Resource Block Utilization Research Articles

QoS-Driven Slicing Management for Vehicular Communications

Network slicing is introduced for elastically instantiating logical network infrastructure isolation to support different application types with diversified quality of service (QoS) class indicators. In particular, vehicular communications are a trending area that consists of massive mission-critical applications in the range of safety-critical, intelligent transport systems, and on-board infotainment. Slicing management can be achieved if the network infrastructure has computing sufficiency, a dynamic control policy, elastic resource virtualization, and cross-tier orchestration. To support the functionality of slicing management, incorporating core network infrastructure with deep learning and reinforcement learning has become a hot topic for researchers and practitioners in analyzing vehicular traffic/resource patterns before orchestrating the steering policies. In this paper, we propose QoS-driven management by considering (edge) resource block utilization, scheduling, and slice instantiation in a three-tier resource placement, namely, small base stations/access points, macro base stations, and core networks. The proposed scheme integrates recurrent neural networks to trigger hidden states of resource availability and predict the output of QoS. The intelligent agent and slice controller, namely, RDQ3N, gathers the resource states from three-tier observations and optimizes the action on allocation and scheduling algorithms. Experiments are conducted on both physical and virtual representational vehicle-to-everything (V2X) environments; furthermore, service requests are set to massive thresholds for rendering V2X congestion flow entries.

Dynamic MBSFN Beam Area Formation in 6G Multibeam Non-Terrestrial Networks

Providing innovative services and ensuring ubiquitous, ultrahigh-density, extreme-data-rate, and very low latency communications are essential for future sixth-generation systems, where the importance of non-terrestrial networks (NTNs) will incredibly rise. However, the management of both the capacity and the radio spectrum remains one of the main challenges, when a huge number of devices require access to dissimilar broadcast services at the same time. To cope with these issues, traditional single-beam satellite systems can be replaced with very high throughput satellite, where multibeam transmissions increase the capacity and improve the spectrum utilization while limiting interbeam interference. In this article, we propose a dynamic multicast/broadcast single-frequency network (MBSFN) beam area formation (D-MBAF) algorithm that dynamically groups beams into dedicated MBSFN beam areas (MBAs) to increase the aggregate data rate (ADR) of the multibeam NTN system and deliver a given video content to all the interested NTN terminals. D-MBAF leverages multicast subgrouping by clustering the NTN terminals into different MBAs that are simultaneously served with different data rates. Furthermore, the D-MBAF algorithm delivers the video content in different flows: the base layer with lower resolution and a set of enhancement layers with higher resolutions. Radio resources are efficiently allocated to avoid interference among the beams belonging to different MBAs. A simulation campaign is carried out under different scenarios to assess the effectiveness of the proposed D-MBAF algorithm in terms of mean throughput, ADR, resource block utilization, and number of sent layers. Obtained results confirm that the D-MBAF algorithm outperforms the single-frequency multibeam transmission and the multilayer video delivery schemes used as benchmarks.

Enhanced performance of long-term evolution small-cell networks using improved mobility algorithms

With an ever-increasing number of user equipment (UE) and bandwidth demands of new applications, the deployment of dense heterogeneous cellular networks has been adopted in many network scenarios. The small cells are unable to unload the traffic due to the random UEs mobility and cells deployment. This degrades the network performance such as handover success, throughput, and load distribution. To address such a problem, we propose an enhanced mobility load-balancing algorithm for small cells. The conventional mobility load balancing (MLB) algorithms study only the fixed or adaptive thresholds of the network separately to consider the load balance process, while other MLB algorithms consider the neighboring cells of the network those experience unnecessary MLB actions. The proposed load balancing algorithms study overloaded cells and neighbors using the proposed efficiency parameter, B. To identify the overloaded cells, we propose B which compares between a pre-defined threshold and the network threshold to categorize medium and overloaded cells, based on B, the algorithm is triggered. Then, to control the shifted load to a target neighbor cell, we propose the Rescue factor, f. the f ensures the load of the target cell after handover is equal or less than Ɓ. The simulation results showed a lower standard deviation and higher throughput and physical resource block utilization than surveyed algorithms.

