Near-Field Nulling Control Beamfocusing Optimization for Multi-User Interference Suppression Article Swipe
Yuanzhe Gong
,
Mohammad Hossein Karimi
,
Tho Le‐Ngoc
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/ojcoms.2025.3548457
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/ojcoms.2025.3548457
This paper presents comprehensive full-wave simulation-based studies of near-field beam radiation patterns for large-scale arrays, accounting for realistic electromagnetic wave characteristics, heterogeneous element radiation patterns, and array element interactions. These simulations thoroughly investigate and illustrate the radiation behaviors of antenna arrays at different observation distances. To leverage the advantages offered by distance-dependent radiation patterns in the near-field, we consider two nulling control beamfocusing algorithms to effectively mitigate multi-user interference (MUI) in massive multiple-input multiple-output (mMIMO) systems by achieving considerable focusing gain differences between the desired and interference locations. Firstly, a linear constraint minimum variance (LCMV) scheme to effectively control radiation nulls in the Fresnel region is developed. By adjusting the array feeding magnitudes and phase shifters, an average gain difference of 29.2 dB between desired and undesired users can be achieved, with minimal gain degradation of 0.4 dB at the desired user compared to the maximum directivity beamfocusing scheme. Moreover, a constant-modulus beamfocusing scheme based on a perturbation-based nulling control beamfocusing algorithm employing particle swarm optimization is proposed. Using only phase shifters, an average gain difference of 26.1 dB between desired and undesired users can be achieved. Iterative full-wave simulations are conducted to investigate how the achievable beamfocusing gain difference varies with different desired and interference user locations. Finally, a deep neural network (DNN) is trained for MUI suppression based on the LCMV-generated beamfocusing vectors. The model achieves a phase error of less than 0.021 radians and a magnitude error of 0.17 dB in the predicted feeding weights. The resulting near-field beam patterns using the LCMV-based vector and the DNN-predicted vector show good agreement.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/ojcoms.2025.3548457
- OA Status
- gold
- Cited By
- 1
- References
- 46
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4408166166Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/ojcoms.2025.3548457Digital Object Identifier
- Title
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Near-Field Nulling Control Beamfocusing Optimization for Multi-User Interference SuppressionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
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2025Year of publication
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2025-01-01Full publication date if available
- Authors
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Yuanzhe Gong, Mohammad Hossein Karimi, Tho Le‐NgocList of authors in order
- Landing page
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https://doi.org/10.1109/ojcoms.2025.3548457Publisher 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/ojcoms.2025.3548457Direct OA link when available
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Interference (communication), Computer science, Field (mathematics), Control (management), Telecommunications, Mathematics, Artificial intelligence, Channel (broadcasting), Pure mathematicsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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46Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.paper | 1 |
| abstract_inverted_index.phase | 114, 170, 229 |
| abstract_inverted_index.swarm | 164 |
| abstract_inverted_index.users | 127, 183 |
| abstract_inverted_index.using | 253 |
| abstract_inverted_index.(LCMV) | 94 |
| abstract_inverted_index.arrays | 40 |
| abstract_inverted_index.linear | 90 |
| abstract_inverted_index.neural | 211 |
| abstract_inverted_index.region | 104 |
| abstract_inverted_index.scheme | 95, 153 |
| abstract_inverted_index.varies | 200 |
| abstract_inverted_index.vector | 256, 260 |
| abstract_inverted_index.(mMIMO) | 74 |
| abstract_inverted_index.Fresnel | 103 |
| abstract_inverted_index.antenna | 39 |
| abstract_inverted_index.arrays, | 14 |
| abstract_inverted_index.average | 117, 173 |
