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Monthly Faculty Seminar, Universiti Kebangsaan Malaysia, Nov. 2011

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- 1. Monthly Faculty SeminarSpatial Interference and Its Effect Towards the Implementation of Future 4G Wireless Networks Dr. Rosdiadee Nordin JKEES, FKAB
- 2. LAYOUT• Introduction• Problem Background• Previous Works• Proposed Solution• Results and Analysis• Future Works• Conclusions
- 3. INTRODUCTION• Wireless technologies – Past, Present and Future• Imperfection within the wireless channel present numerous challenges – fading vs. Shannon‟s capacity• Basic mechanisms for wireless propagation: – Reflection Base Station – Diffraction – Scattering lect io n Ref S g Lo in ter S cat io n act ffr Di Local Scattering
- 4. INTRODUCTION• Type of imperfections: – Large-scale fading: • Power varies gradually • Over large distance, terrain contours • Determine by path profile and antenna displacement – Small-scale fading: • Small changes of the reflected, diffracted and scattered signals • Resulting in vector summation of destructive/ constructive interference at Rx, known as multipath wave • Rapid changes of amplitudes, phase or angle • Also known as Rayleigh fading [1] or frequency selectivity [1] J.G. Proakis. Digital Communications. Fourth Edition, The McGraw-Hill Companies, 2001
- 5. SMALL-SCALE FADING• Rayleigh fading induces Inter-symbol Interference (ISI) – major source of impairment in wireless channel• Influenced by following mechanisms: – Time spreading 10 5 – Time variance 0 -5 Power (dB) -10 -15 -20 -25 -30 -35 0 20 40 60 80 100 120 140 160 180 200 time (ms)
- 6. MITIGATION STRATEGIES• Introducing diversity [2]: – Time: coding, interleaving, adaptive modulation, equalization (linear/ non-linear) – Spatial: multiple antenna, involves combining methods – Multiuser: exploit channel quality from different users – Cooperative: relay – Frequency: spread spectrum (DS and FH), SC-FDE [3], OFDM [4][2] B. Sklar. “Rayleigh Fading Channels in Mobile Digital Communications Systems. Part II: Mitigation”, IEEECommunications Magazine, Vol. 35, No. 7, pp. 102-109, July 1997[3] H. Sari, G. Karam, and I. Jeanclaude. “Transmission techniques for digital terrestrial TV broadcasting”. IEEECommunications Magazine, Vol. 33, No. 2, pp. 100-109, Feb., 1995.[4] R. van Nee and R. Prasad. OFDM for Wireless Multimedia Communications. Artech House Publishers, 2000.
- 7. OFDM• Multi-carrier modulation scheme – splitting the high rate data stream into lower rate data streams• From wideband into orthogonal narrow band signals• Addition of Guard Interval (GI) provides effective way to combat ISI• FFT (Rx) and IFFT (Tx) help to reduce the complexity of OFDM implementation
- 8. OFDM• OFDM has several disadvantages: – PAPR, will reduce the ADC/DAC and inefficient power amplifier. Mitigations: clipping, reduction codes and scrambling techniques – Frequency offset synchronization. Mitigation: training sequence – Phase noise. Mitigation: pilot signal
- 9. OFDM(A)• An extension to OFDM, or the multiuser version of OFDM is known as OFDMA• Strong candidate for future 4G air interface downlink transmission• Employs bigger FFT size, flexible timeslots and subchannels size, support different type of QoS, feedback information to enhance resource allocation.• Subcarrier as the smallest unit in an OFDMA transmission
