Sensor Selection and Distributed Quantization for Energy Efficiency in Massive MTC

Abstract

This paper presents an estimation approach within the framework of uplink massive machine-type-communications (mMTC) that considers the energy limitations of the devices. We focus on a scenario where a group of sensors observe a set of parameters and send the measured information to a collector node (CN). The CN is responsible for estimating the original observations, which are spatially correlated and corrupted by measurement and quantization noise. Given the use of Gaussian sources, the minimum mean squared error (MSE) estimation is employed and, when considering temporal evolution, the use of Kalman filters is studied. Based on that, we propose a device selection strategy to reduce the number of active sensors and a quantization scheme with adjustable number of bits to minimize the overall payload. The set of selected sensors and quantization levels are, thus, designed to minimize the MSE. For a more realistic analysis, communication errors are also included by averaging the MSE over the error decoding probabilities. We evaluate the performance of our strategy in a practical mMTC system with synthetic and real databases. Simulation results show that the optimization of the payload and the set of active devices can reduce the power consumption without compromising the estimation accuracy.

Performance Analysis of Multi-user NOMA Wireless-Powered mMTC Networks: A Stochastic Geometry Approach

“…MTC networks that consist of a large number of MTC devices (MTCDs) are referred to as massive MTC (mMTC) [5]. MTCDs can be categorized into two classes based on their demands and characteristics [6], namely one that operates continuously and has deterministic traffic demands, and another that operates in a probabilistic manner has an event-driven appearance and has sporadic traffic demands. To handle the coexistence of different classes of MTCDs in the same mMTC network, group-based mMTC networks, in which diverse MTCDs are divided into groups based on different criteria, such as location, delay, and traffic characteristics, have been proposed [7], [8].…”