scholarly journals Base Station Sleeping Mechanism for Reduced Delay Using Traffic Load Prediction

2017 ◽  
Keyword(s):  
Base Station ◽  
Traffic Load ◽  
Load Prediction ◽  
Sleeping Mechanism

scholarly journals Online Supervised Learning for Traffic Load Prediction in Framed-ALOHA Networks

2019 ◽  
Vol 23 (10) ◽  
pp. 1778-1782 ◽  
Author(s):  
Nan Jiang ◽  
Yansha Deng ◽  
Osvaldo Simeone ◽  
Arumugam Nallanathan
Keyword(s):  
Supervised Learning ◽  
Traffic Load ◽  
Load Prediction

Energy Efficient Schemes for Base Station Management in 4G Broadband Systems

2014 ◽  
pp. 100-120 ◽  
Author(s):  
Alexandra Bousia ◽  
Elli Kartsakli ◽  
Angelos Antonopoulos ◽  
Luis Alonso ◽  
Christos Verikoukis
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Energy Savings ◽  
Base Station ◽  
Base Stations ◽  
Traffic Load ◽  
Network Resources ◽  
Load Variations ◽  
Mobile Cellular ◽  
Main Component

Reducing the energy consumption in wireless networks has become a significant challenge, not only because of its great impact on the global energy crisis, but also because it represents a noteworthy cost for telecommunication operators. The Base Stations (BSs), constituting the main component of wireless infrastructure and the major contributor to the energy consumption of mobile cellular networks, are usually designed and planned to serve their customers during peak times. Therefore, they are more than sufficient when the traffic load is low. In this chapter, the authors propose a number of BSs switching off algorithms as an energy efficient solution to the problem of redundancy of network resources. They demonstrate via analysis and by means of simulations that one can achieve reduction in energy consumption when one switches off the unnecessary BSs. In particular, the authors evaluate the energy that can be saved by progressively turning off BSs during the periods when traffic decreases depending on the traffic load variations and the distance between the BS and their associated User Equipments (UEs). In addition, the authors show how to optimize the energy savings of the network by calculating the most energy-efficient combination of switched off and active BSs.

Extension of Modified Joint Uplink and Downlink Scheduling algorithm for LTE-A in Femtocell Networks

2019 ◽  
Vol 09 ◽  
Author(s):  
Ravi Gatti ◽  
Shiva Shankar
Keyword(s):  
Real Time ◽  
Spectral Efficiency ◽  
Scheduling Algorithm ◽  
Base Station ◽  
Traffic Load ◽  
Mobile Users ◽  
Small Scale ◽  
Central Research ◽  
Macro Cell ◽  
Indoor Coverage

Aim: The 5G LTE-Advanced (LTE-A) intended to provide increased peak data rates for the mobile users with the use of Carrier Aggregation (CA) technology. Due to need of un-interrupted bi-directional communication between the eNodeB and User Equipment (UE) in LTE-A, Joint Scheduling Algorithm is considered as central research topic. Objective: A modified joint Uplink/ Downlink (UL/DL) Scheduling algorithm to meet on demands service request from the UEs is proposed in this paper. Methods: CA is used for calculate the weight factors for the bandwidth allocation among the mobile users based on the QoS Class Identifier (QCI). However due the huge amount of data flow in the indoor coverage yield introduction of the small cell called femtocells. Femtocells are randomly deployed in macro cell area in order to improve indoor coverage as well capacity enhancement. Result: Mixed types of traffic are considered ranging from real time to non real time flows and quality of service is evaluated in term of throughput, packet loss ratio, fairness index and spectral efficiency. The proposed modified joint user scheduling algorithm results better in delay among the end users due the reduction in the traffic load of the macro cell base station. Conclusion: Simulation results shows that, the proposed methodology suits best for the small scale network architecture with increased spectral efficiency and throughput among the UEs.

EA-MAC: A QoS Aware Emergency Adaptive MAC Protocol for Intelligent Scheduling of Packets in Smart Emergency Monitoring Applications

2020 ◽  
Vol 29 (13) ◽  
pp. 2050205
Author(s):  
Asokan Jayaram ◽  
Sanjoy Deb
Keyword(s):  
Access Control ◽  
Medium Access Control ◽  
Mac Protocol ◽  
Cluster Head ◽  
Base Station ◽  
Traffic Load ◽  
Monitoring Systems ◽  
Medium Access ◽  
Intelligent Scheduling ◽  
Emergency Monitoring

The evolution of the wireless sensor network (WSN) in recent years has reached its greatest heights and applications are increasing day by day, one such application is Smart Emergency Monitoring Systems (SMESs) which is in vision of implementation in every urban and rural areas. The implementation of WSN architecture in the Smart Monitoring Systems needs an intelligent scheduling mechanism that efficiently handles the high traffic load as well as the emergency traffic load without sacrificing the energy efficiency of the network. However, the traditional scheduling algorithms such as First Come First Served (FCFS), Round Robin, and Shortest Job First (SJF) cannot meet the requirements of high traffic load in SMESs. To address these shortcomings, this paper presents Emergency Adaptive Medium Access Control protocol (EA-MAC), a fuzzy priority scheduling based Quality-of-service (QoS)-aware medium access control (MAC) protocol for hierarchical WSNs. EA-MAC protocol employs the most powerful fuzzy logics to schedule the sensor nodes with both normal and emergency traffic load without any data congestion, and packet loss and maintaining the better QoS which is considered to be more important in SMESs applications. Moreover, a novel rank-based clustering mechanism in EA-MAC protocol prolongs the network lifetime by minimizing the distance between the Cluster Head (CH) and the Base Station (BS). Both analytical and simulation models demonstrate the superiority of the EA-MAC protocol in terms of energy consumption, transmission delay and data throughput when compared with the existing Time Division Multiple Access (TDMA) based MAC protocols such as LEACH protocol and Cluster Head Election Mechanism-Based On Fuzzy Logic (CHEF) protocol.

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