Power consumption is one of the most important aspects in mobile and wireless communications. Existing research has shown significant power reduction through limited radio reconfiguration based on the channel conditions, especially for short range sensor network applications.

A cognitive radio (CR) is an intelligent wireless communication system which is able to determine the most favorable operating parameters (cognition) based on the radio environment and its own capabilities and characteristics (awareness) and reconfigure the radio accordingly (reconfigurability).

This work leverages the advances in cognitive radio technology to dynamically implement favorable trade-offs in radio parameters to achieve more efficient use of radio resource (e.g., minimizing power consumption) on the required Quality of Service (QoS) of an application and channel. A CR-based approach enables us not only to adjust modulation, coding, and radiated power as in a conventional radio, but also to learn and to control component characteristics (e.g., the power amplifier (PA) efficiency characteristic) to minimize power consumption. Significant power savings using this approach are shown in this work for single input single output (SISO) systems and multiple input multiple output (MIMO) systems.

This work has a broad potential impact on the research of improving power efficiency of communication systems. It establishes a cognitive radio based methodology for system power consumption optimization. It emphasizes the difference between radiated power (power radiated from the transmit antenna) and the consumed power (power drawn from the power source, such as a battery). It provides a way to connect communication (which usually cares about radiated power, received signal to noise ratio, etc.) to hardware (which focuses on speed, efficiency, power consumption, etc.) and software (which emphasizes complexity, speed, etc.). This design methodology enhances the capability to jointly optimize communication, hardware, and software. In addition, this CR-based framework can be adapted for general radio resource management with various radio operation optimization targets, such as spectrum utilization.