Wearable Internet of Things (IoT) devices, used in healthcare, fitness, and industrial safety, rely on compact, streamlined antenna systems to achieve high data rates, reliable connectivity, and seamless multi-device operation. This paper introduces a flexible pulse-nexus MIMO antenna (PN-MIMO) design that achieves wideband CP based on a frequency-selective fractal gate absorber (FGA) for mmWave wearable systems. The single antenna is built on a Rogers RT/Duroid 5880 substrate and features compact dimensions of 0.48λ0×0.16λ0×0.02λ0at 24 GHz. The antenna is further configured into a quad-element MIMO system with a minimal port-to-port spacing of 0.048λ0. A novel concept is introduced whereby distinct resonance frequencies, one generated by the PN-MIMO antenna (23.4–24.2 GHz) and the other by FGA (24–26 GHz), are combined to achieve a wide impedance bandwidth (IBW) of 3.5 GHz (22.2–25.7 GHz) through constructive interference. The integration also results in low mutual coupling (≤ 25 dB), high gain (≥ 7.5 dBi), and a wider 3-dB axial ratio bandwidth (ARBW) of 2.5 GHz. The on-body performance and bending behavior of the antenna are evaluated on the chest, hand, and leg of a human model, demonstrating stable operation under structural deformation and body loading. Later, the simulation results are validated through measurements, showing good agreement. The proposed PN-FGA-MIMO antenna exhibits excellent radiation characteristics while maintaining SAR levels significantly below the US (1.6 W/kg) and EU (2 W/kg) safety limits, indicating its strong suitability for mmWave wearable applications.

Federated learning (FL) has emerged as a promising paradigm for privacy-preserving model training across distributed edge devices, enabling local data utilization without explicit sharing. However, in edge computing environments characterized by heterogeneous resources and intermittent connectivity, FL remains vulnerable to gradient leakage attacks (GLA), where adversaries reconstruct private data from shared model updates. Although the existing defenses, such as differential privacy (DP) and gradient compression, offer partial mitigation, they often result in significant performance degradation or increased communication overhead. In this paper, we analyze that the risk of privacy leakage is highly sensitive to the client-side training configurations and gradient magnitudes. Based on this, we propose a risk-aware FL framework tailored for the edge scenarios, which not only performs per-device privacy risk assessment but also introduces subtractive dithering quantization to the inject controllable Gaussian noise into local models. Additionally, a noise-aware aggregation strategy is presented by adjusting each client’s contribution to preserve the global model utility. Experimental results on FashionMNIST and CIFAR-10 demonstrate that the proposed framework achieves strong defense against the GLA, reduces the communication costs by over 50%, and maintains the competitive accuracy.

This paper describes a metasurface (MS) absorberbased circularly polarized (CP) antenna for 5 G mmWave applications. A simple circular notched edge antenna with rectangular slot and “+ “-shaped slot on the ground plane operating at the fundamental mode of the desired frequency band is utilized. Additionally, rectangular patch-based MS is designed to operate at the same fundamental mode as the antenna, and the MS is positioned above the antenna at a specified distance to achieve the desired 3-dB axial ratio bandwidth (ARBW) and a 2.2-dBi gain enhancement while maintaining an appropriate size of 30$\times 36 \times 0.254 \text{mm}^{3}$. The antenna achieved impedance bandwidth$\left(\left\vert \mathrm{S}_{11}\right\vert \leq-10 \text{dB}\right)$without MS from 27.18 GHz up to 28.63 GHz and realized a gain of around 4.8 dBi at 28 GHz. The antenna with MS achieved an impedance bandwidth (IBW) from 27.00 GHz up to 29.20 GHz and realized a gain of around 7.0 dB at 28 GHz, and radiation efficiency above of 80 %. The proposed antenna is a potential candidate for upcoming 5 G applications.

