Line-of-sight (LOS) communication plays a critical role in enhancing received signal quality in wireless communications. However, presence of blockages located in the way between transmitters and receivers can significantly degrade performance of wireless transmission. To address this challenge, we propose a novel computer vision (CV)-aided approach for maintaining LOS communications. The proposed approach is designed with a two-stage framework and integrates lightweight models for image segmentation and amodal completion, which is specifically tailored for efficient deployment on resource-constrained edge devices in order to enable precise assessment for occlusion at low computational cost. Specifically, image segmentation model features improved network structures of backbone and atrous spatial pyramid pooling (ASPP) module, as well as a specially designed loss function to alleviate imbalance between background and foreground. The amodal completion model is trained via a self-supervised fashion in order for accurate mask completion without the need for manual annotations. Additionally, based on the evaluated occlusion ratio, we present an efficient strategy for adjusting the transmitter’s position to maintain a stable LOS link. A prototype verification system is developed to validate the proposed approach, and experimental results demonstrate its capability of highly efficient position adjustment sustainable LOS links.

Intelligent Reflecting Surfaces (IRS) integrated with unmanned aerial vehicles (UAVs) offer a transformative solution for enhancing wireless communication performance. This paper investigates a dual-UAV-mounted IRS-assisted multi-user system under line-of-sight (LoS) channels. We jointly optimize UAV trajectories, IRS phase shifts, and user scheduling to maximize the minimum achievable rate for users. We decompose the non-convex problem into three subproblems: user scheduling, passive beamforming, and trajectory optimization, and solve them via alternating optimization (AO) with successive convex approximation (SCA). A refinement phase further aligns fine-tuning fading by adjusting UAV positions using interior-point and trust-region methods. Simulations demonstrate significant rate improvements over single-UAV and fixed-IRS benchmarks, validating the efficacy of the proposed framework. The results highlight the potential of dual UAV-IRS collaboration in dynamic channel adaptation and performance enhancement for future 6G networks.

Myocardium segmentation is critical for remote diagnosis and intelligent monitoring of cardiac disease in e-Health systems. Existing deep-learning methods struggle with class imbalance, anatomical heterogeneity, and blurred boundaries in medical images. Standard cross-entropy loss weight all pixels equally and cannot adapt to rare but important regions. Focal loss tend to overly emphasize difficult-to-classify samples and lose edge information. A shape-aware adaptive weighted cross-entropy (AWCE) loss is introduced, which combines a global shape prior with per-pixel uncertainty to focus learning on rare in the dataset but critical regions for each samples. On the ACDC dataset, the proposed AWCE loss achieves stable training convergence, recovers thin-wall structures in qualitative evaluations, and outperforms both cross-entropy and focal loss on key metrics. This shape-guided loss promises to accelerate the adoption of AI-driven cardiac analysis in future digital health applications.

Accurate segmentation of the left ventricular myocardium in cardiac CT images is essential for diagnosing and treating cardiovascular diseases. This is considered particularly critical in clinical workflows enabled by the Internet of Medical Things (IoMT), where real-time remote monitoring and rapid analysis of cardiac function are fundamental to timely and effective intervention. Deep learning techniques have achieved significant progress in automatically outlining target objects with reduced human effort. However, most of the deep learning approaches focus on pixel-level loss minimization in model training, and fail to capture the global shape characteristics of anatomical structures, resulting in inconsistent segmentation. In this paper, we propose an end-to-end deep learning framework, which incorporates shape priors derived using kernel principal component analysis (KPCA) to address such limitation. A shape loss function is introduced to enforce anatomical shape consistency by integrating shape prior of myocardial structures during model training. Comprehensive experiments on both convolutional and transformer-based architectures demonstrate that the proposed method significantly enhances segmentation accuracy and anatomical fidelity, particularly in regions critical for diagnosis, such as mitral valve, interventricular septum and papillary muscles. Moreover, we show that integration of learnable Gaussian kernel parameters in KPCA enhances adaptability, outperforming that with fixed kernel parameters. We believe that the improved segmentation accuracy helps doctors make more precise assessments of the heart. This is especially useful in IoMT-based healthcare systems, where reliable segmentation supports quick diagnosis, remote monitoring, and smarter medical decisions, leading to better care and outcomes for patients with heart disease.

