Interference Cancelation and Management Techniques

Radio spectrum is a scarce and precious natural resource that is significantly underutilized with current fixed spectrum-licensing policies [1]. This has inspired the development of hierarchical spectrum-sharing systems, where secondary systems are allowed to access the underutilized spectrum of incumbents without causing harmful interference to legacy/primary systems. In this article, we are interested in an important paradigm of secondary systems known as cognitive radio (CR) networks [2], [3], where the secondary terminals are envisioned to be capable of sensing and reasoning about the operating radio environments and thereby autonomously adjusting their transceiver parameters to exploit the underutilized radio resources in a dynamic fashion. Because of its spectrum-sharing nature, a CR network inevitably operates in interference-intensive environments. Effective interference management is therefore essential to the coexistence of primary and CR networks. Interference management mechanisms can be embedded into a CR network in various aspects of system design from network planning, radio resource management, medium access control (MAC) to physical-layer signal processing. Our interest in this study lies on the physical-layer signal-processing schemes, commonly known as interference cancelation (IC) techniques. In the literature, only a few articles [4]– [7] have studied IC techniques in the context of CR networks. In [4], an opportunistic IC scheme was proposed for CR receivers to adaptively cancel the primary signals when they are decodable. In [5]–[7], active spectrum shaping, transmit beamforming, and transmit precoding techniques were investigated for CR transmitters, respectively. Apart from the aforementioned articles, there exist many other IC techniques [8] that have been proposed and successfully applied to a number of wireless systems to mitigate various types of interference. The widely used IC techniques include the filter-based approach (e.g., Wiener filter), transform-domain approach (e.g., wavelet, chirplet), cyclostationarity-based approach, higher order statistics-based approach, joint detection/multiuser detection (MUD), and spatial processing (e.g., beamforming). The success of these IC techniques inspires us to study their applications to CR networks.