Over-the-air computation (AirComp) exploits the waveform superposition of wireless channels for fast data aggregation from multiple devices. The implementation of AirComp requires amplitude alignment among devices, which requires better channel conditions. Meanwhile, movable antenna (MA) is an emerging method to create better channel states via local antenna movement. To fully utilize the channel gain obtained by adjusting the positions of MA, we consider a MA-enabled AirComp system equipped with one-dimensional MA at transmitter to aggregate wireless data from a large number of devices. We aim to minimize the mean squared error (MSE) by jointly optimizing the antenna position vectors (APV), transmit power, and denoising factor at devices. To address this highly non-convex problem, an alternating optimization (AO) based algorithm is adopted by decomposing it into two sub-problems for power control and APV optimization, respectively. Specifically, we obtain a semi-closed form solution of transmit power control and denoising factor under any given antenna position, and then use second-order Taylor expansion to derive a more tractable MSE counterpart for APV optimization under successive convex approximation (SCA) technique. Experimental results show that, compared with other benchmark schemes, our proposed scheme demonstrates better performance.

Movable antennas (MAs) have emerged as a promising enhancement for integrated sensing and communication (ISAC) by dynamically adjusting antenna positions to significantly improve channel quality and overall ISAC system performance. In this paper, we investigate an MA-enabled ISAC system, where an ISAC base station (BS) equipped with a one-dimensional MA array needs to communicate with devices and detect potential targets at the same time. In particular, we aim to maximize the downlink common (minimum) throughput among all devices by jointly optimizing the transmit beamforming and antenna position vector (APV) at BS, while ensuring beampattern gain constraints in specified directions for sensing tasks and maximum transmit power constraint. Note that the formulated problem is highly non-convex and hard to be solved. To tackle with this problem, we adopt an alternating optimization (AO) based algorithm by decomposing the original problem into two subproblems. In the first subproblem, we construct a surrogate function via second-order polynomial expansion to optimize APV under successive convex approximation (SCA) technique. In the second subproblem, we use the bisection method to obtain feasible transmit beamforming under any given antenna position. The numerical results demonstrate that our proposed scheme not only improves the communication performance of communication devices but also ensures the required sensing performance.

In this article, a low-profile broadband dual-polarized antenna with a bilateral coupled feed and artificial magnetic conductor(AMC) is proposed. The antenna is composed of a radiating patch at the top, parasitic strips around it, a coupling feed structure under the radiating patch, a balun, an AMC structure and a slotted reflector at the bottom. The operating frequency band of the antenna is 0.69 ~ 0.96 GHz, and the overall size is small with low profile characteristics. It is 260 mm x260mm x 34.5 mm (approximately$0.718\lambda_{0}\times 0.718\lambda_{0}\times 0.096\lambda_{0}), S_{11}$and$S_{22}$are below -10 dB in the operating band, and the isolation between the two ports is lower than -24 dB. At the same time, it has a stable realization gain (8.16±1.01 dBi) and maintains excellent cross-polarization discrimination (XPD). Finally, the antenna's front-to-back ratio (FTBR) is higher than 22 dB, which has low backward radiation characteristics. The half-power beamwidth (HPBW) in the XOZ plane is (67.34°±6.13°), which meets the performance requirements for 5G base station antenna covering low frequency band.

Wireless data links and wireless power transfer (WPT) have become emerging topics in IoT applications. A simple-structure high-efficiency rectenna is the key technology for wirelessly powered IoT receivers. Herein, we explore a unified co-design technique that synergizes wideband circular polarization (CP) antennas with high-frequency Gallium Arsenide (GaAs) Schottky diodes across the 10–20-GHz range. Our approach involves a systematic study of the rectifier’s nonlinear behavior over wide frequency ranges, followed by the identification of an effective antenna candidate for co-design. This strategy effectively obviates the need for matching networks, filters, and extra components typically found in conventional wideband rectennas. Consequently, we present a co-design example: the proposed CP antenna, featuring a measured impedance bandwidth of 11.65–16.9 GHz (36.8%), a 3-dB axial ratio bandwidth (ARBW) of 12.55–16.9 GHz (29.54%), and a measured peak gain of 8.5 dBic. This design integrates CP magneto-electric (ME)-dipole units with rectifier topologies. Measured results show an RF-DC efficiency of over 50% within the 11–14.6 GHz (28%) and 10–18 dBm power range, with a maximum conversion efficiency of 61%. This design approach holds broad applicability for all wideband rectennas, offering notable advantages in terms of simplicity, efficiency, and compactness.

