1. 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

2. Midterm Announcements
Midterm: Thursday (11/10), 6-8 pm in
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 pm (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

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. 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)]

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

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

10. 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

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