Powering up nodes with ambient energy sources, thanks to the energy harvesting technology, not only reduces the carbon footprint of information and communication technology (ICT) sector but also increases the autonomy of battery powered communication networks. An energy harvesting node can scavenge energy from the surrounding environment (typical sources are solar, wind, vibration, thermal, etc.). However, time varying nature of the ambient energy makes the design of communication strategies quite different from the traditional communication systems.
Besides energy harvesting, higher throughput can be obtained in a wireless communication system by designing transmission schemes on the basis of propagation channel information. As channel adaptation techniques require to have some knowledge of the wireless channel conditions feedback to the transmitter, the gain in throughput comes at the cost of pilot-based training and feedback which consume resources in a communication system, especially, energy. In addition when the goal in a communication system is to send information about the source to a destination such that mean squared error distortion is minimized, transmission and compression strategies has to be designed based on both the time varying channel conditions and the source statistics.
This dissertation focuses on the design of transmission strategies taking into account the cost of obtaining the channel state information (CSI) at the transmitter, and time varying source statistics when the communication nodes rely on harvested energy (hence time-varying energy) supplies.
