Diffusion Bias-Compensated RLS Estimation with Quantized Measurements over Adaptive Networks

We study the problem of distributed bias-compensated recursive least-squares (BCRLS) estimation with quantized measurements, which means that the nodes in adaptive networks collaborate to estimate a common deterministic parameter using quantized data. Traditional recursive least-squares (RLS) algorithms are biased when both the regressor and the output response are corrupted by stationary additive noise. And considering the limited bandwidth resources and the growing complexity in communication, the measurements transmitted in adaptive networks are always quantized in practical cases, which also contributes to the bias in estimation. Therefore, a distributed bias-compensated RLS algorithm is proposed in this paper to estimate the bias caused by both the background noise and the quantization noise and compensate for it cooperatively. Meanwhile, considering the advantages of diffusion strategies, the algorithm is developed in a diffusion fashion. Simulation results illustrate the good performance of the proposed algorithm.