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Wireless communications group, AU-KBC Research centre 1 CHANNEL ESTIMATION AND LOW COMPLEXITY DETECTION FOR HIGH SPEED WLANs A THESIS Submitted by MUTHURAJA N In partial fulfillment for the award of the degree of MASTER OF SCIENCE (BY RESEARCH) FACULTY OF INFORMATION AND COMMUNICATION ENGINEERING ANNA UNIVERSITY : CHENNAI APRIL 2006

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Wireless communications group, AU-KBC Research centre 2 Outline Introduction Channel estimation for MIMO-OFDM systems MIMO Detection methods Summary

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Wireless communications group, AU-KBC Research centre 3 IEEE evolution b802.11a802.11g802.11n Frequency2.4 GHz 5.X GHz2.4 GHz2.4/5.x GHz Data rate (in Mbps) 1,2Min-1Mbps 6,9,12,18, 24,36,48, 54 1,2,5.5,11,6,9,12,18,24,36,48,54 Up to 600 Mbps ModulationFHSS, DSSSDSSSOFDMDSSS / OFDMMIMO-OFDM Maximum Range300 ft Effective data throughput (in Mbps) 0.5,1532 >100 Channel BW22 MHz20 MHz 20/40 MHz

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Wireless communications group, AU-KBC Research centre 4 Evolution of IEEE The effective application rate offered by the existing WLANs was still far lesser than the LAN (by 2004) WLAN standard has evolved from the basic IEEE (which supports to 1Mbps) to 54 Mbps by modifying PHY and MAC layer. IEEE a/g was the standard widely used for WLANs (by 2004) IEEE n taskgroup (TGn) was formed with the goal of increasing the application throughput to atleast 100Mbps by making changes in the PHY and MAC layer Support to Legacy stations

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Wireless communications group, AU-KBC Research centre 5 IEEE n – Main Features n WLAN Multiple antennasEfficient OFDM MAC Reduced guard interval Reduced guard band Modulation and Coding MIMO 2 Antennas at AP and 1 antenna at the user Aggregation Block Ack Advanced Power save

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Wireless communications group, AU-KBC Research centre 6 IEEE n standard The basic technology for increasing the rate is the use of multiple antennas for spatial multiplexing (SM). However, transmit diversity with space time coding, beamforming, and SVD based schemes are also proposed as optional features. The standard proposes the use of upto 4 antennas The number of useful subcarriers is increased to 52 There is an optional mode with 40MHz BW, wherever the regulatory body allows it Shortened GI, code rate upto 7/8, advanced FEC coding are other optional features. Rates supported vary from 6.5Mbps to 500 Mbps.

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Wireless communications group, AU-KBC Research centre n PHY layer STBC Multiple antennas Spatial multiplexing Tx. Beam forming Efficient OFDM Channel Bandwidth Modulation and Coding Reduced guard band Reduced guard interval

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Wireless communications group, AU-KBC Research centre 8 MIMO-OFDM systems The orthogonal frequency division multiplexing (OFDM) transmission scheme is an efficient technique to combat ISI and simplify the equalization problem The use of multiple antennas at transmitter and/or at the receiver helps in many ways such as diversity gain, spatial multiplexing and beamforming The MIMO signaling can easily be overlayed on an OFDM based system. The MIMO signaling treats each subcarrier in OFDM as an independent narrowband frequency flat channel. It can be viewed as N parallel MIMO systems operating with flat fading channel coefficients. MIMO-OFDM system offers an increase in rate by employing SM at the same time as we combat the ISI problem in an elegant way

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Wireless communications group, AU-KBC Research centre 9 Spatial Multiplexing General MIMO example AP Tx1 Tx2 Rx1 Rx2 Data stream 2 (OFDM symbols)... Data stream 1 (OFDM symbols)... Uncorrelated channels h 11 h 12 h 21 h 22 Matrix channel is an important parameter in analysis and design Received signal at the antennas is the combination of the spatially multiplexed data from the different transmit antennas.

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Wireless communications group, AU-KBC Research centre 10 MIMO-OFDM systems Challenges in MIMO-OFDM systems: –Channel estimation (CE) –Synchronization –MIMO detection Channel estimation – to estimate the channel coefficients corresponding to all transmit receive antenna pair and on all subcarrier positions. MIMO detection also becomes computationally intensive as it has be applied on all the subcarriers.

