Role of Machine Learning in Resource Allocation Strategy over Vehicular Networks: A Survey

Unmanned aerial vehicle (UAV) millimeter wave (mmWave) technologies can provide flexible link and high data rate for future communication networks. By considering the new features of three-dimensional (3D) scattering space, 3D velocity, 3D antenna array, and especially 3D rotations, a machine learning (ML) integrated UAV-to-Vehicle (U2V) mmWave channel model is proposed. Meanwhile, a ML-based network for channel parameter calculation and generation is developed. The deterministic parameters are calculated based on the simplified geometry information, while the random ones are generated by the back propagation based neural network (BPNN) and generative adversarial network (GAN), where the training data set is obtained from massive ray-tracing (RT) simulations. Moreover, theoretical expressions of channel statistical properties, i.e., power delay profile (PDP), autocorrelation function (ACF), Doppler power spectrum density (DPSD), and cross-correlation function (CCF) are derived and analyzed. Finally, the U2V mmWave channel is generated under a typical urban scenario at 28 GHz. The generated PDP and DPSD show good agreement with RT-based results, which validates the effectiveness of proposed method. Moreover, the impact of 3D rotations, which has rarely been reported in previous works, can be observed in the generated CCF and ACF, which are also consistent with the theoretical and measurement results.

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“…Based on the proposed generation method, the PDP of generated U2V channel is calculated by (33) and shown in Fig. 10(a).…”

Section: B Validation Of Proposed Channel Modelmentioning

confidence: 99%

Machine-Learning-Based 3-D Channel Modeling for U2V mmWave Communications

Masuda

Zhu

Song

et al. 2022

IEEE Internet Things J.

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“…DNN compresses the local observation data of each V2V link. Each V2V link first observes the surrounding environment to acquire its transmitting power P d j and the combined interference power from other links I j = (I j [1], . .…”

Section: Link Data Compressionmentioning

confidence: 99%

“…, I j [I]). Considering the impact of V2V links on V2I links, the observation data of the j-th V2V link must also include the interference channel gain from the V2V link to all V2I linkswhich are denoted as H j,B = (H j,B [1], . .…”

Section: Link Data Compressionmentioning

confidence: 99%

“…In recent years, with the development of wireless communication and intelligent vehicle technology, the on-board network has attracted extensive attention from the industry and academia [1][2][3][4]. Vehicle to everything (V2X) includes vehicle to vehicle (V2V), vehicle to infrastructure (V2I), vehicle to pedestrian (V2P) and vehicle to network (V2N).…”

Section: Introductionmentioning

confidence: 99%

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Deep Reinforcement Learning-Based Spectrum Allocation Algorithm in Internet of Vehicles Discriminating Services

Guan

Wang

2022

Applied Sciences

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With the rapid development of global automotive industry intelligence and networking, the Internet of Vehicles (IoV) service, as a key communication technology, has been faced with an increasing spectrum of resources shortage. In this paper, we consider a spectrum utilization problem, in which a number of co-existing cellular users (CUs) and prioritized device-to-device (D2D) users are equipped in a single antenna vehicle-mounted communication network. To ensure a business-aware spectrum access mechanism with delay granted in a complex dynamic environment, we consider optimizing a metric that maintains a trade off between maximizing the total capacity of vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) links and minimizing the interference of high priority links. A low complexity priority-based spectrum allocation scheme based on the deep reinforcement learning method is developed to solve the proposed formulation. We trained our algorithm using the deep Q-learning network (DQN) over a set of public bandwidths. Simulation results show that the proposed scheme can allocate spectrum resources quickly and effectively in a high dynamic vehicle network environment. Concerning improved channel transmission rate, the V2V link rate in this scheme is 2.54 times that of the traditional random spectrum allocation scheme, and the V2I link rate is 13.5% higher than that of the traditional random spectrum allocation scheme. The average total interference received by priority links decreased by 14.2 dB compared to common links, realized service priority distinction and has good robustness to communication noise.

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“…Machine learning and algorithm have been widely used to manage resource allocation. Machine learning has been studied for predictive scheduling and resource allocation in large-scale manufacturing systems and resource allocation strategies in vehicular networks using machine learning have been extensively explored[ 13 , 14 ]. These settings are similar to ICUs in that both need to capture the value from big data processing and extract useful insights to optimize production and protect resources.…”

Section: Resource Allocationmentioning

confidence: 99%

Data science in the intensive care unit

Luo¹,

Huang²,

Luo³

et al. 2022

WJCCM

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In this editorial, we comment on the current development and deployment of data science in intensive care units (ICUs). Data in ICUs can be classified into qualitative and quantitative data with different technologies needed to translate and interpret them. Data science, in the form of artificial intelligence (AI), should find the right interaction between physicians, data and algorithm. For individual patients and physicians, sepsis and mechanical ventilation have been two important aspects where AI has been extensively studied. However, major risks of bias, lack of generalizability and poor clinical values remain. AI deployment in the ICUs should be emphasized more to facilitate AI development. For ICU management, AI has a huge potential in transforming resource allocation. The coronavirus disease 2019 pandemic has given opportunities to establish such systems which should be investigated further. Ethical concerns must be addressed when designing such AI.

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Role of Machine Learning in Resource Allocation Strategy over Vehicular Networks: A Survey

Cited by 27 publications

References 95 publications

Machine-Learning-Based 3-D Channel Modeling for U2V mmWave Communications

Machine-Learning-Based 3-D Channel Modeling for U2V mmWave Communications

Deep Reinforcement Learning-Based Spectrum Allocation Algorithm in Internet of Vehicles Discriminating Services

Data science in the intensive care unit

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