Cell-Edge User Offloading via Flying UAV in Non-Uniform Heterogeneous Cellular Networks

Wu, Huici; Wei, Zhiqing; Hou, Yanzhao; Zhang, Ning; Tao, Xiaofeng

doi:10.1109/twc.2020.2964656

Cited by 44 publications

(24 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, the coverage and spectral efficiency analysis for the mmWave-assisted aerial network are presented in [24]. In [6], the authors consider an aerial network for the application to provide coverage to cell-edge UEs in heterogeneous cellular networks. It can be concluded from the discussion that the mmWave promises unprecedented advantages over microwave RAT at A-BSs.…”

Section: A Related Workmentioning

confidence: 99%

“…could be helpful in load balancing of existing terrestrial cellular network. Although the use of A-BSs in conjunction with terrestrial base stations (T-BSs) reduces some load on terrestrial cellular networks [6], [7], it intensifies the intercell interference (ICI) to the devices and UEs [4]. A visual picture of connectivity and interference impact on reference UE in a typical downlink aerial-terrestrial cellular network is shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

“…UAVs have been identified to serve a variety of use cases [6]- [10]. For instance, UAVs as A-BSs could be helpful in load balancing of the existing terrestrial cellular network [6]- [8], especially in crowded situations (e.g., sports event [7], disaster management [6]). They can also be used to temporarily enhance the connectivity at some hotspots [9].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design and Analysis of Aerial-Terrestrial Network: A Joint Solution for Coverage and Rate

et al. 2021

View full text Add to dashboard Cite

The exploitation of aerial base stations (A-BSs) in conjunction with terrestrial base stations (T-BSs) is envisioned as a promising solution to provide connectivity to devices and user-equipment (UE) in crowded situations (viz. in the sports event) and emergency situations (viz. in the disaster management). However, the use of A-BSs with existing terrestrial networks intensifies the inter-cell interference (ICI) to the devices and UEs, therefore leading to a degraded signal-to-interference-ratio (SIR). This paper addresses this issue by exploiting different radio access technology (RAT) (mmWave/microwave) for aerial and terrestrial networks. Indeed, the network connectivity is always a top priority for all applications. However, there are also some applications such as remote patient monitoring, and remote working, which requires both coverage and high data-rates. But, most of the existing research claims the trade-off between the coverage and the data-rate performance. Whereas this paper aims to improve coverage and rate simultaneously in an aerial-terrestrial networks by employing an optimal combination of mmWave and microwave RAT based on the proposed association strategy. The essential analysis of such an integrated network involves the evaluation of parameters based on the analytic model. Hence, this paper analytically obtains the coverage probability (CP) and average rate expressions for the proposed integrated aerial-terrestrial networks. The analysis is supported by probabilistic models-based simulations that agree closely with analytical results. The results claim that the proposed model leads to improved performance in terms of both CP and average rate. Also, the paper provides parametric analysis for CP and rate with A-BSs height and A-BSs density to enable its practical implementation in 5G/6G technologies.INDEX TERMS Aerial-terrestrial networks, millimeter-wave RAT, microwave RAT, coverage probability, average rate, stochastic geometry.

show abstract

Section: A Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design and Analysis of Aerial-Terrestrial Network: A Joint Solution for Coverage and Rate

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The work of T. Q. Wireless networks supported by UAVs constitute a promising technology for enhancing the network performance [5]. The applications of UAVs in wireless networks span across diverse research fields, such as wireless sensor networks (WSNs) [6], caching [7], heterogeneous cellular networks [8], massive multiple-input multiple-output (MIMO) [9], disaster communications [10], [11] and device-to-device communications (D2D) [12]. For example, in [13], UAVs were deployed to provide network coverage for people in remote areas and disaster zones.…”

Section: Introductionmentioning

confidence: 99%

3D UAV Trajectory and Data Collection Optimisation Via Deep Reinforcement Learning

Nguyen

Duong

Do‐Duy

et al. 2022

IEEE Trans. Commun.

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAVs) are now beginning to be deployed for enhancing the network performance and coverage in wireless communication. However, due to the limitation of their on-board power and flight time, it is challenging to obtain an optimal resource allocation scheme for the UAV-assisted Internet of Things (IoT). In this paper, we design a new UAVassisted IoT system relying on the shortest flight path of the UAVs while maximising the amount of data collected from IoT devices. Then, a deep reinforcement learning-based technique is conceived for finding the optimal trajectory and throughput in a specific coverage area. After training, the UAV has the ability to autonomously collect all the data from user nodes at a significant total sum-rate improvement while minimising the associated resources used. Numerical results are provided to highlight how our techniques strike a balance between the throughput attained, trajectory, and the time spent. More explicitly, we characterise the attainable performance in terms of the UAV trajectory, the expected reward and the total sum-rate.

show abstract

“…For instance, in [9], a sensor offloading framework was developed to design the UAVs trajectories while maximizing the collected data from different sensors and considering the limited energy on-board of UAVs. In [10], an offloading scheme, which schedules the offloaded tasks of the edge users between the ground base stations and the UAVs, was presented. The authors investigated the role of UAVs to improve the average throughput and the spectral efficiency during tasks offloading.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Traffic Offloading in Space-Air-Ground Integrated Networks

Zhang

Abderrahim

Shihada³

2021

IEEE Access

View full text Add to dashboard Cite

While the 5-th generation of communication networks (5G) is taking its first deployment steps, high doubts still concern the fulfilment of the stringent requirements of its slices. To meet these complex requirements, robust access networks should support the current 5G air interface. In this regard, space and air networks, namely satellites and unmanned aerial vehicles (UAVs) are expected to play a key role due to their wide coverage and scalable deployment respectively. Currently, the integration of these platforms in the terrestrial networks is weak though. Therefore, we suggest bridging this gap by designing a heterogeneous traffic offloading approach in the space-air-ground integrated network (SAGIN). Our innovative offloading approach covers the co-existing requirements of two heterogeneous slices of 5G by offloading smartly the traffic to the appropriate segment of SAGIN. Specifically, the ultra-reliable lowlatency communications (URLLC) traffic is offloaded to the UAV link and to the terrestrial link to satisfy its stringent requirements in terms of latency. However, the enhanced mobile broadband (eMBB) traffic is offloaded to the UAV link, to the terrestrial link and to the satellite link because it is less sensitive to delay but needs high data rates. Our offloading approach boosts the network's availability and reduces the latency experienced in SAGIN through an efficient resource allocation and an optimized design of the UAVs trajectory. Our findings highlight the key role that the concrete integration between SAGIN segments plays to achieve a better quality of service (QoS) for different slices with heterogeneous requirements.

show abstract

Cell-Edge User Offloading via Flying UAV in Non-Uniform Heterogeneous Cellular Networks

Cited by 44 publications

References 36 publications

Design and Analysis of Aerial-Terrestrial Network: A Joint Solution for Coverage and Rate

Design and Analysis of Aerial-Terrestrial Network: A Joint Solution for Coverage and Rate

3D UAV Trajectory and Data Collection Optimisation Via Deep Reinforcement Learning

Heterogeneous Traffic Offloading in Space-Air-Ground Integrated Networks

Contact Info

Product

Resources

About