Goodput optimization via dynamic frame length and charging time adaptation for backscatter communication

Abstract

Computational radio frequency identification (CRFID) sensors present a new frontier for pervasive sensing and computing. They exploit ambient light or radio frequency (RF) for energy and use backscatter communication with an RFID reader for data transfer. Unlike conventional RFID tags that only transmit identifiers, CRFID sensors need to transfer potentially large amounts of data to a reader during each contact. Existing EPC Gen2 protocol is inefficient in dealing with a small number of CRFID sensors transferring a large amount of buffered data to the RFID reader and it has no specific design for adaptation to dynamic energy harvesting and channel conditions. In this article, we propose to adopt dynamic frame length and charging time for CRFID backscatter communication, aiming to adapt to the changing energy harvesting and channel conditions and improve the system goodput. First, optimal frame length and charging time that maximizes the goodput are obtained by solving the formulated goodput optimization problem. Then we propose a dynamic frame length and charging time adaptation scheme (DFCA) that increase or decrease the frame length and charging time at runtime based on the goodput measurement. Simulations show that our proposed DFCA scheme outperforms current fixed-frame-length approach and can converge to theoretically optimal under different energy harvesting and channel conditions.