ATLBO-Q: Adaptive TLBO-Based Quantization for MMSE MIMO Detection

Quantization is of significance for multiple-input multiple-output (MIMO) detector design in wireless communication systems. Existing uniform quantization methods suffer from high bitwidth redundancy and inefficient hardware resource utilization, while machine-learning-based automatic quantization frameworks introduce considerable computational burden. In this paper, an adaptive teaching-learning-based-optimization (TLBO) based quantization (ATLBO-Q) framework is proposed for minimum mean square error MIMO detectors, where Cholesky decomposition is utilized to eliminate floating-point matrix inversion operations. The bitwidth assignments are optimized iteratively through an adaptive teacher-learner phase, avoiding the complex training procedures. Simulation results demonstrate that the proposed ATLBO-Q achieves significant reduction in average bitwidth and computational complexity while maintaining near-floating-point bit error rate performance, thereby attaining a desirable trade-off between the detection precision and hardware efficiency.