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Wireless communications group, AU-KBC Research centre 11 Contribution of the thesis The main focus is towards analysing the various PHY layers proposed for n The thesis covers two portion –Channel estimation for n –MIMO detection schemes The performance of several preambles used for MIMO channel estimation and different schemes are analysed Low complexity way of implementing the CE schemes are also discussed by exploiting the SFCF. System performance of various MIMO detection schemes is presented.

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Wireless communications group, AU-KBC Research centre 12 Section2: Channel estimation for n systems

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Wireless communications group, AU-KBC Research centre 13 Outline Channel estimation in MIMO-OFDM systems Different kind of preambles –TM method –SM method and its variation –TO method –SO method Preambles in IEEE n –TGn sync: SM (twice) –WWise: SO –EWC: TO

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Wireless communications group, AU-KBC Research centre 14 Outline IEEE n channel model Different MIMO preambles Preambles in IEEE n proposal Channel estimation schemes –LS –LMMSE –Interpolation based estimation (LCCE) –TMMSE –ML method Complexity of CE schemes Performance for various CE schemes –Mean square error –System performance in terms of BER –MSE results for TGn channels for TGn sync, WWise and EWC proposals

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Wireless communications group, AU-KBC Research centre 15 TGn channel model IEEE n TGn channel model MIMO channel model for indoor and typical office environment in LOS and NLOS conditions Cluster based channel model

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Wireless communications group, AU-KBC Research centre 16 Cluster based channel model The time of arrival of the i th cluster is T i τ j,i is the time of arrival of the ij th path. The clusters and the rays within the cluster decay in amplitude and time The decay rate of cluster and the rays are Λ, λ, Modification of Saleh Valenzula model - By adding of arrival statistics The complete impulse response with respect to both time and angle

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Wireless communications group, AU-KBC Research centre 17 Cluster based channel model The angle of arrival statistics Angular domain Impulse response Θ i ~ uniform [0,2π] - mean angle of arrival in ith cluster. ω ij - correspond to the jth ray angle in ith cluster, modeled as Laplacian distributed random variable. Each cluster has the following angular statistics Mean angle of arrival (AoA) Mean angle of departure (AoD) Azhimuth angluar spread (AS) Elevation angular spread

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Wireless communications group, AU-KBC Research centre 18 Power Angular spectrum Figure 2.2: Laplacian PAS The angle of arrival statistics within a cluster - Laplacian distribution σ - Angular spread

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Wireless communications group, AU-KBC Research centre 19 Cluster based channel model The complex correlation coefficients – PAS, AS, AoA and Individual tap powers To calculate the numerical values of correlation matrices we use a Matlab program developed and distributed by L. Schumacher R XX – crosscorrelation function between the real/imag parts R XY cross correlation between the real part and imaginary part

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Wireless communications group, AU-KBC Research centre 20 TGn channel model - Channel parameters ModelsMax. delay spread (in ns) RMS delay spread NLOS (ns) K factor (in dB)No.of. Clusters LOSNLOS A000-∞1 B80150-∞2 C ∞2 D ∞3 E ∞4 F ∞6 For each cluster in a channel model, AS tx AS rx AoA AoD are specified

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Wireless communications group, AU-KBC Research centre 21 Power delay profile 150ns rms delay spread Cluster 1 Cluster 2

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Wireless communications group, AU-KBC Research centre 22 Channel generation steps

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Wireless communications group, AU-KBC Research centre 23

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Wireless communications group, AU-KBC Research centre 24 Spaced frequency correlation TGn channel models – B toF, NLOS conditions

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Wireless communications group, AU-KBC Research centre 25 MIMO-OFDM system IFFTS/PP/S CP IFFTS/PP/S CP Multipath channel h 11 (n) h 22 (n) h 21 (n) h 12 (n) FFTS/PP/S FFTS/PP/S X 1 (k) X 2 (k) Y 1 (k) Y 2 (k) X 1 (k) X 2 (k) H 11 (k) H 22 (k) H 21 (k) H 12 (k) Flat fading channel Subcarrier domain view Y 1 (k) Y 2 (k)

