On Maximizing the Data Volume in a Wireless Sensor Network with Time-Varying Channels

In this paper we present an analytical solution to the problem of jointly optimizing transmit power and scheduling for a star network of wireless energy-limited sensors around the base station. We have included factors which are usually neglected in modeling sensors and their communication channel. These include: accounting for power utilized for activities other than transmission, i.e. processing power, and time variations of the channel gains throughout the lifetime of the sensor. With an information theoretic approach to this problem, we have avoided the details of modulation and pulse shaping, and accounted for the mutual interference in simultaneous sensor transmissions. We employ the volume of data received at the base station over the lifetime of all sensors as the system performance measure and prove that in the optimal scheduling scheme sensors should avoid simultaneous transmissions. In addition, we show the optimal transmit powers that maximize the data volume can be determined via water-filling over the sensor´s lifetime once the processing power and the single sensor transmission schedule are accounted for. In this work we have assumed prior knowledge of the channel gains throughout the lifetime of all sensors. Nevertheless, the solution will provide an optimistic value for the network´s maximum data volume achievable by a causal scheduler.