PERSPECTIVES ON THE WIRELESS REVOLUTION: Signal Processing and Education Vince Poor

1. Stanford Wireless Seminar: May 16, 2000 PERSPECTIVES ON THE WIRELESS REVOLUTION: Signal Processing and Education Vince Poor (poor@princeton.edu) May 16, 2000 - Perspectives on the Wireless Revolution

2. OUTLINE • The Role of Signal Processing in Wireless • Some Recent Signal Processing Advances • Space-time Multiuser Detection • Turbo Multiuser Detection • Quantum Multiuser Detection • The Wireless Revolution @ Princeton May 16, 2000 - Perspectives on the Wireless Revolution

3. THE ROLE OF SIGNAL PROCESSING IN WIRELESS May 16, 2000 - Perspectives on the Wireless Revolution

4. Motivating Factors • Telecommunications is a $1012/yr. business • c. 2005: wireless > wireline • > 109 subscribers worldwide • Explosive growth in wireless services (3G, WLL’s, WLAN’s, Bluetooth, etc.) • Rapid convergence with the Internet The Role of Signal Processing in Wireless

5. Wireless is Rapidly Overtaking Wireline Source: The Economist Sept. 18-24, 1999 The Role of Signal Processing in Wireless

6. Traffic Increasingly Consists of Data Source:http://www.qualcomm.com The Role of Signal Processing in Wireless

7. Demand Growing Exponentially Source: CTIA - There are now 92,182,894in U.S., according to www.wow-com.com - Every 2.25 secs., a new subscriber signs up for cellular in U.S. The Role of Signal Processing in Wireless

8. Wireless Challenges • High data rate (multimedia traffic) • Networking (seamless connectivity) • Resource allocation (quality of service - QoS) • Manifold physical impairments • Mobility (rapidly changing physical channel) • Portability (battery life) • Privacy/security (encryption) The Role of Signal Processing in Wireless

9. Wireless Channels • Fading: Wireless channels behave like linear systems whose gain depends on time, frequency and space. • Limited Bandwidth (data rate, dispersion) • Dynamism (random access, mobility) • Limited Power (on at least one end) • Interference (multiple-access, co-channel) The Role of Signal Processing in Wireless

10. Not Growing Exponentially • Spectrum - 3G auctions! • Battery power • Terminal size The Role of Signal Processing in Wireless

11. Signal Processor Performance (~Moore’s Law) Battery Capacity (i.e. Eveready’s Law) Moore’s and “Eveready”’s Laws Courtesy of: Ravi Subramanian - ELE391 Lecture (03/24/00) The Role of Signal Processing in Wireless

12. Signal Processing to the Rescue • Source Compression • Transmitter Diversity (Fading Countermeasures): • Spread-spectrum: CDMA, OFDM (frequency selectivity) • Temporal error-control coding (time selectivity) • Space-time coding (angle selectivity) • Advanced Receiver Techniques: • Interference suppression (multiuser detection - MUD) • Multipath combining & space-time processing • Equalization • Channel estimation • Improved Micro-lithography (T. Kailath, et al.) The Role of Signal Processing in Wireless

13. Advances in ASIC Technology Microns Courtesy of: Andy Viterbi - ELE391 Lecture (05/5/00) .8 .5 .35 .25 .18 Time 1991 1995 1997 1998 Future The Role of Signal Processing in Wireless

14. Signal Processing for Wireless (v 1.0) Fleming Valve (British) 1910 Helical Transformer 1919 Marconi Crystal Receiver 1919 DeForest Tubular Audion 1916 The Role of Signal Processing in Wireless

15. SOME RECENT SIGNAL PROCESSING ADVANCES • Space-time MUD (3G) [Wang & Poor (SP’99), Dai & Poor (ISSSTA2000), et al.] • Turbo MUD (2+G) [Wang & Poor (COM’99), et al.] • Quantum MUD (?G) [Concha & Poor (ISIT2000)] May 16, 2000 - Perspectives on the Wireless Revolution

16. First, A Few Words About MUD [Also recall SV’s May 11 talk.] • Multiuser detection (MUD) refers to data detection in non-orthogonal multiplexes • MUD can potentially increase the capacity (e.g., bits-per-chip) of interference-limited systems significantly • MUD comes in various flavors • Optimal (max-likelihood, min-probability-of-error) • Linear (matched filter, decorrelator, MMSE) • Nonlinear interference cancellation Some Recent Signal Processing Advances

17. User 1 User 2 User K Multi-{Access, Antenna, Path} Channel Space-Time MUD

18. Single-Antenna Reception Asynchrony, multipath, fading, dispersion, dynamism, etc. Space-Time MUD

19. Space-Time MA Signal Model • Transmitted signal due to the k-th user: [bk(i): data symbol; sk(t): spreading waveform] • Vector channel of the k-th user: [tkl: path delay; gkl: path gain; akl: array response] • Received signal: Space-Time MUD

20. Sufficient Statistic • Composite data signal • Log-likelihood function of received signal r(t) • Sufficient statistic {yk(i)}: space-time matched filter output. Space-Time MUD

21. Space-Time Multiuser Receiver Maximum Likelihood Sequence Detection OR Iterative Interference Cancellation Space-Time MUD

22. Optimal Space-Time MUD • Maximum likelihood sequence detection maximizes: • Computational complexity: O(2(D+1)K) [D: multipath delay spread] Space-Time MUD

