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Concurrent spectrum access (CSA), which allows different communication systems simultaneously transmit on the same frequency band, has been recognized as one of the most important techniques to realize the dynamic spectrum management (DSM). By regulating the interference to be received by primary users, the secondary users are able to gain continuous transmission opportunity. Without the need of frequent spectrum detection and reconfiguration, the CSA has the merit of low cost and easy implementation in practice. In this chapter, we will present some important CSA models, discuss the key problems existing in these CSA systems, and review the techniques to deal with these problems.

Opportunistic spectrum access (OSA) model is one of the most widely used model for dynamic spectrum access. Spectrum sensing is the enabling function for OSA. The inability for a secondary user (SU) to perform spectrum sensing and spectrum access at the same time requires a joint design of sensing and access strategies to maximize SUs’ own desire for transmission while ensuring sufficient protection to the primary users (PUs). This chapter starts with a brief introduction on the opportunistic spectrum access model and the functionality of sensing-access design at PHY and MAC layers. Then three classic sensing-access design problems are introduced, namely, sensing-throughput tradeoff, spectrum sensing scheduling, and sequential spectrum sensing. Finally, the application of the opportunistic spectrum access to operating LTE in unlicensed band (LTE-U) is discussed.

Blockchain is believed to bring new opportunities to dynamic spectrum management (DSM). With features of blockchain, the traditional spectrum management method, such as the spectrum auction, can be improved. It can also help to overcome the challenges about the security or the lack of incentive mechanisms for collaboration in DSM. Moreover, with blockchain, spectrum usage of the DSM system can be recorded in a decentralized manner. In this chapter, we will discuss the potentials of blockchain for spectrum management in a systematic way and using multiple case studies.

Spectrum sensing is a critical step in cognitive radio based DSM to learn the radio environment. Despite its long history, in the past decade, the study on spectrum sensing has attracted substantial interests from the wireless communications community. In this chapter, we first provide the fundamental theories on spectrum sensing from the optimal likelihood ratio test perspective, then we review the classical methods including Bayesian method, robust hypothesis test, energy detection, matched filtering detection, and cyclostationary detection. After that, we discuss the robustness of the classical methods and review techniques that can enhance the sensing reliability under hostile environment. These methods include eigenvalue based sensing method, covariance based detection method. Finally, we discuss the cooperative sensing that uses data fusion or decision fusion from multiple senors to enhance the sensing performance.