signal will result in multiple peaks Each peak corresponds to the total channel energy between transmit and receive antennas The posit

39、ion corresponding to the first peak of the first receive antenna output sequence is the fine timing estimate For example Let us assume the coarse timing estimate and all the channel impulse responses have the strongest path at zero delay For the 4x4 mixed mode system The cross correlation output bet

40、ween the first transmit antenna signal and the first receive antenna signal will have 4 peaks placed consecutively from Detecting the first peak is quite tricky due to multiple peaks that corresponds to different channel power between transmit and receive antennas To choose the first peak, we propos

41、e a simple technique Proposed FTO estimator contd.Cross correlated output - ExampleFor a 4x4 system0 1 2 13, 14, 150 1 2 13, 14, 150 1 2 13, 14, 150 1 2 13, 14, 15Antenna1Antenna2Antenna4Antenna3Proposed FTO estimator contd.With reference to the table1a for mixed mode, we propose the metrics , and f

42、or different antenna configurations as shown below The cyclic shift 50us, 100us, 150us and 200us applied at the transmit antenna corresponds to numerical shift 15, 14, 13 and 12 that is applied at the correlated output obtained from different transmit signals. The index corresponding to the maximum

43、of absolute of the metric is determined as the fine timing offset. Step2:Complexity analysisIn case of the conventional LTF based FTO estimator, the complex cross correlation should be performed between 64 samples length long symbol and the received signal. In the proposed FTO estimator, the cross c

44、orrelation is performed between 16 samples length short symbol and the received signal Simulation and performance analysisPerformance of coarse timing estimatorProbability distribution of CTO estimate is plottedCompared to the performance of threshold based techniqueSystem model2x2, 3x3 and 4x4 ante

45、nna configurationMIMO Channel modelTGn channel modelsSNR = 8dBParameters of coarse timing estimatorFor threshold based technique as in 7Mixed mode and green field modeThreshold c2=0.6 and Q2=15 samplesFor proposed techniqueMixed mode and green field modeThreshold =0.45 and Q=8 samplesSmoothing filte

46、r weight = 0.5 for both the metricsProbability of coarse timing offset estimate of conventional and the proposed technique.Estimation accuracy of the CTO estimator is 0, Probability of getting zero CTO is high for the algorithm proposed in threshold based techniqueSignificant probability of the CTO

47、obtained using this algorithm is going beyond the defined estimation accuracy In the proposed algorithm, estimates are more stable and lie within the estimation range Comparison of probability of CTO estimates for different antenna configurationsProbability of CTO estimates within the estimation acc

48、uracy Proposed algorithm performs better at the lower SNR values as compared to the CTO estimation algorithm in 7 As the number of antenna increases, the spatial diversity is leveraged resulting in a better performance for higher antenna configuration Impact of channel modelsProbability of CTO estim

49、ates within the estimation accuracy for proposed algorithm in different channel models The maximum probability is achieved at 10dB SNR for a 2x2 systemMotivation to use only the STF for the fine timing offset estimation Performance of fine timing estimatorProbability distribution of fine timing esti

50、mate is plottedCompared to the performance of simple cross correlation based technique using LTFSystem model2x2, 3x3 and 4x4 antenna configurationsMIMO Channel modelTGn channel modelsComparison of probability of FTO estimates with LTF based FTO estimatorThe estimation accuracy is defined with the ra

51、nge 0, 3. Computationally complex LTF based FTO LTF will have slightly better performance as compared to proposed techniqueThe probabilities of the FTO estimates within the estimation accuracy is plotted for the 3x3 and 4x4 systems of mixed mode. Due to better noise averaging ConclusionA low complex

52、ity time synchronization algorithm is proposedThe proposed techniques performs better even at lower SNRs.Using only STF, a single coarse and fine timing estimation technique will be used for both the high throughput modesSame performance is achieved as LTF based timing synchronizationThereby reducin

53、g total complexity of the systemReferences1. IEEE P802.11n/D2.00, “Draft standard for Information Technology-Telecommunications and information exchange between systems-Local and metropolitan area networks-Specific requirements-“, Feb 20072. IEEE 802.11a standard, ISO/IEC 8802-11:1999/Amd 1:2000(E),

54、 /getieee802/download/802.11a-1999.pdf 3. IEEE 802.11g standard, Further Higher-Speed Physical Layer Extension inthe2.4GHzBand, /getieee802/download /802. 11g-2003.pdf4 T. M. Schmidl and D.C. Cox, “Robust Frequency and Timing Synchronization for OFDM”, IEEE Trans. on Communications, vol. 45, no. 12,

55、 pp. 1613-1621, Dec. 1997.5. A. N. Mody and G.L. Stuber, “Synchronization for MIMO-OFDM systems,” in Proc. IEEE Global Commun. Conf., vol. 1, pp.509-513, Nov.20016 A. Van Zelst and Tim C. W. Schenk, “Implementation of MIMO-OFMD based Wireless LAN systems”, IEEE Trans. On Signal Proc. Vol. 52, No.2,

56、pp. 483-494, Feb 20047 Jianhua Liu and Jian Li, “A MIMO system with backward compatibility for OFDM based WLANs”, EURASIP journal on Applied signal processing. Pp. 696-706, May 20048 IEEE P802.11 TGn channel models, May 10 2004,http:/www.ece. ariz /yanli/files/11-03-0940-04-000n-tgn-channel-models.d

57、oc Low Complexity MIMO-OFDM System for High Speed WLANsPresentation OutlineIntroductionSystem model and channel modelMIMO-OFDM1 detection techniquesProposed Group ordered MMSE V-BLAST2 detectionSimulation resultsConclusion 1MIMO-OFDM Multiple input multiple output Orthogonal frequency division multi

58、plexing2MMSE V-BLAST Minimum mean square error Vertical bell labs layered space time systemIntroductionMIMO-OFDM is a promising technique to increase data transmission rate in wireless frequency selective fading channels1,2The key technique behind the MIMO-OFDM system is the spatial detection at the

59、 receiverSpatial DemuxOFDM TXSpatial DetectionOFDM TXOFDM RXOFDM RXSpatial MuxchannelTransmitterReceiver802.11n MIMO OFDM baseband transmitter1Spatial mappingStream ParserFEC EncoderEncoder ParserScrambler1FEC EncoderInterleaverQAM MapperInterleaverQAM MapperIFFT&CPIFFT&CP11802.11n MIMO-OFDM baseban

60、d transmitter802.11n MIMO-OFDM baseband receiver11802.11n MIMO OFDM baseband receiverCP&FFTSpatial Detector and demapping(Zero forcing, MMSE, SIC, etc)CP&FFTQAM De-MapperDeinterleaverQAM De-MapperDeinterleaverDescramblerMUXDECODERDECODERStream De-parser1Decoded bitsRX antennasSignal model and MIMO c