23. Linear S-T Interference Cancellers [ Decorrelator: sgn(R {H-1y}); MMSE: sgn(R {(H+s2A-2)-1y}) ] Problem: Solve with • Gauss-Seidel Iteration: (Serial IC) • Jacobi Iteration: (Parallel IC) • Computational complexity: O(K D mmax) Space-Time MUD

24. Simulation [K = 8; N = 16; L = 3; P = 3] Space-Time MUD

25. Nonlinear S-T Interference Cancellers • Decision Feedback: Cholesky Decomposition: • Successive Cancellation: • EM/SAGE-Based IC: (Interfering symbols are “hidden” data) • Turbo MUD: - Coded channels (b has constraints). Space-Time MUD

26. Turbo CDMA Channel and Receiver Channel Output Channel Input Information Bits Convolutional Encoders Interleaver CDMA Channel Channel Output SISO MUD Int. De-Int. Output Decision SISO Decoders • Soft-input/soft-output (SISO) • Iterative • Interleaving removes correlations vs. Turbo MUD

27. SISO MUD • To get posterior probabilities, we should use MAP detection. • MAP MUD is prohibitively complexO(2K) [K = # users] • Other MUD’s (e.g., MMSE) don’t give posteriors. • But, the MMSE detector output is approx. equal to the desired symbol + Gaussian error. [Poor & Verdu IT’97] • From this, posterior probabilities can be estimated from the MMSE detector output. Turbo MUD

28. Simulation Example [K = 4; r = 0.7] Turbo MUD

29. Quantum MUD • A basic element of MUD is the (space-time) matched-filter-bank sufficient statistic. • With quantum measurements, the type of measurements is restricted (uncertainty principles apply) • In this case, the observation instrument must be designed jointly with the detector. • Photon counting is usually not optimal. Quantum MUD

30. A Two-User Quantum Channel Quantum MUD

31. Quantum MUD Design Problem Quantum MUD

32. Error Probabilities Quantum MUD

33. THE WIRELESS REVOLUTION @ PRINCETON http://courseinfo.princeton.edu/courses/ELE391_S2000 May 16, 2000 - Perspectives on the Wireless Revolution

34. ELE391: The Wireless Revolution(Telecommunications for the 21st Century) • What: A new course (Spring2000) for majors and non-majors (approximately 120 undergraduates). • Motivation: Significant student curiosity about the current wireless boom, both within/without EE. • Prerequisite: Freshman calculus. The Wireless Revolution @ Princeton

35. Objectives: Things to Learn • Wireless technology (digital transmission, access techniques, networking, applications). • Economic/business aspects of wireless. • Social dimensions of wireless. • Politics of wireless (regulation, standards). The Wireless Revolution @ Princeton

36. “Wireless for Poets”? - Not exactly. The Wireless Revolution @ Princeton

37. Course Organization • Part I: Wireless Technology • Part II: Economic, Political & Social Issues • “Wireless News” - Daily e-letter • Final Papers The Wireless Revolution @ Princeton

38. Part I: Wireless Technology • Organization of telecommunications networks • Multimedia transmission (mod/demod, A/D, compression, etc.) • Radio network management (access methods, protocols) • Physical limitations on wireless networks • The radio spectrum (physical characteristics, allocation) • History and evolution of wireless technology • Profile of current wireless services (cellular, WLL, WLAN, etc.) • Cellular telephony (current & emerging systems) • Other emerging technologies (m-Internet, Bluetooth, PDA’s, etc.) The Wireless Revolution @ Princeton

39. Part II: Economic, Political & Social Issues • The main businesses involved in wireless (OEM’s, service providers, etc.) • Ed Zschau (Harvard Business School): the wireless market space • Ravi Subramanian (MorphICs): deconstruction of the wireless industry • Ed Felten (CS): security and privacy • Chris Fine (Goldman-Sachs): a Wall Street perspective (M&A, etc.) • Wayne Wolf (EE): comparison of Marconi and Internet eras • Eszter Hargittai (Sociology): technology diffusion • Dale Hatfield (FCC): spectrum management • Ruby Lee (EE): multimedia information appliances • Mike Feher (wireless antiquary): demo of antique wireless apparatus • Andy Viterbi (Viterbi Fund): how new technology created the wireless mania The Wireless Revolution @ Princeton

40. “Wireless News” (Greatest Hits) • Mergers: Vodafone/Mannesmann ($185B); Bell Atlantic/ Airtouch/GTE (Verizon); SBC/Bellsouth; Pacific Century/Cable & Wireless HKT; Royal KPN/DoCoMo • IPO’s: Palm, AT&T Wireless ($10B), etc. • European 3G Auctions: UK - £22 billion/150 rounds • Iridium: Crispy satellites. • New Devices: Nokia, NEC, Palm, Pocket PC, etc. • m-Everything: stocks, banking, food, golf, bingo, etc. The Wireless Revolution @ Princeton

41. Things to Remember • Wireless is one of the most exciting technologies of our time. • It’s enormous, global, and growing very rapidly. • The opportunities for innovation and impact - technical, economic, social, political - are limitless. • We are living in a period like the Marconi era, with convergence of wireless and the Internet likely to make major changes in society. • Signal processing is the great enabler. May 16, 2000 - Perspectives on the Wireless Revolution