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Spectrum sensing enables secondary users in a cognitive radio network to opportunistically access portions of the spectrum left idle by primary users. Tracking spectrum holes jointly in time and frequency over a wide spectrum band is a challenging task. In one approach to wideband temporal sensing, the spectrum band is partitioned into narrowband subchannels of fixed bandwidth, which are then characterized via hidden Markov modeling using average power or energy measurements as observation data. Adjacent, correlated subchannels are recursively aggregated into channels of variable bandwidths, corresponding to the primary user signals. Thus, wideband temporal sensing is transformed into a multiband sensing scenario by identifying the primary user channels in the spectrum band. However, future changes in the configuration of the primary user channels in the multiband setup cannot generally be detected using an energy detector front end for spectrum sensing. We propose the use of a cepstral feature vector to detect changes in the spectrum envelope of a primary user channel. Our numerical results show that the cepstrum-based spectrum envelope detector performs well under moderate to high signal-to-noise ratio conditions. 

This paper considers a planar multi-agent coordination problem. Unlike other related works, we explicitly consider a globally shared wireless communication channel where individual agents must choose both a frequency and power to transmit their messages at. This problem is motivated by the pressing need for algorithms that are able to efficiently and reliably operate on overcrowded wireless networks or otherwise poor-performing RF environments. We develop a self-triggered coordination algorithm that guarantees convergence to the desired set of states with probability 1. The algorithm is developed by using ideas from event/self-triggered coordination and allows agents to autonomously decide for themselves when to broadcast information, at which frequency and power, and how to move based on information received from other agents in the network. Simulations illustrate our results. 

To protect primary users from interference caused by secondary users (SUs) in a cognitive radio network, a geographic area called an exclusion zone can be defined in which SUs are prohibited from transmitting using a specified spectrum band. We propose a Gaussian Random Field Model (GRFM) framework for determining an exclusion zone with the desired properties in practical scenarios where analytical specifications may not be available. Based on the GRFM, we derive the radius of a disk determining the exclusion zone, assuming that the SUs are distributed geographically over a planar coverage area. Using measurement data obtained from SUs, the GRFM is applied to approximate the equivalent received signal power and aggregate interference at specified locations. Simulation results show that the GRFM approximation yields an accurate characterization of the exclusion zone.