{"title":"Cognitive Radio Based Resource Allocation for Sum Rate Maximization in Dual Satellite Systems","authors":"Dai Nguyen, T. M. Nguyen, L. Le","doi":"10.1109/VTCFall.2017.8287984","DOIUrl":null,"url":null,"abstract":"Satellites operating on same frequency bands with overlapping coverage can suffer from co-channel interference from the others. Hence, resource allocation and interference management for the multi-satellite system are very important to maintain the reliable communications and effective utilization of the radio resources. Such design typically requires the channel state information (CSI) of both desirable and interfering communication links; however, the large round trip delay in satellite communication renders the estimation of instantaneous CSI a difficult task. In this paper, we study the resource allocation for the uplink communications of two satellites using the cognitive radio concept where the two satellites are treated as the primary and secondary satellites. Many of conventional resource allocation problems for sum rate maximization deal with power management. Our design which does not require the instantaneous CSI knowledge aims to maximize the average sum rate of the secondary satellite by optimize both the secondary users' (SU) powers and angles toward the secondary satellite. To tackle the underlying non-convex resource allocation problem, we propose a block coordinate descent based iterative algorithm where in each iteration, the power allocation is solved optimally by using the dual based algorithm and the angles of SUs are determined to achieve the maximum average sum rate while maintaining the average interference constraints. We then conduct numerical studies and show significant performance improvement of the proposed algorithm compared to other conventional algorithms.","PeriodicalId":375803,"journal":{"name":"2017 IEEE 86th Vehicular Technology Conference (VTC-Fall)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 86th Vehicular Technology Conference (VTC-Fall)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VTCFall.2017.8287984","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
Satellites operating on same frequency bands with overlapping coverage can suffer from co-channel interference from the others. Hence, resource allocation and interference management for the multi-satellite system are very important to maintain the reliable communications and effective utilization of the radio resources. Such design typically requires the channel state information (CSI) of both desirable and interfering communication links; however, the large round trip delay in satellite communication renders the estimation of instantaneous CSI a difficult task. In this paper, we study the resource allocation for the uplink communications of two satellites using the cognitive radio concept where the two satellites are treated as the primary and secondary satellites. Many of conventional resource allocation problems for sum rate maximization deal with power management. Our design which does not require the instantaneous CSI knowledge aims to maximize the average sum rate of the secondary satellite by optimize both the secondary users' (SU) powers and angles toward the secondary satellite. To tackle the underlying non-convex resource allocation problem, we propose a block coordinate descent based iterative algorithm where in each iteration, the power allocation is solved optimally by using the dual based algorithm and the angles of SUs are determined to achieve the maximum average sum rate while maintaining the average interference constraints. We then conduct numerical studies and show significant performance improvement of the proposed algorithm compared to other conventional algorithms.