A Review of Heterogeneous Resource Allocation in LTE-A based Femtocell Networks with CQI Feedback

Rajesh, A.; Achar, Rakesh

doi:10.17485/ijst/2016/v9i36/102109

Cited by 3 publications

(2 citation statements)

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“…In the first step, based on the determined SINR threshold from (13), we need to adapt transmit power of BSs by finding out an adequate threshold value (T SINR ) which can keep P C above the minimum requirement of network operators (Fig.4, step a) for enhancing the network throughput while the coverage in the whole network is preserved. In the second step, the appropriate modulation and coding scheme (MCS) can be obtained based on the channel quality indicator (CQI) value that can be obtained by BSs from their associated UEs (Fig.4, step b) [23].…”

Section: Power Adaptation (Pa) Algorithmmentioning

confidence: 99%

Density-Aware, Energy- and Spectrum-Efficient Small Cell Scheduling

Mollahasani

Onur

2019

IEEE Access

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Future mobile networks have to be densified by employing small cells to handle the upsurge in traffic load. Although the amount of energy each small cell consumes is low, the total energy consumption of a large-scale network may be enormous. To enhance energy efficiency, we have to adapt the number of active base stations to the offered traffic load. Deactivating base stations may cause coverage holes, degrade the quality of service and throughput while redundant base stations waste energy. That is why we have to adapt the network to an effective density. In this paper, we show that achieving an optimal solution for adapting the density of base stations to the demand is NP-hard. We propose a solution that consists of two heuristic algorithms: a base station density adaptation algorithm and a cell-zooming algorithm that determines which base stations must be kept active and adapts transmit power of base stations to enhance throughput, energy, and spectral efficiency. We employ a multi-access edge cloud for taking a snapshot of the network state in nearly real time with a wider perspective and for collecting network state over a large area. We show that the proposed algorithm conserves energy up to 12% while the spectral efficiency and network throughput can be enhanced up to 30% and 26% in comparison with recent works, respectively. INDEX TERMS 5G mobile networks, densification, density-aware networking, energy-efficiency, green networks, multi-access edge cloud (MEC), self-organizing networks.

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Section: Power Adaptation (Pa) Algorithmmentioning

confidence: 99%

Density-Aware, Energy- and Spectrum-Efficient Small Cell Scheduling

Mollahasani

Onur

2019

IEEE Access

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“…However, at high velocities, OLSM-RR is the preferred combination. Resource allocation study in Femto cell for LTE-A networks is reviewed in [7]. A resource allocation strategy is also suggested.…”

Section: Introductionmentioning

confidence: 99%

Modified Proportional Fair Scheduling Algorithm for Heterogeneous LTE-A Networks

Elshennawy

2020

Int. J. Interact. Mob. Technol.

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Growing of time-sensitive applications such as streaming multimedia, voice over IP and online gaming required strongly support from mobile communication technology. So, the persistent need for wireless broadband technologies such as LTE-A is essential. LTE-A can achieve QoS in an efficient manner by using a reliable packet scheduling algorithm. It also supports good cell coverage by using heterogeneous capability. In this paper, modifications of proportional fair (PF) algorithm are proposed with different methods to compute the average throughput, which is the main and important parameter in the PF cost function. These methods are geometric mean, root mean square and arithmetic mean. Vienna simulator is used to study the performance of the proposed algorithms. A comparison between PF modifications and the most famous algorithms (the original PF and Best CQI algorithms) with various UE velocities is introduced. Average UE throughput, spectral efficiency, energy per bit, cell throughput and fairness are used as performance indicators. The results expose that QPF has best improved values for spectral efficiency, energy per bit and fairness by 8.4%, 14%, and 9.3%, respectively than original PF. However, Best CQI has a better value of average UE and cell throughput than all algorithms of 2% and 1.8% in low and medium UE velocity, respectively, but the best value of all types of throughput at high speed is gained by QPF. QPF and GMPF has the same performance in fairness with all UEs speeds.

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