Joint Design of Fronthaul and Access Links in Massive MIMO Multi-UAV-Enabled CRANs

Huang, Yingjia; Ikhlef, Aïssa

doi:10.1109/lwc.2021.3100320

Cited by 10 publications

(5 citation statements)

References 12 publications

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“…However a multi-antenna technique has better performance in terms of energy consumption, signal transmission and reception, and improving the channel capacity. Some related work like [ 15 ] has adopted a multi-antenna technique in UAV-assisted MEC systems, such a scenario is worthy of investigation in future work [ 28 ].…”

Section: Discussionmentioning

confidence: 99%

Resource Allocation and 3D Deployment of UAVs-Assisted MEC Network with Air-Ground Cooperation

Huang

Zhang

et al. 2022

Sensors

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Equipping an unmanned aerial vehicle (UAV) with a mobile edge computing (MEC) server is an interesting technique for assisting terminal devices (TDs) to complete their delay sensitive computing tasks. In this paper, we investigate a UAV-assisted MEC network with air–ground cooperation, where both UAV and ground access point (GAP) have a direct link with TDs and undertake computing tasks cooperatively. We set out to minimize the maximum delay among TDs by optimizing the resource allocation of the system and by three-dimensional (3D) deployment of UAVs. Specifically, we propose an iterative algorithm by jointly optimizing UAV–TD association, UAV horizontal location, UAV vertical location, bandwidth allocation, and task split ratio. However, the overall optimization problem will be a mixed-integer nonlinear programming (MINLP) problem, which is hard to deal with. Thus, we adopt successive convex approximation (SCA) and block coordinate descent (BCD) methods to obtain a solution. The simulation results have shown that our proposed algorithm is efficient and has a great performance compared to other benchmark schemes.

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Section: Discussionmentioning

confidence: 99%

Resource Allocation and 3D Deployment of UAVs-Assisted MEC Network with Air-Ground Cooperation

Huang

Zhang

et al. 2022

Sensors

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“…The authors in [20] proposed using millimeter-wave with orthogonal frequency division multiple access for wireless fronthaul-based CRAN at the cost of an additional bandwidth requirement. For the CRAN with massive MIMO at BBU, several studies in published literature have addressed the improvement in fronthaul capacity and sum-rate maximization in [21]- [24]. The work in [21] and [22] proposed remoteradio-head cluster (RRHC) based millimeter wave (mmwave) massive MIMO heterogeneous cloud radio access networks but still used a non-flexible wired fronthaul link.…”

Section: Introductionmentioning

confidence: 99%

“…A massive MIMO based BBU is indeed proposed in [23] for a sub-6GHz link and for an unmanned aerial vehicle (UAV)enabled cloud radio access network in [24]. However, their proposed architectures do not consider mmWave bands to facilitate the accommodation of a huge number of antennas, and thus lacks deployment feasibility.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Massive MIMO Over mmWave Fronthaul Link for Throughput Maximization in Cloud Radio Access Networks

Farhat,

Rashid

2024

IEEE Access

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Massive data traffic over capacity-limited fronthaul link of a cloud radio access network (CRAN) poses a severe challenge in modern wireless systems. We propose a novel CRAN architecture to address this challenge that leverages a large array of antennas or massive MIMO at the baseband unit (BBU) to communicate with a set of distributed remote radio heads (RRHs) and subsequently serve multiple user equipment (UEs) over the access link. Transmission of data from massive MIMO at BBU takes place over a millimeter wave (mmWave) channel to exploit large bandwidth. The proposed architecture leverages a substantial array gain of massive MIMO in conjunction with a large idle mmWave frequency spectrum that can deal with a wide range of throughput requirements. In addition, a data compression strategy is designed by finding an optimal quantization noise covariance matrix across all the RRHs to handle the fronthaul traffic load. To this end, an optimization problem to maximize the sum rate of the UEs is thus formulated subject to constraints on fronthaul capacity, data compression, and total transmit powers at the BBU and RRHs. To solve this non-convex and intractable problem, a path following an iterative algorithm based on the difference of convex (DC) programming is used. The performance of the proposed architecture is evaluated in comparison with nontrivial benchmark schemes/architectures. Simulation results reveal that the proposed architecture can significantly improve the throughput of a CRAN by overcoming the fronthaul bottleneck by using massive MIMO over the mmWave channel.

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“…For example, in difficult terrains and rural areas, the use of the wireless medium for fronthaul links might be preferable due to its lower deployment cost and its flexible deployment and adjustment. Together with the emergence of millimeter wave technology and unmanned aerial vehicle (UAV) techniques, wireless fronthaul links for CRAN is gaining momentum [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we consider a CRAN architecture that utilizes massive MIMO for the wireless fronthaul links. While massive MIMO has been widely studied and is well established for the access links in the cellular system, to the best of authors' knowledge, only [8] studied massive MIMO for the fronthaul links in the context of the downlink of a CRAN system with UAVs deployed as flying RRHs. Other than this work, massive MIMO fronthaul has only been studied for smallcell networks [10,11], which can differ from CRAN in the baseband processing and user association.…”

Section: Introductionmentioning

confidence: 99%

Sum-rate Maximization in Uplink CRAN with a Massive MIMO Fronthaul

Maryopi¹,

Huang²,

Ikhlef³

2021

Preprint

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The limited fronthaul capacity is known to be one of the main problems in cloud radio access networks (CRANs), especially in the wireless fronthaul links. In this paper, we consider the uplink of a CRAN system, where massive multipleinput multiple-output (MIMO) is utilized in the fronthaul link. Considering multi-antenna user equipment (UEs) and multiantenna remote radio heads (RRHs), we maximize the system sum-rate by jointly optimizing the precoders at the UEs and the quantization noise covariance matrices and transmit powers at the RRHs. To solve the resulting nonconvex problem, an iterative algorithm based on the majorization-minimization (MM) method is proposed. Two schemes at the central unit are considered, namely maximum ratio (MR) and zero-forcing (ZF) combining. Numerical results show that the sum-rate has an asymptotic behaviour with respect to the maximum available power at RRHs and that the MR scheme goes to its asymptote faster than the ZF scheme.

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Joint Design of Fronthaul and Access Links in Massive MIMO Multi-UAV-Enabled CRANs

Cited by 10 publications

References 12 publications

Resource Allocation and 3D Deployment of UAVs-Assisted MEC Network with Air-Ground Cooperation

Resource Allocation and 3D Deployment of UAVs-Assisted MEC Network with Air-Ground Cooperation

Leveraging Massive MIMO Over mmWave Fronthaul Link for Throughput Maximization in Cloud Radio Access Networks

Sum-rate Maximization in Uplink CRAN with a Massive MIMO Fronthaul

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