Intelligent Transportation Systems (ITS) rely on the Internet of Vehicles (IoV) to sustain high data rates and low latency under dynamic and heterogeneous conditions. Joint power and spectrum control across macro and micro tiers remains challenging due to mobility, interference coupling, and large continuous action spaces. EdgeSAC is a graph-aware Soft Actor Critic (SAC) framework executed at the edge for power control in hierarchical Fifth-Generation New Radio (5G NR) Multiple-Input Multiple-Output (MIMO) networks. A permutation-equivariant Graph Neural Network (GNN) with edge updates encodes co-channel interference among Base Stations (BSs) and outputs node-level power fractions under tier budgets. An on-demand scheduler activates fixed-size channels and assigns at most one macro and one micro resource per user to realize dual connectivity. Signal-to-Interference-plus-Noise Ratio (SINR) is mapped to rate using a Shannon with gap model with rank adaptive MIMO, enabling tier aggregation without action discretization. In simulation with Third Generation Partnership Project (3GPP) TR 38.901 path loss and Manhattan mobility, EdgeSAC increases throughput over SAC and Proximal Policy Optimization (PPO) and reduces power relative to Twin Delayed Deep Deterministic Policy Gradient (TD3), which raises energy efficiency and fairness. The findings indicate that interference-aware graph embeddings combined with entropy regularized continuous control provide a scalable and power-efficient solution for hierarchical IoV resource management.

Existing AI-based meeting summarization tools have enabled rapid generation of meeting notes, yet their reliability and user controllability remain limited. This paper explores human-AI collaboration for mobile meeting summarization and presents MeetSumAid, a multifunctional system that integrates summarization algorithms with an interactive user interface. The system is designed to support users in understanding, validating, and refining AI-generated summaries through natural interactions and flexible control mechanisms. By enabling real-time inspection, editing, and feedback, MeetSumAid facilitates reliable collaboration between humans and AI in dynamic meeting scenarios. A user study with 20 participants shows that MeetSumAid significantly improves summary quality, generation efficiency, and user-perceived reliability compared with baseline AI summarizers, while reducing cognitive load. Further analysis reveals how different interface components enhance users' engagement and confidence during collaboration. This work provides a practical step toward reliable and user-centered human-AI collaboration in mobile meeting summarization and offers actionable design implications for future intelligent collaborative systems.

Estimating the soluble solids content (SSC) in fruits is essential for meeting consumer expectations, ensuring the quality of processed products, and minimizing food waste. Current methods often rely on destructive sampling, expensive equipment, and complex procedures, which limit their practicality. This paper presents VNiScan-Fruit, a non-invasive, low-cost, and easy-to-use optical sensing system that utilizes visible-near-infrared (Vis-NIR) spectroscopy to estimate SSC by analyzing the interaction of light with fruit tissues to generate characteristic absorption spectra. Our approach diverges from traditional high-precision spectrometers, employing commercial LEDs and photodetectors (PD) to construct the optical sensing unit. We design and implement innovative ring-shaped rubber enclosure, interference elimination algorithms, and reconstruction strategy to extract low-dimensional reflectance spectral features from various fruits. These features are then processed using a specially designed nonlinear regression model, incorporating sucrose values obtained from a commercial refractometer to estimate SSC accurately. We tested VNiScan-Fruit on a range of fruits with diverse peel characteristics, demonstrating its ability to penetrate exocarps and effectively analyze fruits with rough surfaces and delicate tissues. The system achieved normalized mean absolute errors (NMAE) of 8%, imperceptible to consumers in sweetness difference, and remained stable under varying lighting and temperature conditions. Our findings highlight the potential of VNiScan-Fruit as a practical tool for non-destructive fruit quality assessment.

