Three-Dimensional Modeling of mmWave Doubly Massive MIMO Aerial Fading Channels

Michailidis, Emmanouel T.; Νομικός, Νικόλαος; Trakadas, Panagiotis; Kanatas, Αthanasios G.

doi:10.1109/tvt.2019.2956460

Cited by 62 publications

(24 citation statements)

References 48 publications

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“…On another front, massive multiple-input-multiple-output (MIMO) technology has recently received unprecedented attention as a key enabler for increased spectral and energy efficiency, drastically reduced round-trip latency, and support of highly-intensive computation tasks for a vast number of connected users [33]. Beyond the conventional massive MIMO, where only the base stations employ multiple antennas [33], large-scale antenna configurations in vehicles [34]- [38] and UAVs [39], [40] were previously considered, without focusing on MEC applications. Besides, the double-sided massive MIMO concept [35], [37], [39], [41]- [44] was also envisioned to offer notably increased beamforming gain and counterbalance the signal propagation losses.…”

Section: A Backgroundmentioning

confidence: 99%

“…Beyond the conventional massive MIMO, where only the base stations employ multiple antennas [33], large-scale antenna configurations in vehicles [34]- [38] and UAVs [39], [40] were previously considered, without focusing on MEC applications. Besides, the double-sided massive MIMO concept [35], [37], [39], [41]- [44] was also envisioned to offer notably increased beamforming gain and counterbalance the signal propagation losses. Nevertheless, there exist some practical barriers (e.g., energy consumption and hardware complexity) towards the implementation of large-scale antennas, especially when fully digital beamforming is used.…”

Section: A Backgroundmentioning

confidence: 99%

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Energy Optimization in Massive MIMO UAV-Aided MEC-Enabled Vehicular Networks

et al. 2021

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This paper presents a novel unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) architecture for vehicular networks. It is considered that the vehicles should complete latencycritical computation-intensive tasks either locally with on-board computation units or by offloading part of their tasks to road side units (RSUs) with collocated MEC servers. In this direction, a hovering UAV can serve as an aerial RSU (ARSU) for task processing or act as an aerial relay and further offload the computation tasks to a ground RSU (GRSU). To significantly reduce the delay during data offloading and downloading, this architecture relies on the benefits of line-of-sight (LoS) massive multiple-input-multipleoutput (MIMO). Therefore, it is considered that the vehicles, the ARSU, and the GRSU employ large-scale antennas. A three-dimensional (3-D) geometrical representation of the MEC-enabled network is introduced and an optimization method is proposed that minimizes the computation-based and communication-based weighted total energy consumption (WTEC) of vehicles and ARSU subject to transmit power allocation, task allocation, and time slot scheduling. The results verify the theoretical derivations, emphasize on the effectiveness of the LoS massive MIMO transmission, and provide useful engineering insights.INDEX TERMS Computation offloading, energy efficiency, massive multiple-input multiple-output (MIMO), mobile edge computing (MEC), unmanned aerial vehicle (UAV), vehicular networks.

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Section: A Backgroundmentioning

confidence: 99%

Section: A Backgroundmentioning

confidence: 99%

Energy Optimization in Massive MIMO UAV-Aided MEC-Enabled Vehicular Networks

et al. 2021

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“…Additionally, new requirements for enhanced spectral efficiency, higher throughput, and sufficient bandwidth continuously emerge. In this respect, the multiple-input multiple-output (MIMO) [59] and massive MIMO [60] technology can revolutionize NTNs by exploiting spatial diversity and multiplexing as well as serving multiple ground nodes through multi-user (MU) MIMO [61]. In this direction, the extension of the DVB-Satellite to Handheld (DVB-SH) [62] and DVB-Next Generation Handheld (DVB-NGH) [63] standards to introduce MIMO configurations has been suggested with minor modifications.…”

Section: State Of the Art In Wireless Communication And Networking Tementioning

confidence: 99%

“…In this direction, the extension of the DVB-Satellite to Handheld (DVB-SH) [62] and DVB-Next Generation Handheld (DVB-NGH) [63] standards to introduce MIMO configurations has been suggested with minor modifications. Besides the above, the mm-wave frequencies have been proposed as key enablers of 5G and IoT systems to attain sufficient bandwidth [60]. As the performance of radar systems is limited by target scintillations, the application of MIMO techniques to synthetic aperture radars (SARs) can also greatly improve the resolution and sensitivity as well as detection and estimation performance of NTN-based applications for the IIoT by exploiting the diversity of target scattering [64].…”

