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Wireless Access Systems: Introduction and Course Outline

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2 Communication Technology Laboratory Wireless Communication Group Some Wireless Access Systems Wireless Access Systems provide short to medium range tetherless access to a backhaul network, a central unit or peer nodes Examples include –Bluetooth –WLAN –Vehicular Networks –WiMax –RFID –WBAN –WPAN

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3 Communication Technology Laboratory Wireless Communication Group Wireless Access Systems are Ubiquitous Internet

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4 Communication Technology Laboratory Wireless Communication Group Some More Applications intelligent home ambient intelligence security wearable computing shopping defence surveillance supply chain management logistics industrial information exchange pervasive computing virtual reality health care home care communications instant messaging enterprise communication environment traffic security surveillance access control Internet

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5 Communication Technology Laboratory Wireless Communication Group Characteristics of Wireless Access Systems Heterogeneous standards IEEE WLAN IEEE WPAN IEEE WMAN (Hiperlan) Bluetooth DECT various RFID standards RFID tags, readers sensors, actors communication appliances information access information processing backhaul access points Heterogeneous nodes Lots of spectrum (approx.) (ISM) (ISM) (ISM) (ISM) (UWB) WLAN Internet backhaul Sensor network RFID cellular: GSM UMTS Bluetooth WPAN WMAN Pervasive wireless access

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6 Communication Technology Laboratory Wireless Communication Group The Throughput - Range Tradeoff RFID Body Area Networks 100M 10M 1M 100k 10k 1k range [m] link throughput [bps] 11b 11a 11g Sensor Networks 15.3a WLAN WPAN UWB Bluetooth ZigBee UWB region (conceptional)

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7 Communication Technology Laboratory Wireless Communication Group Outline of Course Fundamentals 1.Fundamentals of short/medium range wireless communication 1 –digital transmission systems –equivalent baseband model –digital modulation and ML-detection 2.Fundamentals of short/medium range wireless communication 2 –fading channels –diversity –MIMO wireless 3.Fundamentals of short/medium range wireless communication 3 –Multicarrier modulation and OFDM Systems I: OFDM based broadband access 4.WLAN 1: IEEE g, a 5.WLAN 2: IEEE n 6.WMAN: (mobile) WiMAX 7.Vehicular Networks Systems II: Wireless short range access technolgies and systems 8.UWB 1: Promises and challenges of Ultra Wideband Systems 9.UWB 2: Physical Layer options 7.Wireless Body Area Network case study: UWB based human motion tracking 8.The IEEE x family of Wireless Personal Area Networks (WPAN): Bluetooth, ZigBee, UWB Systems III: RF identification (RFID) and sensor networks 12.RFID 1 13.RFID 2

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8 Communication Technology Laboratory Wireless Communication Group Exercises: Motivation Simulate, practice, verify, learn and have fun We will simulate the theoretical ideas/methods/techniques that we learn throughout the lecture. MATLAB (matrix laboratory) will be used for simulations. In general we will simulate –Single carrier transmission –Multi-carrier transmission –Wireless Channel –Channel coding –Simple UWB transceiver

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9 Communication Technology Laboratory Wireless Communication Group Exercises: Organization Students organize in 2 or 4 groups There will be three exercises with two tasks each during the semester. Each group will perform one of the two tasks and then present the results. The general schedule of tasks: –Introduction of tasks. –Working period (2 weeks). Present afterwards. Each group will work individually. –Combining period (1 week). Present afterwards. Two groups will work in collaboration. For further details will be presented in the first exercise lecture next week 8:15

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10 Communication Technology Laboratory Wireless Communication Group Schedule: 8:15-9:009:15-10:0010:15-11:0011:15-12:00 Week 1 Fundamentals of wireless communications. 1 Week 2 Introduction – First Exercise Fundamentals of wireless communications. 2 Week 3 Fundamentals of wireless communications. 3 Week 4Presentation of Ex 1/ 1Presentation of Ex 1/2WLAN - 1 Week 5 optional: wrap up of simulation basics optional: revised solutions of Ex 1/1 and EX 1/2WLAN - 2 Week 6 Introduction- Second Exercise Presentation of Ex 1 - Combination stepWiMAX 1 Week 7 Vehicular Networks Week 8Presentation of Ex 2/1Presentation of Ex 2/2UWB 1 Week 9 Introduction – Third Exercise Presentation of Ex 2 - Combination stepUWB 2 Week 10 WBAN Week 11Presentation of Ex 3/1Presentation of Ex 3/2WPAN Week 12 Presentation of Ex 3 - Combination stepRFID 1 Week 13 RFID 2

