MATLAB Code Implementation for Signal Detection Using Maximum Likelihood Detection with QPSK Example

In this article, we will use QPSK (Quadrature Phase Shift Keying) as an example to describe the maximum likelihood detection process for signal detection. Signal detection represents a crucial communication technology used to determine which specific signal type a received signal belongs to. Maximum likelihood detection serves as a common signal detection method that operates by maximizing the similarity between the received signal and various signal types to classify the signal accurately.

From an implementation perspective, maximum likelihood detection for QPSK signals typically involves calculating the Euclidean distance between the received signal point and all possible constellation points in the QPSK constellation diagram. The MATLAB implementation would require creating a QPSK constellation array, computing distance metrics using vectorized operations, and employing the min function to identify the constellation point with minimum distance. Key functions would include array creation for constellation points, distance calculation using norm or manual Euclidean distance computation, and decision logic for signal classification.

Through maximum likelihood detection implemented in MATLAB, we can precisely identify received signals and proceed with subsequent signal processing and analysis tasks. The algorithm's efficiency can be enhanced using MATLAB's matrix operations to handle multiple signal samples simultaneously, making it suitable for real-time signal processing applications.