EE359 – Lecture 13 Outline Announcements HW solutions posted Midterm announcements No HW this week (HW posted this Fri, due next week) Introduction to adaptive modulation Variable-rate variable-power MQAM Optimal power and rate adaptation Finite constellation sets

Midterm Announcements Midterm: Thursday (11/10), 6-8 pm in 200-303 Food will be served after the exam! SCPD students can take 2 hour exam in any 24 hour period starting at exam time Review sessions My midterm review will be during lecture (this Th or next Tue) TA review: Tuesday 4-6 pm in 364 Packard Midterm logistics: Open book/notes Bring textbook/calculators (have extras; adv. notice reqd) Covers Chapters 1-7 (sections covered in lecture and/or HW) Special OHs next week: Me: Tue 10:30am-12pm, Wed: 1:30-3pm, Wed: 10:30am-12pm, Thu: 10:30am-12pm all in 371 Packard TAs: Mon 4-5 pm (Mainak outside Packard 340), Tue 6-7 (Milind, 364 Packard), Wed 3.30-4.30pm (Milind, 364 Packard 364), Thu 3-5 pm (Mainak, outside Packard 340) No HW this week Midterms from past 3 MTs posted today: 10 bonus points for “taking” a practice exam Solutions for all exams given when you turn in practice exam

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

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

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

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

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

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

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

Power Adaptation and Average Rate Fixed BER within each region Es/N0=(Mj-1)/K Channel inversion within a region Requires power increase when increasing M(g) Average Rate Practical Considerations: Update rate/estimation error and delay

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

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. Constellations cannot be updated faster than 10s to 100s of symbol times: OK for most dopplers. Estimation error/delay causes error floor