arXiv subjects

Yingchao Jiao

Publications and source records attributed to Yingchao Jiao.

3 recordsLinked to original sources

arXiv2026

6DMA-Enabled ISAC for Low-Altitude Economy

In this paper, we investigate a six dimensional movable antenna (6DMA) enable integrated sensing and communications (ISAC) network in low-altitude economy. The studied 6DMA can move in a three-dimensional space and rotate around its surface center, making full use of spatial freedom to adapt to the different location distributions of uncrewed aerial vehicles (UAVs) adjust channel conditions in time. However, since the rotation and location change of 6DMA requires the assistance of a physical device, it is unreasonable for 6DMA to change locations too frequently. Therefore, we propose a hierarchical deep reinforcement learning algorithm based on twin delayed deep deterministic policy gradient. The first layer optimizes the rotation and location of 6DMA with infrequent updates, and the second layer optimizes the UAV flight direction and base station transmit beamforming matrix in each time slot. Under the condition of satisfying the sensing intensity of the sensing target, the total data transmission rate to the UAVs is maximized. The numerical results show that the proposed 6DMA-enable ISAC algorithm through joint optimization of multiple variables performs significantly better than the partially movable scheme and the fixed antenna position scheme.

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arXiv2026

Hierarchical Learning for IRS-Assisted MEC Systems with Rate-Splitting Multiple Access

Intelligent reflecting surface (IRS)-assisted mobile edge computing (MEC) systems have shown notable improvements in efficiency, such as reduced latency, higher data rates, and better energy efficiency. However, the resource competition among users will lead to uneven allocation, increased latency, and lower throughput. Fortunately, the rate-splitting multiple access (RSMA) technique has emerged as a promising solution for managing interference and optimizing resource allocation in MEC systems. This paper studies an IRS-assisted MEC system with RSMA, aiming to jointly optimize the passive beamforming of the IRS, the active beamforming of the base station, the task offloading allocation, the transmit power of users, the ratios of public and private information allocation, and the decoding order of the RSMA to minimize the average delay from a novel uplink transmission perspective. Since the formulated problem is non-convex and the optimization variables are highly coupled, we propose a hierarchical deep reinforcement learning-based algorithm to optimize both continuous and discrete variables of the problem. Additionally, to better extract channel features, we design a novel network architecture within the policy and evaluation networks of the proposed algorithm, combining convolutional neural networks and densely connected convolutional network for feature extraction. Simulation results indicate that the proposed algorithm not only exhibits excellent convergence performance but also outperforms various benchmarks.

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arXiv2025

UAV-Enabled Data Collection for IoT Networks via Rainbow Learning

Unmanned aerial vehicles (UAVs) enabled Internet of things (IoT) systems have become an important part of future wireless communications. To achieve higher communication rate, the joint design of UAV trajectory and resource allocation is crucial. In this paper, a multi-antenna UAV is dispatched to simultaneously collect data from multiple ground IoT nodes (GNs) within a time interval. To improve the sum data collection (SDC) volume from the GNs, the UAV trajectory, the UAV receive beamforming, the scheduling of the GNs, and the transmit power of the GNs are jointly optimized. Since the problem is non-convex and the variables are highly coupled, it is hard to be solved using traditional methods. To find a near-optimal solution, a double-loop structured optimization-driven deep reinforcement learning (DRL) algorithm, called rainbow learning based algorithm (RLA), and a fully DRL-based algorithm are proposed to solve the problem effectively. Specifically, the outer-loop of the RLA utilizes a fusion deep Q-network to optimize the UAV trajectory, GN scheduling, and power allocation, while the inner-loop optimizes receive beamforming by successive convex approximation. Simulation results verify that the proposed algorithms outperform two benchmarks with significant improvement in SDC volumes, energy efficiency, and fairness.

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