Space Division Multiple Access (SDMA) for Wireless Local Area Network (LAN)

1. S-88.4221 Postgraduate Seminar on Signal Processing 1 (6 cp)DSP SYSTEM DESIGN FOR WIDEBAND WIRELESS COMMUNICATIONS Space Division Multiple Access (SDMA) for Wireless Local Area Network (LAN) Fernando Gregorio Signal Processing Laboratory HUT This presentation is based on: -P.Vandenameele, Space Division Multiple Access for Wireless Local Area Networks, Kluwer Academic Publisher, 2001.

2. Outline • Introduction • Spectrally Efficient WLAN • Bandwidth reuse. • Pico-cellular WLAN vs. Intra-cell bandwidth reuse. • SDMA • SDMA-OFDM • Model • Receiver structures • Advanced Receiver structures • Single-Carrier SDMA • Advantages over OFDM • Practical SDMA system • Conclusions • References Fernando Gregorio

3. 1-Introduction • The internet traffic double every 100 days. • Digital mobile phones must be cheap, small and power efficient. • Laptops has become widely available. Future users will expect universal wireless internet access from their laptops in order to obtain a wide range of services and multimedia contents. Spectrally efficient WLAN Fernando Gregorio

4. 2-Spectrally efficient WLAN • In the second generation WLAN (IEEE 802.11a) high spectral efficiency can be obtained using high-order constellation size. • The implementation of high-order constellation is reduced to good quality channels or small coverage area. • Is it possible to increase spectral efficiency? • By reusing the bandwidth in adjacent cells (Pico-cellularization) • By reusing the bandwidth within one cell by array processing (SDMA) Fernando Gregorio

5. 2-Spectrally efficient WLAN • Pico-cellular WLAN • Each cell is partitioned in multiple smaller cells and to reuse the same frequency bands in some of these smaller cells. • Millimeter wave length carrier frequency is needed. • Low penetration through obstacles. • High path loss. Fernando Gregorio

6. 2-Spectrally efficient WLAN • Pico-cellular WLAN • Disadvantages • Cell size vs. network reinstallation cost • Cell size vs. cell planning effort • Cell size vs. total system capacity • Cell size vs. hand-over and routing Fernando Gregorio

7. 2-Spectrally efficient WLAN • SDMA WLAN • In this structure the bandwidth can be reused within each cell. • The base station is equipped with an antenna array and with digital signal processing that allows to separate the signals from multiple users sharing the same frequency band and time slot. • The users have only a single antenna giving a reduced impact over the system cost. • Spatial diversity exploitation is preferred over beamforming because of the strong multipath propagation. Fernando Gregorio

8. 2-Spectrally efficient WLAN • SDMA WLAN • L different users • User-specific spatial signature • The signal signature generated by the channel over the transmitted signal acts like spreading code in a CDMA system. • Multiuser detection techniques known from CDMA can be applied in SDMA-OFDM Fernando Gregorio

9. 2-Spectrally efficient WLAN • SDMA WLAN vs. Pico-cell Fernando Gregorio

10. 3-SDMA-OFDM • Model -L users -Base Station with P antennas Fernando Gregorio

11. 3-SDMA-OFDM • Model • per-carrier basis Discrete Fourier Transformed Channel User 1, Antenna 2 1 User 1 2 User 2 P User L Base Station Fernando Gregorio

12. 4-Receiver structures • Multiuser detection • Cellular telephony , satellite communication, high-speed data transmission lines, digital TV, fixed wireless local loops are subject to multi-access interference • Several transmitters share a common channel. • The receiver obtains a superposition of the signals sent by active transmitters. • Multiuser detection exploits the considerable structure of the multiuser interference in order to increase the efficiency with which channel resources are employed. Fernando Gregorio

13. 4-Receiver structures • Linear receivers • Different users transmitted signals are estimated with the aid of a linear combiner. • The residual interference caused by remaining users is neglected. Fernando Gregorio

14. 4-Receiver structures • Linear receivers • L-user • Statistical characterization AWGN Desired user Interferening users’ Fernando Gregorio

15. 4-Receiver structures • Linear Receivers • Least Squares: • LS Combiner combiner attempts to recover the signals transmitted by the different users regardless of the signal quality quantified in terms of the signal to noise ratio (SNR) at the reception antennas. • The linear combiner for user l is designed to fully suppress the contribution of all users other than user l. Fernando Gregorio

16. 4-Receiver structures • Linear Receivers • Minimum Mean Squared Error: • Exploits the available statistical knowledge concerning the signals transmitted. • MMSE combiner is designed to minimize the expected variance of the error on the combined signal, reducing the noise amplification. • Balance between the recovery signals transmitted and the suppression of the AWGN. Fernando Gregorio

17. 4-Receiver structures • Linear Receivers – Implementation Complexity R • There are two alternatives to solve this equation: • LU Factorization • It converts a general linear system into two subsystems know as the LU factorization, • where L is unit lower triangular and U is upper triangular. • LDLH Factorization’ • The correlation matrix R is hermitian and positive definite. • In this factorization L is a unit lower triangular and D a diagonal matrix. Fernando Gregorio

18. 4-Receiver structures • Linear Receivers – Implementation Complexity 4 Receive antennas and 4 Users Case Fernando Gregorio

19. 5-Advanced receiver structures • Nonlinear receivers • Linear detector assumes that the different users’ associated linear combiner output are corrupted only by AWGN • Linear combiner output signal contain residual interference which is not Gaussian distributed. • LS and MMSE have sequential structure. • The operation of classification can be included into the linear combination process. • The residual multi-user interference observed at the classifier’s input is reduced • Successive Interference Cancellation (SIC) • Parallel Interference Cancellation (PIC) Fernando Gregorio

20. 5-Advanced receiver structures • Successive Interference Cancellation (SIC) • If a decision has been made about an interfering user’s bit, then that interfering signal can be recreated at the receiver and subtracted from the receiver waveform. • This will cancel the interfering signal provided that the decision is correct, otherwise it will double the contribution of the interferer. • Users with high received power will be demodulated in first order best performance Fernando Gregorio

21. 5-Advanced receiver structures • Successive Interference Cancellation (SIC) • Only the specific user having the highest SINR (or SIR or SNR) in each iteration at the output of the LS or MMSE combiner is detected. • Having detected this user’s signal, the corresponding demodulated signal is subtracted from the composite signal received by the different antenna elements. • With this reduced set of received signal a new iteration is executed. Fernando Gregorio

22. 5-Advanced receiver structures • Successive Interference Cancellation (SIC) • Initialization • Detection • Calculation of remaining user’s weight matrix • Selection of the most dominant user • Detection of the most dominant user • Demodulation of the most dominant user. • Removing of the most important user contribution. • New iteration Fernando Gregorio

23. 5-Advanced receiver structures • Parallel Interference Cancellation (PIC) • The order in which users are canceled affects the performance of SIC receivers. • The basic idea of PIC is to estimate the transmitted symbols of each user using a conventional MMSE method in the first stage. In the second stage, the interfering signals can be reproduced and removed from the received signal. • Assuming that the symbols had been estimated correctly, a new symbol estimation is carried out using the free of interference signal. This process can be repeated several times to obtain a satisfactory result. Fernando Gregorio

24. 5-Advanced receiver structures • Parallel Interference Cancellation (PIC) Fernando Gregorio

25. 5-Advanced receiver structures • Parallel Interference Cancellation (PIC) Fernando Gregorio

26. 5-Advanced receiver structures • Maximum Likelihood detection • Optimum from a statistical point of view. • Potentially excessive computational complexity. • Join detection of the L different users. • McLpossible combinations of symbols transmitted by the L different users are considered by evaluating their Euclidean distance from the received signal, upon taking into account the effects of the channel. Fernando Gregorio

27. 5-Advanced receiver structures • ML estimation • The estimation procedure can be expressed as • The estimation of a ML symbol requires comparing the Euclidean distance between the vector x of the received signals by the different antenna elements for all the different vector of symbol combinations. Fernando Gregorio

28. Simulation Results PIC MMSE ML LS Fernando Gregorio

29. Implementation Complexity L=P=4 QPSK Fernando Gregorio

30. OFDM-SDMA Case of study Fernando Gregorio

31. Implementation Complexity Fernando Gregorio

32. 6-Single-Carrier SDMA • Multicarrier systems requires a more linear power amplifier and more accurate carrier frequency oscillator than single –carrier systems. • Low cost terminals are required in commercial applications. • Single-carrier with cyclic prefix (SC-CP) Avoids high Peak To Average Power Ratio (PAPR) and carrier offset sensitivity. Robust to multipath distortion Low cost terminals Fernando Gregorio

33. 6-Single-Carrier SDMA Fernando Gregorio

34. 6-Single-Carrier SDMA • Linear multiuser detection receivers can be applied in the same way than OFDM-SDMA. • Per-carrier implementation of Non-linear detection can not be implemented. • SIC and PIC can be implemented in the time domain. • Requires back and forward Fourier transform during each iteration step. • High latency. • Non-linear detection is not a promising technique for SC-SDMA Fernando Gregorio

35. 7-Practical SDMA implementations • Real world problems in WLAN implementations • Channel estimation • Symbol timing • Carrier frequency synchronization • Power control (imbalance in the received power from different users) • Implementation complexity • Low cost terminals Fernando Gregorio

36. 6-Single-Carrier SDMA • An implementation case of study • Functional specification Performance requirements : BER=10-3 Eb/No=15 dB Fernando Gregorio

37. 6-Single-Carrier SDMA • An implementation case of study U- number of users A – Number of receive antennas w Fernando Gregorio

38. 6-Single-Carrier SDMA • An implementation case of study • Complexity Results RU = Reused Fernando Gregorio

39. The total area of the SC-SDMA is amounts to 5 mm2 • A SDMA-OFDM modem with similar characteristics requires a chip area of 16 mm2 Fernando Gregorio

40. 8-Conclusions • SDMA-OFDM is a good alternative for WLAN systems. • SC-SDMA shows interesting properties in order to be considered as a candidate for future WLAN implementations. • Nonlinear detection techniques are not suitable for SC-SDMA • Multiuser channel estimation and frequency synchronization are open topics to be considered in SDMA-OFDM . • Nonlinear detection structures provide diversity gain. • Diversity gain vs. Implementation complexity. Fernando Gregorio

41. 9-References 1)  Sergio Verdu ,  Multiuser Detection, 1998  2)  L.Hanzo, M. Munster, B.J. Choi, and T.Keller, OFDM and MC-CDMA for broadband Multi-User Communication , WLANs   and Broadcasting, John Wiley Sons, 2003 3) P. Vandenameele, et. al,   ”A combined OFDM-SDMA approach”, IEEE Journal on  Selected Area on Communications , Nov. 2000 Fernando Gregorio

42. Homework • LS detector reaches similar performance than MMSE in high SNR levels. • Explain • The order in which users are canceled affects the performance of SIC receivers. • Explain Fernando Gregorio