The rapid advancement of 6G technology has driven extensive research on integrated sensing, communication, and computation (ISCC), enabling applications in smart transportation, digital twins, and edge intelligence. ISCC aims to integrate communication, sensing, and computation functions to enhance system performance (e.g., energy efficiency, spectrum efficiency, and reduced latency) by designing an integrated architecture that comprehensively considers system resources and energy consumption. This paper provides an overview of key ISCC technologies, research contents, challenges, and prospects. It starts by analyzing key technical points, introducing their development history and metrics, and then discusses the reasons for integrating these key technologies to lay the groundwork for ISCC research. Subsequently, this paper categorizes and discusses existing ISCC research, ranging from different computational paradigms to emerging communication paradigms, highlighting the current research trends in ISCC. Additionally, it explores the interaction between ISCC and AI and how they can be mutually beneficial in research. Finally, we propose challenges for future ISCC research based on existing studies and suggest potential directions and ideas for research combining new technologies. The integration of ISCC and AI is expected to offer strong support for intelligent 6G by enabling intelligent resource management, reducing AI task handling latency, and improving AI inference accuracy.

The widespread adoption of computation- and communication-intensive applications, such as object detection, VR/AR, and telemedicine, has significantly alleviated transmission pressure on backbone networks and improved user experience. However, efficiently managing and computing these tasks on user sides remains a significant challenge, particularly under resource-constrained conditions. To address this problem, we propose a new service caching decision method based on deep dueling double Q-network (D3QN) by employing a learnable policy to handle the unknown task requests and determine the optimal caching strategies. Additionally, the limited storage capacity of edge servers (ES) is mitigated by forwarding the resource-intensive or infrequently requested tasks to the cloud data centers (CDC). The channel selection problem is modeled as a multiuser game and a distributed method is developed to achieve the Nash Equilibrium (NE). Simulation results demonstrate that the proposed method outperforms the existing benchmarks, showcasing its effectiveness in managing complex, dynamic environments.

Computing offloading optimization for energy saving is becoming increasingly important in low-Earth orbit (LEO) satellite-terrestrial integrated networks (STINs) since battery techniques have not kept up with the demand of ground terminal devices. In this paper, we design a delay-based deep reinforcement learning (DRL) framework specifically for computation offloading decisions, which can effectively reduce the energy consumption. Additionally, we develop a multi-level feedback queue for computing allocation (RAMLFQ), which can effectively enhance the CPU’s efficiency in task scheduling. We initially formulate the computation offloading problem with the system delay as Delay Markov Decision Processes (DMDPs), and then transform them into the equivalent standard Markov Decision Processes (MDPs). To solve the optimization problem effectively, we employ a double deep Q-network (DDQN) method, enhancing it with an augmented state space to better handle the unique challenges posed by system delays. Simulation results demonstrate that the proposed learning-based computing offloading algorithm achieves high levels of performance efficiency and attains a lower total cost compared to other existing offloading methods.

Reconfigurable intelligent surfaces (RIS) play a crucial role in enhancing the security of integrated sensing and communication (ISAC) systems. In this paper, RIS is explored to assist the secure transmission of user data in ISAC system. Through the joint design of the transmit beamforming and RIS discrete phase shifter, we aim to maximize user's secure rates while ensuring target sensing performance. Due to the coupling of optimization variables, conventional optimization methods are hard to address this formulated problem. Therefore, a deep reinforcement learning (DRL) scheme by utilizing the soft actor-critic (SAC) and alternating optimization (AO) algorithms is employed to design the transmit beamforming and the RIS discrete phase shifter, respectively. Simulation results indicate that the problem scheme could obtain a significant improvement in enhancing user secure rates compared to other benching scheme.

