Why is MIMO the Next Big Thing?

Why is MIMO the Next Big Thing? …because everything is going wireless! Lloyd Nirenberg, Ph.D. Shannon Bayes Venture Corp. Presentation to Litepoint Seminar Taiwan 2 NOV 05

About Shannon Bayes Venture Corp. • Provide guidance for wireless product road map • Value, structure and negotiate Intellectual Property transactions

Overview • IC business model is dramatically changing for Consumer Electronics (CE) products • Wireless will be embedded in many CE products • More capacity needs to come from bad channels • MIMO helps in many situations

Cellphone platform+Operator Cell+WiFi Cell+CCD=Cameraphone Cell+PDA=Smartphone and Treo Cell+Tuner=Handset Video Service Cell+GPS=Personal Locator PC Media Center Platform+Content Provider Video+Audio distribution in home WiFi networks Set-Top Box Platform+Cable Operator+Content Provider HD+”MPEG”+DigiTV=Home Media Recorder Game Machine Platform+Content Provider Game+web+Wireless+TV Tuner=Connected game Consumer Electronics Device Pager+email=Blackberry Camcorder +Wireless HD+”Music Download Service”=music player-> iPOD GPS+Weatherstation= Services, Functions and Consumer Hardware are Converging

…and, Everything has to have Wireless Connections

…and Requirements are getting Harder • Latency-sensitive and bandwidth-intensive multimedia applications • HDTV streaming • Entertainment devices with the ability to distribute and share content • Video and Audio must stay synchronized • Need broadband signals in narrow bands

How can Wireless Technology Support These Needs? History

1996 MIMO Emerges 1955 Maximum Likelihood Radar+Comm Receivers 1948 Shannon: Information Theory+Coding Theorem 1935 Wiener; Kolmogoroff: Minimum Mean Square Estimation (MMSE) 1920 Armstrong: FM systems 1920 Nyquist: Sampling Theory 1904 Marconi closes wireless link 1850 Maxwell’s Equations; Bayes Theorem Communications Signal Processing Milestones 1990 Turbo Codes 1980 Multi-user Detection for CDMA 1967 Viterbi Algorithm; Lucky: MMSE tapped delay line receiver 1965 Cooley-Tukey Algorithm (Fast Fourier Transform); Kalman Filter

Evolution of Antennas • Goals • Gain • Bandwidth • Pattern • Efficiency • Cost • Size 1980 Active Integrated 1960 Phased arrays 1950 Patch 1920 Yagi-Uda Directive 1900 Hertz; Marconi; Popov 1895 Source: A. Paulraj, R. Nabar, D. Gore, Introduction to SpaceTime Wireless Communications, 2003 1850 Maxwell’s Equations

Estimation of Direction of Received Wavefronts Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) 1985 1980 Multiple Source Multiple Signal Classification (MUSIC) 1964 Maximum Likelihood 1970 Sweeny 1935 Wullenweber Single Source Adcock 1919 Loop 1904 Source: A. Paulraj, R. Nabar, D. Gore, Introduction to SpaceTime Wireless Communications, 2003

Antennas for Coverage vs Capacity MIMO 2000 Adaptive SIMO ST Processing 1990 Jammer Cancellation 1980 1935 Butler Non-adaptive Marconi 1904 Source: A. Paulraj, R. Nabar, D. Gore, Introduction to SpaceTime Wireless Communications, 2003

Key MIMO Milestones • A. Paulraj, T. Kailath 1994 • Increasing capacity in wireless broadcast systems using distributed transmission/directional reception USPTO 5345599 • G. Foschini 1996 • Layered Space-Time Architecture for for Wireless Communicaitons in a Fading Environment when Using Multi-Element Antennas, Bell System Tech. Journal, 41-59 • D-BLAST • Diagonal Bell Labs Space Time • G. Raleigh, J. Cioffi 1998 • Spatio-temporal Coding for Wireless Communications, IEEE Trans. Comm., 46(3), 357-366-, March

Standards’ Problems and Solutions

QoS Boundary New user causes Capacity Limit, lowers QoS for all Capacity, Coverage Out of range user has Coverage Limit, cannot get QoS New base station connects new user in Line of Site (LoS)

Out of range user has no LoS, cannot get QoS QoS Boundary New user causes Capacity Limit, lowers QoS for all New base station uses MIMO to connect new user Capacity, Coverage and MIMO

SISO DSP DSP RF RF Bits Bits Bits DSP DSP RF RF Bits Bits DSP RF … SISO-MU Basic Space-Time (ST) Architectures

Mitigate multipath fading by combining multiple, correlated multipath versions of single transmitted signal DSP RF RF Bits Bits DSP Bits RF DSP DSP RF RF Bits Diversity SISO Diversity SISO Advanced ST Architectures-”Diversity Combining”

MISO Beamformer Bits DSP RF DSP RF Bits Smart Antennas • Reduce the amount of energy lost to multipath propagation by focusing antenna pattern

RF RF Bits MIMO DSP DSP Bits RF RF MT MR Big Idea: MIMO Architecture • System Dimensionality • Each signal stream has its own dimensionality ~BT • Number of independent signal streams in same band • Multipath reflections, makes each independent receive chain a linear combination of the multiple transmitted data streams. • Reflections create “virtual wires” • Each multipath route can be treated as a separate channel, creating multiple "virtual wires" over which to transmit signals • Signal streams are separated at the receiver using MIMO algorithms that rely on estimates of all channels between each transmitter and each receiver. • The algorithmic combination improves performance … …

Hrank S C = log2 ( 1+Esln/(MTN0) ) n = 1 MIMO Channel Capacity • H = Channel Matrix of gains • H(i,j) random, complex transfer gain between Transmitter Antenna i and Receiver Antenna j • Transmit power per Antenna = Es /MT • ln=N-th reflection power gain = Eigenvalues of HH* • Shannon SISO capacity increases as a result of having reflections ln > 0 • Channel H must be measured to implement algorithms

Design Constraints • Angle of Dispersion • < 10o MIMO has no advantage • Signal to Noise • Low MIMO has no advantage • Channel Measurement • Must make these for best advantage

Multiband Systems MIMO in many systems Cell Phone UMTS/GSM 2100, 800/900/1800/1900 MHz A-GPS 1500 MHz WiFi 2400, 5700 MHz Bluetooth 2400 MHz DTV 700 MHz RFID 13 MHz FM 100 MHz Game Box WiFi DS-UWB/WiMedia Bluetooth DTV Multiple RF Chains =Big Challenges

Winners will Competent to: • Understand MIMO and develop algorithms • Implement multiple RF chains on single chips

Why is MIMO the Next Big Thing?