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Wireless communications group, AU-KBC Research centre 26 MIMO OFDM system Received signals at kth subcarrier in a simple 2x2 system Rx. Ant 1: Rx. Ant 2: In matrix representation ESTIMATE THE CHANNEL COEFFICIENTS AT ALL SUBCARRIER POSITIONS

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Wireless communications group, AU-KBC Research centre 27 Time Multiplexed method Time Multiplexed (TM) method In each MLTF – Transmission from one antenna Simple channel estimation – LS estimate Ant 1 Ant 2 MLTF1MLTF2 S0S0 S1S1 S N-1 S N-2 …….. S0S0 S1S1 S N-1 S N-2 ……..

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Wireless communications group, AU-KBC Research centre 28 Time Multiplexed method Training symbol at ‘k’ the subcarrier from the two antennas Ant 1 Ant 2 MLTF1 MLTF2 The received signal at kth subcarrier

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Wireless communications group, AU-KBC Research centre 29 Time Multiplexed method The channel estimates at Kth subcarrier is given by Mean square error : MSE in dB SNR in dB MSE is inversely proportional to SNR Average transmit power Total Energy required

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Wireless communications group, AU-KBC Research centre 30 Subcarrier Multiplexed method Subcarrier multiplexed method Odd subcarriers – Transmitted from Antenna 1 Even subcarriers – Transmitted from Antenna 2 Interpolation needs to be done to estimate channel on all subcarriers S0S0 0S2S2 0 0S1S1 0S3S3 ………. S N-2 0 ……….. 0S N-1 Ant1 Ant 2 MLTF1

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Wireless communications group, AU-KBC Research centre 31 Subcarrier Multiplexed method Training symbol at the kth subcarrier from the two antennas Ant 1 Ant 2 MLTF1 The received signal at kth subcarrier Ant 1 Ant 2 MLTF1

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Wireless communications group, AU-KBC Research centre 32 Subcarrier Multiplexed method In all odd subcarrier positions In all even subcarrier positions Even subcarriers of channel coefficients corresponding to TX.ant 1 are obtained by Interpolation. Odd subcarriers of channel coefficients corresponding to TX.ant 2 are obtained by Interpolation.

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Wireless communications group, AU-KBC Research centre 33 Subcarrier Multiplexed method Average transmit power Total Energy required

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Wireless communications group, AU-KBC Research centre 34 Subcarrier Multiplexed method - twice Training symbol at ‘k’ the subcarrier from the two antennas Ant 1 Ant 2 MLTF1MLTF2 The received signal at kth subcarrier Ant 1 Ant 2 MLTF1MLTF2

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Wireless communications group, AU-KBC Research centre 35 Time orthogonal method S0S0 S1S1 ………….S N-2 S N-1 S0S0 S1S1 …………S N-2 S N-1 S0S0 S1S1 ………….S N-2 S N-1 -S 0 -S 1 ………..-S N-2 -S N-1 MLTF1MLTF2 Ant 1 Ant 2

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Wireless communications group, AU-KBC Research centre 36 Time Orthogonal method Training symbol at ‘k’ the subcarrier from the two antennas Ant 1 Ant 2 MLTF1 MLTF2 The received signal at kth subcarrier

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Wireless communications group, AU-KBC Research centre 37 Time Orthogonal method The channel estimates at Kth subcarrier is given by Mean square error : MSE in dB SNR in dB MSE is inversely proportional to SNR Average transmit power Total Energy required

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Wireless communications group, AU-KBC Research centre 38 Subcarrier orthogonal method S0S0 S1S1 ………….S N-2 S N-1 S0S0 -S 1 …………S N-2 -S N-1 S2S2 S2S2 Ant 1 Ant 2 MLTF1

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Wireless communications group, AU-KBC Research centre 39 Subcarrier Orthogonal method Training symbol at ‘k’ the subcarrier from the two antennas Ant 1 Ant 2 MLTF1 The received signal at kth subcarrier Ant 1 Ant 2 MLTF1

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Wireless communications group, AU-KBC Research centre 40 Subcarrier Orthogonal method The channel estimates can be obtained by

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Wireless communications group, AU-KBC Research centre 41 Preambles used in IEEE n proposals Preambles used for channel estimation –TGn sync – SM (twice) –WWise – SO method –EWC – TO method

