DRL-Based Robust Multi-Timescale Anti-Jamming Approaches under State Uncertainty Article Swipe
Haoqin Zhao
,
Zan Li
,
Jiangbo Si
,
Rui Huang
,
Hang Hu
,
Tony Q. S. Quek
,
Naofal Al–Dhahir
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2511.03305
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2511.03305
Owing to the openness of wireless channels, wireless communication systems are highly susceptible to malicious jamming. Most existing anti-jamming methods rely on the assumption of accurate sensing and optimize parameters on a single timescale. However, such methods overlook two practical issues: mismatched execution latencies across heterogeneous actions and measurement errors caused by sensor imperfections. Especially for deep reinforcement learning (DRL)-based methods, the inherent sensitivity of neural networks implies that even minor perturbations in the input can mislead the agent into choosing suboptimal actions, with potentially severe consequences. To ensure reliable wireless transmission, we establish a multi-timescale decision model that incorporates state uncertainty. Subsequently, we propose two robust schemes that sustain performance under bounded sensing errors. First, a Projected Gradient Descent-assisted Double Deep Q-Network (PGD-DDQN) algorithm is designed, which derives worst-case perturbations under a norm-bounded error model and applies PGD during training for robust optimization. Second, a Nonlinear Q-Compression DDQN (NQC-DDQN) algorithm introduces a nonlinear compression mechanism that adaptively contracts Q-value ranges to eliminate action aliasing. Simulation results indicate that, compared with the perfect-sensing baseline, the proposed algorithms show only minor degradation in anti-jamming performance while maintaining robustness under various perturbations, thereby validating their practicality in imperfect sensing conditions.
Related Topics
Concepts
No concepts available.
Metadata
- Type
- preprint
- Landing Page
- http://arxiv.org/abs/2511.03305
- https://arxiv.org/pdf/2511.03305
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W4416019632
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4416019632Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2511.03305Digital Object Identifier
- Title
-
DRL-Based Robust Multi-Timescale Anti-Jamming Approaches under State UncertaintyWork title
- Type
-
preprintOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-05Full publication date if available
- Authors
-
Haoqin Zhao, Zan Li, Jiangbo Si, Rui Huang, Hang Hu, Tony Q. S. Quek, Naofal Al–DhahirList of authors in order
- Landing page
-
https://arxiv.org/abs/2511.03305Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2511.03305Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2511.03305Direct OA link when available
- Cited by
-
0Total citation count in OpenAlex
Full payload
| id | https://openalex.org/W4416019632 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2511.03305 |
| ids.doi | https://doi.org/10.48550/arxiv.2511.03305 |
| ids.openalex | https://openalex.org/W4416019632 |
| fwci | 0.0 |
| type | preprint |
| title | DRL-Based Robust Multi-Timescale Anti-Jamming Approaches under State Uncertainty |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| language | |
| locations[0].id | pmh:oai:arXiv.org:2511.03305 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://arxiv.org/pdf/2511.03305 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2511.03305 |
| locations[1].id | doi:10.48550/arxiv.2511.03305 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | cc-by |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/cc-by |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2511.03305 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5024016682 |
| authorships[0].author.orcid | https://orcid.org/0009-0009-9096-0147 |
| authorships[0].author.display_name | Haoqin Zhao |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Zhao, Haoqin |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5100697315 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-5207-6504 |
| authorships[1].author.display_name | Zan Li |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Li, Zan |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5009796023 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-7419-4042 |