Implementation of platform for long‐term evolution cell perspective resource utilization analysis

As wireless communication continues to develop in limited frequency resource environments, it is becoming important to identify the state of spectrum utilization and predict the amount needed in future. It is essential to collect reliable information for data analysis. This paper introduces a platform that enables the gathering of the scheduling information of a long-term evolution (LTE) cellular system without connecting to the network. A typical LTE terminal can confirm its assigned resource information using the configuration parameters delivered from a network. However, our platform receives and captures only the LTE signal over the air and then enables the estimation of the data relevant to scheduling for all terminals within an LTE cell. After extracting the control channel signal without loss from all LTE subframes, it detects valid downlink control information using the proposed algorithm, which is based on the error vector magnitude of depatterned symbols. We verify the reliability of the developed platform by comparing it with real data from mobile phones and service operators. The average difference in resource block utilization is only 0.28%.

Probabilistic Analysis of Spectral Efficiency for LTE Based on PDCCH Measurement Data

As the number of mobile subscribers of long-term evolution (LTE) service increases, it becomes important for various parties, such as operators, policymakers, and researchers, to examine how well LTE cells are deployed in terms of actual performance. To this end, we focus on spectral efficiency (SE) with the cell edge user throughput (TP) and average cell SE which can be calculated from the spectrum data of an LTE physical downlink control channel decoding device—Rohde & Schwarz TSME. For these two aspects, crucial probabilities for the performance evaluations are defined using a joint distribution of resource block utilization and cell TP. We derive novel transformation methods that make them approximately follow a joint Gaussian distribution and use it to compute the probabilities. Furthermore, a deep neural network is adopted to analyze not only limited cases but also a wider range.

Adaptive Mobility Load Balancing Algorithm for LTE Small-Cell Networks

Small cells were introduced to support high data-rate services and for dense deployment. Owing to user equipment (UE) mobility and small-cell coverage, the load across a small-cell network recurrently becomes unbalanced. Such unbalanced loads result in performance degradation in throughput and handover success and can even cause radio link failure. In this paper, we propose a mobility load balancing algorithm for small-cell networks by adapting network load status and considering load estimation. To that end, the proposed algorithm adjusts handover parameters depending on the overloaded cells and adjacent cells. Resource usage depends on signal qualities and traffic demands of connected UEs in long-term evolution. Hence, we define a resource block-utilization ratio as a measurement of cell load and employ an adaptive threshold to determine overloaded cells, according to the network load situation. Moreover, to avoid performance oscillation, the impact of moving loads on the network is considered. Through system-level simulations, the performance of the proposed algorithm is evaluated in various environments. Simulation results show that the proposed algorithm provides a more balanced load across networks (i.e., smaller standard deviation across the cells) and higher network throughput than previous algorithms.

Review of Adaptive Cell Selection Techniques in LTE-Advanced Heterogeneous Networks

Poor cell selection is the main challenge in Picocell (PeNB) deployment in Long Term Evolution- (LTE-) Advanced heterogeneous networks (HetNets) because it results in load imbalance and intercell interference. A selection technique based on cell range extension (CRE) has been proposed for LTE-Advanced HetNets to extend the coverage of PeNBs for load balancing. However, poor CRE bias setting in cell selection inhibits the attainment of desired cell splitting gains. By contrast, a cell selection technique based on adaptive bias is a more effective solution to traffic load balancing in terms of increasing data rate compared with static bias-based approaches. This paper reviews the use of adaptive cell selection in LTE-Advanced HetNets by highlighting the importance of cell load estimation. The general performances of different techniques for adaptive CRE-based cell selection are compared. Results reveal that the adaptive CRE bias of the resource block utilization ratio (RBUR) technique exhibits the highest cell-edge throughput. Moreover, more accurate cell load estimation is obtained in the extended RBUR adaptive CRE bias technique through constant bit rate (CBR) traffic, which further improved load balancing as against the estimation based on the number of user equipment (UE). Finally, this paper presents suggestions for future research directions.

