Set optimization for efficient interference alignment in heterogeneous networks
| Main Author: | |
|---|---|
| Publication Date: | 2014 |
| Other Authors: | , |
| Format: | Article |
| Language: | eng |
| Source: | Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
| Download full: | http://hdl.handle.net/10773/12528 |
Summary: | To increase capacity and offload traffic from the current macro-cell cellular system operators are considering the deployment of small-cells. It is expected that both the small and macro-cells will coexist in the same spectrum resulting in unsustainable levels of interference. Interference alignment is considered as an effective method to deal with such interfer- ence. By using interference alignment the small-cells align their transmission along a common direction to allow the macro-cell receiver to completely remove it. It is clear that if the two systems have no limitations on the information that may be exchanged between them to perform the signal design, then the performance may be improved in comparison to the case of no or partial cooperation. However, this full cooperation strategy requires a high-rate connection between the macro and small-cells, which may not be available. To overcome this problem we consider that the alignment direction is selected from a finite set, known to both macro and small-cell terminals. We provide sufficient conditions for this set that guarantee full-diversity, at the macro- cell, and propose an efficient method to optimize the set elements. Results show that an alignment set with a description length of 1 bit is enough to achieve the same diversity as in the case where an infinite amount of information is exchanged between both systems. The proposed set optimization method achieves better performance than random vector quantization and similar performance to Grassmannian quantization. |
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Set optimization for efficient interference alignment in heterogeneous networksSmall-cellsInterference AlignmentZero-ForcingMIMO SystemsDiversity MethodsCodebook DesignRayleigh ChannelsFeedbackRandom Vector QuantizationTo increase capacity and offload traffic from the current macro-cell cellular system operators are considering the deployment of small-cells. It is expected that both the small and macro-cells will coexist in the same spectrum resulting in unsustainable levels of interference. Interference alignment is considered as an effective method to deal with such interfer- ence. By using interference alignment the small-cells align their transmission along a common direction to allow the macro-cell receiver to completely remove it. It is clear that if the two systems have no limitations on the information that may be exchanged between them to perform the signal design, then the performance may be improved in comparison to the case of no or partial cooperation. However, this full cooperation strategy requires a high-rate connection between the macro and small-cells, which may not be available. To overcome this problem we consider that the alignment direction is selected from a finite set, known to both macro and small-cell terminals. We provide sufficient conditions for this set that guarantee full-diversity, at the macro- cell, and propose an efficient method to optimize the set elements. Results show that an alignment set with a description length of 1 bit is enough to achieve the same diversity as in the case where an infinite amount of information is exchanged between both systems. The proposed set optimization method achieves better performance than random vector quantization and similar performance to Grassmannian quantization.Institute of Electrical and Electronics Engineers (IEEE)2014-07-28T10:37:20Z2014-06-01T00:00:00Z2014-06-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/12528eng1536-127610.1109/TWC.2014.2322855Castanheira, DanielSilva, AdãoGameiro, Atílioinfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-06T03:50:55Zoai:ria.ua.pt:10773/12528Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T13:48:36.047036Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse |
| dc.title.none.fl_str_mv |
Set optimization for efficient interference alignment in heterogeneous networks |
| title |
Set optimization for efficient interference alignment in heterogeneous networks |
| spellingShingle |
Set optimization for efficient interference alignment in heterogeneous networks Castanheira, Daniel Small-cells Interference Alignment Zero-Forcing MIMO Systems Diversity Methods Codebook Design Rayleigh Channels Feedback Random Vector Quantization |
| title_short |
Set optimization for efficient interference alignment in heterogeneous networks |
| title_full |
Set optimization for efficient interference alignment in heterogeneous networks |
| title_fullStr |
Set optimization for efficient interference alignment in heterogeneous networks |
| title_full_unstemmed |
Set optimization for efficient interference alignment in heterogeneous networks |
| title_sort |
Set optimization for efficient interference alignment in heterogeneous networks |
| author |
Castanheira, Daniel |
| author_facet |
Castanheira, Daniel Silva, Adão Gameiro, Atílio |
| author_role |
author |
| author2 |
Silva, Adão Gameiro, Atílio |
| author2_role |
author author |
| dc.contributor.author.fl_str_mv |
Castanheira, Daniel Silva, Adão Gameiro, Atílio |
| dc.subject.por.fl_str_mv |
Small-cells Interference Alignment Zero-Forcing MIMO Systems Diversity Methods Codebook Design Rayleigh Channels Feedback Random Vector Quantization |
| topic |
Small-cells Interference Alignment Zero-Forcing MIMO Systems Diversity Methods Codebook Design Rayleigh Channels Feedback Random Vector Quantization |
| description |
To increase capacity and offload traffic from the current macro-cell cellular system operators are considering the deployment of small-cells. It is expected that both the small and macro-cells will coexist in the same spectrum resulting in unsustainable levels of interference. Interference alignment is considered as an effective method to deal with such interfer- ence. By using interference alignment the small-cells align their transmission along a common direction to allow the macro-cell receiver to completely remove it. It is clear that if the two systems have no limitations on the information that may be exchanged between them to perform the signal design, then the performance may be improved in comparison to the case of no or partial cooperation. However, this full cooperation strategy requires a high-rate connection between the macro and small-cells, which may not be available. To overcome this problem we consider that the alignment direction is selected from a finite set, known to both macro and small-cell terminals. We provide sufficient conditions for this set that guarantee full-diversity, at the macro- cell, and propose an efficient method to optimize the set elements. Results show that an alignment set with a description length of 1 bit is enough to achieve the same diversity as in the case where an infinite amount of information is exchanged between both systems. The proposed set optimization method achieves better performance than random vector quantization and similar performance to Grassmannian quantization. |
| publishDate |
2014 |
| dc.date.none.fl_str_mv |
2014-07-28T10:37:20Z 2014-06-01T00:00:00Z 2014-06-01 |
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info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/article |
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article |
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publishedVersion |
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http://hdl.handle.net/10773/12528 |
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http://hdl.handle.net/10773/12528 |
| dc.language.iso.fl_str_mv |
eng |
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eng |
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1536-1276 10.1109/TWC.2014.2322855 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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Institute of Electrical and Electronics Engineers (IEEE) |
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Institute of Electrical and Electronics Engineers (IEEE) |
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