- 10. MIMO• Another potential technology for future 4G. Have been around since 1970 [5]• Leveraging multipath – exploiting fading (instead of mitigating) for the benefit of MIMO users• CMIMO = min(Nt,Nr). Increasing the number of Tx, Rx antenna allows more data to be transmitted Radio Channel, H x1 1 1 y1 ŷ1 Tx1 Rx1 2 x2 2 y2 ŷ2 Tx2 Rx2 MIMO Processing x Nt Nt Nr yNr ŷNr Tx Nt Rx Nr[5] A.R. Kaye and D.A. George. “Transmission of multiplexed PAM signals over multiple channel and diversity systems,”IEEE Trans. on Comms. Technology. Vol. COMM-18, pp. 520-526, Oct. 1970
- 11. MIMO• Can takes many forms for different type of configurations• SISO vs. MIMO capacity [6]: 2 SNR C SISO log 2 (1 SNR H ) vs. C MIMO log 2 det I N HH* Nt r• Two forms of MIMO: – Space-Time Code (STC) – Spatial Multiplexing (SM)[6] D. Gesbert, M. Shafi, D.S Shiu, P.J. Smith, and A. Naguib. “From theory to practice: an overview of MIMO space-timecoded wireless systems”, Tutorial paper. IEEE Journal on Selected Areas in Communications (JSAC), Vol. 21, No. 3, pp.281-302, Apr. 2003
- 12. MIMO – STC• STC aim to reduce the effect of fading by: – Achieve maximum antenna diversity – Improve wireless link reliability• Combines the use of channel coding and multiple transmit antennas• Achieve spatial diversity at the expense of throughput• Common forms of STC: – Space-Time Trellis Code (STTC) – Space-Time Block Code (STBC) – Space-Frequency Block Code (SFBC)• Practical and common representation of STBC was introduced by Alamouti [7] [7] S. Alamouti, “A simple transmit diversity technique for wireless communications”, IEEE Journal on Selected Areas in Communications (JSAC), Vol. 16, No. 8, pp. 1451-1458, Oct. 1998
- 13. MIMO – SM• Capable of increasing the data rate by higher spectral efficiencies at no additional power or bandwidth• Dividing the high rate data stream input into parallel independent data streams.• Thus, increased the nominal spectral efficiencies by a factor of Nt.• V-BLAST [8] generally regarded as the common form of SM Antenna Index Antenna Index Interference a b c d Nulled a a a a Wasted a b c d b b b b a b c d c c c c Wasted a b c d Time d d d d Cancelled Detection Order Time D-BLAST V-BLAST[8] G. J. Foschini, “Layered Space-Time Architecture for Wireless Communication in a Fading Environment when UsingMultielement Antennas,” Bell Labs Tech. J., pp. 41–59, Autumn 1996
- 14. MIMO – SM• SM performance relies on the: – Detection techniques at Rx – Presence of (rich) scatterers• Some of the well-known SM detection techniques: – Successive Interference Cancellation (SIC) – Zero Forcing (ZF)/ Interference nulling/ Linear decorrelator – Min. Mean Square Error (MMSE)• Scatterers: characteristics of the scatters can be determine from the propagation profile, e.g. separation, location, surrounding terrain, etc.
- 15. PROBLEM BACKGROUND• SM relies on the linear dependence between the channel responses corresponding to each transmit antenna.• Suffer considerably from – Spatial subchannels correlation and/or, – Ricean fading component• Result in an near rank one MIMO channel matrix.• Cause degradation of downlink capacity. Retransmission and combining techniques does not improve the BER performance.
- 16. PROBLEM BACKGROUND• Most MIMO implementations consider ideal propagation conditions, i.e. uncorrelated channel• For realistic approach, spatial correlation does exist between antenna pairs – affects MIMO capacity• The effect is known as self-interference [6] 12 RBS=0.0,RMS=0.0 RBS=0.4,RMS=0.4 10 Spatial layer 1 RBS=0.5,RMS=0.5 T1 R1 RBS=0.0,RMS=0.9 Spatial layer 2 8 RBS=0.9,RMS=0.0 capacity (bps/Hz) Interference RBS=0.9,RMS=0.9 from T2 6 RBS=1.0,RMS=1.0 BS Interference MS from T1 4 T2 Spatial layer 1 R2 Spatial layer 2 2 0 -10 -5 0 5 10 15 20 SNR (dB)[6] D. Gesbert, M. Shafi, D.S Shiu, P.J. Smith, and A. Naguib. “From theory to practice: an overview of MIMO space-timecoded wireless systems”, Tutorial paper. IEEE Journal on Selected Areas in Communications (JSAC), Vol. 21, No. 3, pp.281-302, Apr. 2003