The rapid development of the low-altitude economy (LAE) has significantly enhanced the service diversity of the Internet of Things (IoT) networks, necessitating efficient coordination among the unmanned aerial vehicles (UAVs). In this work, we utilize multiple UAVs to provide both wireless data collection (WDC) service and wireless energy transfer (WET) service, and divide the IoT devices into the I-devices that only need the WDC service and the E-devices that only need the WET service, respectively, from the multiple UAVs. Due to their conflicting service demands on the UAVs with limited resources, we formulate the bi-objective optimization problem (BOOP) to minimize the age of information (AoI) for the I-devices and the hungry-level of energy (HoE) for the E-devices at the same time, by jointly optimizing all the UAVs' trajectories and WET decisions over time slots and their WDC decisions over sub-slots. To efficiently solve the complex BOOP, we innovatively transform it into a single-objective optimization problem (SOOP), in which the two conflicting objectives are scalarized via a self-adaptive objective weight. Unlike the conventional approach reliant on fixed and pre-defined objective weights, we optimize the objective weight jointly with other decision variables, enabling automatic adaptation to various network environments without human intervention. However, the proposed SOOP is NP-hard with a large number of decision variables. Accordingly, we propose a new Multi-Agent Adaptive and Hierarchical Deep Reinforcement Learning (MA$^{2}$HDRL) framework, which leverages a central controller (CC) to guide the local training of multiple individual UAV agents. In this framework, each UAV agent employs a two-tier hierarchical DRL model: tier-1 optimizes the trajectory and WET policies over the time slots, while tier-2 optimizes the WDC policy across the sub-slots. Meanwhile, the CC trains the global reward preference for all the UAV agents over the training episodes, to adaptively balance the WDC and WET service demands. Finally, extensive simulation results are conducted to demonstrate the outstanding performance of the proposed MA$^{2}$HDRL approach as compared to state-of-the-art benchmarks.

This paper proposes a novel scan-then-Iocalize scheme for the unmanned aerial vehicle (UAV) to localize a wireless sensor (WS). In both of the scan and the localizing phases, the UAV estimates its range and/or radial speed to the WS, by exploiting the orthogonal frequency division multiplexing (OFDM) sensing signal. We first derive the UAV's maximally allowable sensing radius to the WS to assure a sufficiently high correct estimation probability. Next, in the scan phase, we design the UAV's scan trajectory along a series of waypoints, where the number of the waypoints and their locations are found by solving the classic region least circle coverage problem. The scan phase ends when the UAV's estimated horizontal range to the WS becomes no larger than the maximally allowable sensing radius. The UAV then begins the localizing phase, where unlike the traditional three-measurement based localization, we propose that only two measurements of the UAV's range and radial speed to the WS are sufficient to localize the WS, but may generate a falsely-found ghost WS. By further identifying the UAV's improper flight angles that generate the ghost WSs, we newly propose the two-measurement based accurate localization algorithm. Finally, various simulation results are provided to validate the localizing performance of our proposed scheme.

This paper proposes a novel design on the wireless powered communication network (WPCN) in dynamic environments under the assistance of multiple unmanned aerial vehicles (UAVs). Unlike the existing studies, where the low-power wireless nodes (WNs) often conform to the coherent harvest-then-transmit protocol, under our newly proposed double-threshold based WN type updating rule, each WN can dynamically and repeatedly update its WN type as an E-node for non-linear energy harvesting over time slots or an I-node for transmitting data over sub-slots. To maximize the total transmission data size of all the WNs over$T$slots, each of the UAVs individually determines its trajectory and binary wireless energy transmission (WET) decisions over times slots and its binary wireless data collection (WDC) decisions over sub-slots, under the constraints of each UAV's limited on-board energy and each WN's node type updating rule. However, due to the UAVs’ tightly-coupled trajectories with their WET and WDC decisions, as well as each WN's time-varying battery energy, this problem is difficult to solve optimally. We then propose a new multi-agent based hierarchical deep reinforcement learning (MAHDRL) framework with two tiers to solve the problem efficiently, where the soft actor critic (SAC) policy is designed in tier-1 to determine each UAV's continuous trajectory and binary WET decision over time slots, and the deep-Q learning (DQN) policy is designed in tier-2 to determine each UAV's binary WDC decisions over sub-slots under the given UAV trajectory from tier-1. Both of the SAC policy and the DQN policy are executed distributively at each UAV. Finally, extensive simulation results are provided to validate the outweighed performance of the proposed MAHDRL approach over various state-of-the-art benchmarks.

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 multi-agent 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.

Abstract This paper applies the wireless powered communication network (WPCN) to an indoor communication system with two energy harvesting (EH) enabled users. Unlike the existing WPCN works designed for outdoor communications, where each user harvests energy only from the signals transmitted by a dedicatedly deployed hybrid access point (H‐AP), due to the short device‐to‐device distance in the indoor scenario, each user additionally harvests a sufficient amount of energy from the information signals transmitted by the H‐AP and the other user. First, the joint downlink and uplink throughput are maximized for the wireless powered indoor communication system. This problem is non‐convex. Thus, the authors manage to transform this problem into a convex one and solve it using convex optimization techniques. The solutions reveal that the total throughput increases largely over a reduced device‐to‐device distance due to the resultant harvested energy from both energy and information signals at each user. However, an unfair downlink versus uplink rate allocation phenomenon is observed. Thus, considering the importance of uplink communication quality for various indoor applications, a new problem is further proposed to maximize the uplink sum‐throughput over both users with an additional constraint to ensure a sufficient downlink rate. This problem is also shown to be non‐convex and is solved optimally by using a method similar to that in the first problem. Numerical results demonstrate the effectiveness of the proposed approach for improving the downlink versus uplink rate allocation fairness in the indoor wireless‐powered communication system.

In this paper, we investigate the uplink communication of a reconfigurable intelligent surface (RIS) assisted system, in which an user equipment (UE) with single radio frequency (RF) chain delivers information to an access point (AP) by adopting the spatial modulation (SM). Specifically, we first investigate the transmit SM (TSM) scheme and jointly optimize the UE’s power allocation matrix and the RIS reflection coefficients to enhance the system reliability. We formulate a non-convex optimization problem to reduce the system symbol-error-rate (SER) and propose a novel penalty-alternative optimizing algorithm to obtain a near-optimal solution. Following this, we show that with the assistant of RIS, receive SM (RSM) scheme can also be performed even if the UE has only one RF chain. Based on this observation, a novel RIS-assisted RSM scheme is proposed, which can provide a low cost and complexity solution for system realization. The reflection coefficients of the RIS are also optimized for the proposed RSM. Numerical results show that the RIS-assisted TSM can achieve a lower SER than the conventional communication scheme (CTS) without SM and the proposed RSM scheme has lower detection complexity than that of CTS. It is also shown that the performance of the TSM and RSM schemes is more sensitive to the quantization accuracy of the phase of the RIS coefficients than that of the amplitude.

To develop sustainable fifth generation (5G) wireless networks and utilize the unused spectrum, this paper focuses on cognitive radio (CR) based wireless information and energy transmissions from an unmanned aerial vehicle (UAV) to multiple low-power ground terminals (GTs). By practically considering the location-dependent air-to-ground (A2G) channel states and the non-linear energy harvesting (EH), we propose a dynamic fly-hover-transmit scheme, where the UAV successively flies between GTs, and hovers close to each GT for efficient wireless energy transfer (WET) or wireless information transfer (WIT) when the primary user (PU) is idle. By causally and optimally determining the UAV's mobility and transmit power for each selected transmission mode (WIT, WET, or being silent), we formulate the UAV's sum-throughput maximization over all GTs as a constrained Markov decision process (MDP) problem with battery energy constraints at all GTs and the UAV. Due to the infinitely large MDP system state space, this problem is difficult to solve. We then decompose this problem into two subproblems, by first deciding the UAV's transmission mode and power above a given GT, and then optimizing the UAV movement policy over multiple GTs. In the first subproblem, we propose an approximate to the complicated MDP value function of low complexity in closed-form, and then analytically derive the threshold-based suboptimal transmission policies. In the second subproblem, we optimally solve a simple-but-fundamental two-GT case, and then extend the general location-dependent GT weight design to an efficient suboptimal UAV movement policy. Simulation results show the significantly improved system performance under the proposed suboptimal policies over various benchmarks in dynamic networks.

This paper investigates a spectrum sharing based cognitive unmanned aerial vehicle (UAV) network. To protect the primary users (PUs) from harmful co-channel interference from the UAVs' transmissions, and at the same time, to control the resultant downlink interference at the secondary users (SUs), each UAV is equipped with a directional antenna of adjustable beamwidth. We adopt a probability-based air-to-ground (AtG) channel model to reflect both line-of-sight (LoS) and non-line-of-sight (NLoS) effects from the UAVs' transmissions to the users on ground. By studying the interference distributions in both primary network and cognitive UAV network, we successfully characterize the coverage probabilities for both PUs and SUs. We then optimally design the UAV density and the UAV beamwidth, so as to maximize the UAV coverage probability under a PU coverage probability constraint. Despite of the complicated network performance characterizations that make this problem non-convex, we find an efficient method to solve this problem. Finally, numerical results are provided to validate the theoretical analysis, and show the performance of our proposed spectrum sharing method via UAV beamwidth over the benchmark scheme.

