LTE-U or mmWave Network: A Network Selection Scheme

Xu, Xiaoxia; Chen, Qimei; Jiang, Hao

doi:10.1109/wcsp.2018.8555581

Cited by 2 publications

(5 citation statements)

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“…An effective iterative algorithm has been proposed in [45], in which the inner loop finds the optimal RAT selection with fixed bandwidth allocation through an exhaustive search and outer loops find the optimal bandwidth allocation with known RAT selection with the simplex method. Besides multiple radios operate on the same unlicensed spectrum, the triple-band heterogeneous RAT selection scheme in [46] addresses an LTE licensed network, an LTE-U network, and a millimeter-wave (mmWave) network. Different from the multi-homing technology, which enables simultaneous transmission, the proposed scheme is designed for improving communication experience under different circumstances by accessing only one RAT.…”

Section: A Rat Selectionmentioning

confidence: 99%

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Resource Management in LTE-U Systems: Past, Present, and Future

Chen

et al. 2020

IEEE Open J. Veh. Technol.

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With the tremendous growth in mobile data traffic, wireless cellular networks are facing a rigorous challenge to increase network capacity. Despite that many advanced technologies are used, the shortage of spectrum resource is still the main bottleneck for capacity enhancement. To address the challenge, the cellular networks have been motivated to seek for more fruitful radio spectra. Amongst many others, the unlicensed 5 GHz spectrum is a promising candidate due to its low channel attenuation, large available bandwidth, and easy to utilize. Therefore, the innovative technology of long-term evolution (LTE) using the unlicensed spectrum, known as LTE-Unlicensed (LTE-U), has been widely investigated as a promising means to increase the data rate of cellular networks. The unique characteristics of the unlicensed spectrum bring new challenges to resource management in the LTE-U system. During the last few years, there have been a lot of resource management designs for the newly-born but vigorous LTE-U technologies. This paper provides a comprehensive overview of the state-of-the-art resource management scenarios in LTE-U systems, including single small cell base station (SBS), multiple SBSs, device-to-device (D2D) networks, vehicular networks, and unmanned aerial vehicle (UAV) systems. The future research issues of resource management in LTE-U for 5G are also outlined.

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Section: A Rat Selectionmentioning

confidence: 99%

“…The first one is the coordination of RAT selection. As stated in [46], users with different data traffic requirements prefer to be associated with different types of cells and APs. When LTE-U SBSs/mmWave APs are not enough, there must be users who cannot access to their first choice.…”

Section: ) User Associationmentioning

confidence: 99%

Resource Management in LTE-U Systems: Past, Present, and Future

Chen

et al. 2020

IEEE Open J. Veh. Technol.

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“…PL) is a function of free space path loss (FSPL), path loss exponent (k), distance (d), nuation (ɣ), and a gaussian random variable with zero mean (Δ) and standard and Δ are given by Equations (2) and 3, respectively. dB = 20 log ( 4 × (3.14) × ( ) × 10 3 × 10 ),…”

Section: Path Loss Modelsmentioning

confidence: 99%

“…where path loss (PL) is a function of free space path loss (FSPL), path loss exponent (k), distan atmospheric attenuation (ɣ), and a gaussian random variable with zero mean (Δ) and sta deviation (σ). FSPL (in dB) and Δ are given by Equations (2) and 3 where is the frequency in GHz, and is the attenuation constant. With reference to models, the following models are discussed here:…”

Section: Path Loss Modelsmentioning

confidence: 99%

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Applications of Extreme Gradient Boosting for Intelligent Handovers from 4G To 5G (mm Waves) Technology with Partial Radio Contact

2020

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In a network topology, where 5G (mm Waves) have better coverage footprint compared to 4G (LTE or LTE-A) technology, mobile devices would generally be handed over from 4G to 5G. In this work, a supervised intelligent prediction technique for improved handover success rate (HSR) from 4G to 5G technology is proposed. The technique is applicable for base stations enabled with sub-6-GHz and mm-wave bands. This technique is novel since it can predict HSR even before switching to 5G radio circuitry or initiating its measurement gap for acquisition of mm-wave reference signal received power (RSRP) unlike conventional algorithms. Thus, preempting all handovers which are likely to fail will provide improvements in latency, delay, and handover success rate, as well as decrease call drops. Therefore, this research work answers previous research shortcomings and can unleash applications of supervised intelligent algorithms for predicting the HSR from 4G to 5G. The proposed algorithm is validated by showing improvements obtained through simulation results performed using Python-based framework. The proposed algorithm is tested for reliability with increasing parameters such as the intensity number of UEs and simulation time. Improvements in standard handover algorithm are also proposed.

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LTE-U or mmWave Network: A Network Selection Scheme

Cited by 2 publications

References 14 publications

Resource Management in LTE-U Systems: Past, Present, and Future

Resource Management in LTE-U Systems: Past, Present, and Future

Applications of Extreme Gradient Boosting for Intelligent Handovers from 4G To 5G (mm Waves) Technology with Partial Radio Contact

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