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Linear Modulation Tradeoffs in Wireless Communication

Explore the performance of linear modulation techniques in fading channels, outage probabilities, and average error rates. Learn about capacity in flat-fading scenarios, modulation tradeoffs, and key Q-function representations. Understand the implications of fading on signal quality and discover strategies for robust communication in challenging environments. This lecture covers topics such as amplitude/phase modulation, frequency modulation, and the impact of fading on signal reliability. Gain insights into adapting transmission strategies for optimal wireless communication in varying channel conditions.

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Linear Modulation Tradeoffs in Wireless Communication

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  1. EE359 – Lecture 9 Outline • Announcements: • Project proposals due this Friday at 5pm • Midterm will likely be @ Nov. 10, 6-8pm. • HWs will be due Friday 5pm SHARP going forward • Linear Modulation Review • Linear Modulation Performance in AWGN • Q-Function representations • Probability of error in fading • Outage probability • Average Ps (Pb)

  2. 1 g g0 g Review of Last Lecture • Capacity in Flat-Fading: g known at TX/RX • Optimal Rate and Power Adaptation • Channel Inversion and Truncated Inversion • Received SNR constant; Capacity is Blog2(1+s) above an outage level associated with truncation • Capacity of ISI channels • Water-filling of power over freq; or time and freq. • Capacity of ISI Channels Waterfilling

  3. Our focus Passband Modulation Tradeoffs • Want high rates, high spectral efficiency, high power efficiency, robust to channel, cheap. • Amplitude/Phase Modulation (MPSK,MQAM) • Information encoded in amplitude/phase • More spectrally efficient than frequency modulation • Issues: differential encoding, pulse shaping, bit mapping. • Frequency Modulation (FSK) • Information encoded in frequency • Continuous phase (CPFSK) special case of FM • Bandwidth determined by Carson’s rule (pulse shaping) • More robust to channel and amplifier nonlinearities

  4. Amplitude/Phase Modulation • Signal over ith symbol period: • Pulse shape g(t) typically Nyquist • Signal constellation defined by (si1,si2) pairs • Can be differentially encoded • M values for (si1,si2)log2 M bits per symbol • Ps depends on • Minimum distance dmin (depends on gs) • # of nearest neighbors aM • Approximate expression:

  5. Alternate Q Function Representation • Traditional Q function representation • Infinite integrand • Argument in integral limits • New representation (Craig’93) • Leads to closed form solution for Ps in PSK • Very useful in fading and diversity analysis

  6. Linear Modulation in Fading • In fading gsand therefore Psrandom • Performance metrics: • Outage probability: p(Ps>Ptarget)=p(g<gtarget) • Average Ps , Ps: • Combined outage and average Ps (next lecture)

  7. Ts Outage Ps Ps(target) t or d Outage Probability • Probability that Ps is above target • Equivalently, probability gs below target • Used when Tc>>Ts

  8. Average Ps • Expected value of random variable Ps • Used when Tc~Ts • Error probability much higher than in AWGN alone • Alternate Q function approach: • Simplifies calculations (Get a Laplace Xfm) Ts Ps Ps t or d

  9. Main Points • Linear modulation more spectrally efficient but less robust than nonlinear modulation • Psapproximation in AWGN: • Alternate Q function representation simplifies calculations • In fading Psis a random variable, characterized by average value, outage, or combined outage/average • Outage probability based on target SNR in AWGN. • Fading greatly increases average Ps .

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