- 17. PROBLEM BACKGROUND• Factors contribute towards self-interference: – Insufficient antenna separation – Small scattering angle, e.g. AoA, AoD, etc – Height of BS antennas – Separation between Tx and Rx antenna• Design antenna based on degree of correlation [9], e.g. 100 separation and wider angle (max. 900)• Not possible due to RF planning, safety, environmental and installation issue several km small small[9] W. Lee, "Effects on Correlation between Two Mobile Radio Base-Station Antennas," IEEE Transactions onCommunications, Vol.21, No.11, pp. 1214-1224, Nov 1973
- 18. PREVIOUS WORKS• Efficient design techniques for MIMO antenna implementation – Antenna separation – Orthogonality: angle, space, polarization• Challenges: – Environmental and safety concern – Array blindness – Reduction of antenna effective gain – „Keyhole‟ or „pinhole‟ effect [10] [10] D. Chizhik, G. J. Foschini, and R.A. Valenzuela, “Capacities of multi-element transmit and receive antennas: Correlations and Keyholes,” Electronic Letters, Vol. 36, pp. 1099–1100, June 2000
- 19. PREVIOUS WORKS• Optimum power allocation scheme – known CSI at the Tx via SVD [11]• Singular values as the decision criteria for power allocation – to identify effective independent channel• Challenges: – Inaccurate CSI in fast fading channels – Different eigenvalues in each channel – errors in selection criteria – High spatial correlation low eigenvalues low gain (power loss)[11] R.R. Ramirez and F. De Flaviis, "A mutual coupling study of linear and circular polarized microstrip antennas fordiversity wireless systems", IEEE Transactions on Antennas and Propagation, Vol. 51, No. 2, pp. 238-248, Feb. 2003
- 20. PREVIOUS WORKS• Enhanced version of [11]: antenna selection + power allocation [12]• Using the correlation matrix, instead of CSI as feedback: less overhead, low feedback req. and faster allocation process• „Water-filling‟ approach• Challenges: • Requires continuous bit assignment • But modulations is discrete; can be overcome by AMC • At the expense of data rate loss[12] M.T. Ivrlac, W. Utschick, J.A. Nossek, "Fading correlations in wireless MIMO communication systems",IEEE Journal on Selected Areas in Communications, Vol. 21, No. 5, pp. 819- 828, June 2003
- 21. PREVIOUS WORKS• Constellation multiplexing [13]: the use of power scaling by scaling down the desired M-QAM constellation size• Adjust the power and phase of the input constellations• In a 16-QAM, superposed of 2×4-QAM s2 signals), scaled down to ¼ with BER te xt te xt te xt te xt loss of 4 dB te te te s1 te xt xt xt xt• But, requires one transmit antenna to te te te te Tx and Rx xt xt xt xt• Only „dual mode‟ operation te xt te xt te xt te xt[13] J. Akhtar, D. Gesbert, "A closed-form precoder for spatial multiplexing over correlated MIMO channels", IEEEGlobal Telecommunications Conference, 2003. GLOBECOM 03, Vol. 4, pp. 1847-1851, Dec. 2003
- 22. PREVIOUS WORKS• Subcarrier allocation scheme based on the knowledge of the adjacent spatial sub-channels – DSA Scheme 5 [14]• Avoid selection of (i) similar subcarrier from the adjacent spatial subchannel, and (ii) the near subcarriers• Depends on the separation between current and next allocated subcarrier, j• Depends on the channel model profile The queue by metric of channel gain of subcarrier at the certain subchannel A and B 21 38 89 128 328 437 Previously considered spatial subchannel (subchannel A) 21 26 30 71 105 128 The considered spatial The allocated subchannel for the same subcarrier user (subchannel B)[14] Y. Peng, S. Armour, A. Doufexi, J. McGeehan, “An Investigation of Optimal Solution for Multiuser Sub-carrierAllocation in OFDMA Systems”, IEEE Multi-Carrier Spread-Spectrum Workshop (MCSS): Proceedings from the 5thInternational Workshop: pp. 337-344. Germany, Sep. 2005
