Min-Max Decoding Error Probability Optimization in RIS-Aided Hybrid TDMA-NOMA Networks Article Swipe
Tra Huong Thi Le
,
Yan Kyaw Tun
,
Ngo Thien Thu
,
Luong Vuong Nguyen
,
Eui‐Nam Huh
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3428928
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3428928
One of the primary objectives for future wireless communication networks is to facilitate the provision of ultra-reliable and low-latency communication services while simultaneously ensuring the capability for vast connection. In order to achieve this objective, we examine a hybrid multi-access scheme inside the finite blocklength (FBL) regime. This system combines the benefits of non-orthogonal multiple access (NOMA) and time-division multiple access (TDMA) schemes with the aim of fulfilling the objectives of future wireless communication networks. In addition, a reconfigurable intelligent surface (RIS) is utilized to facilitate the establishment of the downlink transmission between the base station and mobile devices in situations when impediments impede their direct communication linkages. This paper aims to minimize the worst-case decoding-error probability for all mobile users by jointly optimizing transmission power, transmitting beamforming, blocklength, RIS reflection, and user pairing. To deal with the coupled variables in the formulated mixed-integer non-convex optimization problem, we decompose it into three sub-problems, namely, 1) decoding order determination problem, 2) joint transmission power, receiving beamforming, RIS reflection, and blocklength optimization problem, and 3) optimal user pairing problem. Then, we provide the sequential convex approximation (SCA) and semidefinite relaxation (SDR)-based algorithms as potential solutions for iteratively addressing the deconstructed first two sub-problems at a fixed random user pairing. In addition, the Hungarian matching approach is employed to address the challenge of optimizing user pairing. In conclusion, we undertake a comprehensive simulation, which reveals the advantageous qualities of the proposed algorithm and its superior performance compared to existing benchmark methods.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2024.3428928
- OA Status
- gold
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400645510Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2024.3428928Digital Object Identifier
- Title
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Min-Max Decoding Error Probability Optimization in RIS-Aided Hybrid TDMA-NOMA NetworksWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
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Tra Huong Thi Le, Yan Kyaw Tun, Ngo Thien Thu, Luong Vuong Nguyen, Eui‐Nam HuhList of authors in order
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https://doi.org/10.1109/access.2024.3428928Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/access.2024.3428928Direct OA link when available
- Concepts
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Noma, Decoding methods, Time division multiple access, Computer science, Algorithm, Computer network, Telecommunications linkTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 1, 2024: 2Per-year citation counts (last 5 years)
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42Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.the | 2, 13, 24, 42, 50, 64, 68, 86, 89, 93, 113, 137, 141, 180, 196, 209, 217, 232, 236 |
| abstract_inverted_index.two | 199 |
| abstract_inverted_index.This | 47, 108 |
| abstract_inverted_index.aims | 110 |
| abstract_inverted_index.base | 94 |
| abstract_inverted_index.deal | 135 |
| abstract_inverted_index.into | 150 |
| abstract_inverted_index.this | 33 |
| abstract_inverted_index.user | 132, 174, 205, 221 |
| abstract_inverted_index.vast | 27 |
| abstract_inverted_index.when | 101 |
| abstract_inverted_index.with | 63, 136 |
| abstract_inverted_index.(FBL) | 45 |
| abstract_inverted_index.(RIS) | 81 |
| abstract_inverted_index.(SCA) | 184 |
| abstract_inverted_index.Then, | 177 |
| abstract_inverted_index.first | 198 |
| abstract_inverted_index.fixed | 203 |
| abstract_inverted_index.joint | 160 |
| abstract_inverted_index.order | 30, 156 |
| abstract_inverted_index.paper | 109 |
| abstract_inverted_index.their | 104 |
| abstract_inverted_index.three | 151 |
| abstract_inverted_index.users | 120 |
| abstract_inverted_index.which | 230 |
| abstract_inverted_index.while | 21 |
| abstract_inverted_index.(NOMA) | 56 |
| abstract_inverted_index.(TDMA) | 61 |
| abstract_inverted_index.access | 55, 60 |
| abstract_inverted_index.convex | 182 |
| abstract_inverted_index.direct | 105 |
| abstract_inverted_index.finite | 43 |
| abstract_inverted_index.future | 6, 71 |
| abstract_inverted_index.hybrid | 38 |
| abstract_inverted_index.impede | 103 |
| abstract_inverted_index.inside | 41 |
| abstract_inverted_index.mobile | 97, 119 |