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Wireless communications group, AU-KBC Research centre 42 TGn sync proposal

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Wireless communications group, AU-KBC Research centre 43 Packet structure LSTFLLTFHT-SIGLSIG HT STF HTLTF1DATAHTLTF2 8μs8μs 8μs8μs4μs4μs8μs8μs2.4μs7.2μs LSTFLLTFHT-SIGLSIG HT STF HTLTF1DATAHTLTF2 8μs8μs 8μs8μs4μs4μs8μs8μs2.4μs7.2μs Ant 1 Ant 2 CDD Simplified PPDU format in 2x2 system-TGn sync proposal for IEEE n MIMO Channel estimation Is done during this part

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Wireless communications group, AU-KBC Research centre 44 Long preamble structure in TGn sync Set 1 GI Set 1 SISet 2 GI Set 2 GI Ant 1 Ant 2 HTLTF1 (7.2μs)HTLTF2 (7.2μs) Time domain view Subcarrier domain view S -26 0S Set 1 S S2S2 0S 24 0S 26 ….. S -25 0S -23 0S S1S1 0S 23 0S 25 ….. K=0 Set 2

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Wireless communications group, AU-KBC Research centre 45 Least squares channel estimation Linear relationship between the channel and the received signal Solving the linear equations leads to Least squares (LS) channel estimates Mean square error (MSE) is directly proportional to the noise variance

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Wireless communications group, AU-KBC Research centre 46 WWise proposal

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Wireless communications group, AU-KBC Research centre 47 WWise preamble – Mixed mode SS 20 LS 20 SIG-MM LS 20 DATA Mixed mode SS 20 LS 20 SIG-MM LS 20 DATA Ant 1 Ant 2 8μs8μs 8μs8μs4μs4μs 8μs8μs SIG-N 4μs4μs Cyclic delay of 400ns Cyclic delay of 1600ns Cyclic delay of 3100ns

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Wireless communications group, AU-KBC Research centre 48 WWise preamble – Green field mode SS 20 SIG-NDATA Green field mode SS 20 SIG-NDATA Ant 1 Ant 2 8μs8μs 4μs4μs Cyclic delay of 400ns Cyclic delay of 1600ns LS 20 8μs8μs

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Wireless communications group, AU-KBC Research centre 49 WWISE method Training symbol at ‘k’ the subcarrier from the two antennas Ant 1 Ant 2 MLTF1 The received signal at kth subcarrier during first repetition Ant 1 Ant 2 MLTF1

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Wireless communications group, AU-KBC Research centre 50 CE method for WWISE The channel estimates are obtained by

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Wireless communications group, AU-KBC Research centre 51 MSE closed form The spaced frequency correlation is obtained from F.T of PDP

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Wireless communications group, AU-KBC Research centre 52 EWC proposal

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Wireless communications group, AU-KBC Research centre 53 EWC preamble – Mixed mode L-STFL-LTFLSIGHTSIG HT STF HT LTF1 DATA Mixed mode L-STFL-LTFLSIGHTSIG HT STF HT LTF1 DATA Ant 1 Ant 2 8μs8μs 8μs8μs4μs4μs 8μs8μs 4μs4μs4μs4μs HT LTF2 HT LTF2 4μs4μs Cyclic delay of 200ns Cyclic delay of 400ns

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Wireless communications group, AU-KBC Research centre 54 EWC preamble – Green field mode L-STFHTSIG HT LTF2 DATA Green field mode L-STFHTSIG HT LTF2 DATA Ant 1 Ant 2 8μs8μs 8μs8μs 4μs4μs Cyclic delay of 200nsCyclic delay of 400ns HTLTF 1 8μs8μs

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Wireless communications group, AU-KBC Research centre 55 EWC Set 1 GI Set 2GISet 2 GI -Set 1GI Ant 1 Ant 2 HTLTF1 (7.2μs)HTLTF2 (4μs) Time domain view Subcarrier domain view S Set 1 S Set 2 Set 2 is Cyclic shifted by 400ns of Set 1 S S S S ……...S 1 24 S 1 25 S 1 26 ……..S 2 24 S 2 25 S S Set 1 S Set 2 -S S S S ……...-S S S 1 26 ……..S 2 24 S 2 25 S 2 26 HTLTF 1 HTLTF 2

