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EE359 – Lecture 3 Outline

EE359 – Lecture 3 Outline. Announcements HW posted, due Friday 10/4 at noon (extended deadline) . Regular OHs today – 10:45-11:45. Extra OHs for me Thursday 5-6pm. Mainak’s 2 nd OH: Wed 7-8 pm (via Hangout/ Webex ; see web)

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EE359 – Lecture 3 Outline

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  1. EE359 – Lecture 3 Outline • Announcements • HW posted, due Friday 10/4 at noon (extended deadline). • Regular OHs today – 10:45-11:45. • Extra OHs for me Thursday 5-6pm. • Mainak’s 2nd OH: Wed 7-8 pm (via Hangout/Webex; see web) • Mainak email OHs: Th 11am-1pm (preferred via Piazza) • Log Normal Shadowing • Combined Path Loss and Shadowing • Cell Coverage Area • Model Parameters from Measurements

  2. Lecture 2 Review • Ray Tracing Models • Free Space Model • Power falloff with distance proportional to d-2 • Two Ray Model • Power falloff with distance proportional to d-4 • Empirical Models • Simplified Model: Pr=PtK[d0/d]g, 2g8. • Captures main characteristics of path loss • Two slope model • Power fall off K[d0/d]g1below dc, K[d0/d]g2above dc

  3. Shadowing • Models attenuation from obstructions • Random due to random # and type of obstructions • Typically follows a log-normal distribution • dB value of power is normally distributed • m=0 (mean captured in path loss), 4<s<12 (empirical) • CLT used to explain this model • Decorrelated over decorrelation distance Xc Xc

  4. Slow Combined Path Loss and Shadowing • Linear Model: y lognormal • dB Model 10logK Pr/Pt (dB) Very slow -10g log d

  5. Outage Probabilityand Cell Coverage Area • Path loss: circular cells • Path loss+shadowing: amoeba cells • Tradeoff between coverage and interference • Outage probability • Probability received power below given minimum • Cell coverage area • % of cell locations at desired power • Increases as shadowing variance decreases • Large % indicates interference to other cells

  6. K (dB) 2 sy 10g log(d0) Model Parameters fromEmpirical Measurements • Fit model to data • Path loss (K,g), d0 known: • “Best fit” line through dB data • K obtained from measurements at d0. • Exponent is MMSE estimate based on data • Captures mean due to shadowing • Shadowing variance • Variance of data relative to path loss model (straight line) with MMSE estimate for g Pr(dB) log(d)

  7. Main Points • Random attenuation due to shadowing modeled as log-normal (empirical parameters) • Shadowing decorrelates over decorrelation distance • Combined path loss and shadowing leads to outage and amoeba-like cell shapes • Cellular coverage area dictates the percentage of locations within a cell that are not in outage • Path loss and shadowing parameters are obtained from empirical measurements

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