Simple Antenna Diversity techniques with inherit directional information for SDMA operation

1. Simple Antenna Diversity techniques with inherit directional information for SDMA operation Project group 997: Julien Gonidec Thibaut Ingrain François Net Mauro Pelosi Aurélie Villemont Supervisors: Patrick Eggers Chenguang Lu Censor: Jesper Ø. Nielsen

2. Introduction Why diversity techniques ? Wireless technologies comparison depending on data rate and mobility

3. Introduction Why WLAN ? • A widespread technology • Problems of security • New localisation services • Convergence of technologies

4. Introduction Choices made • 802.11G standard • Open office environments • Jitter diversity • Implementation of diversity techniques only at the base station • Algorithm will provide directional information

5. Introduction Experiment process • Study the recquired theory • Apply the Jitter Diversity algorithm to deduce tendencies • Model a more realistic channel model and apply the jitter diversity on it • Study the gain provided by the diversity • See how the algorithm can provide directional information

6. Theory Antenna Array • Ordered repetition in space of identical radiating elements • 5 degrees of freedom: • Geometry • Element spacing • Excitation signal amplitude • Excitation signal phase • Element pattern

7. Theory Antenna Array: features • Portability • Compact configuration • Adaptability • Feeding network • Dynamic beamforming

8. Theory SDMA • Multiplexing technique used together with another multiple access process • Spatial separation of the users at the BS • Advantages: - improves antenna gain - reduces interferences - improves capacity

9. Theory SDMA • Principle: concentrate on the desired user and nullify the others • Directional information

10. Theory Diversity techniques • Temporal based techniques • Time diversity • Frequency diversity • Antenna pattern techniques • Space diversity • Polarisation diversity • Angle diversity • Direction information bearing techniques • Phase diversity • Beam diversity • Jitter diversity

11. Theory Jitter diversity • Aim: reduce deep fading effects • Principle: slightly move the antenna beam • Advantages: simple and easy to implement

12. Theory Jitter diversity algorithm

13. Theory Fading types • Large- and medium-scale fading • Pathloss • Shadowing • Small-scale or multipath fading • Frequency selective fading • Flat fading • Fast fading • Slow fading

14. Rapidity of fluctuations of signal’s strength Pathloss Shadowing Multipath fading Theory Large- and medium-scale fading • Pathloss: average power decay caused by distance d between Tx and Rx Where γis the pathloss exponent • Shadowing: absorption by the local surrounding media

15. Theory Small-scale fading

16. Theory Small-scale fading

17. Theory Models of small-scale fading • Rayleigh fading: NLOS between Tx and Rx • Rice fading: LOS between Tx and Rx • Nakagami-m fading: Rician Factor K

18. Theory Models of small-scale fading • Rician PDF

19. Theory Models of small-scale fading • Nakagami-m PDF

20. Theory Scatterers repartition: Lee’s model • Scatterers uniformly spaced on a circle centred on the MS • Useful for correlation calculation • Not the best model for indoor description

21. Theory Scatterers repartition: GBSB model • Spatial scatterer density function • GBSB Elliptical Model

22. Theory Scatterers repartition: Saleh-Valenzuela model • Accurate indoor channel representation • Clustered scatterers • Extended model including AOA

23. Theory Choices for our model • Rayleigh fading • Clustered scatterers • Elliptical repartition

24. Simulation 1 Jitter diversity simulation in a simplified environment Steps of the simulation • Modelling a simplified indoor channel • Generation of an ideal antenna pattern • Jitter process description • Results and tendencies Monte-Carlo simulations • the user’s location is randomly defined at each step

25. Simulation 1 Environment implementation (1) Clustered scattering • Investigations concentrated on rays from an unique cluster • AOA power distribution approximated by a Laplacian distribution PowerLaplace_a(θAOA) Environment response Where • The amplitude is defined by • The phase is defined by

26. Simulation 1 Environment implementation (2) • “a“ parameter controls the shape of the environment 10-6 < a < 10-1 • BWenv: half-power width of the mean environment response • Simulation of various type of environment by varying the a parameter

27. Simulation 1 Antenna pattern • Choice of an ideal beam pattern (no side and back lobes) • Amplitude of the pattern • “α“ parameter controls the antenna beamwidth

28. Simulation 1 Transfer function • At each realisation all beam’s orientation are performed • Discrete transfer function • Influence of the environment width on the fades

29. Simulation 1 Jitter process We want to compare 3 different algorithms: • JRDA (Jitter with respect to the Reference Direction Algorithm) • BPP (best possible process algorithm) • FB (fixed beam algorithm) as a reference Explanation of the JRDA process • Reference direction θrefk is found at the kth step • is compared to and • The orientation of the maximum value is chosen θpathk • is the whole of the collected h module

30. Simulation 1 JRDA results

31. Simulation 1 Standard deviation of the JRDA

32. Total power gain at the 1% level of probability: We define the total power gain at the 1% level of probability as the difference between the cumulative density values of and at the 1% level of probability Simulation 1 Total power gain of the JRDA

33. We define the diversity gain at the 1% level of probability as the difference between the cumulative density values of and at the 1% level of probability Simulation 1 Diversity gain of the JRDA (1) • Definition of the normalised power : • Diversity gain at the 1% level of probability :

34. Simulation 1 Diversity gain of the JRDA (2)

35. Simulation 2 Simulations with a more realistic channel model Simulation aim: • Derive a more realistic channel model • Compare the efficiency of diversity techniques in the aforementioned channel model • Provide simple Directional Information

36. Simulation 2 Environment description:modified elliptical model

37. Simulation 2 Beam scanning power at the base station over spatial iterations

38. Simulation 2 Location of the maxima of the beam scanning power over spatial iterations

39. Simulation 2 Jittering algorithms Optimum jittering • We do periodical updating of the global maxima on the beam scanning power Dominant jittering • We do not have updating; we only initialise the algorithm with an angular value corresponding to the angular center of gravity of the dominant cluster

40. Simulation 2 Reference algorithm Dominant fixed beam algorithm (no diversity) • In this case we choose a fixed beam orientation for our antenna, which will remain the same for all the spatial iterations of the mobile station; the algorithm is first initialised with the angular position of the center of gravity of the dominant cluster

41. Simulation 2 Diversity gain calculation

42. Simulation 2 Simulation sets • Dominant cluster’s angular width variation • Scatterer’s complex gain variation • Scatterer’s distribution variation • Modified elliptical model with 4 clusters

43. Simulation 2 Modified elliptical model with 4 clusters

44. Simulation 2 Simulation set 1 • Scatterer’s complex gain variation • 50°< Dominant cluster < 90°

45. Simulation 2 Simulation set 2 • Scatterer’s distribution variation • 50°< Dominant cluster < 90°

46. Simulation 2 Simulation set 3 • Scatterer’s complex gain variation • 60°< Dominant cluster < 90°

47. Simulation 2 Simulation set 4 • Scatterer’s distribution variation • 60°< Dominant cluster < 90°

48. Simulation 2 Simulation set 5 • Scatterer’s complex gain variation • 70°< Dominant cluster < 90°

49. Simulation 2 Simulation set 6 • Scatterer’s distribution variation • 70°< Dominant cluster < 90°

50. Simulation 2 Simulation results • All the previous results show that we get a fair diversity gain even when we change the width of the dominant cluster. • Though the optimum jittering algorithm shows the best performances, the dominant jittering, that has a lower complexity, behaves also good. This would suggest the sub-optimality of the dominant jittering, that though has no updating part in the jittering process, leads to good results.