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Wireless communications group, AU-KBC Research centre 56 Mixed mode – Least squares Ant 1 Ant 2 HTLTF1 HTLTF2 The channel estimates at Kth subcarrier is given by Received signal at Kth subcarrier is given by

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Wireless communications group, AU-KBC Research centre 57 Green field mode – Least squares The channel estimates at Kth subcarrier is given by Received signal at Kth subcarrier is given by

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Wireless communications group, AU-KBC Research centre 58 Enhanced CE schemes LMMSE Interpolation based estimation (LCCE) TMMSE ML method

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Wireless communications group, AU-KBC Research centre 59 LMMSE channel estimation The spaced frequency correlation in the channel is used to get better estimate compared to LS estimate. LMMSE estimate : Autocorrelation, ‘R’ matrix captures the frequency domain correlation in the channel Bx1 vector

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Wireless communications group, AU-KBC Research centre 60 LMMSE channel estimation LMMSE filter requires the autocorrelation matrix and the noise variance Imperfect estimation of R and the noise variance leads to the irreducible error floor in the MSE Computational complexity of the LMMSE scheme is very high as it requires B 2 multiplications and a matrix inversion A block wise LMMSE - Reduce the complexity at the expense of performance degradation

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Wireless communications group, AU-KBC Research centre 61 LMMSE channel estimation The spaced frequency correlation in the channel is used to get better estimate compared to LS estimate. LMMSE estimate : Autocorrelation, ‘R’ matrix captures the frequency domain correlation in the channel Bx1 vector

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Wireless communications group, AU-KBC Research centre 62 Blockwise LMMSE B L – Reduced block length B – Original Block length N B – Number of B L blokcs in B The autocorrelation matrix block of length B L LMMSE estimate for p th block

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Wireless communications group, AU-KBC Research centre 63 Interpolation based Low complexity channel estimation (LCCE) Interpolation based channel estimation Correlation among the adjacent subcarriers are used without the need for the autocorrelation matrix, R and huge computations. Channel estimates are got by weighted average of LS estimates and the interpolation estimates

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Wireless communications group, AU-KBC Research centre 64 Block diagram RX. 1 LS est RX. 2 LS est RX. 1 LS est RX. 2 LS est Int W1-WW HTLTF 2 HTLTF 1

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Wireless communications group, AU-KBC Research centre 65 Low complexity channel estimation The final channel estimates is the weighted average of direct LS estimate and the interpolated estimate Simple linear interpolation filter – Low computational overhead Linear interpolation : Weights of the linear interpolation are chosen to be powers of 2 to use shifting instead of multiplication Other Interpolations like cubic, spline can be done – Complexity increases

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Wireless communications group, AU-KBC Research centre 66 Low complexity channel estimation Weight valueLow SNR regionHigh SNR region W = dB improvement over LS estimation Error floor depends upon RMS delay spread W>0.5<3.5 dB improvement over LS estimation Error floor is less compared to W=0.5 W<0.5>3.5dB improvement over LS estimation. (But not more that 4.25dB) Error floor is high compared to W=0.5 Error floor is directly proportional to the RMS delay spread

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Wireless communications group, AU-KBC Research centre 67 MSE closed form for linear interpolation method The spaced frequency correlation is obtained from F.T of PDP

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Wireless communications group, AU-KBC Research centre 68 Truncated MMSE (TMMSE) - CE The MMSE solution matrix V p of a truncated R matrix is obtained as follows R p is the correlation matrix of dimension PxP The middle row of Vp matrix is used as weight vector Smoothing LS estimates by weight values obtained from MMSE solution

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Wireless communications group, AU-KBC Research centre 69 TMMSE - CE Filter the LS CEs using these weight values as filter coefficients There is a loss in performance compared to LMMSE, due to truncation and smoothing with less number of weights To reduce complexity the modulus of the complex weights is considered and quantized to the nearest power of 2 LCCE method is a special case of TMMSE method when the weights are real

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Wireless communications group, AU-KBC Research centre 70 ML method ML channel estimation – with assumption that the maximum length of the channel impulse response is not greater than the guard time. Step 1: Step 2: Step 3: Step 4: Where, F is the Fourier matrix and F red is the reduced Fourier matrix whose dimension is L x L Suitable only for symbol spaced channel

