This paper presents a unified software and hardware wireless AI platform (USHWAP) for developing and evaluating machine learning in wireless systems. The platform integrates multi-software development such as MATLAB and Python with hardware platforms like FPGA and SDR, allowing for flexible and scalable device and edge computing application development. The USHWAP is implemented and validated using FPGAs and SDRs. Wireless signal classification, wireless LAN sensing, and rate adaptation are used as examples to showcase the platform's capabilities. The platform enables versatile development, including software simulation and real-time hardware implementation, offering flexibility and scalability for multiple applications. It is intended to be used by wireless-AI researchers to develop and evaluate intelligent algorithms in a laboratory environment.

In this paper, we propose a modified Tomlinson Harashima precoding (THP) method with less increase of computational complexity for the multi-user MIMO downlink system. The proposed THP scheme minimizes the influence of noise enhancement at the receivers by placing the diagonal weighted filters at both transmitter side and receiver side with square root. Compared to previously proposed non-linear precoding methods including vector perturbation (VP), the proposed THP achieves high BER performance. Furthermore, we show that the proposed THP method is implemented with lower computational complexity than that of existing modified THP and VP in literature.