Primary user behavior estimation and channel assignment for dynamic spectrum access in energy-constrained cognitive radio sensor networks

Cognitive radio technology improves spectrum utilization by allowing secondary users (SUs) to access the licensed spectrum bands in an opportunistic manner as long as it does not interfere with the activity of the primary users (PUs). This technology may also be used for wireless sensor networks (WSNs) to solve the problem of spectrum scarcity and bursty traffic. With the knowledge of PU behavior, sensors can transmit packets on the channels which are currently not occupied and vacate the bands by the detection of PU signals. In this dissertation, the spectrum sensing and spectrum access problems are investigated in a cognitive radio sensor network (CRSN), in which a cognitive radio is installed in each sensor and it can be tuned to any available channel.;Modeling and estimating the PU behavior is critical to implement dynamic spectrum access. For perfect sensing without sensing errors, we investigate the estimation accuracy of the PU behavior based on the Markov model. The performance of Maximum Likelihood (ML) estimation is evaluated by its distribution. To meet the requirement of estimation accuracy while reducing the unnecessary sensing time, we propose a learning algorithm to dynamically estimate the required length of the sample sequence. For the imperfect sensing with sensing errors, a two-state HMM is employed to model PU behavior with imperfect sensing. Baum-Welch algorithm is used to estimate the transition probabilities. The estimation accuracy is compared with that of perfect sensing.;Due to the inherent power and resource constraints of sensor networks, energy efficiency is the primary concern for the network design. We investigate the residual energy aware channel assignment problem in a cluster-based multi-channel CRSN. An R-coefficient is developed to estimate the predicted residual energy using sensor information (current residual energy and expected energy consumption) and channel conditions (PU behavior). An Optimization-based channel assignment scheme which maximizes the total residual energy of the network is proposed to reduce energy consumption and prolong the network lifetime.;We also consider another important concern for proposing an appropriate opportunistic spectrum access scheme, the total energy consumption needed to successfully transmit a certain amount of information bits. It helps sensors to transmit as much information as possible during their lifetime. We dynamically choose the optimal packet size to minimize energy-per-bit (the ratio of the total energy consumption to the amount of successfully transmitted information bits), which adapts to the time-varying channel states depending on both the behavior of primary users and the activity of sensors. Moreover, we increase the network lifetime by balancing residual energy among sensors.