1 / 51

THE WIRELESS REVOLUTION: A Signal Processing Perspective Vince Poor (poor@princeton.edu)

Federal Communications Commission May 29, 2001. THE WIRELESS REVOLUTION: A Signal Processing Perspective Vince Poor (poor@princeton.edu). May 29, 2001 - The Wireless Revolution. OUTLINE. The Role of Signal Processing in Wireless Some Recent Signal Processing Advances

lynsey
Download Presentation

THE WIRELESS REVOLUTION: A Signal Processing Perspective Vince Poor (poor@princeton.edu)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Federal Communications Commission May 29, 2001 THE WIRELESS REVOLUTION: A Signal Processing Perspective Vince Poor (poor@princeton.edu) May 29, 2001 - 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 • Conclusion May 29, 2001 - The Wireless Revolution

  3. THE ROLE OF SIGNAL PROCESSING IN WIRELESS May 29, 2001 - The Wireless Revolution

  4. Motivating Factors • Telecommunications is a $1012/yr. business • c. 2005: wireless > wireline • > 109 subscribers worldwide • Explosive growth in wireless services • Use of a public resource (the radio spectrum) • Convergence with the Internet The Role of Signal Processing in Wireless

  5. Wireless Applications • Mobile telephony/data/multimedia (3G) • Nomadic computing • Wireless LANs • Bluetooth (piconets) • Wireless local loop • Wireless Internet/m-commerce The Role of Signal Processing in Wireless

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

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

  8. Demand Growing Exponentially Source: CTIA - As of 05/01/01, there were 114,546,113, in 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

  9. 60 51 50 42 41 40 45 35 44 Mobile Subscriptions as a % of all telephone Subscriptions 34 43 30 41 36 34 32 29 37 26 26 20 30 25 27 22 30 23 26 10 24 18 0 Germany Belgium France Greece Austria Britain Australia Portugal Iceland Denmark Sweden Finland Hungary Canada Spain Ireland Japan Norway Poland Italy South Korea Switzerland Netherlands New Zealand United States Source: ITU There’s Plenty of Room to Grow - I Mobile Phones Subscribers per 100 inhabitants, 1998 The Role of Signal Processing in Wireless

  10. There’s Plenty of Room to Grow - II Mobile Phones Market Penetration, 2000 Courtesy of: Tom Sugrue (FCC) The Role of Signal Processing in Wireless

  11. 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

  12. 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

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

  14. Signal Processor Performance (~Moore’s Law) Battery Capacity (i.e. Eveready’s Law) Moore’s and “Eveready”’s Laws Courtesy of: Ravi Subramanian (MorphICs) The Role of Signal Processing in Wireless

  15. 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 (phase-shifting masks) The Role of Signal Processing in Wireless

  16. Advances in ASIC Technology Microns 5/30/00: 25 nm gate announced with optical lithography using phase-shifting masks (T. Kailath, et al.). .8 .5 .35 Courtesy of: Andy Viterbi .25 .18 Time 1991 1995 1997 1998 Future The Role of Signal Processing in Wireless

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

  18. SOME RECENT SIGNAL PROCESSING ADVANCES • Introduction • Space-time Multiuser Detection (3G) • Turbo Multiuser Detection (2.5G) • Quantum Multiuser Detection (?G) May 29, 2001 - The Wireless Revolution

  19. INTRODUCTION Some Recent Signal Processing Advances

  20. First, A Few Words About MUD • Multiuser detection (MUD) refers to data detection in a non-orthogonal multiplex; it’s of interest, e.g., in • CDMA channels • TDMA channels with channel imperfections • DSL with crosstalk • MUD can potentially increase the capacity (e.g., bits-per-chip) of interference-limited systems significantly • MUD comes in various flavors • Optimal (max-likelihood, MAP) • Linear (decorrelator, MMSE) • Nonlinear interference cancellation Some Recent Signal Processing Advances

  21. Some Recent Developments • The basic idea of MUD is to exploit (rather than ignore) cross-correlations among signals to improve data detection. Recent developments in this area: • Space-Time MUD • Joint exploitation of spatial and temporal structure. • Turbo MUD • Joint exploitation of temporal structure induced by channel coding, and the multi-access channel. • Quantum MUD • Joint exploitation of quantum measurements and the multi-access channel. Some Recent Signal Processing Advances

  22. SPACE-TIME MUD Some Recent Signal Processing Advances

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

  24. + + - - - - - - Single-Antenna Reception Non-orthogonal signaling, multipath, fading, dispersion, dynamism, etc. Space-Time MUD

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

  26. A Sufficient Statistic: Space-Time Matched Filter Bank • Log-likelihood function of the received signal r(t): [tkl: path delay; gkl: path gain; akl: array response] • H is a matrix of cross-correlations among the received signals • Sufficient statistic {yk(i)}: space-time matched filter output Space-Time MUD

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

  28. Optimal Space-Time MUD • Maximum likelihood sequence detection maximizes (over b): • Computational complexity: O(2KD) [D: multipath delay spread] Space-Time MUD

  29. Linear S-T Interference Cancellers [ Decorrelator: sgn(Re {H-1y}); MMSE: sgn(Re {(H+s2I)-1y}) ] Problem: Solve with • Gauss-Seidel Iteration: (Serial IC) • Jacobi Iteration: (Parallel IC) • Computational complexity: O(K D mmax) Space-Time MUD

  30. Simulation [K = 8; L = 3; P = 3] Direct-sequence spread-spectrum (16 chips/bit). Space-Time MUD

  31. 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

  32. TURBO MUD Some Recent Signal Processing Advances

  33. MUD & The Decoding of Error-Control Codes • Recall: the basic idea of MUD is to exploit cross-correlations among signals to improve data detection. • Similarly, error-control coding exploits dependencies among channel symbols to improve data detection. • Turbo MUD is a technique for jointly exploiting these two types of dependencies. Turbo MUD

  34. Channel Output Channel Input Information Bits Convolutional Encoders Interleavers Multiaccess Channel Coded Multiple-Access Channel Basic Idea of Turbo MUD: • The convolutional code & the multiaccess channel form a concatenated code. • Like other concatenated codes, this code can be efficiently decoded via a turbo-style receiver. Turbo MUD

  35. Rate-R-Coded Multiaccess Signal Model Received Signal: • K = # active users. • B = # channel symbols per frame • dk = set of RB data symbols transmitted by user k • bk(dk)= vector of channel symbols obtained by encoding dk • pk = rec’d waveform of user k ; 1/T = per-user signaling rate. • {n(t)} = unit AWGN; s = noise intensity Turbo MUD

  36. Sufficient Statistic As before, the vector y of matched-filter outputs: is sufficient for inferring b1(d1) b2(d2) ... bK(dK) and d1 d2 ...dK. Turbo MUD

  37. Optimal MUD/Decoding ML Detection (b)/Decoding (d): MAP Detection/Decoding: Complexity per Symbol (Assume Binary Symbols): O(2KD) - uncoded symbols, delay spread D [MLSD; MAP MUD] O(2n) - convolutionally encoded symbols, constraint length n ; orthogonal signaling [BCJR, Viterbi algo, etc.] Turbo MUD

  38. Turbo MUD: The Main Idea • For constraint-length-n convolutionally coded transmission on an asynchronous K-user multiaccess channel, optimal detection/decoding has complexity O(2Kn) [Giallorenzi & Wilson]. • This complexity can be reduced to O(2K) + O(2n) via the turbo principle [Moher]. • I.e., iterate between MUD and channel decoding, exchanging soft (channel) symbol information at each iteration. Turbo MUD

  39. Channel Output Channel Input Information Bits Convolutional Encoders Interleavers Multiaccess Channel Multiaccess Channel & Turbo Receiver Channel Output SISO MUD Int. De-Int. Output Decision SISO Decoders • Soft-input/soft-output (SISO) • Iterative • Interleaving removes correlations vs. Turbo MUD

  40. SISO MUD • To get posterior probabilities from the multiuser detector, we should use MAP MUD. • MAP MUD is prohibitively complexO(2K) [K = # users] • This differs from standard turbo decoding, in which the constituent decoders are of similar complexity. • Many lower complexity approaches: [Alexander et al.; Honig et al., Lu & Wang, Müller & Huber, Naguib & Sheshadri, Reed et al., Schlegel, Tarköy, Wang & Chen, Wang & Poor (COM’99), & others] Turbo MUD

  41. Recall: Low Complexity MUD Recall the Model: • MUD fits this model to the observations. • As noted before, in addition to ML/MAP, there are many low-complexity techniques for doing this; e.g., • Linear MUD: decorrelator, MMSE, bootstrap (v. efficient iterative implementation as linear interference cancellers (IC’s)) • Nonlinear IC’s: successive cancellation, multistage, EM/SAGE • Generally, these don’t allow the computation of the posterior probabilities needed for turbo MUD. Turbo MUD

  42. Low Complexity SISO MUD • Conventional MMSE MUD: • MMSE output desired symbol + Gaussian error [Poor & Verdú, IT’97] • From this, posterior probabilities can be estimated from the MMSE detector output. • This yields an effective low-complexity SISO MUD. • MMSE w/ Priors: Turbo MUD

  43. Simulation Example [K = 4; r = 0.7] Rate-1/2 convolutional code; constraint length 5; 128-long random interleavers Turbo MUD

  44. QUANTUM MUD Some Recent Signal Processing Advances

  45. Quantum MUD • A basic element of MUD is the matched-filter-bank sufficient statistic. • With quantum measurements, observation 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

  46. Quantum MUD Design Problem Quantum MUD

  47. A Two-User Quantum Channel Quantum MUD

  48. Two-User Example: Error Probabilities Quantum MUD

  49. Conclusion • The transformation from wireless voice to wireless data is causing exponentially increasing demand for wireless capacity. • Signal processing is the great enabler: • Source compression • Fading countermeasures/transmitter diversity • Interference suppression/space-time processing • Micro-lithography • Recent advances: May 29, 2001 - The Wireless Revolution

  50. Conclusion - Cont’d • MUD exploits signal cross-correlations to substantially improve data detection. • Space-time MUD • Combines exploitation of temporal & spatial cross-correlations. • Turbo MUD • Combines exploitation of cross-correlations introduced by the channel with exploitation of dependence introduced by coding. • Quantum MUD • Combines exploitation of cross-correlations with the instrument design for the quantum channels. • Some Open Issues • Space-time MUD: Hardware implementation • Turbo MUD: Adaptivity, convergence behavior • Quantum MUD: Relevance in applications May 29, 2001 - The Wireless Revolution

More Related