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EE359 – Lecture 13 Outline

EE359 – Lecture 13 Outline. Annoucements Midterm announcements No HW this week (study for MT; HW due next week) Midterm r eview Introduction to adaptive modulation Variable-rate variable-power MQAM O ptimal power and rate adaptation Finite constellation s ets. Midterm Announcements.

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EE359 – Lecture 13 Outline

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  1. EE359 – Lecture 13 Outline • Annoucements • Midterm announcements • No HW this week (study for MT; HW due next week) • Midterm review • Introduction to adaptive modulation • Variable-rate variable-power MQAM • Optimal power and rate adaptation • Finite constellation sets

  2. Midterm Announcements • Midterm Thur Nov. 7, 6-8pm, Thornton 110 • Open book/notes (bring textbook/calculators) • Covers Chapters 1-7 • No computers • Short review today • OHs this week: • Andrea: Tuesday 5-6pm and Wednesday 6-7pm • Mainak: Mainak’s: Tues 6-7 pm, Packard 364, Wed 7-8pm Packard 106, Thurs 1:30-2:30 pm, Packard 106 • No HW this week (new HW posted Thurs) • Midterms from past 3 MTs posted, 10 pts for taking one and solns to all exams

  3. Review of Last Lecture • Maximal Ratio Combining • MGF Approach for Performance of MRC • Transmit diversity • With channel knowledge, similar to receiver diversity, same array/diversity gain • Without channel knowledge, can obtain diversity gain through Alamouti scheme over 2 consecutive symbols

  4. Adaptive Modulation • Change modulation relative to fading • Parameters to adapt: • Constellation size • Transmit power • Instantaneous BER • Symbol time • Coding rate/scheme • Optimization criterion: • Maximize throughput • Minimize average power • Minimize average BER Only 1-2 degrees of freedom needed for good performance

  5. One of the M(g) Points log2 M(g) Bits To Channel M(g)-QAM Modulator Power: P(g) Point Selector Uncoded Data Bits Delay g(t) g(t) 16-QAM 4-QAM BSPK Variable-Rate Variable-Power MQAM Goal: Optimize P(g) and M(g) to maximize R=Elog[M(g)]

  6. Optimization Formulation • Adaptive MQAM: Rate for fixed BER • Rate and Power Optimization Same maximization as for capacity, except for K=-1.5/ln(5BER).

  7. gk g Optimal Adaptive Scheme • Power Adaptation • Spectral Efficiency g Equals capacity with effective power loss K=-1.5/ln(5BER).

  8. K2 K1 K=-1.5/ln(5BER) Spectral Efficiency Can reduce gap by superimposing a trellis code

  9. Constellation Restriction • Restrict MD(g) to {M0=0,…,MN}. • Let M(g)=g/gK*, where gK* is later optimized. • Set MD(g) to maxj Mj: Mj M(g). • Region boundaries are gj=MjgK*, j=0,…,N • Power control maintains target BER M3 M(g)=g/gK* MD(g) M3 M2 M2 M1 M1 Outage 0 g0 g1=M1gK* g2 g3 g

  10. Power Adaptation and Average Rate • Power adaptation: • Fixed BER within each region • Es/N0=(Mj-1)/K • Channel inversion within a region • Requires power increase when increasing M(g) • Average Rate

  11. Efficiency in Rayleigh Fading Spectral Efficiency (bps/Hz) Average SNR (dB)

  12. Main Points • Adaptive modulation leverages fast fading to improve performance (throughput, BER, etc.) • Adaptive MQAM uses capacity-achieving power and rate adaptation, with power penalty K. • Comes within 5-6 dB of capacity • Discretizing the constellation size results in negligible performance loss.

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