Assessing network energy consumption of mobile applications

Continuous growth in the energy consumption of mobile networks has become a major concern for mobile carriers. Since current mobile networks are dominated by mobile data traffic generated by over-the-top mobile applications, it is crucial for mobile carriers to understand how much network energy is used to deliver these applications. Here, we use real network and application measurements to comprehensively analyze the energy consumption of 12 common mobile applications by breaking down their total energy consumption into data and signaling energy components. The results provide insights into the different proportions of data and signaling energy (due to LTE signaling) for different mobile applications. They show that the energy consumption of a mobile application can vary at different base station cell sites due to different ratios of throughput to physical resource block utilization. We estimate the total monthly energy consumption of all 4G users of a major mobile carrier using conventional mobile services, such as voice and the short messaging service, and two over-the-top applications, i.e. a popular instant messaging application in China and an online video application. The results show that signaling energy consumption may become a major concern for mobile carriers, and that this issue will be exacerbated as the usage of over-the-top applications continues to grow. Energy assessment of mobile applications provides valuable information to enable mobile carriers to improve the energy efficiency of their networks. An energy assessment tool that enables real-time network and service energy monitoring will also assist in developing energy-efficient network policies for diverse applications with different energy consumption profiles. Furthermore, given our signaling energy consumption findings for over-the-top applications, there may be benefits for mobile operators to introduce a surcharge for signaling traffic to mitigate the high signaling energy consumption of some over-the-top mobile applications.

LTE-WLAN 이종 네트워크 환경에서 자원예약률 변화에 따른 통합 호 수락 제어의 성능분석

본 논문에서는 LTE와 WLAN이 존재하는 이종 네트워크 환경에서 네트워크 선택과 자원 예약 기반의 호 수락 제어 기법을 결합한 통합 호 수락 제어 기법을 제시하고 성능을 분석한다. 이를 위해 LTE 와 WLAN이 중첩된 네트워크 환경에서 단말이 네트워크를 선택할 때의 네트워크 결정률과 자원예약기법이 적용된 통합 호 수락 제어시스템을 제안하고 마코프 체인(Markov Chain) 모델링을 통해 제안된 시스템의 성능을 분석한다. 본 논문에서는 성능지표로서 LTE와 WLAN 각 네트워크 신규호 차단률(New call Blocking Probability), 핸드오프호의 절단률(Handoff call droppng Probability), RB 사용률(Resource Block utilization)을 사용한다. 본 논문의 분석결과로서 통합 호 수락 제어 기법을 적용한 시스템이 적용되지 않은 시스템에 비해 나은 성능을 보였고, 통함 호 수락 제어 기법을 적용할 경우에는 전체의 10%를 자원예약비율로 선택하는 것이 적합함을 보였다. 본 논문의 결과는 향후 LTE와 WLAN의 중첩된 네트워크 환경에서 자원예약기법이 적용된 통합 호 수락 제어 시스템 도입 시에 참고할 만한 자원예약률의 가이드라인을 제시할 수 있을 것이다. In this paper, we analyze and propose the Joint Call Admission Control(JCAC) scheme to combine network selection scheme and radio resource reservation based Call Admission Control(CAC) in LTE-WLAN heterogeneous networks. First, We propose the JCAC system that uses network decision rate to select a network for terminal and radio resource reservation scheme in overlaying LTE-WLAN network environment. And we analyze the performance of a proposed system using markov chain model. The performance is presented in terms of the new call blocking probability, handoff call dropping probability, and channel utilization of each network. As a performance result of the our research, the system using JCAC is better than the system using non-JCAC. We found a suitable resource reservation rate that is 10% in the system using JCAC. Our work may be useful as a guideline of resource reservation rate to introduce JCAC system using resource reservation scheme in overlaying LTE-WLAN network environment.