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6: Opportunistic Communication and Multiuser Diversity

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Presentation on theme: "6: Opportunistic Communication and Multiuser Diversity"— Presentation transcript:

1 6: Opportunistic Communication and Multiuser Diversity

2 Traditional Approach to Wireless System Design
Compensates for deep fades via diversity techniques over time, frequency and space. (Glass is half empty.)

3 Example: CDMA frequency diversity via Rake combining
time diversity via interleaving and coding macro-diversity via soft handoff transmit/receive antenna diversity interference diversity: averaging of interference from many users.

4 Multipath Fading: Another Look
Multipath fading provides high peaks to exploit. Channel capacity is achieved by such an opportunistic strategy. Point-to-point performance benefits mainly in the energy-limited rather than the bandwidth-limited regime.

5 Multiuser Opportunistic Communication

6 Multiuser Diversity In a large system with users fading independently, there is likely to be a user with a very good channel at any time. Long term total throughput can be maximized by always serving the user with the strongest channel (Knopp&Humblet 95)

7 Application to CDMA 2000 1x EV-DO
Multiuser diversity provides a system-wide benefit. Challenge is to share the benefit among the users in a fair way.

8 Symmetric Users Serving the best user at each time is also fair in terms of long term throughputs.

9 Asymmetric Users: Hitting the Peaks
Want to serve each user when it is at its peak. A peak should be defined with respect to the latency time-scale tc of the application.

10 Proportional Fair Scheduler
Schedule the user with the highest ratio Rk = current requested rate of user k Tk = average throughput of user k in the past tc time slots. (Tse 99) De-facto scheduler in Ev-DO and similar algorithms used in HSDPA.

11 Performance

12 Channel Dynamics Channel varies faster and has more dynamic range in mobile environments.

13 Why No Gain with High Mobility?
3 km/hr 30 km/hr 120 km/hr Can only predict the average of the channel fluctuations, not the instantaneous values.

14 Inducing Randomness Scheduling algorithm exploits the nature-given channel fluctuations by hitting the peaks. If there are not enough fluctuations, why not purposely induce them?

15 Dumb Antennas (Viswanath,Tse & Laroia 02) The information bearing signal at each of the transmit antenna is multiplied by a time-varying phase.

16 Slow Fading Environment: Before

17 After

18 Beamforming Interpretation
Antenna array: Beamforming Omni-directional antenna Beamforming direction is controlled by the relative phase (t). Dumb antennas sweeps a beam over all directions.

19 Dumb Antennas in Action: One User
Most of the time, the beam is nowhere near the user.

20 Many users: Opportunistic Beamforming
In a large system, there is likely to be a user near the beam at any one time. By transmitting to that user, close to true beamforming performance is achieved, without knowing the locations of the users.

21 Performance Improvement
Mobile environment: 3 km/hr, Rayleigh fading Fixed environment: 2Hz Rician fading with Efixed/Escattered =5.

22 Smart vs Dumb Antennas Space-time codes improve reliability of point-to-point links but reduce multiuser diversity gain. Dumb antennas add fluctuations to point-to-point links but increases multiuser diversity gains.

23 Cellular Systems: Opportunistic Nulling
In a cellular systems, users are scheduled when their channel is strong and the interference from adjacent base-stations is weak. Dumb antennas provides opportunistic nulling for users in other cells. In practice, performance may be limited by interference averaging: a user may be within range of several base-stations.

24 Conventional vs Opportunistic Communication

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