Lecture 5. Announcements Mid term test on Wednesday April 24. Project proposals.

14
Wireless Modulation Schemes Lecture 5

Transcript of Lecture 5. Announcements Mid term test on Wednesday April 24. Project proposals.

Page 1: Lecture 5. Announcements Mid term test on Wednesday April 24. Project proposals.

Wireless ModulationSchemes

Lecture 5

Page 2: Lecture 5. Announcements Mid term test on Wednesday April 24. Project proposals.

Announcements Mid term test on Wednesday April 24.Project proposals

Page 3: Lecture 5. Announcements Mid term test on Wednesday April 24. Project proposals.

Wireless Modulation TradeoffsWant high rate, low power, robust to channel

variations, low cost.

Amplitude/Phase Modulation (MPSK,MQAM)Linear: Information encoded in amplitude/phaseHigh spectrum efficiencyMajor issue: sensitive to channel variations

Frequency Modulation (FSK)Nonlinear: Information encoded in frequencyMore robust to channel variationsMajor issue: Low spectrum efficiency

Page 4: Lecture 5. Announcements Mid term test on Wednesday April 24. Project proposals.

Amplitude/Phase ModulationSignal over ith symbol period:

Signal constellation defined by (si1,si2) pairs

M possible sets for (si1,si2): log2 M bits per symbol

Probability of symbol error (Ps ) depends on:

Minimum distance dmin (depends on gs)

Number of nearest neighbors aM

Approximate expression:

)2sin()()2cos()()( 0201 tftgstftgsts cici

Page 5: Lecture 5. Announcements Mid term test on Wednesday April 24. Project proposals.

Alternate Q Function RepresentationTraditional Q function representation

Infinite integrandArgument in integral limits

New representation (Craig’93)

Leads to closed form solution for Ps in PSKVery useful in fading and diversity analysis

)1,0(~,2

1)()( 2/2 NxdxezxpzQ x

z

dezQ z )/(sin2/

0

221)(

Page 6: Lecture 5. Announcements Mid term test on Wednesday April 24. Project proposals.

Receiver Structure in AWGN Channel

Si1(t)

SiN(t)

MAX

Page 7: Lecture 5. Announcements Mid term test on Wednesday April 24. Project proposals.

Performance Comparison

Goldsmith, Table 6.1 Notice that for higher order constellation become

higher the modulation scheme become less efficient

Page 8: Lecture 5. Announcements Mid term test on Wednesday April 24. Project proposals.

Linear Modulation in FadingIn fading gs and therefore Ps randomPerformance metrics:

Outage probability: Prob(Ps>Ptarget)=Prob(g<gtarget)

Average Ps , Ps:

dpPP ss )()(0

Page 9: Lecture 5. Announcements Mid term test on Wednesday April 24. Project proposals.

Outage Probability

Probability that Ps is above target

Equivalently, probability gs below target

Used when Tc>>Ts

Ps

Ps(target)

Outage

Ts

t or d

Page 10: Lecture 5. Announcements Mid term test on Wednesday April 24. Project proposals.

Average Ps

Expected value of random variable Ps

Used when Tc~Ts

Error probability much higher than in AWGN aloneAlternate Q function approach:

Simplifies calculations

sssss dpPP )()(Ps

Ps

Ts

t or d

dezQ z )/(sin2/

0

221)(

Page 11: Lecture 5. Announcements Mid term test on Wednesday April 24. Project proposals.

Average BER for Common Schemeswith Rayleigh fading

In general:

Page 12: Lecture 5. Announcements Mid term test on Wednesday April 24. Project proposals.

Loss of Fading (BPSK)

Page 13: Lecture 5. Announcements Mid term test on Wednesday April 24. Project proposals.

Fading Performance of MQAM

Page 14: Lecture 5. Announcements Mid term test on Wednesday April 24. Project proposals.

Main PointsLinear modulation more spectrally efficient but

less robust than nonlinear modulation

Ps approximation in AWGN:

In fading Ps is a random variable, characterized by average value

Fading greatly increases average Ps .