| authorships[2].author.display_name | Jiangbo Si |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Si, Jiangbo |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5101675121 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-3708-6421 |
| authorships[3].author.display_name | Rui Huang |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Huang, Rui |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5038155012 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-5391-010X |
| authorships[4].author.display_name | Hang Hu |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Hu, Hang |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5030858163 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-4037-3149 |
| authorships[5].author.display_name | Tony Q. S. Quek |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Quek, Tony Q. S. |
| authorships[5].is_corresponding | False |
| authorships[6].author.id | https://openalex.org/A5081018704 |
| authorships[6].author.orcid | https://orcid.org/0000-0001-9894-8382 |
| authorships[6].author.display_name | Naofal Al–Dhahir |
| authorships[6].author_position | last |
| authorships[6].raw_author_name | Al-Dhahir, Naofal |
| authorships[6].is_corresponding | False |
| has_content.pdf | True |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2511.03305 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-11-07T00:00:00 |
| display_name | DRL-Based Robust Multi-Timescale Anti-Jamming Approaches under State Uncertainty |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-09T23:09:16.995542 |
| primary_topic | |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2511.03305 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2511.03305 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2511.03305 |
| primary_location.id | pmh:oai:arXiv.org:2511.03305 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://arxiv.org/pdf/2511.03305 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2511.03305 |
| publication_date | 2025-11-05 |
| publication_year | 2025 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 31, 94, 116, 132, 145, 152 |
| abstract_inverted_index.To | 87 |
| abstract_inverted_index.by | 51 |
| abstract_inverted_index.in | 72, 181, 194 |
| abstract_inverted_index.is | 125 |
| abstract_inverted_index.of | 4, 24, 64 |
| abstract_inverted_index.on | 21, 30 |
| abstract_inverted_index.to | 1, 13, 161 |
| abstract_inverted_index.we | 92, 103 |
| abstract_inverted_index.PGD | 138 |
| abstract_inverted_index.and | 27, 47, 136 |
| abstract_inverted_index.are | 10 |
| abstract_inverted_index.can | 75 |
| abstract_inverted_index.for | 55, 141 |
| abstract_inverted_index.the | 2, 22, 61, 73, 77, 171, 174 |
| abstract_inverted_index.two | 38, 105 |
| abstract_inverted_index.DDQN | 148 |
| abstract_inverted_index.Deep | 121 |
| abstract_inverted_index.Most | 16 |
| abstract_inverted_index.deep | 56 |
| abstract_inverted_index.even | 69 |
| abstract_inverted_index.into | 79 |
| abstract_inverted_index.only | 178 |
| abstract_inverted_index.rely | 20 |
| abstract_inverted_index.show | 177 |
| abstract_inverted_index.such | 35 |
| abstract_inverted_index.that | 68, 98, 108, 156 |
| abstract_inverted_index.with | 83, 170 |
| abstract_inverted_index.Owing | 0 |
| abstract_inverted_index.agent | 78 |
| abstract_inverted_index.error | 134 |
| abstract_inverted_index.input | 74 |
| abstract_inverted_index.minor | 70, 179 |
| abstract_inverted_index.model | 97, 135 |
| abstract_inverted_index.state | 100 |
| abstract_inverted_index.that, | 168 |
| abstract_inverted_index.their | 192 |
| abstract_inverted_index.under | 111, 131, 187 |
| abstract_inverted_index.which | 127 |
| abstract_inverted_index.while | 184 |
| abstract_inverted_index.Double | 120 |
| abstract_inverted_index.First, | 115 |
| abstract_inverted_index.across | 44 |
| abstract_inverted_index.action | 163 |
| abstract_inverted_index.caused | 50 |
| abstract_inverted_index.during | 139 |
| abstract_inverted_index.ensure | 88 |
| abstract_inverted_index.errors | 49 |
| abstract_inverted_index.highly | 11 |
| abstract_inverted_index.neural | 65 |
| abstract_inverted_index.ranges | 160 |
| abstract_inverted_index.robust | 106, 142 |
| abstract_inverted_index.sensor | 52 |
| abstract_inverted_index.severe | 85 |
| abstract_inverted_index.single | 32 |
| abstract_inverted_index.Q-value | 159 |
| abstract_inverted_index.Second, | 144 |
| abstract_inverted_index.actions | 46 |
| abstract_inverted_index.applies | 137 |
