Transmission Power Allocation for Remote Estimation with Multi-packet Reception Capabilities

Abstract

In this paper we consider the problem of transmission power allocation for remote estimation of a dynamical system in the case where the estimator is able to simultaneously receive packets from multiple interfering sensors, as it is possible e.g. with the latest wireless technologies such as 5G and WiFi. To this end we introduce a general model where packet arrival probabilities are determined based on the received Signal-to-Interference-and-Noise Ratio and with two different receivers design schemes, one implementing standard multi-packet reception technique and one implementing Successive Interference Cancellation decoding algorithm in addition. Then we cast the power allocation problem as an optimization task where the mean error covariance at the remote estimator is minimized, while penalizing the mean transmission power consumption. For the infinite-horizon problem we show the existence of a stationary optimal policy, while for the finite-horizon case we derive some structural properties under the special scenario where the overall system to be estimated can be seen as a set of independent subsystems. Numerical simulations illustrate the improvement given by the proposed receivers over orthogonal schemes that schedules only one sensor transmission at a time in order to avoid interference.

Power Allocation for Remote Estimation Over Known and Unknown Gilbert-Elliott Channels

“…This is mainly motivated by the placement of wireless sensors in inaccessible environments or other limitations that restrict easy replacement of the on-board batteries (Singh et al, 2020). Moreover, transmission with low power is desirable when interference among users can exacerbate the packet dropout probability in several wireless standards (Pezzutto et al, 2021). Consequently, it is imperative to design efficient transmission power schedules which take into consideration the time-varying nature of the wireless channels for allocating the limited available energy to achieve desirable estimation performance.…”