Mobility management in HetNets consider on QOS and improve Throughput

Niasar, Fereshteh Atri; Aghdam, Mohammad Jalilvand; Nabipour, Mohammad; Momen, Amir Reza

doi:10.1109/ccwc51732.2021.9375832

Cited by 1 publication

(1 citation statement)

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“…A context-aware mobility management (MM) approach using RL is proposed for small-cellular networks in Niasar et al [58]; the BSs learn their long-term traffic loads and the best way to increase their cell range collectively, and they schedule their UDs in accordance with the speed and rate of historical data which is transferred in bits while also taking into account QoS for the users.…”

Section: Intelligent Techniques In Wireless Networkmentioning

confidence: 99%

An intelligent load balancing algorithm for 5G‐satellite networks

Bello,

Pillai,

Sadiq

2024

Satell Commun Network

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SummaryCellular networks are projected to deal with an immense rise in data traffic, as well as an enormous and diverse device, plus advanced use cases, in the nearest future; hence, future 5G networks are being developed to consist of not only 5G but also different radio access technologies (RATs) integrated. In addition to 5G, the user's device (UD) will be able to connect to the network via LTE, WiMAX, WiFi, Satellite and other technologies. On the other hand, Satellite has been suggested as a preferred network to support 5G use cases. However, achieving load balancing is essential to guarantee an equal amount of traffic distributed between different RATs in a heterogeneous wireless network; this would enable optimal utilisation of the radio resources and lower the likelihood of call blocking/dropping. This study presented an artificial intelligent‐based application in heterogeneous wireless networks and proposed an enhanced particle optimisation (EPSO) algorithm to solve the load balancing problem in 5G‐Satellite networks. The algorithm uses a call admission control strategy to admit users into the network to ensure that users are evenly distributed on the network. The proposed algorithm was compared with the Artificial Bee Colony and Simulated Annealing algorithm using three performance metrics: throughput, call blocking and fairness. Finally, based on the experimental findings, results outcomes were analysed and discussed.

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Section: Intelligent Techniques In Wireless Networkmentioning

confidence: 99%