In this paper, we propose a low-complexity blind estimator for the average noise power, average signal power, and signal-to-noise ratio (SNR) in millimeter-wave (mmWave) massive multi-antenna uplink systems. In particular, the proposed method is designed to operate using only a single received signal sample, without relying on pilot signals, iterative optimization, or multiple observations, and without requiring prior knowledge of the transmitted signal. By exploiting the inherent sparsity of mmWave channels in the beamspace domain, the estimator identifies noise-dominant components through a sorting-based procedure combined with a finite-difference criterion. This separation is further supported by the order statistics of noise power under Gaussian assumptions, enabling statistically grounded discrimination between signal and noise elements. The average noise power is estimated from the identified noise-only components, and the signal power and SNR are subsequently obtained through simple arithmetic operations. The proposed algorithm achieves low computational complexity and is well-suited for real-time implementation. To demonstrate its practical feasibility, a hardware-efficient very large-scale integration (VLSI) architecture is developed and implemented on a AMD-Xilinx Kintex UltraScale+ KCU116 Evaluation Kit, with corresponding field-programmable gate array (FPGA) results provided. The implementation exhibits low latency and sublinear scaling of hardware resource utilization with respect to the number of antennas, and enables parameter estimation within a duration shorter than a single symbol of conventional wireless systems. Simulation results verify that the proposed estimator achieves high estimation accuracy compared to existing single-sample-based methods.

翻译：本文提出一种用于毫米波（mmWave）大规模多天线上行链路系统的低复杂度盲估计器，可同时估计平均噪声功率、平均信号功率及信噪比（SNR）。具体而言，该方法仅需单个接收信号样本即可运行，无需依赖导频信号、迭代优化或多观测数据，亦不要求发射信号的先验知识。通过利用毫米波信道在波束空间域固有的稀疏性，该估计器采用基于排序的流程结合有限差分准则识别噪声主导分量。该分离过程进一步得到高斯假设下噪声功率次序统计量的支撑，从而实现对信号与噪声元素具备统计基础的区分。平均噪声功率由识别出的纯噪声分量估计得到，而信号功率与信噪比则通过简单算术运算获得。所提算法计算复杂度低，非常适用于实时实现。为验证其实用可行性，本文开发了一种硬件高效的超大规模集成（VLSI）架构，并在AMD-Xilinx Kintex UltraScale+ KCU116评估套件上实现，同时给出了相应的现场可编程门阵列（FPGA）结果。该实现展现出低延迟特性，且硬件资源利用率随天线数量呈次线性增长，参数估计时长优于传统无线系统的单符号周期。仿真结果验证，与现有基于单样本的方法相比，所提估计器具有更高的估计精度。