BER Performance Simulation of ML and ZF Detection in MIMO 2×3 Systems

In this document, I will present simulation results for the Bit Error Rate (BER) performance of Maximum Likelihood (ML) and Zero Forcing (ZF) detection in a 2×3 MIMO system. MIMO (Multiple-Input Multiple-Output) systems utilize multiple antennas to significantly enhance wireless communication performance. The 2×3 configuration indicates 2 transmit antennas and 3 receive antennas. ML detection and ZF detection are two widely-used MIMO detection algorithms employed to decode received signals and recover original data. For the simulation implementation, the ML detector typically involves an exhaustive search through all possible transmit symbol combinations, calculating the Euclidean distance between received signals and all potential transmitted symbol vectors to minimize error probability. The ZF detector employs linear detection by computing the pseudo-inverse of the channel matrix to eliminate interference, though this approach may amplify noise. The simulation code would include channel matrix generation, signal transmission modeling, noise addition, and detection algorithm implementation with BER calculation through Monte Carlo simulations. Through these performance simulations, we can evaluate and compare the BER performance of the 2×3 MIMO system under different detection algorithms, providing valuable references for the design and optimization of wireless communication systems. The results demonstrate the trade-offs between computational complexity (ML being more complex) and performance degradation (ZF being more noise-sensitive) in practical implementations.