A. Azarfar, Chun-Hao Liu, J. Frigon, B. Sansò, D. Cabric
{"title":"Joint transmission and cooperative spectrum sensing scheduling optimization in multi-channel dynamic spectrum access networks","authors":"A. Azarfar, Chun-Hao Liu, J. Frigon, B. Sansò, D. Cabric","doi":"10.1109/DySPAN.2017.7920789","DOIUrl":null,"url":null,"abstract":"Dynamic spectrum access (DSA) for secondary networks has the potential to improve spectrum utilization and thus mitigate the problem of spectrum scarcity by finding spectrum opportunities and exploiting them efficiently. A key factor in DSA networks with multiple channels and multiple users is to establish efficient spectrum sensing and transmission schedules. Multi-user cooperative spectrum sensing reduces the sensing time, thus increasing transmission throughput. At the same time, it may remove transmission opportunities for users participating in the sensing, thereby decreasing the throughput. Furthermore, in a multi-channel network, where the users experience different channel qualities, the problem of designing optimal sensing and transmission schedules becomes more complex. Sensing schedule indicates to each user the channel that it must sense at different sensing moments, and transmission schedules indicates which user should use a found opportunity. In this paper, we explore this problem and then investigate optimal ways to find a joint sensing and transmission schedule. We propose three joint sensing-transmission strategies. Within each one of them, several solutions striking a balance between throughput performance, memory usage, and computational complexity are proposed. Due to the complex nature of optimal solutions, we also propose different heuristics. Simulation results show that the proposed heuristics perform well and thus can be employed in practical scenarios.","PeriodicalId":221877,"journal":{"name":"2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)","volume":"149 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DySPAN.2017.7920789","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Dynamic spectrum access (DSA) for secondary networks has the potential to improve spectrum utilization and thus mitigate the problem of spectrum scarcity by finding spectrum opportunities and exploiting them efficiently. A key factor in DSA networks with multiple channels and multiple users is to establish efficient spectrum sensing and transmission schedules. Multi-user cooperative spectrum sensing reduces the sensing time, thus increasing transmission throughput. At the same time, it may remove transmission opportunities for users participating in the sensing, thereby decreasing the throughput. Furthermore, in a multi-channel network, where the users experience different channel qualities, the problem of designing optimal sensing and transmission schedules becomes more complex. Sensing schedule indicates to each user the channel that it must sense at different sensing moments, and transmission schedules indicates which user should use a found opportunity. In this paper, we explore this problem and then investigate optimal ways to find a joint sensing and transmission schedule. We propose three joint sensing-transmission strategies. Within each one of them, several solutions striking a balance between throughput performance, memory usage, and computational complexity are proposed. Due to the complex nature of optimal solutions, we also propose different heuristics. Simulation results show that the proposed heuristics perform well and thus can be employed in practical scenarios.