EE Large Scale Path Loss Log Normal Shadowing. The Flat Fading Channel

Similar documents
Indoor Propagation Models

White Paper: Comparison of Narrowband and Ultra Wideband Channels. January 2008

EE 577: Wireless and Personal Communications

Probability distributions relevant to radiowave propagation modelling

EENG473 Mobile Communications Module 3 : Week # (11) Mobile Radio Propagation: Large-Scale Path Loss

The Impact of Fading on the Outage Probability in Cognitive Radio Networks

EE6604 Personal & Mobile Communications. Week 7. Path Loss Models. Shadowing

EELE 5414 Wireless Communications. Chapter 4: Mobile Radio Propagation: Large-Scale Path Loss

EELE 6333: Wireless Commuications

Wireless Communications

Table of Contents. Kocaeli University Computer Engineering Department 2011 Spring Mustafa KIYAR Optimization Theory

Pathloss and Link Budget From Physical Propagation to Multi-Path Fading Statistical Characterization of Channels. P r = P t Gr G t L P

Unit 1: The wireless channel

Radio Propagation Modelling

Continuous Distributions

EE6604 Personal & Mobile Communications. Week 9. Co-Channel Interference

PROPAGATION PATH LOSS IN URBAN AND SUBURBAN AREA

The Wireless Communication Channel. Objectives

Indoor Measurement And Propagation Prediction Of WLAN At

AN021: RF MODULES RANGE CALCULATIONS AND TEST

2.1 Properties of PDFs

Edinburgh Research Explorer

Lecture Stat 302 Introduction to Probability - Slides 15

Path Loss Models and Link Budget

Path Loss Prediction in Wireless Communication System using Fuzzy Logic

Performance of Path Loss Model in 4G Wimax Wireless Communication System in 2390 MHz

Review of Comparative Analysis of Empirical Propagation model for WiMAX

Seasonal Pathloss Modeling at 900MHz for OMAN

An Investigation on the Use of ITU-R P in IEEE N Path Loss Modelling

ECE 5325/6325: Wireless Communication Systems Lecture Notes, Fall Link Budgeting. Lecture 7. Today: (1) Link Budgeting

A Model of Coverage Probability under Shadow Fading

Financial Risk Management

Radio Path Loss and Penetration Loss. Measurements in and around Homes. and Trees at 5.85 GHz. Mobile and Portable Radio Research Group

Suburban Area Path loss Propagation Prediction and Optimisation Using Hata Model at 2375MHz

Er. Neha Sharma and Dr. G.C.Lall HCTM, Kaithal(affiliated to KUK, Haryana, India)

Empirical Path Loss Models for n Wireless networks at 2.4Ghz in rural regions

Project: IEEE P Working Group for Wireless Personal Area Networks (WPANs)

Coverage Planning for LTE system Case Study

ECE 5325/6325: Wireless Communication Systems Lecture Notes, Spring 2013

Statistical Analysis of On-body Radio Propagation Channel for Body-centric Wireless Communications

Analysis of kurtosis-based LOS/NLOS Identification based on indoor MIMO Channel Measurements

Integrated structural approach to Counterparty Credit Risk with dependent jumps

arxiv: v2 [cs.it] 22 Feb 2016

LTE RF Planning Training LTE RF Planning, Design, Optimization Training

ECE6604 PERSONAL & MOBILE COMMUNICATIONS. Lecture 3. Interference and Shadow Margins, Handoff Gain, Coverage

Proposed Propagation Model for Dehradun Region

Empirical Characterization of Propagation Path Loss and Performance Evaluation for Co-Site Urban Environment

Parameter Estimation II

Lecture 2: Wireless Propagation Channels

On Predicting Large Scale Fading Characteristics with the MR-FDPF Method

Value at Risk Ch.12. PAK Study Manual

4.3 Normal distribution

ISSN: [Chinedu, Nkwachukwu, Cosmas* et al., 6(5): May, 2017] Impact Factor: 4.116

IJESRT. Scientific Journal Impact Factor: (ISRA), Impact Factor: 2.114

Information Processing and Limited Liability

Version A. Problem 1. Let X be the continuous random variable defined by the following pdf: 1 x/2 when 0 x 2, f(x) = 0 otherwise.

Saddlepoint Approximation Methods for Pricing. Financial Options on Discrete Realized Variance

Linear Dispersion Over Time and Frequency

Link Budget Calculation. Ermanno Pietrosemoli Marco Zennaro

Efficient and Consistent Path loss Model for Mobile Network Simulation

A Measurement-Based Model For The Analysis Of Pathloss In A Given Geographical Area

Optimization of Path Loss Models Based on Signal Level Measurements in 4G LTE Network in Sofia

Path Loss Modeling Based on Field Measurements Using Deployed 3.5 GHz WiMAX Network

Financial Econometrics

Optimizing the Existing Indoor Propagation Prediction Models

Online Appendix Optimal Time-Consistent Government Debt Maturity D. Debortoli, R. Nunes, P. Yared. A. Proofs

