Adaptive Power Saving Algorithm for Mobile Subscriber Station in 802.16e

2007 ◽  
Author(s):  
Omanand Jha Vatsa ◽  
Mayank Raj ◽  
Ritesh Kumar K ◽  
Deepak Panigrahy ◽  
Debabrata Das
Keyword(s):  
Power Saving ◽  
Subscriber Station ◽  
Adaptive Power ◽  
Saving Algorithm ◽  
Mobile Subscriber

scholarly journals An Adaptive Energy Saving Algorithm for an RSSI-Based Localization System in Mobile Radio Sensors

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3987
Author(s):  
Adam Olesiński ◽  
Zbigniew Piotrowski
Keyword(s):  
Radio Signal ◽  
Operating Time ◽  
Power Saving ◽  
Signal Propagation ◽  
Localization System ◽  
Received Power ◽  
Power Saving Mode ◽  
Power Control Algorithm ◽  
Adaptive Power ◽  
Saving Mode

In localization systems based on the emission of reference radio signals, an important issue related to the reliability of sensor operation is the problem of operating time and power of the emitted reference radio signal. There are many localization methods that have proven useful in practice and that use a reference radio signal for this purpose. In the issue of determining the location of radio emitters, various radio signal propagation models are used to determine the effective range and distance of the sensor-receiver from the radio emitter. This paper presents an adaptive power control algorithm for a transmitter, as a reference emitter, operating in power-saving mode. An important advantage of the presented solution is the adjustment of the localization system accuracy at the assumed level of energy radiated by radio emitters based on the RSSI signal received power estimation.

Adaptive Power-Saving Mechanism for VoIP Over WiMAX Based on Artificial Neural Network

2019 ◽  
pp. 158-177
Author(s):  
Tamer Emara
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Saving ◽  
Ieee 802.16 ◽  
Feed Forward Neural Network ◽  
Artificial Neural ◽  
Adaptive Power ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

The IEEE 802.16 system offers power-saving class type II as a power-saving algorithm for real-time services such as voice over internet protocol (VoIP) service. However, it doesn't take into account the silent periods of VoIP conversation. This chapter proposes a power conservation algorithm based on artificial neural network (ANN-VPSM) that can be applied to VoIP service over WiMAX systems. Artificial intelligent model using feed forward neural network with a single hidden layer has been developed to predict the mutual silent period that used to determine the sleep period for power saving class mode in IEEE 802.16. From the implication of the findings, ANN-VPSM reduces the power consumption during VoIP calls with respect to the quality of services (QoS). Experimental results depict the significant advantages of ANN-VPSM in terms of power saving and quality-of-service (QoS). It shows the power consumed in the mobile station can be reduced up to 3.7% with respect to VoIP quality.

scholarly journals Adaptive Power Saving Protocol Based on Traffic Estimation for Wi-Fi Direct Networks

2015 ◽  
Vol 10 (4) ◽  
pp. 207-212
Author(s):  
Hongseok Yoo ◽  
Yang-Woo Park ◽  
Chae-Soo Lee ◽  
Tae-Jin Yun
Keyword(s):  
Power Saving ◽  
Traffic Estimation ◽  
Adaptive Power ◽  
Direct Networks

Load Based Sleep Scheduling with Reduced State Transitions for IEEE 802.16e Networks

2018 ◽  
Vol 14 (2) ◽  
pp. 71-85
Author(s):  
Gopalan M. ◽  
Marthandan G. ◽  
Eswaran C.
Keyword(s):  
High Speed ◽  
Power Saving ◽  
Window Size ◽  
Base Station ◽  
State Transitions ◽  
Sleep Scheduling ◽  
Ieee 802.16E ◽  
Network Load ◽  
Mobile Subscriber ◽  
Reduced State

IEEE 802.16e is extensively used these days for both data and voice communications as it makes available high-speed wireless access. However, in view of the fact that mobile subscriber stations are powered by a limited capacity battery, power saving mechanism is necessary to improve network performance. In this article, the authors introduce a load based sleep scheduling mechanism with reduced state transitions for IEEE 802.16e Networks. The mechanism encompasses two phases, load-based sleep scheduling and a reduced state transition (RST) algorithm. Considering network load information, the Base Station (BS) assigns a sleep window size for each mobile subscriber station (MS). The proposed RST algorithm reduces the number of state transitions between sleep and active mode by watchfully combining any two adjacent active bands. Further, the technique considers network load and delay as QoS metrics. Through simulation results, the authors prove the proficiency of their mechanism.

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