Data · dataset · 2020
Simulation data and Supervised ML algorithms for Detecting nodes in LAA’s Hidden Zones.
Listed in Teesside University Research Data Repository
LTE operation in unlicensed spectrum bands, based on Licensed-Assisted Access (LAA), is considered as an option to increase the capacity of 4G wireless networks.
Description
These solutions use a Listen Before Talk (LBT) protocol that enables the eNodeB (eNB) to access the medium opportunistically, avoiding collisions from/to other eNBs. However, the hidden node problem must be addressed in LAA networks to reduce or prevent the degradation of the network.
The efficiency of the LTE-LAA system will improve by identifying hidden nodes and after deciding if user equipment (UE) affected by the hidden condition should remain or should change from unlicensed to licensed band. This dataset includes the simulation data of the performance parameters RSSI, RSRQ, RSRP, and CQI for nodes in hidden (overlapped) zone and free of collisions areas for a LTE-LAA network. The simulations were carried out in NS-3.
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Also, we include the code of two supervised machine learning algorithms, logistic regression, and a neural network to determine if UEs located in the border cell are affected by hidden nodes.
Links
Where it is published
- DOI doi.org/10.17632/vzyxkc2jxf.1 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
- From keywords
- Computer Science & AI · Earth & Environmental Science · Engineering · Humanities · Life Sciences · Machine learning · Social Science
- Inferred from text
- Simulation 75%
Provenance · 1 source records, 12 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/vzyxkc2jxf.1 | 11 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:group:4611 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Machine Learning'] |
| concepts[field].local:field:computer-science-ai | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:engineering | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:humanities | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:social-science | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[method].local:method:simulation | enrichment · researchdata tees ac uk | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license_text | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |