Critical Analysis of Learning Algorithms in Random Neural Network Based Cognitive Engine for LTE Systems
Abstract
In this paper, we critically analyze the performance of an intelligent Long-Term Evolution-Uplink (LTE-UL) system having a cognitive engine (CE) embedded in e-NodeB. Performance characterization, optimal radio parameters prediction, and inter-cell-interference coordination (ICIC) are studied. The embedded CE allocates the optimal radio parameters to serving users and suggests the acceptable transmit power to users served by adjacent cells for ICIC. The desired cognition has been achieved with a novel random neural network (RNN) based CE architecture. To achieve the best learning performance, we critically analyzed three learning algorithms, gradient descent (GD), adaptive inertia weight particle swarm optimization (AIW-PSO) and differential evolution (DE). The analysis showed that AIW-PSO was 10.57% better than GD and 8.012% better than DE in terms of learning accuracy (based on MSE), but with considerable compromise on computational time as compared to GD. Moreover, in terms of convergence time (to achieve the MSE less than 1.04E-03), AIW-PSO took 60% less iterations than GD and 50% less than DE.
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AIWPSO's better accuracy is achieved at the expense of longer convergence time due to extra computational complexity.
“However, AIWPSO's better accuracy is achieved at the expense of longer convergence time due to extra computational complexity.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [6]
AIWPSO outperforms GD and DE with respect to accuracy (based on MSE) for training RNN in LTE-Uplink resource allocation.
“Evaluation results show that AIWPSO outperforms the other learning algorithms, with respect to accuracy (based on MSE).”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [6]
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Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036 · see citing works
- Authors
- Adeel, A., Larijani, H., Javed, A., & Ahmadinia, A.
- Affiliations
- Glasgow Caledonian University (GB)
- Venue
- 2015 IEEE 81st Vehicular Technology Conference (VTC Spring)
- Publisher
- IEEE
- Published
- 2015-05
- Pages
- 1-5
- ISSN
- 1090-3038 (ISSN-L)
- OpenAlex
- W1529459676
- Cited by
- 21
- References
- 12
- Citations by year
- 2024: 2 · 2023: 1 · 2022: 1 · 2021: 1 · 2020: 4 · 2019: 2 · 2018: 3 · 2017: 2 · 2016: 1 · 2015: 3 · 2014: 1
- FWCI
- 2.23
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Vehicular Technology Conference (publishedVersion)
- ResearchOnline (Glasgow Caledonian University) (acceptedVersion)
- ResearchOnline (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Cognitive Radio Networks and Spectrum Sensing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced MIMO Systems Optimization
- Keywords
- cognitive radio, LTE uplink, random neural networks, cognitive engine, inter-cell interference coordination, gradient descent, differential evolution, learning algorithm comparison, transmit power allocation
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