Research On Generation Technique Of Radio Environment Map In WRAN

Cognitive radio relies on the support of artificial intelligence to sense the wireless communication environment. Based on certain learning and decision-making algorithms, the working parameters of system are changed adaptively in real-time, and then the idle spectrum is tested dynamically and can be used effectively. Cognitive radio technology is considered as an effective way to alleviate the contradiction between the current increasing spectrum demand and the limited spectrum resources.IEEE802.22 is a Wireless Regional Area Network (WRAN) specification to support the unlicensed operation in TV bands (54-862 MHz), which is to coexist with incumbent users and provide wideband internet access to rural and suburban areas. Radio Environment Map (REM) is a comprehensive database which contains radio environment information. Knowledge derived from querying a local or global REM can contribute to the radio's environment awareness. Reasoning and learning help the CR to identify the specific radio scenario. The paper focus on the generation technique of REM and the main contents are:(1)It analyses the composition of environmental parameters and the influence of various parameters of cognitive radio and the role and significance of REM in WRAN.(2) Location and environment awareness which are two prominent features of cognitive radios are researched. Based on the traditional position method of radio wave propagation, WRAN CPE location model and method are designed. And a positioning accuracy adaptive algorithm is mentioned; Entities of geographical environmental information are mentioned and we have introduced some methods of how to acquire this information, too.(3) Spectrum sensing methods for cognitive radio are researched. The technical background of researching spectrum sensing methods in REM is introduced. Then, we have analyzed the relationship between REM and spectrum sensing methods and compared strengths and weaknesses of the existing various spectrum sensing methods. At last, we present a WRAN-REM information assisted Collaborative spectrum sensing algorithm.(4) Data format and database structure of REM are designed. The generation steps of REM are described. The E-R model of a global REM and the retrieving REM information are found for supporting calling REM information in cognitive radios.