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Wireless Access Systems: Fundamentals of Short Range Wireless Communications

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12 Communication Technology Laboratory Wireless Communication Group Fundamentals of Short Range Wireless: Outline 1.Digital transmission and detection on the AWGN channel –digital transmission systems –equivalent baseband model –digital modulation and ML-detection 2.Fading channels –fading channels –diversity –MIMO wireless 3.Modulation schemes for frequency selective channels –multicarrier modulation –Orthogonal Frequency Division Multiplexing (OFDM)

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13 Communication Technology Laboratory Wireless Communication Group Equivalent Baseband Representation

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5.14 Communication Technology Laboratory Wireless Communication Group Narrowband Case: Equivalent Baseband Model with Bandpass Channel, Different TX and RX Lowpasses and Frequency/Phase Offset Notes f 0 and are called the reference frequency and phase of the BB model for f 0 = f 1 the BB model is time- invariant (a filter) + Narrowband case: Notation:

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5.15 Communication Technology Laboratory Wireless Communication Group Narrowband Case: Relation of Physical Signals and Their Complex Baseband Representation The spectrum of the analytic signal in terms of the physical signal is given by Re{} Im{}Re{}Im{} Names and Notation:

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16 Communication Technology Laboratory Wireless Communication Group Transmission of Digital Information I: Generation of Finite Signal Sets (Modulation)

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17 Communication Technology Laboratory Wireless Communication Group General Block Diagram of a Digital Modulator The information bit vector contains N bit It is mapped onto a message index (i) We use a look-up table with 2 N transmit waveforms The transmit signal is selected according to the message index The process of selecting a transmit signal according to an information bit vector is called modulation For finite N this structure models a block transmission Mapper

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18 Communication Technology Laboratory Wireless Communication Group Signal Space Representation of Digital Modulator The signal space is defined by a set of orthonormal basepulses The basepulses are stacked to form the basepulse vector –orthonormality implies The signal space representation of the transmit signals is obtained by the projection –we refer to as transmit symbol vector Mapper Look-up table

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19 Communication Technology Laboratory Wireless Communication Group Linear Modulation For linear modulation schemes the transmit symbol vector is obtained by a linear transformation of the input symbol vector –precoding matrix G TX Dramatically reduces the size of the look-up table –general modulation: exponential growth with the number N of information symbols –linear modulation: linear growth

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20 Communication Technology Laboratory Wireless Communication Group Some Popular Linear Modulation Schemes name symbol alphabet 2-PAM 4-QAM (QPSK)

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21 Communication Technology Laboratory Wireless Communication Group Filter Implementation of Linear Modulator: Nyquist Basepulses Nyquist basepulses (orthonormal) Nyquist criterion tT t T

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22 Communication Technology Laboratory Wireless Communication Group Transmission of Digital Information II: Transmission and Detection

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23 Communication Technology Laboratory Wireless Communication Group Channel is modelled as additive noise source In many cases of practical interest the noise can be characterized as zero mean stationary Gaussian random process w(t) –any set of samples is jointy normally distributed –autocorrelation function –power density spectrum Additive White Gaussian Noise (AWGN) Channel For analytical tractability usually a white noise process is assumed –for physical system models (real- valued signals) we have –for complex baseband representations as used herein we have

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24 Communication Technology Laboratory Wireless Communication Group Sufficient Statistic and Symbol Discrete System Model Bank of correlators generates the decision vector the decision vector is a sufficient statistics (for additive white Gaussian noise; AWGN) –contains all information for the optimal estimation of the transmit symbol vector With the impulse correlation matrix we obtain the symbol discrete system model –the elements of the noise vector are statistically independent and identically distributed Gaussian random variables Bank of correlators Symbol discrete system model Continuous time system model