- 23. PREVIOUS WORKS• Swapping of subcarriers between users, known as MGSS [15] to achieve max. power gain• Total perceived gain as the performance metric• Involved two stages: – Initial allocations: fast & rough 0 10 version of the allocation matrix – Sort-swap: iterative process to Bit Error Rate (BER) -1 10 refine the allocation• However, MGSS has poor -2 10 Uncorr HL HH performance against self- CH Full -3 interference 10 -5 0 5 10 15 20 Signal-to-Noise Ratio (SNR) in dB 25 30• Modification is required[15] S. Pietrzyk, G.J.M Janssen, “Multiuser subcarrier allocation for QoS provision in the OFDMA systems”, IEEE56th Vehicular Technology Conference, 2002. VTC 2002-Fall, Vol.2, pp. 1077- 1081, Sept. 2002
- 24. PROPOSED SOLUTION • OFDMA allows multiple users to Tx simultaneously on different subcarriers by exploiting channel fading • Initial work done based on SISO transmission [16] • ESINR as performance metric, known as DSA-ESINR • Involves sorting, comparing and simple arithmetic • Ranks users from lowest to highest ESINR – fairness MMSE filterq= spatial layer Main spatial layer 2 Gk H k qq Es q ESINRk 2 2 2 Gk H k Es Gk Gk N Channel Gain qj, j q qq qj, j q M A FD k= subcarrier index Knowledge of TDMA self-interference [16] A. Doufexi and S. Armour, "Design Considerations and Physical Layer Performance Results for a 4G OFDMA System Employing Dynamic Subcarrier Allocation", IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications, 2005. PIMRC 2005, Vol. 1, pp. 357-361, Sept. 2005
- 25. PROPOSED SOLUTION ESINRs0 INR r0 ES BS ES INR MS ‘All’ ESINR `s1 r1 ESINR ESINR ain lg r0 s0 nn e C ha BS Ch an MS ‘Partial’ ESINR ne ` l ga in s1 r1 ESINR
- 26. SIMULATION SETUPS • Nsub= 768, NFFT= 1024 for 16 users, 48 subcarriers per user, 2×2 MIMO configuration • Six MCS schemes, consists of BPSK, QPSK, 16-QAM and 64-QAM with ½ or ¾ coding rate 0.9 1 0.8 • Two channel models: 0.7 Normalised power 0.6 – ETSI HiperLAN „Channel E‟ [17] 0.5 0.4 0.3 – 3GPP-SCM „Urban Micro‟ [18] 0.2 0.1 HIPERLAN ‘E’ 0 200 400 600 800 1000 1200 1400 1600 1800 Parameters Urban Micro Excess delay (ns) 1 Environment Large open space NLOS Outdoor urban NLOS 0.9 0.8 Bandwidth 100 MHz 5 MHz 0.7 Normalised power Excess Delay Spread 1760 ns 923 ns 0.6 0.5 Mean Delay Spread 250 ns 251 ns 0.4 Carrier Frequency 5 GHz 2 GHz 0.3 0.2 0.1[17] J. Medbo and P. Schramm, "Channel Models for HIPERLAN/2," ETSI/BRAN 200 300 400 500 600 700 800 900 1000 Excess delay (ns)document no. 3ERI085B, 1998.[18] 3GPP, “Spatial channel model for MIMO simulations”, TR 25.996 V7.0.0, 3GPP,2007. [Online]. Available: http://www.3gpp.org/
- 27. SIMULATION SETUPS• Correlation model based on Kronecker product, RMIMO=RMS RBS [19] Correlation• „Fully‟ correlated channel: worst case Correlation Coefficient Modes RBS RMS scenario, i.e. SISO case „Full‟ 0.99 0.99• Uncorrelated channel: „ideal‟ channel „CH‟ 0.96 0.96 „HH‟ condition 0.91 0.91 „HL‟ 0.91 0.30• Issue: how uncorrelated is an Uncorrelated 0.00 0.00 uncorrelated channel? Correlation Correlation• Uncorrelated: „Default‟ vs „Forced‟ Coefficient Modes RBS RMS – ‘Default’: Generated by the channel models „Default‟ 0.45 0.32 – ‘Forced’: No effect of self-interference „Forced‟ 0.00 0.00[19] K.I. Pedersen, P.E. Mogensen, B.H. Fleury, “Spatial Channel Characteristics in Outdoor Environments and TheirImpact on BS antenna System Performance”, IEEE Proc. Vehicular Technology Conference. VTC ’98, Vol. 2, pp. 719-724,May 1998.
- 28. SIMULATION SETUPS ETSI’s Channel E SCM’s Urban Micro 15 10 10 5 5 0 0 -5 Transmit Power (dB)Transmit Power (dB) -5 -10 -10 -15 -20 -15 -25 h -20 h 11 11 h h -30 12 -25 12 h h 21 21 -35 -30 h 22 h 22 -40 -35 0 100 200 300 400 500 600 700 0 100 200 300 400 500 600 700 Subcarrier Subcarrier Uncorrelated Channel Spatial layer 1 T1 R1 Spatial layer 2 Interference from T2 BS Interference MS from T1 T2 Spatial layer 1 R2 Spatial layer 2
- 29. SIMULATION SETUPS ETSI’s Channel E SCM’s Urban Micro 10 15 5 10 0 5 -5 Transmit Power (dB) 0 -10Transmit Power (dB) -5 -15 -10 -15 -20 -20 h -25 h11 11 -25 h 12 -30 h12 h 21 h21 -30 -35 h 22 h22 -35 -40 0 100 200 300 400 500 600 700 0 100 200 300 400 500 600 700 Subcarrier Subcarrier ‘HH’ Correlated Channel
- 30. SIMULATION SETUPS ETSI’s Channel E SCM’s Urban Micro 10 5 0 0 -5Transmit Power (dB) Transmit Power (dB) -10 -10 -15 -20 -20 -30 h11 -25 h11 h12 -30 h12 -40 h21 h21 -35 h22 h22 -50 -40 0 100 200 300 400 500 600 700 0 100 200 300 400 500 600 700 Subcarrier Subcarrier ‘Fully’ Correlated Channel
- 31. RESULTS AND ANALYSIS 0 Uncorrelated channel 10 SM-Uncorr STC-Uncorr SM-HH• SM vs. STBC performance STC-HH Bit Error Rate (BER) -1 10 SM-CH STC-CH• „Force‟ vs. „Default‟ SM-Full STC-Full• „Partial-SINR‟ vs. „All-SINR‟ 10 -2 0 -3 10 10 -10 -5 0 5 10 15 20 Partial DSA-SINR Signal-to-Noise Ratio (SNR) in dB 0 All DSA-SINR 10 DSA-Sch 1 Force Uncorrelated Bit Error Rate (BER) -1 Default Uncorrelated 10 AWGN Channel Bit Error Rate (BER) -1 10 -2 10 -2 10 -3 10 -5 0 5 10 15 Signal-to-Noise Ratio (SNR) in dB -3 10 -5 0 5 10 15 Signal-to-Noise Ratio (SNR) in dB 2 dB
- 32. RESULTS AND ANALYSIS• Fairness gain 5 Channel Gain 14 4 Channel Gain ESINR 12 ESINR 3 Random Random 2 10 Average (dB) 1 Variance 8 0 6 -1 4 -2 -3 2 -4 2 4 6 8 10 12 14 16 0 User number 2 4 6 8 10 12 14 16 User number (a) Average ESINR metric (b) Mean variance Algorithm ESINR Channel gain Random Mean (dB) 1.815 1.658 -1.336 Variance 0.6061 1.882 4.972
- 33. RESULTS AND ANALYSIS 0 0 10 10 DSA-ESINR M1 DSA-ESINR M1Bit Error Rate (BER) Bit Error Rate (BER) -1 DSA-Sch1 M1 -1 10 10 DSA-Sch1 M1 DSA-ESINR M2 DSA-ESINR M2 DSA-Sch1 M2 DSA-Sch1 M2 DSA-ESINR M3 DSA-ESINR M3 DSA-Sch1 M3 DSA-Sch1 M3 -2 DSA-ESINR M4 10 -2 DSA-ESINR M4 DSA-Sch1 M4 10 DSA-Sch1 M4 DSA-ESINR M5 DSA-ESINR M5 DSA-Sch1 M5 DSA-Sch1 M5 DSA-ESINR M6 DSA-ESINR M6 -3 DSA-Sch1 M6 10 -3 DSA-Sch1 M6 -20 -10 0 10 20 30 10 Signal-to-Noise Ratio (SNR) in dB -20 -10 0 10 20 30 Signal-to-Noise Ratio (SNR) in dB „Forced‟ „Default‟