Graduate School of Business, University of Chicago Business 41202, Spring Quarter 2007, Mr. Ruey S. Tsay. Solutions to Final Exam

Dr. Maddah ENMG 625 Financial Eng g II 10/16/06

PATH LOSS PREDICTION FOR GSM MOBILE NETWORKS FOR URBAN REGION OF ABA, SOUTH-EAST NIGERIA

Normal Probability Distributions

Deriving the Black-Scholes Equation and Basic Mathematical Finance

Basic notions of probability theory: continuous probability distributions. Piero Baraldi

Path Loss Measurements for a Non-Line-of-Sight Mobile-to-Mobile Environment

Quantitative Introduction ro Risk and Uncertainty in Business Module 5: Hypothesis Testing Examples

Realistic Indoor Path Loss Modeling for Regular WiFi Operations in India

A Novel Hybrid Approach For Path Loss Exponent Estimation In Vanet Application

Attenuation over distance and excess path loss for a large-area indoor commercial topology at 2.4 GHz

The University of Chicago, Booth School of Business Business 41202, Spring Quarter 2009, Mr. Ruey S. Tsay. Solutions to Final Exam

COMPARATIVE ANALYSIS OF PATH LOSS PREDICTION MODELS FOR URBAN MACROCELLULAR ENVIRONMENTS

Mobile and Wireless Compu2ng CITS4419 Week 2: Wireless Communica2on

Hedging Under Jump Diffusions with Transaction Costs. Peter Forsyth, Shannon Kennedy, Ken Vetzal University of Waterloo

Statistical Tables Compiled by Alan J. Terry

Valuing volatility and variance swaps for a non-gaussian Ornstein-Uhlenbeck stochastic volatility model

Dokumentnamn. Document - Ref PTS-ER-2004:32

Millimeter Wave Wireless Communications: New Results for Rural Connectivity

Investigating the Best Radio Propagation Model for 4G - WiMAX Networks Deployment in 2530MHz Band in Sub- Saharan Africa

Normal distribution Approximating binomial distribution by normal 2.10 Central Limit Theorem

Financial Econometrics Jeffrey R. Russell. Midterm 2014 Suggested Solutions. TA: B. B. Deng

Bivariate Birnbaum-Saunders Distribution

Research Article BEP/SEP and Outage Performance Analysis of L-Branch Maximal-Ratio Combiner for κ-μ Fading

I. INTRODUCTION II. COVERAGE AREA

[Ekeocha*, 5(5): May, 2016] ISSN: Impact Factor: 3.785

Optimization of Empirical Pathloss Models of WiMax at 4.5 GHz Frequency Band

Approximation Methods in Derivatives Pricing

A Sum-Product Model as a Physical Basis for Shadow Fading

Discounting a mean reverting cash flow

Business Statistics 41000: Probability 3

INDIAN INSTITUTE OF SCIENCE STOCHASTIC HYDROLOGY. Lecture -5 Course Instructor : Prof. P. P. MUJUMDAR Department of Civil Engg., IISc.

High-Frequency Data Analysis and Market Microstructure [Tsay (2005), chapter 5]

7 pages 1. Premia 14

Transcription:

EE447- Large Scale Path Loss Log Normal Shadowing The Flat Fading Channel The channel functions are random processes and hard to characterize We therefore use the channel correlation functions Now assume: The channel impulse response is a random variable We describe the channel at any time t using a pdf Consider a flat fading channel where the delay spread is small compared with the symbol duration.

The Flat Fading Channel FLAT FADING CHANNEL The delay spread does not effect the received signal The channel delay function is reduced to the mean delay τ δt-τ The channel ehibits a time-varying gain gt gt has a short term fading component Zt due to multipath. It is modeled statistically by a Rayleigh, Rician, or Nakagami disitribution and is independent of the distance between the transmitter and the receiver. gt also has a long-term path loss component that is the mean of gt 3 Large scale path loss 4

The Flat Fading Channel The dashed line is the mean m path loss. The variation about the mean is described by the Normal Distribution 5 Plane Earth Loss*-again Antennas and Propagation for Wireless Communication Systems by Saunders 6 3

Log-Distance PL with Shadowing mean path loss Lp d d d 0 k, for d d d Lp Lp d0 + 0kLog0 db, for d d d0 d 0 km for macrocells,m indoors 0 0 Typical Path Loss Eponents for Different Environments Environment Path loss Eponent, κ free space urban cellular radio.7 to 3.5 shadowed urban cellular radio 3 to 5 in building with LOS.6 to.8 obstructed in building 4 to 6 7 Log-Distance PL with Shadowing Statistical component to loss The total path loss L p d with shadowing is then: Statistical path loss component 8 4