Follicular thyroid carcinoma (FTC) is the second most common type of thyroid cancer and is highly aggressive, with a tendency to hematogenous metastasis. A definite diagnosis of FTC requires pathological examination after complete excision of the mass, and preoperative diagnosis of FTC is a challenge for both surgeons and imaging physicians. In this study, we aim to develop a multimodal deep learning model that combines grey scale ultrasound images, color doppler ultrasound images, and patient clinical data to predict the risk of FTC. This retrospective study include a dataset of 323 patients who underwent surgery. We develop and compare different models, including single modal, bimodal, and multi-modal models. The multimodal model performs the best, with an area under the curve (AUC) of 0.97. The results of the study demonstrate that the deep learning multimodal fusion method using grey scale ultrasound images, color doppler ultrasound images, and patient clinical data achieves better prediction performance.

Assessment of the left ventricle segmentation in cardiac magnetic resonance imaging (MRI) is of crucial importance for cardiac disease diagnosis. However, conventional manual segmentation is a tedious task that requires excessive human effort, which makes automated segmentation highly desirable in practice to facilitate the process of clinical diagnosis. The Internet of Medical Things (IoMT) and artificial intelligence (AI) for efficient medical data collection and analysis have been deemed effective approaches to remote and automatic diagnosis. In this article, we propose a novel reinforcement-learning-based framework for left ventricle contouring, which mimics how a cardiologist outlines the left ventricle in a cardiac image. Since such a contour drawing process is simply moving a paintbrush along a specific trajectory, it is thus analogized to a path finding problem. Following the algorithm of proximal policy optimization (PPO), we train a policy network, which makes a stochastic decision on the agent’s movement according to its local observation such that the generated trajectory matches the true contour of the left ventricle as much as possible. Moreover, we design a deep learning model with a customized loss function to generate the agent’s landing spot (or coordinate of its initial position on a cardiac image). We further propose an alternative approach for generating the landing spot based on interventricular septum detection, which is more efficient since no extra effort in data preprocessing and model training is involved. The experimental results show that the coordinates of the generated landing spots with both of the two approaches are sufficiently close to the true contour and the proposed reinforcement-learning-based approach outperforms the existing U-net model and its improved version, even with a limited training set.

In this article, we focus on a downlink cellular network, where multiple unmanned aerial vehicles (UAVs) serve as aerial base stations for ground users through frequency-division multiple access (FDMA). With user locations and channel parameters inaccessible, the UAVs coordinate to make a decision on resource allocation and trajectory design in a decentralized way. Aiming at optimizing both overall and fairness throughput, we model resource allocation and trajectory design as a decentralized partially observable Markov decision process (Dec-POMDP) and propose multiagent reinforcement learning (RL) as a solution. Specifically, we use parameterized deep$Q$-network (P-DQN) for the action space comprising both discrete and continuous actions and the QMIX framework is leveraged to aggregate each UAV’s local critics. For fairness throughput optimization, we introduce an entropy-like fairness indicator to the reward to make the total return decomposable. In addition, we further propose a novel distributed learning framework for overall throughput optimization such that each UAV can contribute its local gradient, and model training can be implemented in parallel without need of observation data sharing among the UAVs. Simulation results show that the proposed multiagent RL approach as well as the distributed learning framework are efficient in model training and present acceptable performance close to that achieved by deterministic optimization, which relies on convention optimization techniques with user locations and channel parameters explicitly known beforehand. For fairness throughput optimization, we also show that ground users achieve individual throughputs close to each other, which verifies the effectiveness of the proposed fairness indicator as the reward definition in the RL framework.

