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EE359 – Lecture 16 Outline

EE359 – Lecture 16 Outline. Announcements: HW due Friday MT announcements Rest of term announcements MIMO Diversity/Multiplexing Tradeoffs MIMO Receiver Design Maximum-Likelihood, Decision Feedback, Sphere Decoder Space-Time Processing Other MIMO Design Issues

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EE359 – Lecture 16 Outline

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  1. EE359 – Lecture 16 Outline • Announcements: • HW due Friday • MT announcements • Rest of term announcements • MIMO Diversity/Multiplexing Tradeoffs • MIMO Receiver Design • Maximum-Likelihood, Decision Feedback, Sphere Decoder • Space-Time Processing • Other MIMO Design Issues • Space/time Coding, Adaptive techniques, Limited feedback

  2. Midterm Announcements 95-98: A+ 90-94: A 85-89: A- 80-84: B+ You guys ROCK Will explain some common mistakes Note: Bonus points count different from exam and homework points

  3. Common MT Mistakes Q1b did not use the average fade duration stated in question Q1c did not write out the rate adaptation (just wrote power adaptation and capacity) Q1d did not use the maximum outage capacity definition in the text Q2 was well done in general Q3b/c assumed SC chooses the signal with highest average SNR, not instantaneous SNR

  4. End of Quarter Announcements • Remaining lectures: • Today MIMO • Next week: Thanksgiving • Nov. 29 week: OFDM, Intro to SS • Dec. 5 week: SS, Summary, Advanced topics • No lecture Dec 7 • Can start class early on previous lecture (9am) • Can have a bonus lecture Dec. 5 and/or 6 to wrap up lecture material and do summary and advanced topics

  5. Review of Last Lecture • MIMO Channel Decomposition • MIMO Channel Capacity • Depends on what is known about H at TX/RX • Beamforming y=uHHvx+uHn

  6. Error Prone Low Pe Diversity vs. Multiplexing • Use antennas for multiplexing or diversity • Diversity/Multiplexing tradeoffs (Zheng/Tse)

  7. ST Code High Rate High-Rate Quantizer Decoder Error Prone ST Code High Diversity Low-Rate Quantizer Decoder Low Pe How should antennas be used? • Use antennas for multiplexing: • Use antennas for diversity Depends on end-to-end metric: Solve by optimizing app. metric

  8. MIMO Receiver Design • Optimal Receiver: • Maximum likelihood: finds input symbol most likely to have resulted in received vector • Exponentially complex # of streams and constellation size • Decision-Feedback receiver • Uses triangular decomposition of channel matrix • Allows sequential detection of symbol at each received antenna, subtracting out previously detected symbols • Sphere Decoder: • Only considers possibilities within a sphere of received symbol. • Space-Time Processing: Encode/decode over time & space

  9. Other MIMO Design Issues • Space-time coding: • Map symbols to both space and time via space-time block and convolutional codes. • For OFDM systems, codes are also mapped over frequency tones. • Adaptive techniques: • Fast and accurate channel estimation • Adapt the use of transmit/receive antennas • Adapting modulation and coding. • Limited feedback: • Partial CSI introduces interference in parallel decomp: can use interference cancellation at RX • TX codebook design for quantized channel

  10. Main Points • MIMO introduces diversity/multiplexing tradeoff • Optimal use of antennas depends on application • MIMO RX design trades complexity for performance • ML detector optimal; exponentially complex • DF receivers prone to error propagation • Sphere decoders allow performance tradeoff via radius • Space-time processing (i.e. coding) used in most systems • Adaptation requires fast/accurate channel estimation • Limited feedback introduces interference between streams: requires codebook design

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