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Wireless communications group, AU-KBC Research centre 71 Computational complexity MethodComlex multiplicationsComplex additionsOther LSB-- LMMSE-52B 2 + B(B-1)BFinding common LMMSE filter O(B 3 ) complex multiplications LMMSE-13N B B L 2 + BN B (B L -1)B L Finding common LMMSE filter O(B L 3 ) complex multiplications LCCE (Linear intp) B2B2B shifting operation. TMMSE-PB(P+1)B(P-1)Finding the P filter coefficients B is the number of subcarrier; For TGnSycn B=52, WWISE & EWC B = 56

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Wireless communications group, AU-KBC Research centre 72 Results for all the methods Performance of various preambles Performance - TGn sync preamble Performance - EWC preamble Performance - WWISE preamble Effect of various channel estimation schemes on system performance interms of BER & PER – Section3

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Wireless communications group, AU-KBC Research centre 73 Simulation model The SNR used here refers to signal to noise ratio per receive antenna per subcarrier, are the ideal and estimated CEs on the k th subcarrier The performance measure is the MSE of the channel estimate

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Wireless communications group, AU-KBC Research centre 74 Performance of various preambles Noise Power in dB MSE performance of different preambles in channel D, NLOS conditions MSE

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Wireless communications group, AU-KBC Research centre 75 TGn sync

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Wireless communications group, AU-KBC Research centre 76 Performance of LCCE method MSE performance of LCCE for TGn sync preamble, Channel D NLOS.

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Wireless communications group, AU-KBC Research centre 77 LMMSE method MSE performance of LMMSE for TGn sync preamble, Channel D NLOS.

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Wireless communications group, AU-KBC Research centre 78 LMMSE method – Mismatch Correlation matrix MSE performance of LMMSE for TGn sync preamble, Channel D NLOS Autocorrelation matrix mismatch

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Wireless communications group, AU-KBC Research centre 79 Figure 2.22: MSE performance of LMMSE method for TGn sync preamble in channel D, NLOS

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Wireless communications group, AU-KBC Research centre 80 TMMSE method, P=3 Figure 2.24: MSE performance of TMMSE scheme with P=3 for TGn sync preamble, Channel D NLOS

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Wireless communications group, AU-KBC Research centre 81 TMMSE method, P=5 Figure 2.25: MSE performance of TMMSE scheme with P=5 for TGn sync preamble, Channel D NLOS

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Wireless communications group, AU-KBC Research centre 82 Performance of various CE methods MSE performance of various CE scheme

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Wireless communications group, AU-KBC Research centre 83 Gain at 0dB Gain at 0 dB for all CE schemes

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Wireless communications group, AU-KBC Research centre 84 Cutoff point of all schemes Cutoff point of all CE scheme

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Wireless communications group, AU-KBC Research centre 85 WWISE

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Wireless communications group, AU-KBC Research centre 86 Performance of smoothing window method for all channel models MSE performance of 2x2 WWISE preambles for all channel models – SW method

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Wireless communications group, AU-KBC Research centre 87 WWISE – MSE Performance for different schemes

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Wireless communications group, AU-KBC Research centre 88 Performance of ML based method MSE performance of ML method

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Wireless communications group, AU-KBC Research centre 89 Enhanced Wireless Consortium

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Wireless communications group, AU-KBC Research centre 90 LCCE scheme MSE performance of LCCE with linear interpolation for EWC Greenfield mode in Channel D NLOS.

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Wireless communications group, AU-KBC Research centre 91 EWC - Performance for various CE schemes MSE performance of CE schemes for 2x2 of EWC

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Wireless communications group, AU-KBC Research centre 92 Figure 2.35: MSE performance of LMMSE, LCCE scheme for GF and MM in Channel D NLOS.

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Wireless communications group, AU-KBC Research centre 93 Effect of CE errors on BER and PER

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Wireless communications group, AU-KBC Research centre 94 BER performance – Uncoded system BER performance of MMSE detection with different CE schemes for 2x2 system in channel D, NLOS.

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Wireless communications group, AU-KBC Research centre 95 BER performance – TGn sync system QPSK – ½ rate BER performance of LC MIMO detection schemes for 2x2 TGn sync system in channel D, NLOS.

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Wireless communications group, AU-KBC Research centre 96 PER performance – TGn sync system QPSK – ½ rate PER performance of LC MIMO detection schemes for 2x2 TGn sync system in channel D, NLOS.

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Wireless communications group, AU-KBC Research centre 97 Complexity of CE schemes MethodComlex multiplications Complex additions Other LSB-- LMMSE-52B 2 + B(B-1)BFinding common LMMSE filter O(B 3 ) complex multiplications LMMSE-13N B B L 2 + BN B (B L -1)B L Finding common LMMSE filter O(B L 3 ) complex multiplications LCCE (Linear intp) B2B2B shifting operation. TMMSE-PB(P+1)B(P-1)Finding PxP LMMSE filter O(P 3 ) complex multiplications

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Wireless communications group, AU-KBC Research centre 98 Section2 : MIMO Detection methods for n >> A simple uncoded system >> TGn Sync system

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Wireless communications group, AU-KBC Research centre 99 MIMO Detection schemes MIMO detection schemes –Decorrelator / ZF –MMSE –Successive Interference Cancellation (SIC) –ZF/MMSE VBLAST (Ordered SIC) –Maximum Likelihood (ML) Explain in detail about each of these schemes.

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Wireless communications group, AU-KBC Research centre 100 Maximum Likelihood (ML) Optimum and most complex detection method Zero-Forcing (ZF) Pseudo inverse of the channel, simplest detection method Minimum mean-squared error (MMSE) : Intermediate complexity and performance MIMO Detection schemes

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Wireless communications group, AU-KBC Research centre 101 V-BLAST Ordered successive interference cancellation (SIC) detector MIMO Detection schemes Finding Nulling Solution (Zero Forcing ) (MMSE filter ) Ordering Choosing the best channel is the j th row of G Nulling Nullifying the effect of the channel faced by k th stream

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Wireless communications group, AU-KBC Research centre 102 Non-feedback MIMO Receivers (contd..) Slicing Quantizing the nullified symbol with appropriate constellation Canceling Cancel the effect of detected stream from the received signal Finding new channel matrix Find new channel matrix by replacing the columns corresponding to the detected streams with zeros Iteration Repeat from step 1 with new channel matrix until all the streams are detected

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Wireless communications group, AU-KBC Research centre 103 V-BLAST – 2 x 2 Example Let the received signal be where Finding Nulling Solution Using zero forcing or MMSE solution Ordering Find the energy of all the rows of G Choosing the best channel Nulling Assume k=2, then the nulling vector

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Wireless communications group, AU-KBC Research centre 104 Slicing Quantizing the nullified symbol with appropriate constellation Canceling Cancel the effect of detected stream from the received signal Iteration Repeat from step 1 another time to get the 1 st stream. V-BLAST – 2 x 2 Example Nullifying the effect of the channel faced by 2 th stream Finding new channel matrix

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Wireless communications group, AU-KBC Research centre 105 Mean square error in detection The mean square error (MSE) between the transmitted data symbols and the output of the detection algorithm is a good measure for the performance of MIMO detection algorithms MSE easy to derive for MIMO detection. From simulation, the reduction in MSE leads to BER reduction.

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Wireless communications group, AU-KBC Research centre 106 Low complexity MIMO detection We need to employ N independent MIMO detectors in a MIMO system with N subcarrier. The frequency correlation among the subcarriers can be used to reduce the complexity of the MIMO-OFDM system Instead of independently employing MIMO detector in all subcarriers, only the solution for the MIMO detector on alternate subcarrier positions are found The solution for the other subcarriers is found by interpolating the solutions obtained for the neighboring subcarriers.

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Wireless communications group, AU-KBC Research centre 107 Low complexity MIMO detection Linear interpolation using weights which are simple to implement can be used. –Let k-1 and k+1 be the subcarrier positions where the direct solution –let k be the subcarrier position in which the solution is obtained by linear interpolation –Where Vk is the matrix solution for MIMO detection 50% reduction in the complexity when compared to the normal MIMO-OFDM detection methods This idea can be used for ZF, MMSE, MMSE-SIC, ZF-SIC detection method. It cannot be directly applied to the VBLAST based detection schemes, since the order in which the detection is performed varies for each subcarrier.