- 34. RESULTS AND ANALYSIS• Correlated channel – comparison between two different allocation schemes in a QPSK, ½ MCS SM-OFDMA downlink (16 users) 0 10 0 10 Uncorr HL HH Bit Error Rate (BER) -1 CH Bit Error Rate (BER) 10 -1 10 Full -2 10 Uncorr -2 10 HL HH CH -3 Full 10 -3 -10 0 10 20 30 10 Signal-to-Noise Ratio (SNR) in dB -10 -5 0 5 10 Signal-to-Noise Ratio (SNR) in dB (a) Channel gain (b) ESINR
- 35. RESULTS AND ANALYSIS 10 0 Partial SINR (Uncorr) All SINR (Uncorr) Partial SINR (HH) ‘Partial-ESINR’ vs. Bit Error Rate (BER) -1 All SINR (HH) 10 ‘All-ESINR’ Partial SINR (Full) All SINR (Full) -2 10 -3 10 -10 -5 0 5 10 15 20 Signal-to-Noise Ratio (SNR) in dB 10 10 5 5Channel Gain (dB) Channel Gain (dB) 0 0 -5 -5 Source Source -10 -10 Interferer Interferer ESINR Metric ESINR Metric -15 ESINR Metric (Interferer) -15 ESINR Metric (Interferer) DSA-ESINR DSA-ESINR DSA-ChG (Interferer) DSA-ESINR (Interferer) -20 -20 0 100 200 300 400 500 600 700 0 100 200 300 400 500 600 700 Subcarrier Subcarrier (a) ‘Partial ESINR’ scheme (b) ‘All ESINR’ scheme
- 36. RESULTS AND ANALYSIS Comparisons of sub-optimal allocation schemes ETSI ‘Channel E’ SCM ‘Urban Micro’ 0 0 10 10 DSA-ESINR DSA-ESINR MGSS-ESINR MGSS-ESINR DSA-Sch5 Uncorrelated DSA-Sch5 DSA-Sch1 DSA-Sch1Bit Error Rate (BER) Bit Error Rate (BER) -1 -1 10 10 -2 -2 10 10 -3 -3 10 10 -4 -2 0 2 4 -5 0 5 10 15 Signal-to-Noise Ratio (SNR) in dB Signal-to-Noise Ratio (SNR) in dB 0 0 10 10 DSA-ESINR MGSS-ESINR ‘Fully’ correlated DSA-Sch5 DSA-Sch1 Bit Error Rate (BER) Bit Error Rate (BER) -1 -1 10 10 -2 -2 10 10 DSA-ESINR MGSS-ESINR DSA-Sch5 -3 -3 DSA-Sch1 10 10 -5 0 5 10 15 20 25 -5 0 5 10 15 20 25 Signal-to-Noise Ratio (SNR) in dB Signal-to-Noise Ratio (SNR) in dB
- 37. RESULTS AND ANALYSIS• „DSA-ESINR‟ vs. „DSA-Scheme 5‟• DSA-Scheme 5 suffer from propagation error due to subcarrier separation (j parameter) 0 0 10 10 Sch5 M1 Sch5 M1 ESINR M1 ESINR M1 Sch5 M2 Sch5 M2 ESINR M2 ESINR M2Bit Error Rate (BER) Bit Error Rate (BER) -1 -1 10 Sch5 M3 10 Sch5 M3 ESINR M3 ESINR M3 Sch5 M4 Sch5 M4 ESINR M4 ESINR M4 Sch5 M5 Sch5 M5 -2 -2 10 ESINR M5 10 ESINR M5 Sch5 M6 Sch5 M5 ESINR M6 ESINR M6 -3 -3 10 10 -10 -5 0 5 10 15 20 25 30 -10 -5 0 5 10 15 20 25 30 35 40 Signal-to-Noise Ratio (SNR) in dB Signal-to-Noise Ratio (SNR) in dB (a) ETSI ‘Channel E’ (b) SCM ‘Urban Micro’
- 38. RESULTS AND ANALYSIS Effective correlation coefficient Uncorrelated ‘Fully’ correlated 1 1 0.8 0.8Effective Correlation Co-efficients Effective Correlation Co-efficients 0.6 0.6 0.4 0.4 0.2 0.2 0 0 -0.2 -0.2 -0.4 DSA-ESINR -0.4 DSA-ESINR MGSS-ESINR -0.6 MGSS-ESINR DSA-Sch1 -0.6 DSA-Sch5 DSA-Sch1 -0.8 ChG (Before Allocation) -0.8 DSA-Sch5 -1 ChG (Before Allocation) -5 0 5 10 15 20 25 30 -1 Signal-to-Noise Ratio (SNR) in dB -5 0 5 10 15 20 25 30 Signal-to-Noise Ratio (SNR) in dB