| abstract_inverted_index.between | 82, 123, 179 |
| abstract_inverted_index.control | 61, 98, 159 |
| abstract_inverted_index.desired | 84, 124, 140, 180, 203 |
| abstract_inverted_index.element | 22, 27 |
| abstract_inverted_index.feeding | 111, 246 |
| abstract_inverted_index.massive | 71 |
| abstract_inverted_index.maximum | 145 |
| abstract_inverted_index.minimal | 132 |
| abstract_inverted_index.minimum | 92 |
| abstract_inverted_index.network | 212 |
| abstract_inverted_index.nulling | 60, 158 |
| abstract_inverted_index.offered | 49 |
| abstract_inverted_index.radians | 235 |
| abstract_inverted_index.scheme. | 148 |
| abstract_inverted_index.studies | 6 |
| abstract_inverted_index.systems | 75 |
| abstract_inverted_index.trained | 215 |
| abstract_inverted_index.Finally, | 208 |
| abstract_inverted_index.Firstly, | 88 |
| abstract_inverted_index.achieves | 227 |
| abstract_inverted_index.compared | 142 |
| abstract_inverted_index.consider | 58 |
| abstract_inverted_index.focusing | 79 |
| abstract_inverted_index.leverage | 46 |
| abstract_inverted_index.mitigate | 66 |
| abstract_inverted_index.particle | 163 |
| abstract_inverted_index.patterns | 11, 53, 252 |
| abstract_inverted_index.presents | 2 |
| abstract_inverted_index.variance | 93 |
| abstract_inverted_index.vectors. | 224 |
| abstract_inverted_index.weights. | 247 |
| abstract_inverted_index.Iterative | 187 |
| abstract_inverted_index.Moreover, | 149 |
| abstract_inverted_index.achieved, | 130 |
| abstract_inverted_index.achieved. | 186 |
| abstract_inverted_index.achieving | 77 |
| abstract_inverted_index.adjusting | 108 |
| abstract_inverted_index.algorithm | 161 |
| abstract_inverted_index.behaviors | 37 |
| abstract_inverted_index.conducted | 191 |
| abstract_inverted_index.different | 42, 202 |
| abstract_inverted_index.employing | 162 |
| abstract_inverted_index.full-wave | 4, 188 |
| abstract_inverted_index.magnitude | 238 |
| abstract_inverted_index.patterns, | 24 |
| abstract_inverted_index.predicted | 245 |
| abstract_inverted_index.proposed. | 167 |
| abstract_inverted_index.radiation | 10, 23, 36, 52, 99 |
| abstract_inverted_index.realistic | 17 |
| abstract_inverted_index.resulting | 249 |
| abstract_inverted_index.shifters, | 115, 171 |
| abstract_inverted_index.undesired | 126, 182 |
| abstract_inverted_index.LCMV-based | 255 |
| abstract_inverted_index.accounting | 15 |
| abstract_inverted_index.achievable | 196 |
| abstract_inverted_index.advantages | 48 |
| abstract_inverted_index.agreement. | 263 |
| abstract_inverted_index.algorithms | 63 |
| abstract_inverted_index.constraint | 91 |
| abstract_inverted_index.developed. | 106 |
| abstract_inverted_index.difference | 119, 175, 199 |
| abstract_inverted_index.distances. | 44 |
| abstract_inverted_index.illustrate | 34 |
| abstract_inverted_index.locations. | 87, 207 |
| abstract_inverted_index.magnitudes | 112 |
| abstract_inverted_index.multi-user | 67 |
| abstract_inverted_index.near-field | 8, 250 |
| abstract_inverted_index.thoroughly | 31 |
| abstract_inverted_index.degradation | 134 |
| abstract_inverted_index.differences | 81 |
| abstract_inverted_index.directivity | 146 |
| abstract_inverted_index.effectively | 65, 97 |
| abstract_inverted_index.investigate | 32, 193 |
| abstract_inverted_index.large-scale | 13 |
| abstract_inverted_index.near-field, | 56 |
| abstract_inverted_index.observation | 43 |
| abstract_inverted_index.simulations | 30, 189 |
| abstract_inverted_index.suppression | 218 |
| abstract_inverted_index.beamfocusing | 62, 147, 152, 160, 197, 223 |
| abstract_inverted_index.considerable | 78 |
| abstract_inverted_index.interference | 68, 86, 205 |
| abstract_inverted_index.optimization | 165 |
| abstract_inverted_index.DNN-predicted | 259 |
| abstract_inverted_index.comprehensive | 3 |
| abstract_inverted_index.heterogeneous | 21 |
| abstract_inverted_index.interactions. | 28 |
| abstract_inverted_index.LCMV-generated | 222 |
| abstract_inverted_index.multiple-input | 72 |
| abstract_inverted_index.electromagnetic | 18 |
| abstract_inverted_index.multiple-output | 73 |
| abstract_inverted_index.characteristics, | 20 |
| abstract_inverted_index.constant-modulus | 151 |
| abstract_inverted_index.simulation-based | 5 |
| abstract_inverted_index.distance-dependent | 51 |
| abstract_inverted_index.perturbation-based | 157 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.9120894 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |