RF Signal Processing Training

RF Signal Processing Training

Introduction:

RF Signal Processing Training by ENO

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 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.

Customize It:

With onsite Training, courses can be scheduled on a date that is convenient for you, and because they can be scheduled at your location, you don’t incur travel costs and students won’t be away from home. Onsite classes can also be tailored to meet your needs. You might shorten a 5-day class into a 3-day class, or combine portions of several related courses into a single course, or have the instructor vary the emphasis of topics depending on your staff’s and site’s requirements.

RF Signal Processing TrainingRelated Courses:

Duration: 4-5 days

Course Content:

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
◾Convolution Operation
◾IIR and FIR Filter StructuresPole-Zero Representations

Fourier and Z Transforms
◾Power Spectral Density (PSD)
◾Linear Filtering
◾Discrete Fourier Transforms (DFT)
◾FFT and IFFT

Probability Overview
◾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
◾Matrix Inversion
◾Eigen Decomposition

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)

Equalization Methods
◾Decision Feedback Equalization (DFE)
◾Maximum Likelihood Sequence Equalizer (MLSE)

Communications Applications
◾DC Offset Estimation
◾Automatic Frequency Correction (AFC)
◾Channel Estimation
◾Likelihood Ratio Testing
◾Phase Noise

Estimators
◾Properties of Estimators
◾Digital Communications Application (BER)

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Time Frame: 0-3 Months4-12 Months

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