Abstract
This paper introduces an effective yet simple and practical solution to improve small-cell performance in heterogeneous networks (HetNets). The proposed solution is based on deploying a switched multielement antenna (MEA) system capable of generating a variety of antenna patterns at small-cell base stations (BSs). Then, antenna patterns are assigned to user equipment (UE) in a dynamic basis. The antenna pattern selection for each UE is considered to be a supervised machine learning classification problem, in which the small-cell BS seek to find the optimal antenna pattern to serve each UE according to its measurement reports (i.e., UE radio-frequency fingerprint). Simulation results confirm the feasibility of the proposed approach, despite potential inaccuracies in UE measurement reports. Compared with the existing solutions comprising a single omnidirectional antenna (ODA), the proposed approach results in a 68% additional network-wide capacity increase. In addition, a technoeconomic analysis is presented in this paper, indicating the economic advantages of deploying the proposed scheme.
| Original language | English |
|---|---|
| Article number | 6879477 |
| Pages (from-to) | 3140-3151 |
| Number of pages | 12 |
| Journal | IEEE Transactions on Vehicular Technology |
| Volume | 64 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - 1 Jul 2015 |
| Externally published | Yes |
Keywords
- Classification
- heterogeneous networks (HetNets)
- interference management
- machine learning
- multielement antenna (MEA)
- picocells
- radio frequency (RF) fingerprint
- small cells
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