Multi-user diversity in slow fading channels

1. Multi-user diversity in slow fading channels Reference: “Opportunistic Beamforming Using Dumb Antennas” P. Vishwanath, D. Tse, R. Laroia, IT 2002 Presented by: Sarandeep Bhatia sb344@njit.edu

2. Review • Fading : Rapid fluctuations of signal strength due to constructive and destructive interference between multi-paths. • Diversity : Technique to compensate for fading channel impairments. It can be obtained over: Time - Interleaving of coded bits Frequency – Spread spectrum & frequency hopping Space – Multiple antennas Fast fading channels : Diversity inherent Slow fading channels: Diversity induced sb344@njit.edu

3. Focus on downlink of wireless communication • Multiple antennas at the base station to transmit the same signal. • Fundamental difference : • “Multi-user diversity takes advantage of rather than Compensate fading” sb344@njit.edu

4. Opportunistic Beam forming The information bearing signal at each of the transmit antenna is multiplied by a random complex gain. Formation of random beam. sb344@njit.edu

5. Slow Fading Environment : Before sb344@njit.edu

6. Slow Fading Environment : After sb344@njit.edu

7. Fading channel is Better Than AWGN Total average SNR = 0 dB. Long term total throughput can be maximized by always serving the user with the strongest channel. sb344@njit.edu

8. Maximizing information theoretic capacity • Strategy – --In a large system with users channels fading independently, there is likely to be a user with a very good channel at any time. --Schedule to the user with best channel to transmit to base station. • Assumption – --Channel tracked by receiver and SNR fed back to BS. --Peak transmit power constraint. sb344@njit.edu

9. Issues in scheduling • Fading statistics identical: -- Strategy not only maximizes the total capacity but also throughput of individual users. • Fading statistics different : Two major issues -- Fairness -- Delay sb344@njit.edu

10. Proportional Fair Scheduling At time slot t, given • User’s average throughputs T1(t), T2(t)…in past window of time tc • Feedback of channel quality in terms of requested data rate R1(t), R2(t)… • Schedule the user ‘k’ with the highest ratio Rk = current requested rate of user k Tk = average throughput of user k in the past tc time slots. Average throughputs Tk (t) updated by an exponential filter. sb344@njit.edu

11. Inspection of algorithm • When tc is small- Serves all users • When tc is large -- Case-1 : Identical channels Tk remains same .Pick user with greater Rk. -- Case-2 : Different channels If Tk is large then Rk is also large. Pick user with greater sb344@njit.edu

12. Comparison with space time code • Space time code : Intelligent use of transmit diversity to improve reliability of point-to-point link but reduce multi-user diversity gain. • In contrast, opportunistic beam forming requires no special multi-antenna encoder or decoder nor MIMO channel estimation. • Use of separate pilot signals for each antenna in space time codes. • Antennas are truly dumb, but yet can surpass performance of space time code (with proportional scheduling). sb344@njit.edu

13. Overall performance Improvement sb344@njit.edu

14. Cellular Environment : Opportunistic Nulling • In a cellular systems, users are scheduled when their channel is strong and interference from adjacent base station is weak. • Multi-user diversity allows interference avoidance as there is beamforming to some users and null to other users. • Opportunistic beamforming combined with opportunistic nulling. sb344@njit.edu

15. Conclusion • Modern design principle : “Large and Rapid channel fluctuations are preferable” • Proactive Stance : “Induce Larger and Faster Channel fluctuations” • Requirement : -- Sufficient number of users in the system -- Scheduling algorithm sb344@njit.edu

16. Questions???? sb344@njit.edu