Wireless Avionics Intracommunications: A Survey of Benefits, Challenges, and Solutions

Park, Pangun; Marco, Piergiuseppe Di; Nah, Junghyo; Fischione, Carlo

doi:10.1109/jiot.2020.3038848

Cited by 29 publications

(10 citation statements)

References 118 publications

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“…Note that the applications for in-X subnetworks are complementary to other radio technologies involved in vehicular communication, such as vehicular-to-vehicular services for platooning, lane change, forward collision warning, and intersection movement assist [40]. Besides cars and trucks, we envision another important application of the in-vehicle subnetworks in avionics, where wireless communications can be used to get rid of the large weight of cables to connect all sensors, controllers and actuators in an aircraft [41]. Multiple in-X subnetworks may need to be installed across the multiple segments of the aircraft, including wings, and a centralized controller can take care of coordinating their operations for ensuring flight stability control via e.g., flaps, spoilers and slats actuation.…”

Section: B In-vehicle Subnetworkmentioning

confidence: 99%

Extreme Communication in 6G: Vision and Challenges for ‘in-X’ Subnetworks

Berardinelli¹,

Baracca

Adeogun

et al. 2021

IEEE Open J. Commun. Soc.

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The 6th Generation (6G) radio access technology is expected to support extreme communication requirements in terms of throughput, latency and reliability, which can only be achieved by providing capillary wireless coverage. In this paper, we present our vision for short-range low power 6G 'in-X' subnetworks, with the 'X' standing for the entity in which the cell is deployed, e.g., a production module, a robot, a vehicle, a house or even a human body. Such cells can support services that can be life-critical and that traditionally relied on wired systems. We discuss potential deployment options, as well as candidate air interface components and spectrum bands. Interference management is identified as a major challenge in dense deployments, which needs to handle also non-cellular types of interference like jamming attacks and impulsive noise. A qualitative example of interference-robust system design is also presented.

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Section: B In-vehicle Subnetworkmentioning

confidence: 99%

Extreme Communication in 6G: Vision and Challenges for ‘in-X’ Subnetworks

Berardinelli¹,

Baracca

Adeogun

et al. 2021

IEEE Open J. Commun. Soc.

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“…To solve these problems, extensive researches on wireless communication in aircraft have been carried out [3]- [6]. Wireless Avionics Intra-Communications (WAIC) is an emerging technology to provide wireless connectivity for safety-related applications of next-generation aircraft [7]. Compared with conventional wired avionics communication, WAIC can ef-fectively reduce the overall weight of aircraft and improve the maintainability of avionics system.…”

Section: A Backgroundmentioning

confidence: 99%

“…On the other hand, most research efforts on resource allocation scheme have been devoted to channel allocation [24] and power control [21] separately. Hence, it is urgent to propose a scheme of joint channel allocation and power control to meet the high reliability and bounded latency requirements of the WAIC system [7].…”

Section: B Motivationsmentioning

confidence: 99%

Deep Reinforcement Learning for Power Controlled Channel Allocation in Wireless Avionics Intra-Communications

Zuo

et al. 2021

IEEE Access

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Wireless avionics intra-communications (WAIC) can play an important role in alleviate the issue of fuel consumption, stability and maintenance costs over traditional wired avionics systems. However, in order for WAIC system to coexist with other systems in aircraft, the transmitted power level of WAIC is strictly limited to 50 mW. Meanwhile, WAIC require extremely low outage probability for the safety-critical avionics applications. Hence, it is urgently needed to effectively allocate channels and utilize the limited power while ensuring the reliability and real-time performance of the WAIC system. In this paper, a deep reinforcement learning (DRL)-based power controlled channel allocation (DRL-PCCA) scheme for WAIC network is proposed, with physical layer of frequency hopping orthogonal frequency division multiplexing (FH-OFDM). First, we formulate a sub-bands allocation and power control optimization problem whose aim is to minimize the overall transmit power provided that all end nodes achieve their requested data rates and desired bit error rate (BER). However, the problem formulated is non-convex and NP-hard. To tackle this problem, we propose a DRL-based scheme, which can effectively solve the optimization problem of sequence decision making in complex environment by using neural networks to extract spatial correlation features of WAIC. In particular, a reliability framework for WAIC is presented to analyse the performance of the proposed scheme against the system requirements of the flight certification. Simulation results demonstrate that the performance of proposed DRL-based scheme is superior to the traditional power control and channel allocation scheme.INDEX TERMS channel allocation, deep reinforcement learning, UWB, WAIC, wireless communication.

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“…At network level, a variety of networking architectures and techniques have been studied for aircraft applications, including cooperative schemes [19], structural acoustic wave communication [20], sensor deployment optimization [21], dynamic reconfigurability [22] and game theory based medium access control (MAC) protocol design [23]. An overview of adhoc aeronautical networking technology can be found in [24] and an assessment of wireless networking benefits for avionics has been presented in [25].…”

Section: Introductionmentioning

confidence: 99%

Autonomous Electrical Current Monitoring System for Aircraft

Blagojevic¹,

Dieudonne

Kamecki³

et al. 2023

IEEE Trans. Aerosp. Electron. Syst.

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Aircraft monitoring systems offer enhanced safety, reliability, reduced maintenance cost and improved overall flight efficiency. Advancements in wireless sensor networks (WSN) are enabling unprecedented data acquisition functionalities, but their applicability is restricted by power limitations, as batteries require replacement or recharging and wired power adds weight and detracts from the benefits of wireless technology. In this paper, an energy autonomous WSN is presented for monitoring the structural current in aircraft structures. A hybrid inductive/hall sensing concept is introduced demonstrating 0.5 A resolution, < 2% accuracy and frequency independence, for a 5 A -100 A RMS, DC-800 Hz current and frequency range, with 35 mW active power consumption. An inductive energy harvesting power supply with magnetic flux funnelling, reactance compensation and supercapacitor storage is demonstrated to provide 0.16 mW of continuous power from the 65 μT RMS field of a 20 A RMS, 360 Hz structural current. A lowpower sensor node platform with a custom multi-mode duty cycling network protocol is developed, offering cold starting network association and data acquisition/transmission functionality at 50 μW and 70 μW average power respectively. WSN level operation for 1 minute for every 8 minutes of energy harvesting is demonstrated. The proposed system offers a unique energy autonomous WSN platform for aircraft monitoring.

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Wireless Avionics Intracommunications: A Survey of Benefits, Challenges, and Solutions

Cited by 29 publications

References 118 publications

Extreme Communication in 6G: Vision and Challenges for ‘in-X’ Subnetworks

Extreme Communication in 6G: Vision and Challenges for ‘in-X’ Subnetworks

Deep Reinforcement Learning for Power Controlled Channel Allocation in Wireless Avionics Intra-Communications

Autonomous Electrical Current Monitoring System for Aircraft

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