access icon free Analysis of partial diffusion recursive least squares adaptation over noisy links

© The Institution of Engineering and Technology
Received 23/09/2016, Accepted 20/03/2017, Revised 06/01/2017, Published 28/03/2017

Partial diffusion-based recursive least squares (PDRLS) is an effective way of lowering computational load and power consumption in adaptive network implementation. In this method, every single node distributes a fraction of its intermediate vector estimate with its immediate neighbours at each iteration. In this study, the authors examine the steady-state performance of PDRLS algorithm in the presence of noisy links by means of an energy conservation argument. They consider the mean-square-deviation (MSD) as the performance metric in the steady-state and derive a theoretical expression for PDRLS algorithm with noisy links. The authors’ analysis reveals that unlike the established statements on PDRLS scheme under ideal links, the trade-off between MSD performance and the number of selected entries of the intermediate estimate vectors, as a sign of communication cost, is mitigated. They further examine the convergence behaviour of the PDRLS algorithm. The obtained results show that under certain statistical assumptions for the measurement data and noise signals, under noisy links the PDRLS algorithm is stable in both mean and mean-square senses. Finally, they present some simulation results to verify the theoretical findings.

Inspec keywords: least squares approximations; telecommunication power management; power consumption; radio links

Other keywords: statistical assumptions; adaptive network implementation; noisy links; PDRLS; energy conservation argument; mean-square-deviation; power consumption; intermediate vector estimate; MSD; partial diffusion-based recursive least squares

Subjects: Radio links and equipment; Interpolation and function approximation (numerical analysis); Telecommunication systems (energy utilisation); Numerical approximation and analysis

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