| abstract_inverted_index.power, | 125, 162 |
| abstract_inverted_index.random | 204 |
| abstract_inverted_index.scheme | 40 |
| abstract_inverted_index.system | 48 |
| abstract_inverted_index.achieve | 32 |
| abstract_inverted_index.address | 216 |
| abstract_inverted_index.between | 92 |
| abstract_inverted_index.coupled | 138 |
| abstract_inverted_index.devices | 98 |
| abstract_inverted_index.examine | 36 |
| abstract_inverted_index.jointly | 122 |
| abstract_inverted_index.namely, | 153 |
| abstract_inverted_index.optimal | 173 |
| abstract_inverted_index.pairing | 175 |
| abstract_inverted_index.primary | 3 |
| abstract_inverted_index.provide | 179 |
| abstract_inverted_index.regime. | 46 |
| abstract_inverted_index.reveals | 231 |
| abstract_inverted_index.schemes | 62 |
| abstract_inverted_index.station | 95 |
| abstract_inverted_index.surface | 80 |
| abstract_inverted_index.approach | 212 |
| abstract_inverted_index.benefits | 51 |
| abstract_inverted_index.combines | 49 |
| abstract_inverted_index.compared | 243 |
| abstract_inverted_index.decoding | 155 |
| abstract_inverted_index.downlink | 90 |
| abstract_inverted_index.employed | 214 |
| abstract_inverted_index.ensuring | 23 |
| abstract_inverted_index.existing | 245 |
| abstract_inverted_index.matching | 211 |
| abstract_inverted_index.methods. | 247 |
| abstract_inverted_index.minimize | 112 |
| abstract_inverted_index.multiple | 54, 59 |
| abstract_inverted_index.networks | 9 |
| abstract_inverted_index.pairing. | 133, 206, 222 |
| abstract_inverted_index.problem, | 146, 158, 170 |
| abstract_inverted_index.problem. | 176 |
| abstract_inverted_index.proposed | 237 |
| abstract_inverted_index.services | 20 |
| abstract_inverted_index.superior | 241 |
| abstract_inverted_index.utilized | 83 |
| abstract_inverted_index.wireless | 7, 72 |
| abstract_inverted_index.Hungarian | 210 |
| abstract_inverted_index.addition, | 76, 208 |
| abstract_inverted_index.algorithm | 238 |
| abstract_inverted_index.benchmark | 246 |
| abstract_inverted_index.challenge | 218 |
| abstract_inverted_index.decompose | 148 |
| abstract_inverted_index.linkages. | 107 |
| abstract_inverted_index.networks. | 74 |
| abstract_inverted_index.potential | 191 |
| abstract_inverted_index.provision | 14 |
| abstract_inverted_index.qualities | 234 |
| abstract_inverted_index.receiving | 163 |
| abstract_inverted_index.solutions | 192 |
| abstract_inverted_index.undertake | 226 |
| abstract_inverted_index.variables | 139 |
| abstract_inverted_index.addressing | 195 |
| abstract_inverted_index.algorithms | 189 |
| abstract_inverted_index.capability | 25 |
| abstract_inverted_index.facilitate | 12, 85 |
| abstract_inverted_index.formulated | 142 |
| abstract_inverted_index.fulfilling | 67 |
| abstract_inverted_index.non-convex | 144 |
| abstract_inverted_index.objective, | 34 |
| abstract_inverted_index.objectives | 4, 69 |
| abstract_inverted_index.optimizing | 123, 220 |
| abstract_inverted_index.relaxation | 187 |
| abstract_inverted_index.sequential | 181 |
| abstract_inverted_index.situations | 100 |
| abstract_inverted_index.worst-case | 114 |
| abstract_inverted_index.(SDR)-based | 188 |
| abstract_inverted_index.blocklength | 44, 168 |
| abstract_inverted_index.conclusion, | 224 |
| abstract_inverted_index.connection. | 28 |
| abstract_inverted_index.impediments | 102 |
| abstract_inverted_index.intelligent | 79 |
| abstract_inverted_index.iteratively | 194 |
| abstract_inverted_index.low-latency | 18 |
| abstract_inverted_index.performance | 242 |
| abstract_inverted_index.probability | 116 |
| abstract_inverted_index.reflection, | 130, 166 |
| abstract_inverted_index.simulation, | 229 |
| abstract_inverted_index.advantageous | 233 |
| abstract_inverted_index.beamforming, | 127, 164 |
| abstract_inverted_index.blocklength, | 128 |
| abstract_inverted_index.multi-access | 39 |
| abstract_inverted_index.optimization | 145, 169 |
| abstract_inverted_index.semidefinite | 186 |
| abstract_inverted_index.sub-problems | 200 |
| abstract_inverted_index.transmission | 91, 124, 161 |
| abstract_inverted_index.transmitting | 126 |
| abstract_inverted_index.approximation | 183 |
| abstract_inverted_index.communication | 8, 19, 73, 106 |
| abstract_inverted_index.comprehensive | 228 |
| abstract_inverted_index.deconstructed | 197 |
| abstract_inverted_index.determination | 157 |
| abstract_inverted_index.establishment | 87 |
| abstract_inverted_index.mixed-integer | 143 |
| abstract_inverted_index.sub-problems, | 152 |
| abstract_inverted_index.time-division | 58 |
| abstract_inverted_index.decoding-error | 115 |
| abstract_inverted_index.non-orthogonal | 53 |
| abstract_inverted_index.reconfigurable | 78 |
| abstract_inverted_index.simultaneously | 22 |
| abstract_inverted_index.ultra-reliable | 16 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.74028692 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |