arXiv subjects

Yueling Che

Publications and source records attributed to Yueling Che.

7 recordsLinked to original sources

arXiv2025

CPMamba: Selective State Space Models for MIMO Channel Prediction in High-Mobility Environments

Channel prediction is a key technology for improving the performance of various functions such as precoding, adaptive modulation, and resource allocation in MIMO-OFDM systems. Especially in high-mobility scenarios with fast time-varying channels, it is crucial for resisting channel aging and ensuring communication quality. However, existing methods suffer from high complexity and the inability to accurately model the temporal variations of channels. To address this issue, this paper proposes CPMamba -- an efficient channel prediction framework based on the selective state space model. The proposed CPMamba architecture extracts features from historical channel state information (CSI) using a specifically designed feature extraction and embedding network and employs stacked residual Mamba modules for temporal modeling. By leveraging an input-dependent selective mechanism to dynamically adjust state transitions, it can effectively capture the long-range dependencies between the CSIs while maintaining a linear computational complexity. Simulation results under the 3GPP standard channel model demonstrate that CPMamba achieves state-of-the-art prediction accuracy across all scenarios, along with superior generalization and robustness. Compared to existing baseline models, CPMamba reduces the number of parameters by approximately 50 percent while achieving comparable or better performance, thereby significantly lowering the barrier for practical deployment.

eess.SP↗

arXiv2025

Population-Free Pareto Tracking for Sample-Efficient Multi-Policy MORL

Multi-objective reinforcement learning (MORL) is a fundamental framework for real-world decision-making problems involving multiple conflicting criteria. Existing multi-policy (MP) methods typically rely on online evolutionary frameworks that maintain large policy populations, leading to high sample complexity and excessive agent-environment interactions. To mitigate these limitations, we present Multi-policy Pareto Front Tracking (MPFT), a framework without a self-evolving population. It leverages an efficient Pareto-tracking mechanism initialized with single-objective extreme policies to trace the Pareto front, and further densifies sparse regions to achieve an accurate approximation of the full Pareto front. MPFT can be seamlessly integrated with advanced offline MORL algorithms, thereby substantially improving sample efficiency. We evaluate MPFT on six robotic control tasks with up to three objectives and three high-dimensional tasks with more than three objectives. Experimental results show that MPFT outperforms state-of-the-art baselines in terms of hypervolume and expected utility. It also significantly reduces agent-environment interactions. These results further demonstrate that MPFT serves as a general-purpose framework that can seamlessly integrate both online and offline MORL algorithms.

cs.LG↗

arXiv2023

Multi-Agent Graph Reinforcement Learning based On-Demand Wireless Energy Transfer in Multi-UAV-aided IoT Network

This paper proposes a new on-demand wireless energy transfer (WET) scheme of multiple unmanned aerial vehicles (UAVs). Unlike the existing studies that simply pursuing the total or the minimum harvested energy maximization at the Internet of Things (IoT) devices, where the IoT devices' own energy requirements are barely considered, we propose a new metric called the hungry-level of energy (HoE), which reflects the time-varying energy demand of each IoT device based on the energy gap between its required energy and the harvested energy from the UAVs. With the purpose to minimize the overall HoE of the IoT devices whose energy requirements are not satisfied, we optimally determine all the UAVs' trajectories and WET decisions over time, under the practical mobility and energy constraints of the UAVs. Although the proposed problem is of high complexity to solve, by excavating the UAVs' self-attentions for their collaborative WET, we propose the multiagent graph reinforcement learning (MAGRL) based approach. Through the offline training of the MAGRL model, where the global training at the central controller guides the local training at each UAV agent, each UAV then distributively determines its trajectory and WET based on the well-trained local neural networks. Simulation results show that the proposed MAGRL-based approach outperforms various benchmarks for meeting the IoT devices' energy requirements.