- 39. RESULTS AND ANALYSIS 1.00 1.0 DSA-ESINR DSA-ESINR MGSS-ESINR MGSS-ESINR 0.83 DSA-Sch5 DSA-Sch5 0.8 DSA-Sch1 DSA-Sch1 p(correlation coefficient)p(correlation coefficient) h11&h12 0.67 0.6 0.5 0.4 0.33 0.2 0.17 0 0 -1 -0.5 0 0.5 1 -1 -0.5 0 0.5 1 correlation coefficient correlation coefficient Uncorrelated ‘Fully’ correlated 1.0 1 SNR= 10 dB SNR= 10 dB 0.9 SNR= 20 dB 0.9 SNR= 20 dB SNR= 30 dB SNR= 30 dB 0.8 0.8 p(correlation coefficient) p(correlation coefficient) 0.7 0.7 0.6 0.6 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0 -1 -0.5 0 0.5 1 0 -1 -0.5 0 0.5 1 correlation coefficient correlation coefficient DSA-ESINR MGSS-ESINR
- 40. Self-Interference in a LTE Downlink Transmission Exploiting limited feedback and multiuser diversity in a spatially correlated channels
- 41. LTE FUNDAMENTALS• Advantages of LTE: – Performance: @ 20 MHz BW offering up to 50 Mbps (UL) and 100 Mbps (DL) – Reduced latency: „flat‟ network architecture – Improved spectrum flexibility: 1.25 to 20 MHz – Operational cost: SON• Work based on 3GPP-LTE Rel. 8 [20]• MIMO-OFDMA as the potential candidate for 4G downlink technology [20] Technical Specification Group Radio Access Network; (E-UTRA) and (EUTRAN): Physical Channels and Modulation‟, 3GPP TS 36.211 V8.4.0, Sept 08. [Online]. Available: http://www.3gpp.org/ftp/Specs/html-info/36211.htm
- 42. LTE FUNDAMENTALS One radio frame, Tt= 307,200Ts= 10 ms One subframe, Tslot= 15,360Ts = 0.5 ms Slot #0 Slot #1 Slot #19• Resource block (RB): a group of 12 One slot Resource Block NRB= Nsub×Nsym resource elements subcarriers, smallest element in LTE OFDMA Subcarrier (Frequency)• Short and long Cyclic Prefix (CP)• 15 MCS schemes [20], only six Resource Element Nsub NRB ×Nsub considered for simulation Coding Coded bits Data bits Nominal BitMode Modulation Rate per carrier per time slot Rate (Mbps) Nsym 1 QPSK ½ 2 7,600 15.2 OFDMA Symbol (Time) 2 QPSK ¾ 2 11,400 22.8 3 16-QAM ½ 4 15,200 30.4 1 frame (10 ms) 4 16-QAM ¾ 4 22,800 45.6 5 64-QAM ½ 6 22,800 45.6 1 subframe (1 ms) 1 slot (0.5 ms) 6 64-QAM ¾ 6 34,200 68.4 0 1 2 3 10 11 19 0 1 2 3 4 5 6 0 1 2 3 4 5 6 7 OFDM symbols (short cyclic prefix) cyclic prefixes LTE generic frame structure
- 43. LIMITED FEEDBACK IN LTE• Capacity gain can be achieved when Nt antennas communicate with k users: MU-MIMO [21], another form of SDMA• Benefit from CSIT. Can be achieved by precoding technique at the expense of feedback overhead – challenging especially in a fast fading channel• Limited Rx1 feedback: provides „incomplete‟ info on the channel Tx1 RxN UE1 Rx1• Three types of feedback Tx2 RxN UE2 schemes in LTE: CQI, RI and eNodeB Rx1 PMI TxM RxN UEk [21] H. Weingarten, Y. Steinberg, S.Shamai, “The capacity region of the Gaussian MIMO broadcast channel”, IEEE Proc. International Symposium on Information Theory, Vol. 52, No. 9, pp. 3936-3964, Sept. 2006
