This Course is available in the following format:
RF Signal Processing Training course with hands-on Exercises
This important RF Signal Processing Training course brings together, in one place, signal processing concepts as well as mathematical techniques that are critical for understanding and effectively analyzing or designing the modern communications systems. It’s a great introduction to the subject for those who may not have been exposed to this material and an excellent refresher for those who learned it long time back in college. Both types of audiences will benefit from this RF Signal Processing Training course’s practical, application-centered instructional approach aimed at bridging the gap between theory and application. This RF Signal Processing Training course is a must for all whose work focuses on the analysis or design of existing or emerging communications systems.
• If you are familiar with some aspects of this RF Signal Processing Training course, we can omit or shorten their discussion.
• We can adjust the emphasis placed on the various topics or build the course around the mix of technologies of interest to you (including technologies other than those included in this outline).
• If your background is nontechnical, we can exclude the more technical topics, include the topics that may be of special interest to you (e.g., as a manager or policy-maker), and present the course in manner understandable to lay audiences.
RF Signal Processing Training – Course Syllabus:
Discrete Time Signal Processing
◾Sampling Theorem: Continuous and Discrete time
◾Interpolation and Up sampling
◾Decimation and Down sampling
◾ADC and DAC Convertors
◾Overview of Transforms
◾IIR and FIR Filter StructuresPole-Zero Representations
Fourier and Z Transforms
◾Power Spectral Density (PSD)
◾Discrete Fourier Transforms (DFT)
◾FFT and IFFT
◾Mean, Variance, Several Theorems
◾PDF Examples: Gaussian, Erlang, Exponential, Uniform, etc.
◾Central Limit Theorem
◾Hypothesis Testing (MAP, ML)
◾Calculating Probability of ErrorDigital Communications Systems Example
◾The importance of the PDF and CDF
Linear Algebra Methods
◾Dot Product and Cross Product
Adaptive Signal Processing
◾Minimum Mean Square Error (MMSE)
◾Least Mean Squared (LMS) and NLMS
◾Recursive Least Squared (RLS)
◾Direct Matrix Inversion (DMI)
◾Maximum Likelihood Estimation (MLE)
◾Interpolation Techniques (Lagrange, Linear)
◾Decision Feedback Equalization (DFE)
◾Maximum Likelihood Sequence Equalizer (MLSE)
◾DC Offset Estimation
◾Automatic Frequency Correction (AFC)
◾Likelihood Ratio Testing
◾Properties of Estimators
◾Digital Communications Application (BER)