In this paper, we investigate the uplink of a millimeter-wave (mmWave) communication system, in which small base stations (SBSs) are densely placed. The positions of the SBSs are modeled as a homogeneous Poisson point process (PPP) and a user equipment (UE) connects to multiple SBSs opportunistically and adopts the spatial modulation (SM) scheme. Two detection methods, namely the centralized detection and distributed detection, are first proposed for the SBSs to demodulate the received signal and the power allocation scheme minimizing the system symbol error rate (SER) is investigated. Following this, by ignoring the side lobe of the UE, the probability that the SM scheme outperforms the conventional transmission scheme in which the UE only communicates with the SBS with a stronger channel is approximated to evaluate the performance of the SM scheme. Numerical results show that the SM scheme can effectively improve reliability of the system.

The wireless local area networks (WLANs) have been widely deployed with the rapid development of mobile devices and have further been brought into new applications with infrastructure mobility due to the growth of unmanned aerial vehicles (UAVs). However, the WLANs still face persistent challenge on increasing the network throughput to meet the customer’s requirement and fight against the node mobility. Interference is a well‐known issue that would degrade the network performance due to the broadcast characteristics of the wireless signals. Moreover, with infrastructure mobility, the interference becomes the key obstacle in pursuing the channel capacity. Legacy interference management mechanism through the channel access control in the MAC layer design of the 802.11 standard has some well‐known drawbacks, such as exposed and hidden terminal problems, inefficient rate adaptation, and retransmission schemes, making the efficient interference management an everlasting research topic over the years. Recently, interference management through exploiting physical layer mechanisms has attracted much research interest and has been proven to be a promising way to improve the network throughput, especially under the infrastructure mobility scenarios which provides more indicators for node dynamics. In this paper, we introduce a series of representative physical layer techniques and analyze how they are exploited for interference management to improve the network performance. We also provide some discussions about the research challenges and give potential future research topics in this area.

Traditional macrocell networks are experiencing an upsurge of data traffic, and small-cells are deployed to help offload the traffic from macrocells. Given the massive deployment of small-cells in a macrocell, the aggregate power consumption of small-cells (though being low individually) can be larger than that of the macrocell. Compared to the macrocell 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 nonuniformly 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.

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. We consider that the BSs are aggregated as a microgrid with hybrid energy supplies and an associated central energy storage, which can store the harvested renewable energy and the purchased on-grid energy over time. 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. However, due to the curse of dimensionality of the DP, it is of high complexity to obtain the optimal on-grid energy purchase policy. We thus 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. Simulation results show that the suboptimal on-grid energy purchase can achieve near-optimal performance. We also compare the proposed policy with the existing schemes to show that our proposed policy can more efficiently save the on-grid energy cost over time.

Wireless charging is a promising way to power wireless nodes' transmissions. This paper considers new dual-function access points (APs), which are able to support the energy/information transmission to/from wireless nodes. We focus on a large-scale wireless powered communication network (WPCN), and use stochastic geometry to analyze the wireless nodes' performance tradeoff between energy harvesting and information transmission. We study two cases with battery-free and battery-deployed wireless nodes. For both cases, we consider a harvest-then-transmit protocol by partitioning each time frame into a downlink (DL) phase for energy transfer, and an uplink (UL) phase for information transfer. By jointly optimizing frame partition between the two phases and the wireless nodes' transmit power, we maximize the wireless nodes' spatial throughput subject to a successful information transmission probability constraint. For the battery-free case, we show that the wireless nodes prefer to choose small transmit power to obtain large transmission opportunity. For the battery-deployed case, we first study an ideal infinite-capacity battery scenario for wireless nodes, and show that the optimal charging design is not unique, due to the sufficient energy stored in the battery. We then extend to the practical finite-capacity battery scenario. Although the exact performance is difficult to be obtained analytically, it is shown to be upper and lower bounded by those in the infinite-capacity battery scenario and the battery-free case, respectively. Finally, we provide numerical results to corroborate our study.

This paper studies spatial capacity in a stochastic wireless ad hoc network. We propose a novel signal-to-interference-ratio (SIR) threshold based scheduling scheme with multi-stage probing and data transmission, where each transmitter iteratively decides to further probe or stay idle, depending on whether the estimated SIR in the proceeding probing is no smaller than a predefined threshold. Though the locations of the initial transmitters can be modeled as a homogeneous Poisson Point Process (PPP), the SIR based scheduling makes the PPP model no longer applicable in the subsequent probing and data transmission phases. We first focus on single-stage probing and find that when the SIR threshold is set sufficiently small to assure an acceptable network interference level, the proposed scheme can greatly outperform the reference scheme without any transmission scheduling in terms of spatial capacity. We clearly characterize the spatial capacity with exact/approximate closed-form expressions, by proposing a new approximate approach to deal with the correlated SIR distributions over non-PPPs. Then, we successfully extend to multi-stage probing, by properly designing the multiple SIR thresholds to assure gradual improvement of the spatial capacity. Furthermore, we analyze the impact of multi-stage probing overhead and present a probing-capacity tradeoff in scheduling design. Finally, extensive numerical results are presented to demonstrate the scheduling performance.

This paper studies the spatial capacity of wireless ad hoc networks. We propose a transmission scheme with threshold-based scheduling, where each transmitter decides to transmit in the data transmission phase if the signal-to-interference-ratio (SIR) at its receiver in the preceding pilot phase is no smaller than a predefined threshold. For comparison, we also consider a reference scheme, where all transmitters transmit independently in both the pilot and data transmission phases. For both schemes, we assume a homogeneous Poisson Point Process (PPP) to model the locations of transmitters that have the intention to transmit. However, for the proposed scheme, the point process formed by the retained transmitters in the data transmission phase is generally not a PPP due to the SIR-based scheduling. First, we show how to set the SIR threshold in the proposed scheme to assure that it outperforms the reference scheme in terms of network spatial capacity. Then, we present exact/approximate spatial capacity expressions for the proposed scheme with different SIR-threshold values. Finally, we provide simulation results to validate our analysis.

Opportunistic spectrum access (OSA) is a key technique enabling the secondary users (SUs) in a cognitive radio (CR) network to transmit over the "spectrum holes" unoccupied by the primary users (PUs). In this paper, we focus on the OSA design in the presence of reactive PUs, where PU's access probability in a given channel is related to SU's past access decisions. We model the channel occupancy of the reactive PU as a 4-state discrete-time Markov chain. We formulate the optimal OSA design for SU throughput maximization as a constrained finite-horizon partially observable Markov decision process (POMDP) problem. We solve this problem by first considering the conventional short-term conditional collision probability (SCCP) constraint. We then adopt a long-term PU throughput (LPUT) constraint to effectively protect the reactive PU transmission. We derive the structure of the optimal OSA policy under the LPUT constraint and propose a suboptimal policy with lower complexity. Numerical results are provided to validate the proposed studies, which reveal some interesting new tradeoffs between SU throughput maximization and PU transmission protection in a practical interaction scenario.

Opportunistic spectrum access (OSA) is a key technique for the secondary user (SU) in a Cognitive Radio network to transmit over the "spectrum holes" unoccupied by the primary user (PU). Most existing work on the design of OSA has assumed a non-reactive (NR) PU model, i.e., the PU transmission on-off status is independent of the SU access policy, which may not be practical. In this paper, we propose a new Reactive Primary User (RPU) model for the study of OSA, where the PU's access probability over a particular channel is related to the SU's past access history. We model the channel occupancy of the RPU as a 4-state memoryless Markov chain, as opposed to the conventional 2-state (on/off) counterpart, where the expanded state space and state transition probabilities are used to model the reactions of the PU subject to the SU transmit collision. Under this model, we formulate the optimal OSA design for the SU's throughput maximization as a finite-horizon partially observable Markov decision process (POMDP) problem, subject to a conditional collision probability constraint for protecting the PU. Because of the high complexity of the proposed problem, we further propose a separation principle to obtain the optimal policy for the SU with implementable complexity. Numerical results show the new tradeoff between the SU's and the PU's throughput under the RPU model, as compared to the conventional NRPU model.

We consider a cognitive network which is operating with licensed networks simultaneously. One of the major concerns lies in the fact that cognitive networks may cause harmful interference to licensed users. This paper focuses on a joint channel and power allocation scheme that can both protect licensed users and meet the quality of service (QoS) of cognitive radio users with fairness. The problem can be formulated as a nonlinear programming. For reducing complexity in obtaining optimal channel and power allocation scheme, we propose a two-step suboptimal scheme that can achieve good performance with lower complexity. In the first step, resources, such as channels and powers, are allocated by cognitive radio base station (CRBS) based on fairness and QoS requirements to get the maximum available resources of cognitive radio customer premises equipment (CRCPE). In the second step, to get the final resource and reduce algorithm complexity, the allocation task is accomplished by each CRCPE simultaneously, rather than accomplished by CRBS in a serial order. Theoretical analysis and simulation results show that our scheme can support the QoS of CRCPEs with both lower power consumption and fair resource allocation.