cs.IT↗

arXiv2018

Throughput Maximization for Laser-Powered UAV Wireless Communication Systems

Laser power has become a viable solution to provide convenient and sustainable energy supply to unmanned aerial vehicles (UAVs). In this paper, we study a laser-powered UAV wireless communication system, where a laser transmitter sends laser beams to charge a fixed-wing UAV in flight, and the UAV uses the harvested laser energy to communicate with a ground station. To maintain the UAV's sustainable operation, its total energy consumption cannot exceed that harvested from the laser transmitter. Under such a laser energy harvesting constraint, we maximize the downlink communication throughput from the UAV to the ground station over a finite time duration, by jointly optimizing the UAV's trajectory and its transmit power allocation. However, due to the complicated UAV energy consumption model, this problem is non-convex and difficult to be solved. To tackle the problem, we first consider a special case with a double-circular UAV trajectory which balances the tradeoff between maximizing the performance of laser energy harvesting versus wireless communication at the UAV. Next, based on the obtained double-circular trajectory, we propose an efficient solution to the general problem, by applying the techniques of alternating optimization and sequential convex programming (SCP). Finally, numerical results are provided to validate the communication throughput performance of the proposed design.

cs.IT↗

arXiv2016

Green 5G Heterogeneous Networks through Dynamic Small-Cell Operation

Traditional macro-cell networks are experiencing an upsurge of data traffic, and small-cells are deployed to help offload the traffic from macro-cells. Given the massive deployment of small-cells in a macro-cell, the aggregate power consumption of small-cells (though being low individually) can be larger than that of the macro-cell. Compared to the macro-cell base station (MBS) whose power consumption increases significantly with its traffic load, the power consumption of a small-cell base station (SBS) is relatively flat and independent of its load. To reduce the total power consumption of the heterogeneous networks (HetNets), we dynamically change the operating states (on and off) of the SBSs, while keeping the MBS on to avoid any service failure outside active small-cells. First, we consider that the wireless users are uniformly distributed in the network, and propose an optimal location-based operation scheme by gradually turning off the SBSs closer to the MBS. We then extend the operation problem to a more general case where users are non-uniformly distributed in the network. Although this problem is NP-hard, we propose a joint location and user density based operation scheme to achieve near-optimum (with less than 1\% performance loss in our simulations) in polynomial time.

cs.IT↗

arXiv2015

Dynamic Base Station Operation in Large-Scale Green Cellular Networks

In this paper, to minimize the on-grid energy cost in a large-scale green cellular network, we jointly design the optimal base station (BS) on/off operation policy and the on-grid energy purchase policy from a network-level perspective. Due to the fluctuations of the on-grid energy prices, the harvested renewable energy, and the network traffic loads over time, as well as the BS coordination to hand over the traffic offloaded from the inactive BSs to the active BSs, it is generally NP-hard to find a network-level optimal adaptation policy that can minimize the on-grid energy cost over a long-term and yet assures the downlink transmission quality at the same time. Aiming at the network-level dynamic system design, we jointly apply stochastic geometry (Geo) for large-scale green cellular network analysis and dynamic programming (DP) for adaptive BS on/off operation design and on-grid energy purchase design, and thus propose a new Geo-DP design approach. By this approach, we obtain the optimal BS on/off policy, which shows that the optimal BSs' active operation probability in each horizon is just sufficient to assure the required downlink transmission quality with time-varying load in the large-scale cellular network. We also propose a suboptimal on-grid energy purchase policy with low-complexity, where the low-price on-grid energy is over-purchased in the current horizon only when the current storage level and the future renewable energy level are both low. We compare the proposed policy with the existing schemes and show that our proposed policy can more efficiently save the on-grid energy cost over time.

cs.IT↗

arXiv2015

Multi-antenna Wireless Powered Communication with Co-channel Energy and Information Transfer

This letter studies a multi-antenna wireless powered communication (WPC) system with co-channel energy and information transfer, where a wireless device (WD), powered up by wireless energy transfer (WET) from an energy transmitter (ET), communicates to an information receiver (IR) over the same frequency band. We maximize the achievable data rate from the WD to the IR by jointly optimizing the energy beamforming at the ET and the information beamforming at the WD, subject to their individual transmit power constraints. We obtain the optimal solution to this problem in closed-form, where the optimal energy beamforming at the ET achieves a best energy/interference tradeoff between maximizing the energy transfer efficiency to the WD and minimizing the co-channel interference to the IR. Numerical results show that our proposed optimal co-channel design is superior to other reference schemes.

cs.IT↗