EL 6023 Wireless Communications: Channel Modeling and Receiver Design

Wireless communication channel models and practical techniques for mitigating transmission impairments. Channel Modeling Parameters: Path loss; Fading: long-term vs. short-term fading, flat vs. frequency selective fading, and slow vs. fast fading; Multipath spread parameters: delay spread, angular spread and Doppler spread, Matrix Channel Modeling for Multiple Input and Multiple Output (MIMO) Systems. Channel Parameter Estimation: training sequence and blind approaches. Mitigation: Mitigation of path loss and fading: Diversity, handoff and power control; Mitigation of intersymbol interference: rake receiver and equalizer; Mitigation of time variation: pilot symbols and dynamic tracking. Processing Techniques: LS, zero forcing, MMSE, LMS, etc.

Prerequisites: EE 3404, MA 3012.


Course Outline

Lecture 1: Spectrum, Signals & Systems

1a Spectrum of Radio Waves
1b Reviews on Theories of Signals & Systems in both Time and Frequency Domains
1c Numerical Simulations of Signals & Systems using Matlab Programming

Lecture 2: Transmission Loss (A Deterministic Approach to Narrowband Channel Modeling)

2a Fundamentals of RF Propagations
2b Propagation Impairments: Transmission Loss on Narrowband RF signals
2c Narrowband Channel Models: Path Loss, Long Term Fading and Short Term Fading

Lecture 3: Multipath & Doppler Propagation Effects (A Deterministic Approach to Broadband Channel Modeling)

3a Propagation Impairments: Multipath Propagation Effects on both Narrowband and Broadband RF Signals
3b Propagation Impairments: Doppler Effects on both Narrowband and Broadband RF Signals in Wireless Channels
3c Basic Multipath and Doppler Channel Models

Lecture 4: Reviews on Probability & Random Variables

4a Reviews on Probability Theory & Random Variables
4b Numerical Generations of Commonly Used Random Variables (Gaussian, Uniform, Rayleigh, Exponential) in Wireless Communications
4c Numerical Simulations for Various Statistical Measurements of Random Variables

Lecture 5: Probabilistic Narrowband Channel Modeling & System Design Applications

5a Numerical Generations of Arbitrarily Distributed Random Variables
5b Probabilistic Modeling for Long Term Fading (Lognormal Fading or Shadow Fading)
5c Probabilistic Modeling for Short Term Fading (Rayleigh Fading or Multipath Fading)
5d Application Example I: Designs of Signal Coverage Area & Cell Boundaries
5e Application Example II: Numerical Analysis of Bit Error Rates for Various Noisy and Fading Channels

Lecture 6: Probabilistic Narrowband Channel Modeling for Multiple Transmitters and/or Receivers: Part I: Theory and Numerical Techniques

6a Narrowband Channel Model for Multiple Transmitters and/or Receivers
6b Numerical Simulations for Correlated Shadow Fading Random Variables
6c Numerical Simulations for Correlated Multipath Fading Random Variables

Lecture 7: Midterm Exam

Lecture 8: Probabilistic Narrowband Channel Modeling for Multiple Transmitters and/or Receivers: Part II: Mitigation of Channel Fading

8a Fading and Diversity Combining Techniques
8b Handoff
8c Power Control

Lecture 9: Probabilistic Broadband Channel Modeling

9a Brief Introduction to Random Processes
9b Practical Broadband Channel Modeling
9c Numerical Procedures for Simulating Time Varying Broadband Channels
  1. Direct Implementation Using Multipath Arrivals

  2. Direct Implementation in the Time Domain Using Autocorrelation

  3. Finite Impulse Response (FIR) Filter Implementation

  4. Infinite Impulse Response (IIR) Filter Implementation

Lecture 10: Narrowband Channel Estimation and Data Detection

10a Narrowband Channel Estimation using Training Sequence (LS and MMSE approaches)
10b Narrowband Channel Estimation using Pilot Symbols
10c Blind and Non-blind Narrowband Data Detection (LS and MMSE approaches)
10d Kalman Filter

Lecture 11: Broadband Channel Estimation and Data Detection

11a Broadband Channel Estimation (LS and MMSE approaches)
11b Broadband Data Detection (LS and MMSE approaches)
11c Iterative Approaches: Steepest Descent (SD), Least Mean Square (LMS) Approaches
Lecture 12: Mitigation of Multipath Effects (Equalizers & Rake Receivers)

12a Overview of Equalizer Techniques
12b Non-Blind Equalizer (with training sequences)
12c Blind Equalizer (Simultaneous Channel Estimation & Equalization)

Lecture 13: Matrix Channel Modeling for Multiple Input Multiple Output (MIMO) Systems

13a Introduction to Smart Antenna Systems
13b Vector & Matrix Channel Modeling
13c Space Processing & Space-time Processing

Lecture 14: Reviews

Lecture 15: Final Exam

Textbook: None. Course materials are provided.

Software: Student version of Matlab with signal processing toolbox is recommended but is not required.

Grading Policy
Midterm Exam: 45%
Final Exam: 45%
Homework and Class Participation (email discussions): 10%

Required Background (You must satisfy All of the following requirements)
BSEE
GPA>3 for technical Courses
Good in Probability
Good in Signals & Systems

Homework submission:
email your homework to me: itailu@poly.edu
or fax your homework to me: 631-755-4404

Discussions:
email your questions or comments to the entire class (including me)

Instructor: I-Tai Lu
(631) 755-4226
itailu@poly.edu