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25 Communication Technology Laboratory Wireless Communication Group Frequency Selective Channel channel The channel is represented by a filter h(t) and AWGN A channel matched filter is required prior to the correlator bank in order to obtain a sufficient statistics These filters may affect the resulting impulse correlation matrix –intersymbol interference (ISI) channel matched filter

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26 Communication Technology Laboratory Wireless Communication Group P Form-Invariant Basepulses Continuous time system model impulse modulator g(t) h(t) h * (-t)g * (-t) S kT Symbol discrete system model

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27 Communication Technology Laboratory Wireless Communication Group Transmission of Digital Information III: Decoding

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28 Communication Technology Laboratory Wireless Communication Group Maximum Likelihood Decoder and Decision Regions With orthonormal basepulse vector the impulse correlation matrix becomes the identity matrix The decoder observes the decision vector and generates an estimate of the transmit symbol vector To minimize the probability of error the decoder selects the hypothesis, which has the minimum Euclidean distance to the decision vector (Maximum Likelihood (ML) decoder) –decision regions in the signal space decoder

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29 Communication Technology Laboratory Wireless Communication Group Example: Error Performance of QPSK Decision regions (1,1) (1,0) (0,1) (0,0) Gray mapping (bit 1, bit 2)

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30 Communication Technology Laboratory Wireless Communication Group Fundamentals of Short Range Wireless: Outline 1.Digital transmission and detection on the AWGN channel –digital transmission systems –equivalent baseband model –digital modulation and ML-detection 2.Fading channels –fading channels –diversity –MIMO wireless 3.Modulation schemes for frequency selective channels –multicarrier modulation –Orthogonal Frequency Division Multiplexing (OFDM)

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31 Communication Technology Laboratory Wireless Communication Group Fading I: Time Selective (Narrowband) Fading Channels

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Communication Technology Laboratory Wireless Communication Group Path Loss and Short Term Fading TXRX power [dB] distance (log(x)) free space 20 dB/dec urban 40 dB/dec rural 30 dB/dec distance (log(x)) 32

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Communication Technology Laboratory Wireless Communication Group Doppler Shift I: Angle of Arrival Received signal in complex passband notation For (small scale effects) we obtain the complex envelope of the receive signal –depends only on the displacement. In the spectral domain we obtain the (spatial) Doppler shift 33

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Communication Technology Laboratory Wireless Communication Group For a linear movement of the receiver the spatial variations translate linearly into equivalent temporal variations –the corresponding frequency shift follows as Doppler Shift II: Arbitrary Angle of Arrival Complex envelope of received signal –in the spectral domain we obtain the spatial Doppler shift 34

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Communication Technology Laboratory Wireless Communication Group Multipath Propagation and Fading Complex envelope of the received signal Due to the different frequency shifts of the components, the magnitude of the received signal varies with the displacement: fading Example: –note the spaced zero crossings

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Communication Technology Laboratory Wireless Communication Group Doppler Spectrum: Power Spectral Density of Fading Process infinite number of scatterers under average receive power constraint: continuous PSD of fading process Scattering coefficients c n modelled as uncorrelated random variables with variance –fading described as random process Power spectral density (PSD) of fading process for –note the relation between Doppler shift f xD,n and the angle of arrival fxDfxD PSD 36

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37 Communication Technology Laboratory Wireless Communication Group Jake's Doppler Spectrum Cumulative power distribution versus frequency Power spectral density –"Jake's Spectrum" uniform continuous scattering around receiver cumulative power distribution Relation of angle of arrival and Dopper shift

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38 Communication Technology Laboratory Wireless Communication Group Jake's Channel Model for Linear Movement Multiplicative fading Speed of movement : v complex white Gaussian noise process fDfD

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39 Communication Technology Laboratory Wireless Communication Group Fading II: Frequency Selective Fading

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Communication Technology Laboratory Wireless Communication Group Broadband Channel Measurement Channel measurement with a short impulse h(t) (broadband) All scatterers, which lead to a given path delay are located on an ellipse Typical received signal: 40

