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

- Direct Implementation Using Multipath Arrivals
- Direct Implementation in the Time Domain Using Autocorrelation
- Finite Impulse Response (FIR) Filter Implementation
- 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