Unmanned aerial vehicles (UAVs) show significant potential in enhancing communication services within the mobile edge computing (MEC) system by taking their advantages on the flexible mobility and reliable line-of-sight links. However, in the scenarios with multiple UAV-MECs (UMs) operating concurrently, potential conflicts in their trajectories need to be mitigated. Thus, the 3-D trajectory needs to be properly designed in a highly reliable manner. Besides, such an infrastructure-free communication paradigm also exposes a potential risk of misuse by malicious parties, which allows them to eavesdrop on private communications, posing a threat to the security and privacy. Therefore, we consider a multi-UAV-assisted MEC communication system, where a UAV maliciously eavesdrops on the data transmission from the user devices (UDs) while a jammer is deployed on the ground to interfere with the eavesdropping channel. In specific, our objective is to minimize the energy consumption and latency while incorporating fairness metrics by optimizing the 3-D trajectories of UMs, transmission power of UDs, and the offloading strategies under the constraints of ensuring communication security and load fairness. Given the complexity of this mixed-integer nonconvex programming problem, we decompose the formulated problem into three subproblems. Specifically, at each time slot, we optimize the transmit power and offloading strategies using theoretical derivation and mathematical analysis, respectively. Additionally, a multiagent deep deterministic policy gradient (MADDPG) algorithm is employed to optimize the trajectories of UMs. Simulation results demonstrate that our proposed joint optimization algorithm successfully minimizes the system energy consumption and delay as compared to benchmarking schemes.

Satellite communication networks with the characteristics of wide coverage, high deployment flexibility, and seamless communication services can provide communication services to users who don’t communicate with ground networks but directly communicate with satellites. In response to the increasing demand for user services, this paper proposes a collaborative computing offloading scheme for satellite edge computing networks with a four-layer architecture. By utilizing collaborative computing between ground users and three layers of satellites (low-orbit satellites, edge, and cloud data centers), the service quality for ground users is improved. Considering the mobility of vehicles and satellite nodes, the frequent changes in link states further complicate the design and implementation of such systems, leading to increased latency and energy consumption. This paper proposes to optimize the computation offloading decision while satisfying the constraint of satellite computing capabilities, aiming to improve the success rate of tasks and minimize the overall cost of the system. However, with the increase in the number of ground users and satellites, the formulated problem becomes a mixed-integer nonlinear programming (MINLP) problem, which is difficult to solve with general optimization algorithms. To address this issue, this paper proposes a dynamic distributed learning offloading (DDLDO) algorithm based on distributed deep learning. The algorithm utilizes multiple parallel deep neural networks (DNN) to dynamically learn computation offloading strategies. Simulation results demonstrate that the algorithm outperforms other benchmark algorithms in terms of latency, energy consumption, and successful execution efficiency.

As the demand for global Internet connectivity continues to grow, the satellite-terrestrial integrated networks (STINs) have become more and more crucial for expanding the service coverage and enhancing the network performance. However, the task offloading problem in STINs faces many significant challenges, such as high processing latency and energy consumption. The current intelligent offloading strategies often rely on the real-time interactions with the environments which not only consume valuable satellite resources but also cause irreversible damage to the satellite equipment due to some operational errors. To address these issues, in this article, we propose an offline deep reinforcement learning (offline DRL) approach to learn and optimize the task offloading decisions by leveraging the stored historical decision data and employing the soft actor-critic (SAC) algorithm specifically. Experimental results show that the proposed strategy outperforms most of the existing methods in terms of latency and energy consumption and effectively reduces the direct interactions with STINs.

The integrated backscatter communication (BackCom) and active communication (AC) scheme can improve wireless powered mobile edge computing (WPMEC) system performance in general single-user and multi-user scenarios. However, there is little research in the cooperation-assisted WPMEC scenario. In this paper, we consider a cooperation-assisted WPMEC system consisting of a source node (SN), a helper and a hybrid access point (HAP) integrated with MEC servers. An innovative user cooperation (UC) scheme with integrated BackCom and AC is proposed to enhance the system performance. As a relay, the helper can help the SN to transmit its computing tasks due to the poor communication link between the SN and the HAP. To be specific, we aim at maximizing the user energy efficiency (EE) by jointly optimizing backscatter reflection coefficient for BackCom, transmission power for AC, system time and tasks allocation while considering the minimum computation bits requirement, the channel capacity and energy constraints. Based on a fractional program, the EE maximization problem first is transformed to an equivalent one. Then, we exploit variable substitution and convex optimization to transform this non-convex problem into a convex problem. In addition, semi-closed form expressions of the optimal solution are deduced. An energy efficiency maximization algorithm is proposed to solve this problem. Simulation results demonstrate that the proposed scheme significantly improves the user EE than the existing schemes.

