Signal Processing for Communication Systems
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Overfitting? Shrink your model! Regularization prevents memorization, enabling true learning & robust success. Discover how. A central problem in machine learning is to develop algorithms that work well both on training data and on new inputs (test data). Most machine learning tasks can be generalized as the estimation of a function \(\hat{f}(X)\) that maps the … Read more
Model distillation, also known as knowledge distillation, is a supervised learning technique that condenses the capabilities and thought processes of a large, pre-trained “teacher” model into a smaller “student” model. This allows the student model to achieve comparable performance to the teacher model, but at a lower cost and with faster performance. Chinese AI lab … Read more
Multiple-Input Multiple-Output (MIMO) is a wireless communication technology that greatly improves data transmission and reception by using multiple antennas at both the transmitter and receiver. It is a fundamental component of modern wireless standards, enhancing data rates, boosting reliability, and increasing network efficiency. Here are the key aspects of MIMO: Overview of MIMO MIMO enables … Read more
Error Vector Magnitude (EVM) is a key performance metric in communication systems, particularly in digital modulation schemes. It quantifies the difference between the actual transmitted signal and the received signal, thereby measuring the quality of the transmitted signal. EVM is expressed as a percentage or in decibels (dB). Applications of EVM EVM is a crucial … Read more
Key focus : Convolutional codes: Error-correcting codes effective in noisy channels and burst error correction. Know about sliding window & shift registers and how they are used in a convolutional encoder. Linear block codes Linear block codes, like Hamming codes, are mainly employed in hard decision decoding, utilizing the inherent algebraic structure of the code … Read more
Key focus: How to implement the basic addition operation on two discrete time signal sequences. Python code for signal addition is provided. Signal addition Given two discrete-time sequences \(x_1[n]\) and \(x_2[n]\), the addition of these two sequences is represented as \(x_1[n]+ x_2[n]\). The start of the sample at \(n=0\) can be different for these two … Read more
In digital signal processing, we utilize various elementary sequences for the purpose of analysis. In this series, we will see such sequences. One such elementary sequence is the real-valued exponential sequence. (see the articles on unit sample sequence, unit step sequence, real-valued exponential sequence) A complex-valued exponential sequence in signals and systems is a discrete-time … Read more
In digital signal processing, we utilize various elementary sequences for the purpose of analysis. In this series, we will see such sequences. One such elementary sequence is the real-valued exponential sequence. (see the articles on unit sample sequence, unit step sequence, complex exponential sequence) An exponential sequence in signals and systems is a discrete-time sequence … Read more
In digital signal processing, we utilize various elementary sequences for the purpose of analysis. In this series, we will see such sequences. One such elementary sequence is the unit step sequence (see the articles on unit sample sequence, unit step sequence, real-valued exponential sequence, complex exponential sequence). Unit Step Sequence A unit step sequence is … Read more
In digital signal processing, we utilize various elementary sequences for the purpose of analysis. In this series, we will see such sequences. One such elementary sequence is the unit sample sequence (see the articles on unit sample sequence, unit step sequence, real-valued exponential sequence, complex exponential sequence) A unit sample sequence, also known as an … Read more