Optimization of Total Power Consumed by Flying Base Station Serving Mobile Users

Nikooroo, Mohammadsaleh; Bečvář, Zdeněk

doi:10.1109/tnse.2022.3169846

Cited by 11 publications

(2 citation statements)

References 63 publications

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“…In the future, we will improve the flexibility of the tracking framework with the combination of centralized and distributed patterns, in which edge servers such as flying base stations are available [47]. The flexible framework may provide centralized management to assist distributed MTT in computing resource scheduling.…”

Section: Discussionmentioning

confidence: 99%

Integrated Sensing and Communication in UAV Swarms for Cooperative Multiple Targets Tracking

Zhou

Leng

Wang

et al. 2022

IEEE Trans. on Mobile Comput.

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Various interconnected Internet of Things (IoT) devices have emerged, led by the intelligence of the IoT, to realize exceptional interaction with the physical world. In this context, UAV swarm-enabled Multiple Targets Tracking (UAV-MTT), which can sense and track mobile targets for many applications such as hit-and-run, is an appealing topic. Unfortunately, UAVs cannot implement real-time MTT based on the traditional centralized pattern due to the complicated road network environment. It is also challenging to realize low-overhead UAV swarm cooperation in a distributed architecture for the real-time MTT. To address the problem, we propose a cyber-twin-based distributed tracking algorithm to update and optimize a trained digital model for real-time MTT. We then design a distributed cooperative tracking framework to promote MTT performance. In the design, both short-distance and long-distance distributed tracking cooperation manners are first realized with low energy consumption in communication by integrating resources of sensing and communication. Resource integration promotes target sensing efficiency with a highly successful tracking ratio as well. Theoretical derivation proves our algorithmic convergence. Hardware-in-the-loop simulation results demonstrate that our proposed algorithm can remarkably save 65.7% energy consumption in communication compared to other benchmarks while efficiently promoting 20.0% sensing performance.

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Section: Discussionmentioning

confidence: 99%

Integrated Sensing and Communication in UAV Swarms for Cooperative Multiple Targets Tracking

Zhou

Leng

Wang

et al. 2022

IEEE Trans. on Mobile Comput.

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“…Previous research studies have focused on the positioning of the flying base station and its interplay and impact on the communication channel to the user. They used the standard path loss model to characterize the channel signal performance with respect to physical distance, e.g., [3,[9][10][11][12], and the corresponding channel capacity, e.g., [1,3,[13][14][15][16]. These approaches and metrics are similar to what we used in our work to quantify the channel performances with respect to the flying base station's position.…”

Section: Related Workmentioning

confidence: 99%

Flying Base Station Channel Capacity Limits: Dependent on Stationary Base Station and Independent of Positioning

Chang,

Park,

Kim

et al. 2024

Electronics

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Flying base stations, also known as aerial base stations, provide wireless connectivity to the user and utilize their aerial mobility to improve communication performance. Flying base stations depend on traditional stationary terrestrial base stations for connectivity, as stationary base stations act as the gateway to the backhaul/cloud via a wired connection. We introduce the flying base station channel capacity to build on the Shannon channel capacity, which quantifies the upper-bound limit of the rate at which information can be reliably transmitted using the communication channel regardless of the modulation and coding techniques used. The flying base station’s channel capacity assumes aerial mobility and ideal positioning for maximum channel capacity. Therefore, the channel capacity limit holds for any digital and signal processing technique used and for any location or positioning of the flying base station. Because of its inherent reliance on the stationary terrestrial base station, the flying base station channel capacity depends on the stationary base station’s parameters, such as its location and SNR performance to the user, in contrast to previous research, which focused on the link between the user and the flying base station without the stationary base station. For example, the beneficial region (where there is a positive flying base station capacity gain) depends on the stationary base station’s power and channel SNR in addition to the flying base station’s own transmission power and whether it has full duplex vs. half-duplex capability. We jointly study the mobility and the wireless communications of the flying base station to analyze its position, channel capacity, and beneficialness over the stationary terrestrial base station (capacity gain). As communication protocols and implementations for flying base stations undergo development for next-generation wireless networking, we focus on information-theoretical analyses and channel capacity to inform future research and development in flying base station networking.

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Power allocation method based on modified social network search algorithm

Gao,

Li,

Lin

et al. 2024

Appl Intell

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Optimization of Total Power Consumed by Flying Base Station Serving Mobile Users

Cited by 11 publications

References 63 publications

Integrated Sensing and Communication in UAV Swarms for Cooperative Multiple Targets Tracking

Integrated Sensing and Communication in UAV Swarms for Cooperative Multiple Targets Tracking

Flying Base Station Channel Capacity Limits: Dependent on Stationary Base Station and Independent of Positioning

Power allocation method based on modified social network search algorithm

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