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Wireless communications group, AU-KBC Research centre 108 Complexity comparison Number of complex multiplication is considered

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Wireless communications group, AU-KBC Research centre 109 Complexity comparison Comparison of computational complexity for various MIMO detection schemes

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Wireless communications group, AU-KBC Research centre 110 A simple MIMO-OFDM system

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Wireless communications group, AU-KBC Research centre 111 Simulation results and discussion Uncoded system Simulation results for MIMO detection algorithms Effect of CE on the system performance Simulation parameters –Number of Subcarriers, N =64 –Cyclic prefix = 16 samples –BW = 20MHz –QPSK modulation –TGn channel D – NLOS –Results presented in terms of MSE performance, BER and PER.

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Wireless communications group, AU-KBC Research centre 112 MIMO detection schemes for 2x2 and 4x4 Figure:MSE performance of various MIMO detection schemes for 2x2 system in channel D, NLOS Figure: MSE performance of various MIMO detection schemes for 4x4 uncoded system in channel D, NLOS 2x24x4

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Wireless communications group, AU-KBC Research centre 113 Low complexity MIMO detection scheme Figure : MSE performance of various low complexity MIMO detection schemes for 2x2 uncoded system in channel D, NLOS NLOS. Figure : BER performance of various MIMO detection schemes for 2x2 uncoded system in channel D, NLOS MSEBER

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Wireless communications group, AU-KBC Research centre 114 BER performance with different CE Figure : BER performance of MMSE detection with different CE schemes for 2x2 system in channel D, NLOS

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Wireless communications group, AU-KBC Research centre 115 TGn Sync system – System model

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Wireless communications group, AU-KBC Research centre 116 TGn Sync - Simulation results ParameterValue CR½ ModulationQPSK N t x N r 2x2 Payload1000 bytes TGn sync Tx TGn sync Rx. Channel Estimation using preambles Info bits Figure : Simulation model Channel Results presented in terms of BER and PER

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Wireless communications group, AU-KBC Research centre 117 MIMO detection schemes – BER & PER Figure : BER performance of various MIMO detection schemes for 2x2 TGn sync system in channel D, NLOS Figure : PER performance of various MIMO detection schemes for 2x2 TGn sync system in channel D, NLOS

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Wireless communications group, AU-KBC Research centre 118 LC-MIMO detection schemes – BER & PER Figure : BER performance of LC-MIMO detection schemes for 2x2 TGn sync system in channel D, NLOS Figure : PER performance of LC-MIMO detection schemes for 2x2 TGn sync system in channel D, NLOS

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Wireless communications group, AU-KBC Research centre 119 Effect of CE schemes on system performance Figure: BER performance for various CE schemes with MMSE detection for 2x2 TGn sync system in channel D, NLOS Figure: PER performance for various CE schemes with MMSE detection for 2x2 TGn sync system in channel D, NLOS

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Wireless communications group, AU-KBC Research centre 120 Effect of CE schemes on system performance for all channel models Figure : G p Loss in performance of BER for different CE method on all channel models G p is defined as the Loss in performance in terms of SNR at a cut off point of BER

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Wireless communications group, AU-KBC Research centre 121 Thank You

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Wireless communications group, AU-KBC Research centre 122 Summary Various MIMO detectors are discussed and their performance in uncoded system in terms of MSE and BER is presented based on simulation. The effect of CE on the performance of uncoded MIMO system is also presented. A low complexity solution for MIMO-OFDM detection is proposed and it reduces the computational complexity by 50%. The performance of the TGn sync system is presented for various MIMO detection methods in terms of BER and PER. It is shown by simulations that the LC MIMO detectors result in very less performance degradation for practical channel conditions. Thus, the LC MIMO detectors can be used for IEEE n proposals as they reduce the computational complexity load at the receiver. The effect of various CE schemes on the performance of IEEE n TGn sync proposal is presented in terms of BER and PER. The results indicate that the LS scheme results in about 3 dB loss in performance at 10-5 BER point, while the low complexity CE schemes such as LCCE, TMMSE have less performance degradation. Thus, the TMMSE and LCCE CE schemes can be used for IEEE n proposals leading to fewer computations and less performance degradation.

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