Spectrum Sensing Issues In Dynamic Spectrum Access System

Due to the rapid development of wireless networks and an increasing demand for higher throughput,there is an urgent need to improve the spectral efficiency of the wireless systems.Dynamic spectrum access(DSA)has been envisioned as a promising solution to alleviate the spectrum scarcity problem as well as to increase the efficiency of spectrum utilization,which allows secondary users(referred to as unlicensed users)to use the spectrum opportunistically at a particular time and location when and where primary users(referred to as licensed users)are not active.And spectrum sensing is the most important component for the establishment of dynamic spectrum access.Due to the hardware limitation,the secondary user can only sense a small portion of the spectrum band or even one channel at a time.In order to reduce the delay in spectrum sensing,we study the spectrum sensing order problem across multiple service providers and the channel selection strategy among multiple channels in proactive sensing.The main contributions of this thesis are summarized as follows:1.We study the spectrum sensing order problem across multiple service providers in the dynamic spectrum access system and propose discounted Thompson sampling and satisficing Thompson sampling algorithms to solve the problem.The goal is to reduce the sensing delay and acquire more idle channels so that the system throughput will be improved.Considering the prior information of the probability of the spectrum being idle cannot be known in advance,the optimization problem is formulated as a multi-armed bandit problem.Moreover,some practical scenarios have been taken into consideration where the probability varies at temporal scale.Numerical results indicate that the proposed methods yield idle channels almost as many as the optimal decision in hindsight and can track the time-varying probability more quickly than other algorithms.2.We consider the channel selection problem among multiple channels in proactive sensing and propose top-two probability sampling and top-two Thompson sampling based on the existing Bayesian allocation rules.The objective is to design a reasonable channel selection strategy so that the secondary user can learn more about the unknown availability probability of each channel and definitively identify the optimal channel quickly after a small number of spectrum sensing trials.Whenever the secondary user has a packet to transmit,the information that has been learned can be exploited to reduce the time searching for an idle channel and an end-to-end packet delay will be minimized.We formulate the problem as a 'pure exploration' problem.As the spectrum sensing trails goes on,the posterior probability assigned to the event that some other channels is optimal converges to zero.Theoretical results prove that our proposed methods converges at an exponential rate,and the corresponding exponent seems to be the best possible.Numerical results show that the proposed algorithms confidently identify the optimal channel with less spectrum sensing overhead.In summary,we investigate the spectrum sensing issues in dynamic spectrum access system.In particular,we focus on the spectrum sensing order and channel selection problem,and propose some efficient methods to solve the problem,which enable efficient spectrum sensing.Numerical results indicate that the proposed methods can locate the idle channel quickly and reduce the time for spectrum sensing,thus improving the system throughput and spectrum utilization efficiency,which shed lights in the optimization of spectrum sensing in the future dynamic spectrum access system.