Energy-efficient reporting model for cooperative spectrum sensing in TDMA-based cognitive radio networks

In cognitive radio networks (CRNs), cooperative spectrum sensing (CSS) incurs a considerable amount of cooperation overhead in terms of reporting energy because of the cooperation activities involved amongst secondary users (SUs). Reduction in energy overhead during reporting plays a vital role in improving the overall energy efficiency of CSS. This paper proposes an energy-efficient reporting model for CSS. While developing the reporting model, we first capture the dynamics of primary user (PU) activities on a channel using a two-state discrete-time Markov chain (TS-DTMC) model. After that, we present a mealy-machine-based reporting model, which enables SUs to reduce energy consumption by selectively reporting local sensing information. The energy overhead during the reporting phase of CSS is formulated by jointly considering prior PU occupancy probability and PU transitional probabilities on the channel. Simulation results demonstrate the proposed model’s performance improvement in reporting energy compared to the conventional reporting model and the exclusive ON/OFF reporting model from the literature.