Complete Data Link Simulation for MIMO Systems

Complete data link simulation for MIMO systems involves multiple critical components, including different types of detectors, antenna selection criteria, and channel environment modeling.

First, detectors are key components at the MIMO receiver end. Common detector types include: ML detector (ML=1): Maximum Likelihood detector providing optimal performance but with high computational complexity. Implementation typically involves exhaustive search over all possible symbol combinations. Joint ML detector (JMSE=2): Enhances detection accuracy through joint optimization strategies, often using advanced search algorithms to reduce complexity. Joint MMSE detector (ZF=3): Minimum Mean Square Error algorithm that balances performance and computational efficiency, implemented through matrix inversion operations. Joint Zero-Forcing detector: A simplified algorithm that sacrifices some performance to reduce computational burden, using pseudo-inverse matrix calculations.

For antenna selection, different criteria impact system performance: MBER (Minimum Bit Error Rate) (MBER=1): Optimizes BER performance through iterative algorithms that minimize error probability. MMI (Maximum Mutual Information) (MMI=2): Enhances channel capacity by selecting antennas that maximize information theoretic measures. MNP (Minimum Noise Power) (MNP=4): Reduces noise interference by choosing antennas with optimal signal-to-noise ratios. LAZY/LAZY2: Simplified selection strategies that reduce computational overhead through heuristic approaches.

The simulation environment can be configured for simulated channels (`real_ch=0`) or real channels. Simulated channels are implemented in MATLAB and can test system performance at specific signal-to-noise ratios (e.g., `SNR_dB=20`). By setting the number of transmit frames (`nr_frames=20`), the Bit Error Rate (BER) performance of different detectors and antenna selection strategies can be evaluated through statistical averaging across multiple transmission instances.

This simulation framework provides a flexible experimental platform for optimizing MIMO system design, suitable for performance analysis and algorithm improvement across various application scenarios. The code structure typically includes modular components for channel modeling, detector implementation, and performance metric calculation.