- 44. LIMITED FEEDBACK IN LTE• Modification: average ESINR metric as the CQI feedback to benefit allocation and feedback scheme• UE only feeds back a single CQI for the preferred matrix for each RB• The preferred precoding matrix for a RB is chosen by selecting the highest average SINR perceived by user• eNodeB chooses the precoding matrix with highest sum of avg. SINR of all the spatial subchannels Preferred Preferred Alternative Alternative Preferred Total Feedback Layer 1 Layer 2 Layer 1 Layer 2 Matrix bits per Scheme CQI CQI CQI CQI Index RB MU-MIMO 4 bits 4 bits 4 bits 4 bits 1 bit 17 bits Full Feedback MU-MIMO Partial 4 bits 4 bits - - 1 bit 9 bits Feedback SU-MIMO 4 bits - 1 bit 5 bits Feedback
- 45. LIMITED FEEDBACK IN LTE• Precoding to achieve accurate CSIT• DFT-based codebook precoding is considered [22]• Amount of feedback increased as the spatial subchannels, Q and codebook size, L increased[22] D. Yang; L. Yang, L. Hanzo, “DFT-Based Beamforming Weight-Vector Codebook Design for Spatially CorrelatedChannels in the Unitary Precoding Aided Multiuser Downlink”, 2010 IEEE International Conference on Communications.ICC 2010, pp. 1-5, May 2010
- 46. RESULTS AND ANALYSIS Performance of different feedback schemes in different correlation scenarios 0 0 10 10Bit Error Rate (BER) Bit Error Rate (BER) -1 -1 10 10 -2 Full MU, Uncorr -2 Partial MU, Uncorr 10 Partial MU, Uncorr 10 Full-MU, Uncorr SU, Uncorr SU, Uncorr SU, Full Corr Full MU, Full Corr Partial MU, Full Corr Partial MU, Full Corr -3 Full MU, Full Corr SU, Full Corr -3 10 10 -10 -5 0 5 10 15 -8 -6 -4 -2 0 2 4 6 8 Signal-to-Noise Ratio (SNR) in dB Signal-to-Noise Ratio (SNR) in dB (a) DSA-Channel gain (b) DSA-ESINR
- 47. RESULTS AND ANALYSIS Effect of codebook sizes 0 0 10 10 0 10 L=1 L=1 L=1 L=2 L=2 L=2 L=4 L=4 L=4Uncorrelated L=8 Bit Error Rate (BER) Bit Error Rate (BER) Bit Error Rate (BER) -1 -1 L=8 -1 L=8 10 10 10 -2 -2 -2 10 10 10 -3 -3 10 -3 10 -10 -5 0 5 10 10 -10 -5 0 5 10 -10 -5 0 5 10 Signal-to-Noise Ratio (SNR) in dB Signal-to-Noise Ratio (SNR) in dB Signal-to-Noise Ratio (SNR) in dB 0 0 0 10 10 10 L=1 L=1 L=1 L=2 L=2‘Fully’ correlated L=2 L=4 L=4 L=4 Bit Error Rate (BER) L=8 Bit Error Rate (BER) L=8 Bit Error Rate (BER) L=8 -1 -1 -1 10 10 10 -2 -2 -2 10 10 10 -3 -3 10 10 -3 10 -10 -5 0 5 10 15 20 -10 -5 0 5 10 -10 -5 0 5 10 Signal-to-Noise Ratio (SNR) in dB Signal-to-Noise Ratio (SNR) in dB Signal-to-Noise Ratio (SNR) in dB SU-MIMO Partial MU-MIMO Full MU-MIMO

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