We present the implementation of an adaptive Transformer-based localization system for 5G massive MIMO targeting sub-10ms real-time positioning. The design exploits propagation characteristics, where beam-delay channel representations exhibit sparsity, enabling a row-wise skipping mechanism that removes low-energy beam components with minimal control overhead. The contribution is focused on hardware realization of the model using a mixed dataflow architecture, combining input- and output-stationary execution, mapped onto a heterogeneous vector processing engine with parallel processing elements and adder trees for efficient matrix computation. Environment-dependent processing is supported through a lightweight runtime model-switching mechanism, where temporally filtered outputs of a single-layer perceptron router enable stable selection between specialized models with reduced latency. Implemented on a Xilinx Zynq UltraScale+ FPGA and evaluated on real-world massive MIMO measurements, the design achieves up to 65% row sparsity, yielding peak computational speedups of approximately 2x while limiting the average localization accuracy degradation to below 10%, relative to the floating-point baseline model. The accelerator attains below 1.15m localization accuracy across scenarios, with inference latency of 0.51-2.11ms and throughput of up to 1961 positions/s. These results demonstrate that propagation-aware sparsity, mixed dataflow execution, and efficient runtime model switching enable a scalable and low-latency hardware realization of adaptive Transformer-based localization for real-time 5G systems.

翻译：我们提出了一种基于自适应Transformer的定位系统实现方案，面向5G大规模MIMO，可实现亚10毫秒的实时定位。该设计利用传播特性，其中波束-时延信道表示具有稀疏性，从而支持逐行跳过机制，能以最小控制开销移除低能量波束分量。本文的贡献聚焦于模型的硬件实现：采用混合数据流架构，结合输入固定与输出固定执行方式，映射到包含并行处理单元和加法器树的异构向量处理引擎上，以实现高效的矩阵计算。通过轻量级运行时模型切换机制支持环境相关处理，其中单层感知器路由器的时域滤波输出能够稳定地在专用模型间进行选择，并降低延迟。该设计在Xilinx Zynq UltraScale+ FPGA上实现，并基于真实大规模MIMO实测数据进行评估：在相对于浮点基线模型，平均定位精度退化限制在10%以内的条件下，实现了最高65%的行稀疏度，峰值计算加速比约为2倍。该加速器在各种场景下的定位精度均优于1.15米，推理延迟为0.51-2.11毫秒，吞吐量可达1961个位置/秒。这些结果表明，传播感知稀疏性、混合数据流执行以及高效的运行时模型切换技术，能够实现可扩展、低延迟的自适应Transformer定位硬件实现，适用于实时5G系统。