A Machine Learning Approach to Achieving Energy Efficiency in Relay-Assisted LTE-A Downlink System
| Autor: | Hammad Hassan, Ghulam M. Bhatti, Irfan Ahmed, Waqas Ahmed, Muhammad Mahtab Alam, Rizwan Ahmad, Hedi Khammari |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
Throughput 02 engineering and technology lcsh:Chemical technology LTE-A Biochemistry Article Analytical Chemistry law.invention 0203 mechanical engineering Relay law Telecommunications link 0202 electrical engineering electronic engineering information engineering lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation Throughput (business) energy efficiency resource block allocation water filling algorithm Artificial neural network proportional rate constraint Water filling algorithm 020206 networking & telecommunications 020302 automobile design & engineering Atomic and Molecular Physics and Optics LTE Advanced machine learning Computer engineering Unsupervised learning bisection based optimal power allocation Efficient energy use |
| Zdroj: | Sensors Volume 19 Issue 16 Sensors (Basel, Switzerland) Sensors, Vol 19, Iss 16, p 3461 (2019) |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s19163461 |
| Popis: | In recent years, Energy Efficiency (EE) has become a critical design metric for cellular systems. In order to achieve EE, a fine balance between throughput and fairness must also be ensured. To this end, in this paper we have presented various resource block (RB) allocation schemes in relay-assisted Long Term Evolution-Advanced (LTE-A) networks. Driven by equal power and Bisection-based Power Allocation (BOPA) algorithm, the Maximum Throughput (MT) and an alternating MT and proportional fairness (PF)-based SAMM (abbreviated with Authors’ names) RB allocation scheme is presented for a single relay. In the case of multiple relays, the dependency of RB and power allocation on relay deployment and users’ association is first addressed through a k-mean clustering approach. Secondly, to reduce the computational cost of RB and power allocation, a two-step neural network (NN) process (SAMM NN) is presented that uses SAMM-based unsupervised learning for RB allocation and BOPA-based supervised learning for power allocation. The results for all the schemes are compared in terms of EE and user throughput. For a single relay, SAMM BOPA offers the best EE, whereas SAMM equal power provides the best fairness. In the case of multiple relays, the results indicate SAMM NN achieves better EE compared to SAMM equal power and BOPA, and it also achieves better throughput fairness compared to MT equal power and MT BOPA. |
| Databáze: | OpenAIRE |
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