Two-timescale Beamforming Optimization for Intelligent Reflecting Surface Aided Multiuser Communication with QoS Constraints Article Swipe
Minjian Zhao
,
An Liu
,
Yubo Wan
,
Rui Zhang
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2011.02237
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2011.02237
Intelligent reflecting surface (IRS) is an emerging technology that is able to reconfigure the wireless channel via tunable passive signal reflection and thereby enhance the spectral and energy efficiency of wireless networks cost-effectively. In this paper, we study an IRS-aided multiuser multiple-input single-output (MISO) wireless system and adopt the two-timescale (TTS) transmission to reduce the signal processing complexity and channel training overhead as compared to the existing schemes based on the instantaneous channel state information (I-CSI), and at the same time, exploit the multiuser channel diversity in transmission scheduling. Specifically, the long-term passive beamforming is designed based on the statistical CSI (S-CSI) of all links, while the short-term active beamforming is designed to cater to the I-CSI of all users' reconfigured channels with optimized IRS phase shifts. We aim to minimize the average transmit power at the access point (AP), subject to the users' individual quality of service (QoS) constraints. The formulated stochastic optimization problem is non-convex and difficult to solve since the long-term and short-term design variables are complicatedly coupled in the QoS constraints. To tackle this problem, we propose an efficient algorithm, called the primal-dual decomposition based TTS joint active and passive beamforming (PDD-TJAPB), where the original problem is decomposed into a long-term problem and a family of short-term problems, and the deep unfolding technique is employed to extract gradient information from the short-term problems to construct a convex surrogate problem for the long-term problem. The proposed algorithm is proved to converge to a stationary solution of the original problem almost surely. Simulation results are presented which demonstrate the advantages and effectiveness of the proposed algorithm as compared to benchmark schemes.
Related Topics
Concepts
Beamforming
Computer science
Channel state information
Optimization problem
Overhead (engineering)
Wireless
Scheduling (production processes)
Convex optimization
Transmission (telecommunications)
Quality of service
Channel (broadcasting)
Mathematical optimization
Computer network
Electronic engineering
Algorithm
Telecommunications
Engineering
Mathematics
Regular polygon
Geometry
Operating system
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2011.02237
- https://arxiv.org/pdf/2011.02237
- OA Status
- green
- Cited By
- 3
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3097053042
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3097053042Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2011.02237Digital Object Identifier
- Title
-
Two-timescale Beamforming Optimization for Intelligent Reflecting Surface Aided Multiuser Communication with QoS ConstraintsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-11-04Full publication date if available
- Authors
-
Minjian Zhao, An Liu, Yubo Wan, Rui ZhangList of authors in order
- Landing page
-
https://arxiv.org/abs/2011.02237Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2011.02237Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2011.02237Direct OA link when available
- Concepts
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Beamforming, Computer science, Channel state information, Optimization problem, Overhead (engineering), Wireless, Scheduling (production processes), Convex optimization, Transmission (telecommunications), Quality of service, Channel (broadcasting), Mathematical optimization, Computer network, Electronic engineering, Algorithm, Telecommunications, Engineering, Mathematics, Regular polygon, Geometry, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2021: 2, 2020: 1Per-year citation counts (last 5 years)
- References (count)
-
49Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2011.02237 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
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| primary_location.landing_page_url | http://arxiv.org/abs/2011.02237 |
| publication_date | 2020-11-04 |
| publication_year | 2020 |
| referenced_works | https://openalex.org/W3009554119, https://openalex.org/W3129486236, https://openalex.org/W3100218246, https://openalex.org/W3038534820, https://openalex.org/W3005476696, https://openalex.org/W1545861347, https://openalex.org/W2968810373, https://openalex.org/W3013719647, https://openalex.org/W2784709112, https://openalex.org/W2105502962, https://openalex.org/W1498436455, https://openalex.org/W2990747873, https://openalex.org/W2735938380, https://openalex.org/W3008356568, https://openalex.org/W2077813311, https://openalex.org/W2047042084, https://openalex.org/W3011952645, https://openalex.org/W3041878473, https://openalex.org/W2950077417, https://openalex.org/W2995554704, https://openalex.org/W3039402952, https://openalex.org/W1831449718, https://openalex.org/W2953226670, https://openalex.org/W2103972037, https://openalex.org/W1970830346, https://openalex.org/W3011747750, https://openalex.org/W3006962285, https://openalex.org/W3008435545, https://openalex.org/W2981792785, https://openalex.org/W2896507689, https://openalex.org/W3049287381, https://openalex.org/W1577245107, https://openalex.org/W2103643922, https://openalex.org/W3035386068, https://openalex.org/W2937693915, https://openalex.org/W2042599231, https://openalex.org/W1545139845, https://openalex.org/W3001977108, https://openalex.org/W3034533409, https://openalex.org/W2992456343, https://openalex.org/W2993316576, https://openalex.org/W3045387830, https://openalex.org/W3021225717, https://openalex.org/W1663973292, https://openalex.org/W2157293015, https://openalex.org/W2036749237, https://openalex.org/W2969424089, https://openalex.org/W3137708979, https://openalex.org/W2011836673 |
