Minimization of Energy and Service Latency Computation Offloading using Neural Network in 5G NOMA System

The future Internet of Things (IoT) era is anticipated to support computation-intensive and time-critical applications using edge computing for mobile (MEC), which is regarded as promising technique. However, the transmitting uplink performance will be highly impacted by the hostile wireless channel...

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Vydáno v:International Journal of Electronics and Telecommunications Ročník 69; číslo 4; s. 661 - 667
Hlavní autoři: Suprith, P.G, Riyaz Ahmed, Mohammed
Médium: Journal Article
Jazyk:angličtina
Vydáno: Warsaw Polish Academy of Sciences 01.01.2023
Témata:
5G mobile communication
Algorithms
Cloud computing
Computation offloading
deep q network algorithm
Edge computing
Electric power distribution
Energy consumption
Internet of Things
latency optimized
Least squares
Mobile computing
mobile edge computing
Network latency
Neural networks
Optimization
Power management
ISSN:2081-8491, 2300-1933
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Shrnutí:The future Internet of Things (IoT) era is anticipated to support computation-intensive and time-critical applications using edge computing for mobile (MEC), which is regarded as promising technique. However, the transmitting uplink performance will be highly impacted by the hostile wireless channel, the low bandwidth, and the low transmission power of IoT devices. Using edge computing for mobile (MEC) to offload tasks becomes a crucial technology to reduce service latency for computation-intensive applications and reduce the computational workloads of mobile devices. Under the restrictions of computation latency and cloud computing capacity, our goal is to reduce the overall energy consumption of all users, including transmission energy and local computation energy. In this article, the Deep Q Network Algorithm (DQNA) to deal with the data rates with respect to the user base in different time slots of 5G NOMA network. The DQNA is optimized by considering more number of cell structures like 2, 4, 6 and 8. Therefore, the DQNA provides the optimal distribution of power among all 3 users in the 5G network, which gives the increased data rates. The existing various power distribution algorithms like frequent pattern (FP), weighted least squares mean error weighted least squares mean error (WLSME), and Random Power and Maximal Power allocation are used to justify the proposed DQNA technique. The proposed technique which gives 81.6% more the data rates when increased the cell structure to 8. Thus 25% more in comparison to other algorithms like FP, WLSME Random Power and Maximal Power allocation.
Bibliografie:ObjectType-Article-1
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ISSN:2081-8491
2300-1933
DOI:10.24425/ijet.2023.147685