1. THE WIRELESS REVOLUTION: A Signal Processing Perspective Vince Poor (poor@princeton.edu) Federal Communications Commission May 29, 2001 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 $10 12 /yr. business c. 2005: wireless > wireline > 10 9 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 The Role of Signal Processing in Wireless Source: The Economist Sept. 18-24, 1999

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

8. Demand Growing Exponentially The Role of Signal Processing in Wireless 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.

9. Mobile Phones Subscribers per 100 inhabitants, 1998 The Role of Signal Processing in Wireless There’s Plenty of Room to Grow - I

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

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. Moore’s and “Eveready”’s Laws Courtesy of: Ravi Subramanian (MorphICs) Battery Capacity (i.e. Eveready’s Law) Signal Processor Performance (~Moore’s Law) 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 Courtesy of: Andy Viterbi Microns.8.5.35.25.18 Time1991Future199819971995 The Role of Signal Processing in Wireless 5/30/00: 25 nm gate announced with optical lithography using phase- shifting masks (T. Kailath, et al.).

17. Fleming Valve 1910 Helical Transformer 1919 Marconi Crystal Receiver 1919 DeForest Tubular Audion 1916 Signal Processing for Wireless (v 1.0) 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. Non-orthogonal signaling, multipath, fading, dispersion, dynamism, etc. Single-Antenna Reception ------ ------ ++ Space-Time MUD

25. Transmitted signal due to the k-th user: [b k (i): data symbol; s k (t): signaling waveform] Vector channel (impulse response) of the k-th user: [  kl : path delay; g kl : path gain; a kl : array response] Received signal: Space-Time MA Signal Model Space-Time MUD

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

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

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

29. [ Decorrelator: sgn( Re {H -1 y}); MMSE: sgn( Re {(H+  2 I) -1 y}) ] – Gauss-Seidel Iteration: (Serial IC) Problem:with – Jacobi Iteration: (Parallel IC) Linear S-T Interference Cancellers Computational complexity: O(K  m max ) Solve Space-Time MUD

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

31. – Decision Feedback: Cholesky Decomposition: – Successive Cancellation: – EM/SAGE-Based IC: (Interfering symbols are “hidden” data) Nonlinear S-T Interference Cancellers – 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. 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. Coded Multiple-Access Channel Convolutional Encoders Interleavers Multiaccess Channel Information Bits Channel Input Channel Output Basic Idea of Turbo MUD : Turbo MUD

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

36. As before, the vector y of matched-filter outputs: is sufficient for inferring b 1 (d 1 ) b 2 (d 2 )... b K (d K ) and d 1 d 2... d K. Sufficient Statistic Turbo MUD

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

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

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

40. SISO MUD To get posterior probabilities from the multiuser detector, we should use MAP MUD. MAP MUD is prohibitively complex O(2 K ) [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;  Rate-1/2 convolutional code; constraint length 5; 128-long random interleavers Turbo MUD

44. QUANTUM MUD Some Recent Signal Processing Advances

45. 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

51. THANK YOU! May 29, 2001 - The Wireless Revolution