How Do Non-Ideal UAV Antennas Affect Air-to-Ground Communications?

Khoshkholgh, Mohammad G.; Navaie, Keivan; Yanıkömeroğlu, Halim; Leung, Victor C. M.; Shin, Kang G.

doi:10.1109/icc.2019.8761330

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…The vertical angel between the receiver k and the UAV-BS is ρ k = tan −1 (H/ X k ). The receiver k is within the main-lobe of the antenna, if [16]. The probability that the channel between UAV-BS and the receiver is in LOS status is obtained from p…”

Section: A Radio Environmentmentioning

confidence: 99%

RSS-Based UAV-BS 3-D Mobility Management via Policy Gradient Deep Reinforcement Learning

Khoshkholgh

Yanıkömeroğlu

2021

ICC 2021 - IEEE International Conference on Communications

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We address the mobility management of an autonomous UAV-mounted base station (UAV-BS) that provides communication services to a cluster of users on the ground while the geographical characteristics (e.g., location and boundary) of the cluster, the geographical locations of the users, and the characteristics of the radio environment are unknown. UAV-BS solely exploits the received signal strengths (RSS) from the users and accordingly chooses its (continuous) 3-D speed to constructively navigate, i.e., improving the transmitted data rate. To compensate for the lack of a model, we adopt policy gradient deep reinforcement learning. As our approach does not rely on any particular information about the users as well as the radio environment, it is flexible and respects the privacy concerns.Our experiments indicate that despite the minimum available information the UAV-BS is able to distinguish between high-rise (often non-line-of-sight dominant) and sub-urban (mainly lineof-sight dominant) environments such that in the former (resp. latter) it tends to reduce (resp. increase) its height and stays close (resp. far) to the cluster. We further observe that the choice of the reward function affects the speed and the ability of the agent to adhere to the problem constraints without affecting the delivered data rate.

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Section: A Radio Environmentmentioning

confidence: 99%

RSS-Based UAV-BS 3-D Mobility Management via Policy Gradient Deep Reinforcement Learning

Khoshkholgh

Yanıkömeroğlu

2021

ICC 2021 - IEEE International Conference on Communications

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“…The vertical angel between the receiver and the UAV is . The receiver is within the mainlobe of UAV , if , or equivalently for [15]. The probability that the channel between UAV and the receiver is in LOS status is obtained from [16], where and are the channel parameters representing the characteristics of the communication environment.…”

Section: Monitored Environmentmentioning

confidence: 99%

Learning Power Control From a Fixed Batch of Data

Khoshkholgh

Yanıkömeroğlu

2021

IEEE Wireless Commun. Lett.

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We address how to exploit power control data, gathered from a monitored environment, for performing power reinforcement learning, whereby the agent learns the policy to produce the transmission powers solely by using the data. Experiments demonstrate that despite discrepancies between the monitored and unexplored environments, the agent successfully learns the power control very quickly, even if the objective functions in the monitored and unexplored environments are be of high-quality and the rest can be from any sub-optimal algorithm.

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