lowSNR

lowSNR refers to a condition in which the signal-to-noise ratio (SNR) is low, meaning the power or strength of the desired signal is not much higher than the background noise. SNR is commonly expressed in decibels as 10 log10(signal power / noise power). A low SNR indicates that the signal is difficult to detect, extract, or interpret accurately.

In communications and data acquisition, low SNR increases the likelihood of errors in demodulation and decoding, reduces achievable data rates, and can cause outages or degraded performance. In imaging and audio, low SNR manifests as graininess, hiss, or blur, reducing clarity and detail. In radar, biomedical imaging, and other sensing applications, a low SNR can limit resolution and sensitivity.

Causes of low SNR include long transmission distances, limited transmit power, high ambient or interference noise, bandwidth constraints, and hardware limitations such as quantization noise. Noise types commonly encountered include thermal noise, shot noise, electrical interference, and quantization or background noise in sensors.

Techniques to cope with low SNR fall into several categories. Increasing the signal power when possible, reducing noise at the source, or using longer observation times can raise SNR. In signal processing, methods such as filtering (matched, Wiener), averaging, or adaptive noise cancellation help. In communications, robust modulation and forward error correction (e.g., LDPC, Turbo codes), coding gains, and multiple-antenna schemes improve resilience. In imaging and audio, denoising algorithms (e.g., non-local means, BM3D, wavelet-based methods) and multi-frame or multi-channel approaches are common.

SNR is typically estimated by comparing signal power to noise power in a chosen analysis window, with decibel representations aiding comparisons across systems and conditions.