Femtocell based green power consumption methods for mobile network

Mukherjee, Anwesha; Bhattacherjee, Srimoyee; Pal, Sucheta; De, Debashis

doi:10.1016/j.comnet.2012.09.007

Cited by 76 publications

(30 citation statements)

References 17 publications

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“…The average power using 16QAM and 64QAM is given in the Fig.17 and Fig.18 respectively. Power consumption analysis in femtocell based LTE network is discussed in [12]. It is observed from TABLE IV that, 16QAM provides the highest channel power of -21.04 dBm/2MHz among the techniques; QPSK obtains better average power -17.49 dBm.…”

Section: E Effects On Average Powermentioning

confidence: 92%

Comparative analysis of different modulation techniques of LTE network

Acharya

Kabiraj

2015

Proceedings of the 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT)

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This paper presents a comparative study on different modulation techniques to enhance application performances in the context of LTE. This work presents a close examination of 4G LTE standard to recognize better the performance characteristics. Long Term Evolution (LTE) is used in 4G mobile telecommunication for high speed wireless communication data transfer in mobile phones and data terminals. Modulation techniques such as QPSK, 16QAM and 64QAM are discussed based on the parameters like channel power, occupied bandwidth, adjacent channel power, average power based on statistical complementary cumulative distribution function, and signal to noise ratio. From the results it is observed that QPSK provides better average power, 16QAM provides better channel power but 64QAM gives lowest noise power, adjacent channel power, lowest occupied bandwidth and maximum channel capacity.

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Section: E Effects On Average Powermentioning

confidence: 92%

Comparative analysis of different modulation techniques of LTE network

Acharya

Kabiraj

2015

Proceedings of the 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT)

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“…Thus if we consider a set Q as A B C D E and the current patient health state is y n where y n ∈ Q, then after t time period the distribution is given by, y n+t = y n+t−1 S = y n+t−2 S S = · · · = y n+t−t S S t−1 = y n S t (4) In this way the patient health status after a time period can be predicted based on the transition matrix. To predict the probability of going to state D or E i.e., cured or dead from the begin state A i.e., affected after t time period can be determined based on the following equation:…”

Section: Health Cycle Model Of a Patientmentioning

confidence: 99%

“…due to which the traditional healthcare system suffers. In our previous work we have proposed femtocell [4][5][6][7][8][9] and MCC 10 11 based a mobile health care system, FMCCH. 8 9 In this paper we have used FMCCH to extend our previous work to develop a home-health care scheme with health state transition model of a patient and for epidemic trends monitoring.…”

Section: Introductionmentioning

confidence: 99%

Femto-Cloud Based Secure and Economic Distributed Diagnosis and Home Health Care System

De¹,

Mukherjee²

2015

J Med Imaging Hlth Inform

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This paper has proposed femtocell and mobile cloud computing based a secure, multi-parameter based economic home health care scheme. In this system health data of a user is captured by body sensor network and then sent to the user's mobile device which is registered under a femtocell. Femtocell is a low power home node base station providing good coverage at indoor region. Using a database maintained at femtocell, captured health data are verified and if abnormality is detected, the data are sent to the cloud through the femtocell for storage. Inside the cloud the data are checked by the health care centre which takes proper action for curing the patient based on the health parameters' values. Analyzing the health data inside the cloud using Markov chain model, patient health state transition model is generated and the probability of cure is determined. Analyzing the health status of a number of patients affected by an infectious disease in a particular region, epidemic trends can be detected and then to aware people alert messages are sent over social networking sites. Simulation results present that using data filtering at femtocell as in the proposed approach the cost wastage due to unnecessary data maintenance in cloud can be reduced to 5% and trustworthiness between the parties involved in transactions can also be increased.

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“…This approach does not focus on network power consumption; instead it focuses on quality of service. In [22] the authors propose an analytical model to characterize the power consumption of macrocell, microcell, picocell and femtocell based networks. In [23] the authors investigate solutions for reducing the number and size of active macrocell to reduce the network energy consumption.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Selective Activation in Femtocell Networks

Lin

Silvestri

Bartolini

et al. 2015

2015 IEEE 12th International Conference on Mobile Ad Hoc and Sensor Systems

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Provisioning the capacity of wireless networks is difficult when peak load is significantly higher than average load, for example, in public spaces like airports or train stations. Service providers can use femtocells and small cells to increase local capacity, but deploying enough femtocells to serve peak loads requires a large number of femtocells that will remain idle most of the time, which wastes a significant amount of power. To reduce the energy consumption of over-provisioned femtocell networks, we formulate a femtocell selective activation problem, which we formalize as an integer nonlinear optimization problem. Then we introduce GREENFEMTO, a distributed femtocell selective activation algorithm that deactivates idle femtocells to save power and activates them on-the-fly as the number of users increases. We prove that GREENFEMTO converges to a locally Pareto optimal solution and demonstrate its performance using extensive simulations of an LTE wireless system. Overall, we find that GREENFEMTO requires up to 55% fewer femtocells to serve a given user load, relative to an existing femtocell power-saving procedure, and comes within 15% of a globally optimal solution.

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Femtocell based green power consumption methods for mobile network

Cited by 76 publications

References 17 publications

Comparative analysis of different modulation techniques of LTE network

Comparative analysis of different modulation techniques of LTE network

Femto-Cloud Based Secure and Economic Distributed Diagnosis and Home Health Care System

Energy-Efficient Selective Activation in Femtocell Networks

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