In this paper, we focus on a downlink cellular network, where multiple UAVs serve as aerial basestations to provide wireless connectivity to ground users through frequency division multi-access (FDMA) scheme. The UAVs are exclusively powered by a wireless charging station located on the ground following save-then-transmit protocol. In such a cellular network joint optimization for user association, resource allocation and basesation placement is investigated to maximize the downlink sum rate. The problem is formulated as a mixed integer optimization problem and is thus challenging to solve. We propose an efficient solution based on alternate optimization by iteratively solving one of the three subproblems at a time and an algorithm based on penalty method and successive convex optimization to binarize the association indicators. Numerical result shows that the downlink sum rate cannot be always enhanced by deploying more UAVs due to non-negligible tradeoff between energy/communication sources and co-channel interference.

In this paper, we focus on a typical cooperative communication system with one pair of source and destination, where a unmanned aerial vehicle (UAV) serves as a mobile relay while flying from a start location to an end location. With simultaneous wireless information and power transfer (SWIPT), the UAV's transmission capability is powered exclusively by radio signal transmitted from the source via time-sharing mechanism. In such a cooperative communication system, we study the end-to-end cooperative throughput maximization problem for amplify-and-forward (AF) protocol. The problem is decomposed into three subproblems for optimizing decision profile, power profile and trajectory, respectively. The first one is solved via standard linear programming techniques, the second one is solved via the dual decomposition while last one is solved via successive convex optimization, by which a lower bound is iteratively maximized. Then the end-to-end cooperative throughput maximization problem is solved by alternately solving the three subproblems. The numerical results show that the proposed optimal solution outperforms both static and naive mobile strategies.

In this paper, we focus on a cellular network with a energy-constrained unmanned aerial vehicle (UAV), which serves as a relay for all the users to improve their uplink rates via cooperative communication. Uplink resource allocation in terms of power and time allocation among users is investigated to optimize the uplink sum-rate. By leveraging the local search strategy, the sum-rate optimization problem is decomposed into two subproblems, i.e., energy allocation and time allocation. We derived the optimal solution for both amplify-and-forward (AF) and decode- and-forward (DF) protocols with in-depth analysis. Numerical results show that path loss exponent has non- trivial impact on the sum-rate while higher energy replenishment rate of the UAV relay brings little performance gain. In addition, location of the UAV is also non-negligible and the cellular network can significantly benefit from proper deployment of the UAV relay.

In this paper, we consider a multiuser downlink communication system, where ground users are served by a UAV base station flying on an aerial plane. For the sake of computational complexity and energy economy, we propose a polygonal-line trajectory for the UAV base station and each ground user is served one by one while the UAV base station is travelling along one of the line segments on the trajectory. With the polygonal- line trajectory, joint optimization for power control and trajectory design for the UAV base station is investigated to maximize total capacity of all the ground users. Such a problem is decomposed into three subproblems for single-user downlink power profile, transmission energy allocation and waypoints location selection, respectively, and solved via alternate optimization, with which one of the three subproblems is solved given solutions to the other two. Simulation results show that the proposed scheme with optimized power profile and trajectory for the UAV base station outperforms two baselines. We also show that trajectory design significantly impact the system performance and is indispensable in UAV-assisted communications.

In this paper, we focus on fundamental point-to-point wireless transmission with a reverse feedback channel for automatic repeat request (ARQ) and propose wireless energy replenishment (WER) for the transmitter with ACK or NACK signal sent back by the receiver. In such a case, transmission power control affects not only transmission reliability at the receiver but also WER performance at the transmitter and transmission sustainability. Hence, dynamic power control for the transmitter is investigated to optimize the expected number of transmitted bits in multiple timeslots and such a problem is modeled as a finite-horizon Markov decision process (MDP), which is further solved via the backward induction algorithm. We evaluate the transmission performance with impact of channel condition, WER performance, number of timeslots as well as modulation scheme and show that transmission with WER outperforms that without WER, especially for moderate channel condition.

In this paper, we consider a cooperative communication system, where the relay node is capable of simultaneous wireless information and power transfer (SWIPT) by splitting the signal from the source node into two power streams for energy harvesting and information relaying, and study the optimal design to maximize the cooperative capacity for both amplify-and-forward (AF) and decode-and- forward (DF) protocols. The closed-form optimal power-split ratio is derived for the two protocols with in-depth analysis and performance in cooperative capacity is evaluated for the optimal power splitting scheme with AF and DF protocols. Experiment results show that AF protocol benefits more from favorable cooperation link condition than the DF protocol and cooperation link condition contributes more to the optimal cooperative capacity compared with direct link condition. Asymptotic analysis also show that signal processing noise is also non-negligible to the optimal cooperative capacity.

In cooperative spectrum sensing, wireless reporting channels (from local sensing nodes to fusion center) may suffer severe unreliability, which would make a correct local sensing result incorrect while received by fusion center. In this study, the reliability of the sensing result transmission from local sensor to fusion center is considered, and to make spectrum sensing more energy efficient, appropriate nodes were selected and activated to participate in spectrum sensing while others remain idle. To further reduce energy consumption, transmission power control for sensing nodes was optimized. Total energy consumption minimization was modeled as a mixed discrete and continuous variable optimization problem and the binary particle swarm optimization with power control (BPSO-PC) was proposed. BPSO-PC adjusted the sensing node transmission power and properly selects nodes for cooperative spectrum sensing. Simulation results showed that total energy consumption was significantly reduced compared with BPSO and three other sensing nodes selection algorithms.

In this paper, we consider an energy harvesting cognitive radio (CR) system operating in slotted mode, where the secondary user (SU) has no wired power supplies and is powered exclusively by energy harvested from ambient environment. The SU can only perform either energy harvesting, spectrum sensing or data transmission at a time due to hardware limitation such that a timeslot is segmented into three non-overlapping fractions. Considering a generalized multi-slot spectrum sensing paradigm and two types of fusion rules: data fusion and decision fusion, we focus on the “harvesting-sensing-throughput” tradeoff and joint optimization for save-ratio, sensing duration, sensing threshold as well as fusion rule to maximize the SU's expected achievable throughput while keeping primary users (PUs) sufficiently protected. For data-fusion spectrum sensing, we translate the original problem into a convex one and show that the optimal solutions for sample number, mini-slot number as well as sensing threshold are non-unique. For decision-fusion spectrum sensing, we propose a two-level algorithm to solve the original problem with in-depth analysis on the convexity of a simplified problem and experiments show that the proposed algorithm is more efficient than differential evolution algorithm. We find that despite the inherent difference between the two types of fusion rules, the optimal data-fusion and decision-fusion strategies both converge to single-slot spectrum sensing while the SU's maximal expected achievable throughput is attained. Simulation results show that the optimal single-slot spectrum sensing strategy outperforms three other multi-slot strategies as well as two existing strategies while the empirical probability of detection is limited under a predefined level.

In this paper, we consider a cognitive radio (CR) system operating in slotted mode, where the secondary user (SU) has no wired power supplies and is powered exclusively through extracting energy from ambient environment. Due to hardware limitation, the SU can only perform either of energy harvesting, spectrum sensing or data transmission at the same time and we assume that a timeslot is partitioned into three non-overlapping fractions. Considering a generalized multi-slot spectrum sensing paradigm with data fusion rule, we focus on the "saving-sensing-throughput" tradeoff and joint optimization for save-ratio, sensing duration as well as sensing threshold to maximize the SU's expected achievable throughput while keeping primary users (PUs) sufficiently protected. By translating the original optimization problem into a convex one, we show that the optimal solutions for sample number, mini-slot number as well as sensing threshold are non-unique and single-slot spectrum sensing is more practically preferable. We also investigate the impact of system parameters to the optimal sensing strategy.

As a promising technology to address the issue of spectrum scarcity, cognitive radio (CR) has been receiving an increasing attention in recent years and existing works suggested that mobile service band could potentially present reuse opportunities for unlicensed access. In this sense, understanding mobile service's temporal and spatial dynamics can be of great help on spectrum management for mobile service band. In this paper, we perform in-depth theoretical analysis on the probability distribution of wireless signal strength received from mobile stations and propose a polylogarithm-like statistical model that characterizes mobile service usage's spatio-temporal dynamics (e.g., temporal and spatial density). To validate the proposed model, we perform empirical studies on the spectrum measurement data collected in an open area and fit the overall signal strength's probability density function (PDF) and cumulative distribution function (CDF) with the proposed model. The results indicate a significant fitting and testify the applicability of the proposed model to estimate the temporal and spatial density of mobile services in open areas.

With the rapid development of small cell, the interference problem in small cell enhancement network is urgent to be solved. This paper focuses on the interference coordination for co-channel deployed macrocell and small cell cluster where interferences exist in both cross-tier and intra- tier. Considering different priorities of macrocell and small cell, a semi-distributed strategy is proposed to optimize network performance. Frequency resource allocation scheme is investigated to get a target of interference limitation and throughput optimization for the network. Non-cooperative game Cournot model is employed for power adjustment in small cell cluster to increase cell-edge throughput by considering power limitation and the influence of macrocell. Simulation results show that the proposed strategy improves system performance in cell-edge throughput and coverage. Moreover, the strategy can also be applied to the scenario that macrocell and small cell have different priorities successfully.

In recent years, the excessive energy consumption in wireless communication systems has been increasingly critical, and environmental and financial considerations have motivated a trend in wireless communication technologies to resort to renewable energy sources. Energy harvesting is considered as a promising solution to alleviate such issues and has received extensive attentions. In this paper, we consider a cognitive radio system with one primary user (PU) and one secondary user (SU) and both of their transmitters operate in time-slotted mode. The SU, which harvests energy exclusively from ambient radio signal, follows a save-then-transmit protocol. In such a scenario, we investigate the SU's optimal cooperation strategy, namely, the optimal decision (to cooperate with the PU or not) and the optimal action (to spend how much time on energy harvesting and to allocate how much power for cooperative relay). We separately investigate the optimal action in non-cooperation and cooperation modes to maximize the SU's achievable throughout and derive the optimal closed-form solutions. Based on the analytical results of the optimal solutions, we propose the optimal cooperation protocol (OCP) to make the optimal decision, which simply involves a two-level test. Simulation results show that the proposed OCP outperforms the other two protocols (non-cooperation protocol and stochastic cooperation protocol) and the optimal underlay (OU) transmission mode.

In recent years, with the rapid growth in wireless communication applications, issues in energy consumption has been increasingly critical, especially in cognitive radio (CR) systems with the exclusive functionality of spectrum sensing. In this paper, we consider a self-powered cognitive radio system, in which the SU has no fixed power supplies (e.g. batteries) and is powered by an energy harvester which extracts energy from the ambient radio signal. It is assumed that the SU operates in a harvesting (also termed “saving”)-sensing-transmitting fashion, which partitions a timeslot into three non-overlapping fractions. Taking the tradeoff between the three operations into account, we focus on optimization for spectrum sensing strategy to maximize the SU's expected achievable throughput. We formulate the expected achievable throughput optimization as a mixe-dinteger non-linear programming (MINLP) problem and derive the optimal spectrum sensing strategy via a modified differential evolution (DE) algorithm. We also present in-depth numerical analysis on the optimal spectrum sensing strategy and the experimental results demonstrate the optimal sensing strategy outperforms the stochastic one in terms of statistical expectation.

In this paper, we consider a CR system operating in slotted mode, in which the SU has no fixed power supplies and extract energy only via wireless energy harvesting from ambient radio signal. It is assumed that the SU operates in a saving-sensing-transmitting (SST) fashion, which partitions a timeslot into non-overlapping fractions for the three operations. Considering the tradeoff between durations of saving, sensing and transmitting, we focus on the optimal structure for the three operations that maximizes the achievable throughput. By formulating the achievable throughput optimization as a mixed-integer non-linear programming (MINLP) problem, we derive the optimal SST structure via a modified differential evolution (DE) algorithm and perform in-depth numerical analysis. The simulation results demonstrate the performance gain of the optimal structure over non-optimal ones as well as affects of energy harvesting rate.

Energy harvesting is considered as a promising solution to efficiently prolong the lifetime of energy-constrained wireless networks. In particular, wireless energy harvesting, which scavenges energy from ambient radio signals, has recently received an increasing attention. In this paper, we consider a self-powered wireless system with one transmitter and one receiver, in which the transmitter has no fixed power supplies and extract energy only via wireless energy harvesting from ambient radio signals. It is assumed that the transmitter follows a save-then-transmit protocol, which specifies that a fraction (referred to as save-ratio) of time is devoted exclusively to energy harvesting while the remaining fraction is used for data transmission. We focus on the optimal save-ratio selection and achievable throughput maximization in two cases with regard to energy harvesting rate: the deterministic case, in which energy harvesting rate is known in advance, and the stochastic case, in which energy harvesting rate is unknown and only its statistical properties (e.g. probability distribution) are available. The optimal save-ratio for the two cases is theoretically derived as a function of energy harvesting rate (or its statistical properties). The experimental results characterize how the optimal save-ratio and the maximal achievable throughput vary with energy harvesting rate and validate the optimality of save-ratio selection.

Nowadays, cyber-physical system (CPS) relies on wireless networks for devices control and information backhaul. But the mass deployment CPS devices make operators' spectrum scarce situations even more worse. Hence, cellular network operators anticipate the Dynamic Spectrum Access (DSA) technology to solve the spectrum shortage problem in the context of cognitive radio (CR). Femtocells, acting as gateways in CPS, integrate CPS devices into cellular networks in a seamless manner. The concept of cognitive femtocell can solve the spectrum congestion problem even within a massive network on the CPS scale. However, in practical systems, cellular white space spectrum should be quantitatively measured to guide cognitive femtocell access algorithms design. We are the first to conduct a comprehensive measurement study for the purpose of measurement, discovery and model features of GSM white space spectrum. We evaluate availabilities of extra 21.4 MHz capacity in GSM white space spectrum as a reason of artificial GSM network spectrum planning. In our study, we find out that perfect results can hardly be obtained because of inherent measurement trade-offs, even when extremely high sweep speed of 16 GHz/s is applied at the receiver. Based on statistical analysis of real-scene traces, we propose an Efficient Duty Cycle (EDC) model to accurately characterize the white space in GSM network by considering miss-detection probabilities. Cross-validating evaluation results show that the EDC model can well decrease interference probabilities at high time-granularity measurement periodicity scenarios. Our results confirm the feasibility of cognitive femtocells access in an intra-operator scenario and can be applied to future wireless networks.

Dynamic spectrum access has been a subject of extensive study in recent years. The increasing volume of literatures calls for a deeper understanding of the characteristics of current spectrum utilization. In this paper, we present a detailed spectrum measurement study, with data collected in the 20 MHz to 3 GHz spectrum band and at four locations concurrently in Guangdong province of China. We examine the statistics of the collected data, including channel vacancy statistics, channel utilization within each individual wireless service, and the spectral and spatial correlation of these measures. Main findings include that the channel vacancy durations follow an exponential-like distribution, but are not independently distributed over time, and that significant spectral and spatial correlations are found between channels of the same service. We then exploit such spectrum correlation to develop a 2D frequent pattern mining algorithm that can predict channel availability based on past observations with considerable accuracy.

Dynamic spectrum access has been a subject of extensive research activity in recent years. The increasing volume of literature calls for a deeper understanding of the characteristics of current spectrum utilization. In this paper we present a detailed spectrum measurement study, with data collected in the 20MHz to 3GHz spectrum band and at four locations concurrently in South China. We examine the first and second order statistics of the collected data, including channel occupancy/vacancy statistics, channel utilization within each individual wireless service, and the temporal, spectral, and spatial correlation of these measures. Main findings include that the channel vacancy durations follow an exponential-like distribution, but are not independently distributed over time, and that significant spectral and spatial correlations are found between channels of the same service. We then exploit such spectrum correlation to develop a 2-dimensional frequent pattern mining algorithm that can accurately predict channel availability based on past observations.