Low-Complexity Blind SNR Estimator for mmWave Multi-Antenna Communications

2026-05-12

AI Summary

This work addresses the absence of low-complexity, pilot-free, single-sample blind signal-to-noise ratio (SNR) estimation methods in millimeter-wave massive MIMO uplink systems. Exploiting channel sparsity in the beam domain, the proposed approach identifies noise-dominated components through ordering and finite-difference criteria, and leverages the order statistics of Gaussian noise power to separate signal and noise from a single received snapshot. This enables joint estimation of average noise power, signal power, and SNR without requiring prior information or iterative optimization—marking the first realization of single-sample blind SNR estimation. A hardware-efficient VLSI architecture is co-designed to support real-time deployment. Simulations and FPGA validation on a KCU116 platform demonstrate that the method achieves higher estimation accuracy than existing single-sample schemes while incurring sublinear hardware resource usage and sub-symbol latency.

Abstract

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.

Problem

• blind SNR estimation
• mmWave communications
• massive MIMO
• low-complexity estimation
• single-sample estimation

Innovation

• blind SNR estimation
• mmWave massive MIMO
• beamspace sparsity
• single-sample estimation
• low-complexity VLSI