Cognitive Interference Alignment Schemes for IoT Oriented Heterogeneous Two-Tier Networks
This paper considers interference management and capacity improvement for Internet of Things (IoT) oriented two-tier networks by exploiting cognition between network tiers with interference alignment (IA). More specifically, we target our efforts on the next generation two-tier networks, where a tie...
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MDPI AG
2018-08-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/18/8/2548 |
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| author | Run Tian Lin Ma Zhe Wang Xuezhi Tan |
| author_facet | Run Tian Lin Ma Zhe Wang Xuezhi Tan |
| author_sort | Run Tian |
| collection | DOAJ |
| description | This paper considers interference management and capacity improvement for Internet of Things (IoT) oriented two-tier networks by exploiting cognition between network tiers with interference alignment (IA). More specifically, we target our efforts on the next generation two-tier networks, where a tier of femtocell serving multiple IoT devices shares the licensed spectrum with a tier of pre-existing macrocell via a cognitive radio. Aiming to manage the cross-tier interference caused by cognitive spectrum sharing as well as ensure an optimal capacity of the femtocell, two novel self-organizing cognitive IA schemes are proposed. First, we propose an interference nulling based cognitive IA scheme. In such a scheme, both co-tier and cross-tier interferences are aligned into the orthogonal subspace at each IoT receiver, which means all the interference can be perfectly eliminated without causing any performance degradation on the macrocell. However, it is known that the interference nulling based IA algorithm achieves its optimum only in high signal to noise ratio (SNR) scenarios, where the noise power is negligible. Consequently, when the imposed interference-free constraint on the femtocell can be relaxed, we also present a partial cognitive IA scheme that further enhances the network performance under a low and intermediate SNR. Additionally, the feasibility conditions and capacity analyses of the proposed schemes are provided. Both theoretical and numerical results demonstrate that the proposed cognitive IA schemes outperform the traditional orthogonal precoding methods in terms of network capacity, while preserving for macrocell users the desired quality of service. |
| first_indexed | 2024-04-11T18:07:03Z |
| format | Article |
| id | doaj.art-fa761f51dba242ca90e72a31e1075dc6 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T18:07:03Z |
| publishDate | 2018-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-fa761f51dba242ca90e72a31e1075dc62022-12-22T04:10:18ZengMDPI AGSensors1424-82202018-08-01188254810.3390/s18082548s18082548Cognitive Interference Alignment Schemes for IoT Oriented Heterogeneous Two-Tier NetworksRun Tian0Lin Ma1Zhe Wang2Xuezhi Tan3School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Engineering, Michigan State University, East Lansing, MI 48824, USASchool of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, ChinaThis paper considers interference management and capacity improvement for Internet of Things (IoT) oriented two-tier networks by exploiting cognition between network tiers with interference alignment (IA). More specifically, we target our efforts on the next generation two-tier networks, where a tier of femtocell serving multiple IoT devices shares the licensed spectrum with a tier of pre-existing macrocell via a cognitive radio. Aiming to manage the cross-tier interference caused by cognitive spectrum sharing as well as ensure an optimal capacity of the femtocell, two novel self-organizing cognitive IA schemes are proposed. First, we propose an interference nulling based cognitive IA scheme. In such a scheme, both co-tier and cross-tier interferences are aligned into the orthogonal subspace at each IoT receiver, which means all the interference can be perfectly eliminated without causing any performance degradation on the macrocell. However, it is known that the interference nulling based IA algorithm achieves its optimum only in high signal to noise ratio (SNR) scenarios, where the noise power is negligible. Consequently, when the imposed interference-free constraint on the femtocell can be relaxed, we also present a partial cognitive IA scheme that further enhances the network performance under a low and intermediate SNR. Additionally, the feasibility conditions and capacity analyses of the proposed schemes are provided. Both theoretical and numerical results demonstrate that the proposed cognitive IA schemes outperform the traditional orthogonal precoding methods in terms of network capacity, while preserving for macrocell users the desired quality of service.http://www.mdpi.com/1424-8220/18/8/2548internet of thingsinterference alignmentheterogeneous networkscognitive radio |
| spellingShingle | Run Tian Lin Ma Zhe Wang Xuezhi Tan Cognitive Interference Alignment Schemes for IoT Oriented Heterogeneous Two-Tier Networks Sensors internet of things interference alignment heterogeneous networks cognitive radio |
| title | Cognitive Interference Alignment Schemes for IoT Oriented Heterogeneous Two-Tier Networks |
| title_full | Cognitive Interference Alignment Schemes for IoT Oriented Heterogeneous Two-Tier Networks |
| title_fullStr | Cognitive Interference Alignment Schemes for IoT Oriented Heterogeneous Two-Tier Networks |
| title_full_unstemmed | Cognitive Interference Alignment Schemes for IoT Oriented Heterogeneous Two-Tier Networks |
| title_short | Cognitive Interference Alignment Schemes for IoT Oriented Heterogeneous Two-Tier Networks |
| title_sort | cognitive interference alignment schemes for iot oriented heterogeneous two tier networks |
| topic | internet of things interference alignment heterogeneous networks cognitive radio |
| url | http://www.mdpi.com/1424-8220/18/8/2548 |
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