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Communication Technology Laboratory Wireless Communication Group Scattering Function The scattering function describes the average power spectral density of the received signal as a function of Doppler shift f x and delay S2 S1 S3 S4 S1 S2 S3 S4 Doppler shift f x 41

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Communication Technology Laboratory Wireless Communication Group Doppler Spectrum of Narrowband System rms Doppler spread with the mean Doppler shift Doppler Spectrum S1 S3 S4 Doppler shift Scattering function –2nd order statistics of the spatio- temporal fading process a narrow band system can not resolve the multiple paths –narrowband fading with Doppler spectrum 42

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Communication Technology Laboratory Wireless Communication Group Delay Power Spectrum of Broadband System Scattering function –2nd order statistics of the spatio- temporal fading process Delay power spectrum –rms delay spread with the mean delay S1 S3 S4 Doppler shift Delay power spectrum 43

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44 Communication Technology Laboratory Wireless Communication Group Classification of Multipath Channels The signal bandwidth and the duration of the transmit burst determine the fading model –flat: no significant variation over the interval of interest –selective: varies significantly over the interval of interest Narrowband systems experience frequency-flat fading Broadband systems experience frequency-selective fading A block fading model is suitable in the time-flat regime –may be either frequency-flat or frequency selective Systems below the red curve are not physically implementable Note the role of Doppler spread and delay spread signal bandwidth B burst duration T BURST time-selective frequency-selective time-flat frequency-selective time-selective frequency-flat time-flat frequency-flat T Burst =1/ B

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Communication Technology Laboratory Wireless Communication Group Typical Time-Selective/Frequency Selective Channel Model A discrete delay power spectrum is specified –paths (delays) are usually clustered For each path (k) (i.e. delay ) a Doppler spectrum is specified –default: Jake's spectrum –if specified in terms of spatial frequency f x, substitute f x = f/v for linear movement with velocity v The filter coefficients are filtered complex normal random processes –in line of sight (LoS) situations: nonzero mean + delay power spectrum delay white complex normal random process Structure Generation of fading processes Specification 45

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Communication Technology Laboratory Wireless Communication Group Special Cases + Block fading channel Note that the coefficients are random variables (not processes) For each channel realization a new set of random variables is generated – –non LoS: Frequency-flat fading white complex normal random process Generation of fading processes 46

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47 Communication Technology Laboratory Wireless Communication Group Fading III: Impact on Error Performance 47

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48 Communication Technology Laboratory Wireless Communication Group Frequency-Flat Fading Channel fading channel matched "filter" Symbol discrete system model with block fading block fading: fading variable instead of fading process note multiplication with magnitude of fading variable due to –channel matched "filter" –normalization of decision vector

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49 Communication Technology Laboratory Wireless Communication Group Error Performance of QPSK in Frequency-Flat Block Fading In frequency-flat block fading the error performance of QPSK is determined by the instantaneous value of the fading variable We can define various figures of merit. Frequently used are –outage probability: probability, that the instantaneous bit error probability is above a target value –fading averaged bit error probability Clearly these figures of merit depend on the probability density function (pdf) of the fading amplitude here is a chi2 random variable with 2 degrees of freedom fading averaged bit error probability

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50 Communication Technology Laboratory Wireless Communication Group Special case L=1 –the fading variable z is complex normally distributed; – is the sum of two statistically independent squared real- valued normal random variable –If, is Rayleigh distributed; Rayleigh fading –if, is Rician distributed. K- factor: General case L=N: N-fold diversity –For, is the sum of 2L squared real-valued Gaussian random variables –chi2-distribution with 2L degrees of freedom –e.g. achievable with L receive antennas Approximation: BER (c/SNR) L Diversity fading averaged bit error probability

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51 Communication Technology Laboratory Wireless Communication Group Fundamentals of Short Range Wireless: Outline 1.Digital transmission and detection on the AWGN channel –digital transmission systems –equivalent baseband model –digital modulation and ML-detection 2.Fading channels –fading channels –diversity –MIMO wireless 3.Modulation schemes for frequency selective channels –multicarrier modulation –Orthogonal Frequency Division Multiplexing (OFDM)

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Vector/Matrix Channels Single Input/Single Output (SISO) channel coefficient Single Input/Multiple Output (SIMO) channel vector Multiple Input/Multiple Output (MIMO) channel matrix 52

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53 Diversity Techniques Wireless channel varies in time, frequency and space Time, frequency and space diversity available Examples: –Time diversity: repeat same codeword after channel varied (Repetition Code) –Frequency diversity: transmit same symbol over two or more OFDM sub- carriers (if fading of the sub-carriers is uncorrelated) –Space diversity: use more than one antenna at RX or TX or on both sides –(But usually pure repetition is not an efficient way to code: repetition of the same information in time or frequency sacrifices bandwidth space diversity seems promising) Diversity, MIMO

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54 Receive diversity: using N RX receive antennas (spatial dimension) High diversity factors available for high carrier frequencies and large bandwidths Transmit diversity: in addition a temporal coding needed Space-Time Codes Diversity, MIMO Spatial (or Antenna) Diversity TX RX

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55 Communication Technology Laboratory Wireless Communication Group System Model with RX Diversity and Maximum Ratio Combining block fading vector channel channel matched "filter" Scalar symbol discrete system model note multiplication with magnitude of fading vector due to –channel matched "filter" –normalization of decision vector

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56 Receive diversity: h i : channel gain between the TX antenna and the RX antenna i Diversity, MIMO Probability of Error TX RX new argument of Q-Function:

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Array Gain and Diversity Gain Array (Power) gain Diversity gain Expression converges to constant for increasing N RX i.e. fading is eliminated in the limit 3 dB gain per doubling of the number of RX antenna 57

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Multiple Input/ Multiple Output Single Input/Single Output (SISO) channel coefficient Single Input/Multiple Output (SIMO) channel vector Multiple Input/Multiple Output (MIMO) channel matrix 58

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Free Space vs. Multipath Propagation scattering fading 59

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Multiple Antennas and Spatial Multiplexing Channel Matrix Singular Value Decomposition rank 1 full rank 3 spatial subchannels spatial multiplexing unitary 60

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61 MIMO Wireless Capacity (1) MIMO channel capacity grows nearly linearly with N = min(N TX, N RX ) [Foschini, Gans, 1998] [Telatar, 1999] TX RX N decoupled spatial sub-channels available (Spatial Multiplexing) Higher data rate without need of higher bandwidth spectral efficiency Diversity, MIMO K-factor of Rician fading

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62 MIMO Wireless Capacity (2) TX RX Diversity, MIMO Telatar, Foschini: N TX : number of TX antennas, N RX : number of RX antennas. K-factor of Rician fading

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63 MIMO Systems: Spatial Subchannels Subchannels A priori transmit channel state information (CSIT) necessary ! TX Diversity: Take only the “best“ subchannel ! Spatial Multiplexing: Take all ! SVD of MIMO channel matrix: Diversity, MIMO

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64 MIMO Systems without CSIT: Spatial Subchannels if no a priori CSIT: TX Combining not possible; Spatial multiplexing leads to ISI Receiver has to compensate ISI due to V (cf. BLAST); Diversity, MIMO

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65 BLAST Architecture [Gesbert, et al.: From Theory to Practice: An Overview of MIMO Space–Time Coded Wireless Systems] Diversity, MIMO

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66 BLAST (2) Antenna array at TX and RX Spatial Data Pipes in rich scattering (MIMO channel H of high rank) without increasing the bandwidth Spatial Multiplexing achieves ergodic MIMO capacity ISI compensation at RX necessary, because no CSIT used in BLAST system BLAST: no combination of diversity techniques and spatial multiplexing Diversity, MIMO

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67 Communication Technology Laboratory Wireless Communication Group Fundamentals of Short Range Wireless: Outline 1.Digital transmission and detection on the AWGN channel –digital transmission systems –equivalent baseband model –digital modulation and ML-detection 2.Fading channels –fading channels –diversity –MIMO wireless 3.Modulation schemes for frequency selective channels –multicarrier modulation –Orthogonal Frequency Division Multiplexing (OFDM)

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68 Communication Technology Laboratory Wireless Communication Group Multicarrier Modulation I: Continuous Time Implementation

