Wireless Physical Layer Concepts Part III Raj Jain Professor of CSE Washington University in Saint Louis Saint Louis MO 63130 Jain cse wustl edu Audio Video recordings of this lecture are available at http www cse wustl edu jain cse574 10 Washington University in St Louis CSE574S 5 1 2010 Raj Jain Overview 1 2 3 4 5 Empirical Channel Models Multi Antenna Systems Beam forming and MIMO Space Time Block Codes Time Division Duplexing OFDM OFDMA SOFDMA Washington University in St Louis CSE574S 5 2 2010 Raj Jain Empirical Channel Models Based on measured data in the field 1 Hata Model 2 COST 231 Extension to Hata Model 3 COST 231 Walfish Ikegami Model 4 Erceg Model 5 Stanford University Interim SUI Models 6 ITU Path Loss Models Washington University in St Louis CSE574S 5 3 2010 Raj Jain Hata Model PL urban d dB 69 55 26 16log10 f c 13 82log10 ht a hr 44 9 6 55log10 ht log10 d Based on 1968 measurement in Tokyo by Okumura Closed form expression by Hata in 1980 fc carrier frequency ht height of the transmitting base station antenna hr height of the receiving mobile antenna a correction factor for the mobile antenna height based on the size of the coverage area Designed for 150 1500 MHz Washington University in St Louis CSE574S 5 4 2010 Raj Jain COST 231 Extension to Hata Model PL urban d dB 46 3 33 9log10 f c 13 82log10 ht a hr 44 9 6 55log10 ht log10 d CM European Cooperative for Scientific and Technical COST Extended Hata model to 2 GHz CM 0 dB for medium sized cities and suburbs 3 dB for metropolitan areas Other Parameters Carrier Frequency 1 5 GHz to 2 GHz Base Antenna Height 30 m to 300 m Mobile Antenna Height 1m to 10 m Distance 1 km to 20 km Washington University in St Louis CSE574S 5 5 2010 Raj Jain ITU Path Loss Models Indoor office outdoor to indoor pedestrian and vehicular Low delay spread A medium delay spread B Pedestrian Tap Channel A 1 2 3 4 5 6 Relative delay ns 0 110 190 410 Doppler spectrum Channel B Average power dB 0 9 7 19 2 22 8 Washington University in St Louis Relative delay ns 0 200 800 1 200 2 300 3 700 CSE574S 5 6 Average power dB 0 0 9 4 9 8 0 7 8 23 9 Classic Classic Classic Classic Classic Classic 2010 Raj Jain ITU Vehicular Channel Model Tap Channel A 1 2 3 4 5 6 Average Relative power delay ns dB 0 0 0 310 1 0 710 9 0 1 090 10 0 1 730 15 0 2 510 20 0 Washington University in St Louis Channel B Doppler spectrum Average Relative power delay ns dB 0 2 5 300 0 8 900 12 8 12 900 10 0 17 100 25 2 20 000 16 0 Classic Classic Classic Classic Classic Classic CSE574S 5 7 2010 Raj Jain Multi Antenna Systems Receiver Diversity Transmitter Diversity Beam forming MIMO Washington University in St Louis CSE574S 5 8 2010 Raj Jain Receiver Diversity a1 a2 a3 aM User multiple receive antenna Selection combining Select antenna with highest SNR Threshold combining Select the first antenna with SNR above a threshold Maximal Ratio Combining Phase is adjusted so that all signals have the same phase Then weighted sum is used to maximize SNR Washington University in St Louis CSE574S 2010 Raj Jain 5 9 Transmitter Diversity a1 a2 a3 aM Use multiple antennas to transmit the signal Ample space power and processing capacity at the transmitter but not at the receiver If the channel is known phase each component and weight it before transmission so that they arrive in phase at the receiver and maximize SNR If the channel is not known use space time block codes Washington University in St Louis CSE574S 5 10 2010 Raj Jain Beam forming Phased Antenna Arrays Receive the same signal using multiple antennas By phase shifting various received signals and then summing Focus on a narrow directional beam Digital Signal Processing DSP is used for signal processing Self aligning Washington University in St Louis CSE574S 5 11 2010 Raj Jain MIMO Multiple Input Multiple Output RF chain for each antenna Simultaneous reception or transmission of multiple streams 2x3 Washington University in St Louis CSE574S 5 12 2010 Raj Jain Space Time Block Codes STBC Invented 1998 by Vahid Tarokh Transmit multiple redundant copies from multiple antennas Precisely coordinate distribution of symbols in space and time Receiver combines multiple copies of the received signals optimally to overcome multipath Example Two antennas Antenna 1 Antenna 2 Time Slot 1 S1 S2 S2 S1 Slot 2 Space S1 is complex conjugate of S1 columns are orthogonal Washington University in St Louis CSE574S 5 13 2010 Raj Jain Time Division Duplexing TDD Duplex Bi Directional Communication Frequency division duplexing FDD Full Duplex Frequency 1 Base Subscriber Frequency 2 Time division duplex TDD Half duplex Base Subscriber Most WiMAX deployments will use TDD Allows more flexible sharing of DL UL data rate Does not require paired spectrum Easy channel estimation Simpler transceiver design Con All neighboring BS should time synchronize Washington University in St Louis CSE574S 5 14 2010 Raj Jain Inter Symbol Interference Power Time Power Time Power Symbols become wider Limits the number of bits s Washington University in St Louis CSE574S 5 15 Time 2010 Raj Jain OFDM Orthogonal Frequency Division Multiplexing Ten 100 kHz channels are better than one 1 MHz Channel Multi carrier modulation Frequency band is divided into 256 or more sub bands Orthogonal Peak of one at null of others Each carrier is modulated with a BPSK QPSK 16 QAM 64QAM etc depending on the noise Frequency selective fading Used in 802 11a g 802 16 Digital Video Broadcast handheld DVB H Easy to implement using FFT IFFT Washington University in St Louis CSE574S 5 16 2010 Raj Jain Advantages of OFDM Easy to implement using FFT IFFT Computational complexity O B log BT compared to previous O B2T for Equalization Here B is the bandwidth and T is the delay spread Graceful degradation if excess delay Robustness against frequency selective burst errors Allows adaptive modulation and coding of subcarriers Robust against narrowband interference affecting only some subcarriers Allows pilot subcarriers for channel estimation Washington University in St Louis CSE574S 5 17 2010 Raj Jain OFDM Design considerations Large number of carriers Smaller data rate per carrier Larger symbol duration Less inter symbol interference Reduced subcarrier spacing Increased inter carrier interference due to Doppler spread in mobile applications Easily implemented as Inverse Discrete Fourier Transform IDFT of data symbol block Fast Fourier Transform FFT is a computationally efficient way of computing DFT 1 Mbps 10 Mbps 0
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