Cellular-connected unmanned aerial vehicles (UAVs) are increasingly being deployed in emerging Internet of Things (IoT) applications, where reliable handover management is critical to ensure uninterrupted communication. Unlike terrestrial users, UAVs face frequent handovers due to antenna side-lobe coverage and fragmented aerial cell overlaps. Existing optimization strategies, however, are often limited to coarse-grained “whether-to-handover” decisions or unidirectional cell individual offset (CIO) tuning, resulting in degraded handover performance. To address these problems, this paper proposes an Event-triggered Bidirectional CIO Optimization with Hybrid prioritized experience replay based Deep Reinforcement Learning (EBCO-HDRL) algorithm. Under the A3 event-triggered mechanism, EBCO-HDRL jointly configures serving-to-neighbor and neighbor-to-serving CIOs, improving handover accuracy and reducing ping-pong events. An event-triggered optimization strategy adaptively reconfigures CIOs only when needed, mitigating computational overhead and enhancing training stability. To further improve sample efficiency, a Hybrid Prioritized Experience Replay (HPER) scheme is introduced, combining temporal-difference error and high-reward sampling, supported by a heterogeneous neural network design. Simulation results show that EBCO-HDRL significantly outperforms baseline algorithms in terms of precision, stability, and robustness, offering an effective solution for UAV handover management in cellular IoT networks.

Passing-object detection is a basic function in an intelligent environment. However, as one of the main functions in an integrated sensing and communication system, it is still challenging to achieve due to the dense multipath propagation in typical indoor environments. First, a Fresnel zone model and a diffraction model are constructed from indoor radio wave propagation characteristics to estimate the first Fresnel zone maximum (FFZM) and phase difference (PD), both of which enable the utilization of antenna pairs to determine the existence and passing direction of an object. Next, using a mixture of the FFZM and PD, a joint detection algorithm (JDA) for passing objects is proposed for a multiantenna system, in which the dynamic time warping method is applied to obtain the signal similarity between antennas. In the preprocessing stage of the proposed JDA, the minimum delay sequence is used to extract the passing period, and an improved trilinear parallel factor decomposition method is used to remove multipath interference. For experimental demonstration, the proposed JDA is implemented using a software radio platform and applied to WLAN sensing. The measurement results show that the proposed JDA can achieve very low missing alarm and direction error rates for both single-passing and multipassing scenarios.

The receiver design for multi-input multi-output (MIMO) ultra-reliable and low-latency communication (URLLC) systems can be a tough task due to the use of short channel codes and few pilot symbols. Consequently, error propagation can occur in traditional turbo receivers, leading to performance degradation. Moreover, the processing delay induced by information exchange between different modules may also be undesirable for URLLC. To address the issues, we advocate to perform joint channel estimation, detection, and decoding (JCDD) for MIMO URLLC systems encoded by short low-density parity-check (LDPC) codes. Specifically, we develop two novel JCDD problem formulations based on the maximuma posteriori(MAP) criterion for Gaussian MIMO channels and sparse mmWave MIMO channels, respectively, which integrate the pilots, the bit-to-symbol mapping, the LDPC code constraints, as well as the channel statistical information. Both the challenging large-scale non-convex problems are then solved based on the alternating direction method of multipliers (ADMM) algorithms, where closed-form solutions are achieved in each ADMM iteration. Furthermore, two JCDD neural networks, called JCDDNet-G and JCDDNet-S, are built by unfolding the derived ADMM algorithms and introducing trainable parameters. It is interesting to find via simulations that the proposed trainable JCDD receivers can outperform the turbo receivers with affordable computational complexities.

In this paper, we investigate the design of statistically robust detectors for multi-input multi-output (MIMO) systems subject to imperfect channel state information (CSI). A robust maximum likelihood (ML) detection problem is formulated by taking into consideration the CSI uncertainties caused by both the channel estimation error and the channel variation. To address the challenging discrete optimization problem, we propose an efficient alternating direction method of multipliers (ADMM)-based algorithm, which only requires calculating closed-form solutions in each iteration. Furthermore, a robust detection network RADMMNet is constructed by unfolding the ADMM iterations and employing both model-driven and data-driven philosophies. Moreover, in order to relieve the computational burden, a low-complexity ADMM-based robust detector is developed using the Gaussian approximation, and the corresponding deep unfolding network LCRADMMNet is further established. On the other hand, we also provide a novel robust data-aided Kalman filter (RDAKF)-based channel tracking method, which can effectively refine the CSI accuracy and improve the performance of the proposed robust detectors. Simulation results validate the significant performance advantages of the proposed robust detection networks over the non-robust detectors with different CSI acquisition methods.