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69 Communication Technology Laboratory Wireless Communication Group P Discrete Channel Impulse Response g(t) h(t) h * (-t)g * (-t) S kT -T TT p0p0 p -1 p1p1 +

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70 Communication Technology Laboratory Wireless Communication Group Low and High Data Rate Systems t t t low data rate: high data rate: no ISI severe ISI transmit basepulse channel impulse response

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71 Communication Technology Laboratory Wireless Communication Group Multicarrier Modulation Transmit in N subbands, for each of which the channel transfer function is approximately constant –minimizes ISI in each subband –subband center frequency f k –for non-overlapping subbands, there is no inter-subband (inter- carrier) interference (ICI) One multicarrier (MC) symbol consists of N transmit symbols: Subbands implemented by letting The symbol rate on each subcarrier is f H(f)

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72 Communication Technology Laboratory Wireless Communication Group Transmit and Receive Window For sinc-windows there is strictly no interference between adjacent subcarriers (non-overlapping spectra) –the ISI matrix in the discrete system model is diagonal Due to their infinite duration sinc-windows are not implementable. Is it possible to design finite length windows without introducing interference? h(t) transmit window receive window

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73 Communication Technology Laboratory Wireless Communication Group Eigenfunction of the Convolution We consider the response of the channel h(t) to a step function with center frequency f k We observe a transient with duration T h After the transient the response is a scaled version of the input signal –scaling factor H(f k ) –complex exponentials are eigenfunctions of convolution After an appropriate window, which blanks the transient, we obtain the input-output relation This observation is the key to the design of a finite window for MC t t t transient t

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74 Communication Technology Laboratory Wireless Communication Group Equivalent Diagonal Channel Matrix h(t) Equivalent model: TX and RX window: equivalent channel matrix

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75 Communication Technology Laboratory Wireless Communication Group Receive Window As the equivalent channel matrix is diagonal, it does not affect orthogonality any more What is the impact of the receive window? Pulse correlation matrix –the Fourier transform P RX (f) of the receive window p RX (t) determines the pulse correlation matrix For a uniform subcarrier spacing we have –the Fourier transform of the receive window needs to have spaced zeros In this case the received window has to fullfil the following condition (temporal dual to the spectral Nyquist condition) –the most compact window thus is given by –this implies A temporal roll off improves the robustness to frequency offsets

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76 Communication Technology Laboratory Wireless Communication Group Transmit Window With we obtain Example for N=21; p TX (t)=rect(t/(T c +T h ) The transmit window has to be constant for at least This window determines the power spectral density of the transmit signal

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77 Communication Technology Laboratory Wireless Communication Group Multicarrier Modulation II: Discrete Implementation (Orthogonal Frequency Division Multiplexing; OFDM)

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78 Communication Technology Laboratory Wireless Communication Group P Discrete Channel Impulse Response g(t) h(t) h * (-t)g * (-t) S kT -T TT p0p0 p -1 p1p1 + Vector signal model

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79 Communication Technology Laboratory Wireless Communication Group Response to Periodic Input Sequence TT p0p0 p1p1 + d d N Discrete channel impulse response with L taps for illustration assumed causal Idea: use periodic input sequence to generate periodic response Equivalent channel matrix is circulant

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80 Communication Technology Laboratory Wireless Communication Group Cyclic Prefix and Circulant Channel Matrix Circulant channel matrix due to cyclic prefix of length larger or equal to L-1 TT p0p0 p1p1 + d d N cyclic prefix N CP Discrete channel impulse response with L taps for illustration assumed causal

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81 Communication Technology Laboratory Wireless Communication Group Diagonalization of Circulant Matrix: Orthogonal Frequency Division Multiplexing (OFDM) All (NxN) circulant matrices are diagonalized by the Fourier matrix F N g(t) h(t) a(t) kT; k=1.. N+N CP insert CP remove CP S P P S diagonal channel matrix circulant channel matrix C kT; k=1.. N+N CP

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82 Communication Technology Laboratory Wireless Communication Group Comparison of Discrete and Continuous Time Implementation of Multicarrier Modulation Low Pass Low Pass kT; k=1.. N+N CP insert CP remove CP S P P S kT; k=1.. N+N CP Multicarrier TransmitterMulticarrier Receiver Note:

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