| abstract_inverted_index.bounded | 112 |
| abstract_inverted_index.derives | 128 |
| abstract_inverted_index.errors. | 114 |
| abstract_inverted_index.implies | 67 |
| abstract_inverted_index.issues: | 40 |
| abstract_inverted_index.methods | 19, 36 |
| abstract_inverted_index.mislead | 76 |
| abstract_inverted_index.propose | 104 |
| abstract_inverted_index.results | 166 |
| abstract_inverted_index.schemes | 107 |
| abstract_inverted_index.sensing | 26, 113, 196 |
| abstract_inverted_index.sustain | 109 |
| abstract_inverted_index.systems | 9 |
| abstract_inverted_index.thereby | 190 |
| abstract_inverted_index.various | 188 |
| abstract_inverted_index.Gradient | 118 |
| abstract_inverted_index.However, | 34 |
| abstract_inverted_index.accurate | 25 |
| abstract_inverted_index.actions, | 82 |
| abstract_inverted_index.choosing | 80 |
| abstract_inverted_index.compared | 169 |
| abstract_inverted_index.decision | 96 |
| abstract_inverted_index.existing | 17 |
| abstract_inverted_index.indicate | 167 |
| abstract_inverted_index.inherent | 62 |
| abstract_inverted_index.jamming. | 15 |
| abstract_inverted_index.learning | 58 |
| abstract_inverted_index.methods, | 60 |
| abstract_inverted_index.networks | 66 |
| abstract_inverted_index.openness | 3 |
| abstract_inverted_index.optimize | 28 |
| abstract_inverted_index.overlook | 37 |
| abstract_inverted_index.proposed | 175 |
| abstract_inverted_index.reliable | 89 |
| abstract_inverted_index.training | 140 |
| abstract_inverted_index.wireless | 5, 7, 90 |
| abstract_inverted_index.Nonlinear | 146 |
| abstract_inverted_index.Projected | 117 |
| abstract_inverted_index.Q-Network | 122 |
| abstract_inverted_index.algorithm | 124, 150 |
| abstract_inverted_index.aliasing. | 164 |
| abstract_inverted_index.baseline, | 173 |
| abstract_inverted_index.channels, | 6 |
| abstract_inverted_index.contracts | 158 |
| abstract_inverted_index.designed, | 126 |
| abstract_inverted_index.eliminate | 162 |
| abstract_inverted_index.establish | 93 |
| abstract_inverted_index.execution | 42 |
| abstract_inverted_index.imperfect | 195 |
| abstract_inverted_index.latencies | 43 |
| abstract_inverted_index.malicious | 14 |
| abstract_inverted_index.mechanism | 155 |
| abstract_inverted_index.nonlinear | 153 |
| abstract_inverted_index.practical | 39 |
| abstract_inverted_index.(NQC-DDQN) | 149 |
| abstract_inverted_index.(PGD-DDQN) | 123 |
| abstract_inverted_index.Especially | 54 |
| abstract_inverted_index.Simulation | 165 |
| abstract_inverted_index.adaptively | 157 |
| abstract_inverted_index.algorithms | 176 |
| abstract_inverted_index.assumption | 23 |
| abstract_inverted_index.introduces | 151 |
| abstract_inverted_index.mismatched | 41 |
| abstract_inverted_index.parameters | 29 |
| abstract_inverted_index.robustness | 186 |
| abstract_inverted_index.suboptimal | 81 |
| abstract_inverted_index.timescale. | 33 |
| abstract_inverted_index.validating | 191 |
| abstract_inverted_index.worst-case | 129 |
| abstract_inverted_index.(DRL)-based | 59 |
| abstract_inverted_index.compression | 154 |
| abstract_inverted_index.conditions. | 197 |
| abstract_inverted_index.degradation | 180 |
| abstract_inverted_index.maintaining | 185 |
| abstract_inverted_index.measurement | 48 |
| abstract_inverted_index.performance | 110, 183 |
| abstract_inverted_index.potentially | 84 |
| abstract_inverted_index.sensitivity | 63 |
| abstract_inverted_index.susceptible | 12 |
| abstract_inverted_index.anti-jamming | 18, 182 |
| abstract_inverted_index.incorporates | 99 |
| abstract_inverted_index.norm-bounded | 133 |
| abstract_inverted_index.practicality | 193 |
| abstract_inverted_index.uncertainty. | 101 |
| abstract_inverted_index.Q-Compression | 147 |
| abstract_inverted_index.Subsequently, | 102 |
| abstract_inverted_index.communication | 8 |
| abstract_inverted_index.consequences. | 86 |
| abstract_inverted_index.heterogeneous | 45 |
| abstract_inverted_index.optimization. | 143 |
| abstract_inverted_index.perturbations | 71, 130 |
| abstract_inverted_index.reinforcement | 57 |
| abstract_inverted_index.transmission, | 91 |
| abstract_inverted_index.imperfections. | 53 |
| abstract_inverted_index.perturbations, | 189 |
| abstract_inverted_index.multi-timescale | 95 |
| abstract_inverted_index.perfect-sensing | 172 |
| abstract_inverted_index.Descent-assisted | 119 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |