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A Novel MIMO Transmission Method proposed herein as 802.11 TGn PHYsical Layer Element

A Novel MIMO Transmission Method proposed herein as 802.11 TGn PHYsical Layer Element. Daniel Nissani (Nissensohn) dnissani@bezeqint.net. The whole story in 5 slides (i). Cross-Talk Interference is a very significant MIMO performance deterioration factor Current Art attempts to combat this

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A Novel MIMO Transmission Method proposed herein as 802.11 TGn PHYsical Layer Element

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  1. A Novel MIMO Transmission Methodproposed herein as802.11 TGn PHYsical Layer Element Daniel Nissani (Nissensohn) dnissani@bezeqint.net Daniel Nissani (Nissensohn)

  2. The whole story in 5 slides (i) • Cross-Talk Interference is a very significant MIMO performance deterioration factor • Current Art attempts to combat this • Interference Cancellation (e.g. BLAST) • Adaptive Modulation (e.g. PARC) • ‘Black Box’ or ‘Force Majeure’ approaches • Assume no Pre-Calculation nor Control of Cross-Talk is possible • As we shall see, we CAN both Predict and Control Cross-Talk Daniel Nissani (Nissensohn)

  3. The whole story in 5 slides (ii) • (Ubiquitous) Imperfect MIMO Channel Estimation is the Sole Cause of Cross-Talk Interference • with Perfect Channel Estimates • No Cross Talk • MIMO problem reduces to a classic (and trivial) Linear Estimation problem • LSE, WLSE, MMSE, ZF • Imperfect Channel Estimate is the result of unavoidable Power / Time constrained Channel Training process Daniel Nissani (Nissensohn)

  4. The whole story in 5 slides (iii) • A very simple functional relationship is found between Cross-Talk Interference Power and the MIMO Channel Matrix structure • This is a Fundamental Enabler to the proposed Class of Methods • We are now able, (for the first time EVER, I believe) to precisely Predict, and most importantly to Control and improve the TOTAL (i.e. Cross-Talk, Channel, and Thermal) Sub-Stream Received mean SNR Daniel Nissani (Nissensohn)

  5. The whole story in 5 slides (iv) • Exploiting this relationship our usually So-So-Bad Channel can be Virtually replaced by other Much-Much-Better Channel, with superior Overall and Cross-Talk performance • By first, calculating this optimal (in Overall or Cross-Talk SNR sense) Channel • and by simple application of Pre/ Post-Equalization Daniel Nissani (Nissensohn)

  6. The whole story in 5 slides (v) • The end result • EXCELLENT BER PERFORMANCE • e.g. 20 db performance gain @1E-6 BER for 1st sub-stream relative to ZF/ LSE/ MMSE • EXTREME REAL TIME SIMPLICITY • single matrix-vector multiplication • O(M) vs. e.g. V-BLAST O(29M3) • LOW OVERHEAD • e.g. just 6 symbols Pre-Amble for L = R = 3 Daniel Nissani (Nissensohn)

  7. The whole story in 5 slides • Of course -- ‘God is in the Details’ • (G. Stein?, F. Nietzsche?), Long Time Ago • THE REST OF THE STORY FOLLOWS Daniel Nissani (Nissensohn)

  8. Talk Outline • The MIMO Cross-Talk Interference Phenomenon • Force Majeure or Docile Monster? • Cross-Talk SNR vs. Channel Matrix – a Simple Functional Relationship • A surprising discovery • Virtual Channel Generation, Pre/Post Equalization, Superior Cross-Talk SNR • Putting it all together • Simulation Results, Conclusions • What next? Daniel Nissani (Nissensohn)

  9. MIMO Basic ModelThe Cross-Talk Interference Component y = H A s + n = H x + n r = (Hn’ C-1 Hn)-1 Hn’ C-1 y e = r – x = ((Hn’ C-1 Hn) -1 (Hn’ C-1 H) – I) x + ((Hn’ C-1 Hn) -1 Hn’ C-1 n Daniel Nissani (Nissensohn)

  10. MIMO Model, Channel Info at Tx and Rx • In a broad class of MIMO systems Channel Matrix information is, or can be available, at both the Transmitting and Receiving sides • a natural situation in TDD based systems (like WLAN) • Channel Reciprocity • possible in FDD based systems (like most Cellular) by means of Return Channel feedback • We’ll focus on these hereon Daniel Nissani (Nissensohn)

  11. MIMO Model, Channel Info at Tx and Rx Hn = Un Dn Vn’ , H = U D V’ x = Vn A s y = H x + n r = Un’ y = Un’ H x + Un’ n = (Un’ U) D (V’ Vn ) A s + Un’ n = B s + Un’ n Daniel Nissani (Nissensohn)

  12. MIMO Model, Channel Info at Tx and RxThe ‘ideal’ and realistic (‘naïve’) Channel Estimation cases • In the ‘ideal’, perfect channel estimate case Hn = H, r = (D A) s + U’ n • r is simply a scaled version of the transmitted data • no Cross-Talk Interference, only Thermal Noise • In the ‘naïve’ case Hn H, Un U, Dn D, etc. • B (=(Un’ U) D (V’ Vn ) A) is a non-diagonal perturbation of of the diagonal matrix (D A) • all components of r include cross-talk power from each other sub-stream Daniel Nissani (Nissensohn)

  13. Cross-Talk SNR, Si CDF L = R = 3, ga = 20db, s1 and s2 data sub-streams x x NOT ALL CHANNEL MATRICES ARE BORN EQUAL! and MOST ARE SO-SO-BAD CHANNELS! Daniel Nissani (Nissensohn)

  14. Talk Outline • The MIMO Cross-Talk Interference Phenomenon • Force Majeure or Docile Monster? • Cross-Talk SNR vs. Channel Matrix – a Simple Functional Relationship • A surprising discovery • Virtual Channel Generation, Pre/Post Equalization, Superior Cross-Talk SNR • Putting it all together • Simulation Results, Conclusions • What next? Daniel Nissani (Nissensohn)

  15. Cross-Talk SNR, Si, representation • It can be shown that Si (H) = Si (D(H)) • a smooth, continuous function in D-space, the Singular Values of H (= U D V’) • this is a convenient, lower dimensionality (and much more efficient) representation Daniel Nissani (Nissensohn)

  16. Example: S2 (D(H)) scatter plot, L = R = 3, ga = 20db D3 D2 D1 Daniel Nissani (Nissensohn)

  17. Typical, Schematic plots of Si (D(H)) in D-space S1 S2 • Fij (D)= 0are‘iso-merit’ curves in D-space • Can be approximated by low order polynomials in D • G23 is a ‘Good’ region Daniel Nissani (Nissensohn)

  18. Talk Outline • The MIMO Cross-Talk Interference Phenomenon • Force Majeure or Docile Monster? • Cross-Talk SNR vs. Channel Matrix – a Simple Functional Relationship • A surprising discovery • Virtual Channel Generation, Pre/Post Equalization, Superior Cross-Talk SNR • Putting it all together • Simulation Results, Conclusions • What next? Daniel Nissani (Nissensohn)

  19. Taking Advantage of this InsightA Pre-Equalizer P Daniel Nissani (Nissensohn)

  20. Taking Advantage of this Insight (cont.) • We modify our original channel H so that a more favorable channel Hm (in the sense of better cross-talk SNR) is observed between Tx and Rx sides P = Vn Dn-1 Dm Hm = Hn P = (Un Dn Vn’ ) (Vn Dn-1 Dm) = Un Dm I Vm = I Um = Un Daniel Nissani (Nissensohn)

  21. Taking Advantage of this InsightA simple, Sub-Optimal scheme • Solve for Dm so that is satisfied, subject to • This minimizes the SNR loss incurred in P application • A is re-scaled by so that transmission power is preserved to (say) unity • other, simple and optimal problem formulations exist Daniel Nissani (Nissensohn)

  22. Taking Advantage of this Insight (cont.) • P equalizes the channel for cross-talk noise resultant from the non-diagonal elements of B above • A Post-Equalizer Q is also added to equalize residual distortion, due to the diagonal elements of B • We have to estimate Hn (and resultant Hm, A, Vm, P, Um) and to calculate the Post-Equalizer Q • 2 short (e.g. L x L) Training Matrices are transmitted as burst pre-amble Daniel Nissani (Nissensohn)

  23. Our Proposed MIMO Model x = (P Vm A) s y = H x + n and z = (Q Um’) y A SINGLE MATRIX-VECTOR MULTIPLICATION AT EACH RX AND TX SIDES !! Daniel Nissani (Nissensohn)

  24. Talk Outline • The MIMO Cross-Talk Interference Phenomenon • Force Majeure or Docile Monster? • Cross-Talk SNR vs. Channel Matrix – a Simple Functional Relationship • A surprising discovery • Virtual Channel Generation, Pre/Post Equalization, Superior Cross-Talk SNR • Putting it all together • Simulation Results, Conclusions • What next? Daniel Nissani (Nissensohn)

  25. Preliminary Simulation ResultsL = R = 3, QPSK, Raleigh, flat fading, uncorrelated H, 3.5% Channel Exception‘proposed’ vs. ‘naïve’ vs. ‘ideal’ models • ~ 20 db s1 sub-stream gain vs. ‘naïve’ ZF @ 1E-6 BER • 5 db s1 gap vs. ‘ideal’ @ 1E-6 BER • Preliminary initial results, sub-optimal, no parameters fine tuning Daniel Nissani (Nissensohn)

  26. Preliminary Simulation Results(cont.) Daniel Nissani (Nissensohn)

  27. WLAN Modeled Expected Rate-Range PerformanceL = R = 3, Path Loss = 27 db/ decade, fo = 5.3 GHz Daniel Nissani (Nissensohn)

  28. So what are our MIMO options for TGn?A zero-order straw-man comparison (L = R =3, etc.) Daniel Nissani (Nissensohn)

  29. Summary • EXCELLENT BER PERFORMANCE • 20 db performance gain @1E-6 BER for 1st sub-stream relative to LSE • 0.2 db performance gap @ 1E-3 BER for 3rd sub-stream relative to utopian perfect channel estimate case • even with sub-optimal implementation, initial results • EXTREME REAL TIME SIMPLICITY • single matrix-vector multiplication at each side • easily adapted to • TDD schemes by inherent Channel Reciprocity • OFDM schemes, at sub-carrier level • FDD schemes by appropriate Channel Information Feedback • LOW OVERHEAD • e.g. just 6 symbols Pre-Amble for L = R = 3 Daniel Nissani (Nissensohn)

  30. Possible Next Step • Call to Set-Up TGn Expert Task Force • To study, evaluate, validate the Proposed Method, compare with Other Options Daniel Nissani (Nissensohn)

  31. BACKUP SLIDES FOLLOW Daniel Nissani (Nissensohn)

  32. A broader perspectiveCross-Talk Interference Precedents • These results may seem new (and even a bit strange) to us all • We have met with Cross-Talk Interference before, e.g. • CDMA • Orthogonality loss due to Synchronization Miss-alignment (reverse) and Inter-Cell Interference (forward) • xDSL • Near and Far End Parasitic bundle coupling Daniel Nissani (Nissensohn)

  33. A broader perspective (cont.)Cross-Talk Interference Precedents • In both CDMA and xDSL cases Cross-Talk Interference is • externally imposed • beyond our model control • within our model we can only attempt to • fight against it • (Sequential or Parallel) Interference Cancellation • live with it • Adaptive Modulation Daniel Nissani (Nissensohn)

  34. A broader perspective (cont.)Cross-Talk Interference Precedents • In Wireless MIMO we also experience Cross-Talk Interference, but this time it is • intrinsically inherent to our model, and • (to a major extent) well under our control Daniel Nissani (Nissensohn)

  35. References [1] G.D. Golden, G.J. Foschini, R.A. Valenzuela, and P.W. Wolniasky, ‘Detection algorithm and initial laboratory results using the V-BLAST space-time communication architecture’, Electronics Letters, Vol. 35, No. 1, pp. 14-15, 1999  [2] Andersen, J.B. ‘Array gain and capacity for known random channels with multiple element arrays at both ends’, Selected Areas in Communications, IEEE Journal on, Volume: 18 Issue: 11 , Nov 2000, Page(s): 2172 -2178  [3] Telatar, E. I., ‘Capacity of Multi-antenna Gaussian Channels’, Technical Memorandum, Bell Laboratories, October 1995 [4] Stewart, G.W., ‘Perturbation Theory for the Singular Value Decomposition’ UMIACS-TR-90-124, September 1999 [5] Nissani (Nissensohn), D.N., ’The MIMO Cross-Talk Interference Problem- a Novel Solution’, Internal Technical Report, March 2003, publicly available Daniel Nissani (Nissensohn)

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