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1 Multi-user diversity in slow fading channels Reference: “Opportunistic Beamforming Using Dumb Antennas” P. Vishwanath, D. Tse, R. Laroia,

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Presentation on theme: "1 Multi-user diversity in slow fading channels Reference: “Opportunistic Beamforming Using Dumb Antennas” P. Vishwanath, D. Tse, R. Laroia,"— Presentation transcript:

1 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

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

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”

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.

5 Slow Fading Environment : Before

6 Slow Fading Environment : After

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.

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.

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

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

11 Inspection of algorithm When t c is small- Serves all users When t c is large -- Case-1 : Identical channels T k remains same.Pick user with greater R k. -- Case-2 : Different channels If T k is large then R k is also large. Pick user with greater

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).

13 Overall performance Improvement

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.

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

16 Questions????


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