Distributed RRM for 5G Multi-RAT Multiconnectivity Networks

Monteiro, Victor Farias; Sousa, Diego Aguiar; Maciel, Tarcísio F.; Cavalcanti, Francisco R. P.; Silva, Carlos F. M. e; Rodrigues, Emanuel B.

doi:10.1109/jsyst.2018.2838335

Cited by 33 publications

(24 citation statements)

References 18 publications

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“…In particular, in the early stage of 5G deployment, interworking with LTE-A based on dual connectivity will be necessary to boost the deployment and has been proposed by 3GPP as a 5G operational requirement [2,36]. Extending the dual connectivity to multi-connectivity in 5G will become an important technique and require further study [37].…”

Section: Discussionmentioning

confidence: 99%

QoS-Guaranteed Radio Resource Management in LTE-A Co-Channel Networks with Dual Connectivity

2019

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Dual connectivity (DC) was first proposed in 3GPP Release 12 which allows one piece of user equipment (UE) to connect to two base stations in heterogeneous networks (HetNet) at the same time, to increase the flexibility of resource utilization. DC has been further extended to multiple connectivity in 5G New Radio (NR). On the other hand, different UE tends to have different bandwidth requirements. Thus, in DC, one of the challenging issues is how to integrate resources from two base stations to enhance the quality of service (QoS) as well as the data transfer rate of each UE. In this paper, we proposed novel resource management mechanisms to improve the QoS of UE in the co-channel dual connectivity network. In terms of resource allocation, we designed the (MTS) which, in principle, allocates a resource block to the UE with the best channel quality while considering the issues of intercell resource allocation and the QoS requirement of each UE. In order to balance the load of different cells, we designed a novel cell selection scheme based on the HetNet Congestion Indicator (HCI) which considers not only the signal quality of UE but also the remaining resources of each base station. To improve the QoS of cell edge UE, cell range expansion (CRE) and the Almost Blank Subframe (ABS) were proposed in 3GPP. In this paper, based on Q-learning, we designed an adaptive mechanism which dynamically adjusts the ABS ratio according to the network condition to improve resource utilization. Our simulation results showed that our MTS scheduler was able to achieve a 31.44% higher data rate than the Proportional Fairness Scheduler; our HCI cell selection scheme yielded a 2.98% higher data rate than the signal-to-interference plus noise ratio (SINR) cell selection scheme; the QoS satisfaction rate of our Q-learning dynamic ABS scheme was 4.06% higher than that of the Static ABS scheme. Finally, for the cell edge users who often suffer poor data transfer rate, by integrating the mechanisms of DC, CRE, and ABS, our experimental results showed that the QoS satisfaction ratio of cell edge UEs could be improved by 10.76% as compared to the single connectivity and no ABS situation.

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Section: Discussionmentioning

confidence: 99%

QoS-Guaranteed Radio Resource Management in LTE-A Co-Channel Networks with Dual Connectivity

2019

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“…Authors in [23][24][25][26][27] recommended a technique where dual connectivity was made with 4G and 5G simultaneously in order to achieve the desired goals. However, this technique concurrently engages dual radio circuitry.…”

Section: Literature Reviewmentioning

confidence: 99%

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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“…We formulate a utility-based optimization problem to maximize the total user utility derived from the network, i.e., the total user satisfaction, where the concept of user satisfaction relates to meeting the users' quality of service (QoS) demands [17], [16]. Our optimization problem is:…”

Section: Problem Formulationmentioning

confidence: 99%

“…The first and last set of constraints state that the RBs of each BS are discrete and that the same RB of a given BS cannot be shared by two or more users in the same TTI. The second set of constraints controls the number of connections of each UD, while the third set of constraints controls which RBs can be used by each BS from each RAT [16].…”

Section: Problem Formulationmentioning

confidence: 99%

“…A joint FCA and RRA scheme for throughput maximization was proposed in [14], whereas the authors in [15] studied a sub-optimum solution for a utility maximization optimization problem focusing on the FCA and UBA. In [16], a mechanism for UBA and RRA was proposed for maximizing the minimum user rate. However, since it is not possible to avoid the deployment of all three functionalities in NSA 5G networks with DC, those works did not propose suitable frameworks for such scenarios.…”

Section: Introductionmentioning

confidence: 99%

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Unified Scheduling Framework for Dual Connectivity in Multi-RAT NSA 5G Networks

Antonioli

Sousa

Rodrigues

et al. 2020

JCIS

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Supporting the dual connectivity technology in non-stand-alone 5G scenarios involves three main functionalities: a user device (UD)-base station (BS) association (UBA) mechanism, a flow control algorithm (FCA) to determine the data split transmitted by each BS and radio resource management (RRM) algorithms. However, previous works only focused on the design of at most two of these functionalities. In this letter, we go beyond and design a unified scheduling framework including the three aforementioned functionalities. The proposed framework targets the user satisfaction maximization and runs in a completely decentralized fashion, where the UBA algorithm runs independently at each UD, the FCA is executed at the master LTE BS and the RRM is performed individually by each BS. Simulations show the gains of the proposed solution compared to existing solution that addressed at most two functionalities.

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Distributed RRM for 5G Multi-RAT Multiconnectivity Networks

Cited by 33 publications

References 18 publications

QoS-Guaranteed Radio Resource Management in LTE-A Co-Channel Networks with Dual Connectivity

QoS-Guaranteed Radio Resource Management in LTE-A Co-Channel Networks with Dual Connectivity

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

Unified Scheduling Framework for Dual Connectivity in Multi-RAT NSA 5G Networks

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