arXiv subjects

Po-Heng Chou

Publications and source records attributed to Po-Heng Chou.

22 records · Page 2Linked to original sources

arXiv2025

Unlicensed Band Allocation for Heterogeneous Networks

Based on the License-Assisted Access (LAA) small cell architecture, the LAA coexisting with Wi-Fi heterogeneous networks provides LTE mobile users with high bandwidth efficiency as the unlicensed channels are shared among LAA and Wi-Fi. However, LAA and Wi-Fi interfere with each other when both systems use the same unlicensed channel in heterogeneous networks. In such a network, unlicensed band allocation for LAA and Wi-Fi is an important issue that may affect the quality of service (QoS) of both systems significantly. In this paper, we propose an analytical model and conduct simulation experiments to study four allocations for the unlicensed band: unlicensed full allocation (UFA), unlicensed time-division allocation (UTA), and UFA/UTA with buffering mechanism (UFAB and UTAB) for the LAA data packets. We evaluate the performance of these unlicensed band allocation schemes in terms of the acceptance rate of both LAA and Wi-Fi packet data in the LAA buffer queue. Our study provides guidelines for designing the channel occupation phase and the buffer size of the LAA small cell.

cs.NI↗

arXiv2025

Agentic DDQN-Based Scheduling for Licensed and Unlicensed Band Allocation in Sidelink Networks

In this paper, we present an agentic double deep Q-network (DDQN) scheduler for licensed/unlicensed band allocation in New Radio (NR) sidelink (SL) networks. Beyond conventional reward-seeking reinforcement learning (RL), the agent perceives and reasons over a multi-dimensional context that jointly captures queueing delay, link quality, coexistence intensity, and switching stability. A capacity-aware, quality of service (QoS)-constrained reward aligns the agent with goal-oriented scheduling rather than static thresholding. Under constrained bandwidth, the proposed design reduces blocking by up to 87.5% versus threshold policies while preserving throughput, highlighting the value of context-driven decisions in coexistence-limited NR SL networks. The proposed scheduler is an embodied agent (E-agent) tailored for task-specific, resource-efficient operation at the network edge.

eess.SY↗

arXiv2025

Green Learning for STAR-RIS mmWave Systems with Implicit CSI

In this paper, a green learning (GL)-based precoding framework is proposed for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided millimeter-wave (mmWave) MIMO broadcasting systems. Motivated by the growing emphasis on environmental sustainability in future 6G networks, this work adopts a broadcasting transmission architecture for scenarios where multiple users share identical information, improving spectral efficiency and reducing redundant transmissions and power consumption. Different from conventional optimization methods, such as block coordinate descent (BCD) that require perfect channel state information (CSI) and iterative computation, the proposed GL framework operates directly on received uplink pilot signals without explicit CSI estimation. Unlike deep learning (DL) approaches that require CSI-based labels for training, the proposed GL approach also avoids deep neural networks and backpropagation, leading to a more lightweight design. Although the proposed GL framework is trained with supervision generated by BCD under full CSI, inference is performed in a fully CSI-free manner. The proposed GL integrates subspace approximation with adjusted bias (Saab), relevant feature test (RFT)-based supervised feature selection, and eXtreme gradient boosting (XGBoost)-based decision learning to jointly predict the STAR-RIS coefficients and transmit precoder. Simulation results show that the proposed GL approach achieves competitive spectral efficiency compared to BCD and DL-based models, while reducing floating-point operations (FLOPs) by over four orders of magnitude. These advantages make the proposed GL approach highly suitable for real-time deployment in energy- and hardware-constrained broadcasting scenarios.

eess.SP↗

arXiv2025

Analysis and Detection of RIS-based Spoofing in Integrated Sensing and Communication (ISAC)

Integrated sensing and communication (ISAC) is a key feature of next-generation 6G wireless systems, allowing them to achieve high data rates and sensing accuracy. While prior research has primarily focused on addressing communication safety in ISAC systems, the equally critical issue of sensing safety remains largely under-explored. In this paper, the possibility of spoofing the sensing function of ISAC in vehicle networks is examined, whereby a malicious reconfigurable intelligent surface (RIS) is deployed to compromise the sensing functionality of a roadside unit (RSU). For this scenario, the requirements on the malicious RIS' phase shifts design and number of reflecting elements are analyzed. Under such spoofing, the practical estimation bias of the vehicular user (VU)'s Doppler shift and angle-of-departure (AoD) for an arbitrary time slot is analytically derived. Moreover, from the attacker's view, a Markov decision process (MDP) is formulated to optimize the RIS' phase shifts design. The goal of this MDP is to generate complete and plausible fake trajectories by incorporating the concept of spatial-temporal consistency. To defend against this sensing spoofing attack, a signal temporal logic (STL)-based neuro-symbolic attack detection framework is proposed and shown to learn interoperable formulas for identifying spoofed trajectories.

cs.IT↗