Log-Distance PL with Shadowing The total path loss L p d with shadowing is then: L d L + ε ε L p db p is the statistical variation of the path loss p p L d 0 db db + 0kLog 0 d d 0 is the mean pathloss L d L p p ma for the TR link to perform correctly note that for L p, d 0 and k may be adjusted to model Friss loss, plane earth loss, or any of the other models for mean path loss at a given frequency. 9 Log-Distance PL with Shadowing Shadowing: When the line of site path is blocked by an obstruction such as a building or a hill that is much larger than the λ of the signal Long term fading is then a combination of the log-distance path loss and the log-normal shadowing -that is statistical. Let εdb be a zero-mean Gaussian distributed random variable d db with a standard deviation σ ε in db. The pdf of εdb is given as: 0/ ln 0 σε fε e πσε A variable transform of will give ε in a linear scale and is is sad to follow a log-normal distribution with pdf: f y ε 0log σ 0/ ln 0 ε e π yσ ε 0 y 0 5

Log Normal Shadowing* *Rappaport Log-Distance PL with Shadowing The statistical term is a log-normal distribution This distribution is fully defined by the mean, m0, and the standard deviation, σ ε in db What we want to determine is the Pε db >ε dbma f y ε 0log σ 0/ ln 0 ε e π yσ ε 0 y σ ε is approimately 8 to0 db outdoors, and 4 to 6 db in a typical room. refer to the class discussion for the calculation of σ ε from field data. 6

EE447 Propagation Small Scale Multipath Fading 3 Review: The Normal, Gaussian, Distribution PDF : f CDF : Φ Φ e πσ P X t m σ normalized, m 0, σ : X X X f X e π t dt dt 4 7

8 5 Review: The Normal Distribution 0 0 0 0 < Φ > e Q or e Q Q Q Q Q Q X P X π Bounds : Upper The Q-function as the area under the tail of the Normal pdf 6 Review: The Normal or Gaussian Distribution > > σ π π m Q X P erf erfc Q Q d e erfc d e Q X P Q calulator function check your is the error where erf function: complementary error

The Log Normal Distribution in path loss Given a Normal Distribution with meanm and standard deviation σ : m P X > Q σ m is the mean path loss as shown in previous slides σ ε is approimately 8 to0 db outdoors, and 4 to 6 db in a typical room. refer to the class discussion for the calculation of σ ε from field data. 7 Review - The Q function 8 9

Log-Normal PL with Shadowing-Eample Eample: It has been determined that a link will operate as long as the path lossdoes not eceed the mean path loss by more than 5 db. The standard deviation of the path loss variation has been determined to σ ε 5dB. f ε Ε What is the probability that the path loss will eceed Lmean+5 db? P L p > Lpma What is the probability that the path loss will eceed Lmean+0 db? Lp Lp ma L p + ε 9 Log-Distance PL with Shadowing-Eample Eample: It has been determined that a link will operate as long as the path loss does not eceed the mean path loss by more than 5 db. The standard deviation of the path loss variation has been determined to σ ε 5dB. What is the probability that the path loss will eceed Lp+5 db? What is the probability that the path loss will eceed Lp+0 db? P ε > 5dB ε ε 5 P ε > 5 Q Q 5 σ ε σ ε P ε > 5 Q from the Q table given σ 5dB is Q 0.59 5.9% ε P ε > 0dB given σ 5dB is ε ε 0 P ε > 0 Q Q 5 σε σ ε P ε > 0 Q from the Q table Q 0.08.3% This is why links are often designed so that the mean received power is about 0 db above the minimum power required for proper operation 0 ε 0

Multipath Propagation 3 Multipath Propagation 4

Multipath Propagation Amplitude and Phase of the Received Signal Vary 5 Multipath Propagation 6

Time-Variant Transfer Function Impulse Response Phase may change more rapidly than Amplitude 7 Time-Variant Transfer Function Impulse Response 8 3

Time-Variant Transfer Function Impulse Response 9 Small-Scale Multipath Fading 30 4

Small-Scale Multipath Fading Given a channel with N scatterers, each with gain α n t and delay τ n t Consider a digital transmission with carrier f c and a symbol interval >> Δτ the delay spread 3 Small-Scale Multipath Fading Let Zt Z c t jz s t 3 5

.5 Rayleigh Fading - NLOS 33 Rayleigh Fading - NLOS 34 6

Rician LOS Propagation 35 Rician LOS Propagation 36 7

Small-Scale Multipath Fading Rayleigh 37 EE447 Propagation LCR and AFD 38 8

Other Statistics The pdfs of the amplitude distortion f α and the phase distortion f θ eplain how the signal will behave at each instant in time. They do not tell us how they change with time. We need to know how fast the channel fading changes with time. LCR- The Level Crossing Rate AFD- The Average Fade Duration LCR and AFD describe the frequency of fading 39 LCR- Level Crossing Rate R is the chosen threshold The observation time is [0,T] The number of positive crossings is M T 5 N R M T /T # per second 40 9

LCR 4 LCR 4 0

LCR 43 LCR 44

LCR ma The maimum LCR is at ρ -3 db because the pdf of α is maimized at threshold 45 AFD- Average Fade Duration 46

AFD- Average Fade Duration 47 AFD- Average Fade Duration 48 3

AFD- Average Fade Duration χ R 49 AFD- Average Fade Duration 50 4