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| abstract_inverted_index.QoS | 173 |
| abstract_inverted_index.TTS | 189 |
| abstract_inverted_index.The | 150, 237 |
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| abstract_inverted_index.(AP), | 139 |
| abstract_inverted_index.(IRS) | 3 |
| abstract_inverted_index.(QoS) | 148 |
| abstract_inverted_index.(TTS) | 50 |
| abstract_inverted_index.I-CSI | 116 |
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| abstract_inverted_index.cater | 113 |
| abstract_inverted_index.joint | 190 |
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| abstract_inverted_index.which | 258 |
| abstract_inverted_index.while | 105 |
| abstract_inverted_index.(MISO) | 43 |
| abstract_inverted_index.access | 137 |
| abstract_inverted_index.active | 108, 191 |
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| abstract_inverted_index.called | 184 |
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| abstract_inverted_index.energy | 27 |
| abstract_inverted_index.family | 208 |
| abstract_inverted_index.links, | 104 |
| abstract_inverted_index.paper, | 35 |
| abstract_inverted_index.proved | 241 |
| abstract_inverted_index.reduce | 53 |
| abstract_inverted_index.signal | 19, 55 |
| abstract_inverted_index.system | 45 |
| abstract_inverted_index.tackle | 176 |
| abstract_inverted_index.users' | 119, 143 |
| abstract_inverted_index.(S-CSI) | 101 |
| abstract_inverted_index.average | 132 |
| abstract_inverted_index.channel | 15, 59, 72, 84 |
| abstract_inverted_index.coupled | 170 |
| abstract_inverted_index.enhance | 23 |
| abstract_inverted_index.exploit | 81 |
| abstract_inverted_index.extract | 220 |
| abstract_inverted_index.passive | 18, 92, 193 |
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| abstract_inverted_index.propose | 180 |
| abstract_inverted_index.quality | 145 |
| abstract_inverted_index.results | 255 |
| abstract_inverted_index.schemes | 67 |
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| abstract_inverted_index.shifts. | 126 |
| abstract_inverted_index.subject | 140 |
| abstract_inverted_index.surely. | 253 |
| abstract_inverted_index.surface | 2 |
| abstract_inverted_index.thereby | 22 |
| abstract_inverted_index.tunable | 17 |
| abstract_inverted_index.(I-CSI), | 75 |
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| abstract_inverted_index.networks | 31 |
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| abstract_inverted_index.overhead | 61 |
| abstract_inverted_index.problem, | 178 |
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| abstract_inverted_index.schemes. | 272 |
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| abstract_inverted_index.spectral | 25 |
| abstract_inverted_index.training | 60 |
| abstract_inverted_index.transmit | 133 |
| abstract_inverted_index.wireless | 14, 30, 44 |
| abstract_inverted_index.IRS-aided | 39 |
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| abstract_inverted_index.benchmark | 271 |
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| abstract_inverted_index.difficult | 158 |
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| abstract_inverted_index.multiuser | 40, 83 |
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| abstract_inverted_index.presented | 257 |
| abstract_inverted_index.problems, | 211 |
| abstract_inverted_index.surrogate | 231 |
| abstract_inverted_index.technique | 216 |
| abstract_inverted_index.unfolding | 215 |
| abstract_inverted_index.variables | 167 |
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| abstract_inverted_index.advantages | 261 |
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| abstract_inverted_index.decomposed | 201 |
| abstract_inverted_index.efficiency | 28 |
| abstract_inverted_index.formulated | 151 |
| abstract_inverted_index.individual | 144 |
| abstract_inverted_index.non-convex | 156 |
| abstract_inverted_index.processing | 56 |
| abstract_inverted_index.reflecting | 1 |
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| abstract_inverted_index.technology | 7 |
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| abstract_inverted_index.reconfigured | 120 |
| abstract_inverted_index.transmission | 51, 87 |
| abstract_inverted_index.Specifically, | 89 |
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| abstract_inverted_index.two-timescale | 49 |
| abstract_inverted_index.multiple-input | 41 |
| abstract_inverted_index.cost-effectively. | 32 |
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| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.8999999761581421 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
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| citation_normalized_percentile.is_in_top_10_percent | False |