A comparative study of ultra-massive MIMO intelligent receivers with adversarial robustness and energy efficiency for 6G applications

• Taguchi method optimizes UM-MIMO parameters for BER reduction and energy efficiency. • Orthogonal Arrays minimize simulations while enhancing power and spectral efficiency. • Ultra-massive MIMO (256 × 256, 512 × 512,1024 × 1024) optimized for 6G applications. • AI-integrated detection outperforms ZF and MMSE for large-scale MIMO setups. • Taguchi-based adaptive optimization enables low-complexity, high-performance MIMO. Ultra-Massive Multiple Input Multiple Output (UM-MIMO) systems are pivotal for 6G wireless networks, enabling unprecedented data rates and spectral efficiency. However, optimizing these systems requires a careful balance between energy efficiency, spectral efficiency, and Bit Error Rate (BER) performance. This paper proposes a novel Taguchi-based optimization framework that systematically fine-tunes power allocation, modulation schemes, and space-time coding parameters, reducing computational complexity by up to 85% compared to exhaustive search. The approach integrates advanced technologies including UM-MIMO, cognitive radio, and Artificial Intelligence (AI)-based detection to enhance robustness against adversarial attacks. Simulation results demonstrate that the optimized Minimum Mean Square Error (MMSE) detector achieves up to 95% reduction in Bit Error Rate (BER) compared to Zero-Forcing (ZF) in small-to-medium UM-MIMO configurations (2 × 2 to 32 × 32), as shown in Tables [ 4 ] and [ 5 ], while maintaining computational feasibility. For ultra-massive configurations (256 × 256 to 1024 × 1024), optimized ZF offers the best trade-off between performance and energy efficiency. The Taguchi method yields up to 40% improvement in energy efficiency while maintaining near-perfect BER. Practical implications of massive power consumption, system complexity, and execution time are discussed, alongside a comparative analysis with recent neural network and genetic algorithm optimizations. Limitations of simulation-based evaluation and a plan for experimental validation are also presented. The findings establish deployment guidelines for mission-critical versus energy-sensitive 6G applications.