Section: State Of the Art In Wireless Communication And Networking Tementioning

confidence: 99%

AI-Inspired Non-Terrestrial Networks for IIoT: Review on Enabling Technologies and Applications

Michailidis

Potirakis

Kanatas

2020

IoT

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During the last few years, various Industrial Internet of Things (IIoT) applications have emerged with numerous network elements interconnected using wired and wireless communication technologies and equipped with strategically placed sensors and actuators. This paper justifies why non-terrestrial networks (NTNs) will bring the IIoT vision closer to reality by providing improved data acquisition and massive connectivity to sensor fields in large and remote areas. NTNs are engineered to utilize satellites, airships, and aircrafts, which can be employed to extend the radio coverage and provide remote monitoring and sensing services. Additionally, this paper describes indicative delay-tolerant massive IIoT and delay-sensitive mission-critical IIoT applications spanning a large number of vertical markets with diverse and stringent requirements. As the heterogeneous nature of NTNs and the complex and dynamic communications scenarios lead to uncertainty and a high degree of variability, conventional wireless communication technologies cannot sufficiently support ultra-reliable and low-latency communications (URLLC) and offer ubiquitous and uninterrupted interconnectivity. In this regard, this paper sheds light on the potential role of artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), in the provision of challenging NTN-based IIoT services and provides a thorough review of the relevant research works. By adding intelligence and facilitating the decision-making and prediction procedures, the NTNs can effectively adapt to their surrounding environment, thus enhancing the performance of various metrics with significantly lower complexity compared to typical optimization methods.

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“…The research in wireless systems and techniques for NTNs spans many domains, from massive-MIMO systems Chandhar et al (2018), Geraci et al (2018), mm-wave technologies and channel issues Zhang et al (2019), Michailidis et al (2020a), UAV selection Bithas et al (2020), beam In this paper, a joint Tx-Rx beamforming with beam selection and combining technique is proposed for improving the performance of an aerial communication system supported by electronically steerable antenna arrays. The main idea of the proposed scheme is to select, using SNR maximization, a pair of beam patterns between each RF chain of the ground station and the aerial platform, and combine the received SNRs under the maximal ratio principle.…”

Section: Introductionmentioning

confidence: 99%

A Low-Complexity Reconfigurable Multi-Antenna Technique for Non-Terrestrial Networks

2021

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Multi-Antenna communication techniques are an efficient and relatively simple approach for the performance improvement of wireless communication systems. However, the direct application of multi-antenna techniques to an aerial communication system is not always feasible due to the constraints induced by the aerial platforms. Reconfigurable intelligent antenna technologies could provide an efficient solution to these problems and thus they are considered as ideal candidates for adaption in the aerial communication platforms that will be used in the 5G and beyond communication networks. In this paper, a joint Tx-Rx beamforming with beam selection and combining technique is proposed for improving the performance of an aerial communication system supported by electronically steerable antenna arrays. The main idea of the proposed scheme is to select, using an SNR maximization criterion, a pair of beam patterns between each RF chain of the ground station and the aerial platform, and combine the received SNRs under the maximal ratio principle. Initially, an analytical stochastic framework has been developed that is based on a Markov chain model, which is used to investigate the statistics of the received SNR. Then, an implementation of the novel beamforming and pattern adaptation scheme is presented, with the use of Electronically Steerable Parasitic Array Radiators (ESPAR), properly designed for Ground Station to UAV links. In addition, a realistic simulator is also developed with proper channel model selection, by the aid of which, the performance of the proposed scheme has been evaluated in conjunction with the extracted analytical results.

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Three-Dimensional Modeling of mmWave Doubly Massive MIMO Aerial Fading Channels

Cited by 62 publications

References 48 publications

Energy Optimization in Massive MIMO UAV-Aided MEC-Enabled Vehicular Networks

Energy Optimization in Massive MIMO UAV-Aided MEC-Enabled Vehicular Networks

AI-Inspired Non-Terrestrial Networks for IIoT: Review on Enabling Technologies and Applications

A Low-Complexity Reconfigurable Multi-Antenna Technique for Non-Terrestrial Networks

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