We explore the waveform design problem of a multi-input multi-output (MIMO) integrated sensing and communication (ISAC) system, which minimizes the weighted sum of downlink multi-user interference (MUI) energy and directional beampattern dissimilarity under the total power constraint. To solve the optimization problem, we adopt the alternating direction method of multipliers (ADMM). The ADMM algorithm solves this optimization problem by decomposing the original optimization problem into smaller and easier-to-solve subproblems. Numerical results show that the proposed scheme is superior to the low-complexity algorithm in average sum rate performance and the symbol error rate (SER) performance.

As an emerging technology, mobile edge computing (MEC) can provide users with higher quality of service (Qos) such as reducing tasks computing latency and energy consumption of user equipments. Unmanned Aerial Vehicle (UAV) -assisted MEC can apply this technology to more scenarios. In this paper, we design a joint optimization algorithm to optimize the user's task offloading strategy and the trajectory of the UAV. When the MEC server interacts with multiple users at the same time, we adopt the differential evolution (DE) algorithm to obtain the offloading policy of each user in the current time slot based on the user location and UAV location. Aiming at the trajectory optimization problem of the UAV, we adopt the optimistic actor-critic (OAC) algorithm, which can minimize the weighted sum of energy consumption and delay of the system, and derive the optimal path through training. Simulation results show that the proposed algorithm is superior to other algorithms in terms of energy consumption and convergence performance.

Intelligent reflecting surface (IRS) consisting of a plane including many passive components can control even-t signals and create a programmable wireless environment by reconfigurable passive components. Passive components are typically composed of electronic components such as positive intrinsic-negative (PIN) diodes, field effect transistors, and micro electromechanical system switches, whose inherent characteristics show without active circuitry. Given that intelligent reflecting surfaces can strengthen the property of wireless transmission channels by adjusting the phase of incident signals, we investigate a multi-input single-output smart home model for downlink multi-users in this paper. An alternating optimization (AO) algorithm is proposed to cope with the challenge of maximizing the weighted sum of objectives of multiple users. To increase the sum rate, the beamforming vector at the access point and the phase shift matrix at the IRS are jointly tuned. In perfect channel state information, the AO approach is employed to achieve the effect of user maximization. Typically, fractional programming is utilized to optimize the beamforming vector at AP, while the Riemannian conjugate gradient approach is used to design the phase shift at IRS. Compared with the baseline methods, our proposed method can significantly improve convergence of the system.

Unmanned aerial vehicles (UAVs)-assisted mobile-edge computing (MEC) communication system has recently gained increasing attention. In this article, we investigate a 3-D multi-UAV trajectory optimization based on ground devices (GDs) selecting the target UAV for task computing. Specifically, we first design a 3-D dynamic multi-UAV-assisted MEC system in which GDs have real-time mobility and task update. Next, we formulate the system communication, computation, and flight energy consumption as objective functions based on fairness among UAVs. Then, to pursue fairness among UAVs, we theoretically deduce and mathematically prove the optimal GDs’ selectivity and offloading strategy, that is, how GDs select the optimal UAV for task offloading and how much to offload. While ensuring the optimal offloading strategy and GDs’ selectivity between UAVs and GDs at each step, we model UAV trajectories as a sequence of location updates of all UAVs and apply a multiagent deep deterministic policy gradient (MADDPG) algorithm to find the optimal solution. Simulation results demonstrate that we achieve the minimum energy consumption under the premise of fairness and the efficiency of model processing tasks.

This paper presented a 76–81 GHz FMCW MIMO Radar transceiver with mixed-mode PLL. Utilizing series-fed patch antenna array, a prototype system is developed based on the proposed transceiver. On-chip Measurements show that reconfigurable sawtooth chirps could be generated with a bandwidth up to 4 GHz and a period as short as 30${\mu s}$. Real-time experiments demonstrate that the prototype MIMO radar has the ability of target detection and achieves an angular resolution of 9°

In this paper, a low‐profile circularly polarized (CP) conical‐beam antenna with a wide overlap bandwidth is presented. Such an antenna is constructed on the two sides of a square substrate. The antenna consists of a wideband monopolar patch antenna fed by a probe in the center and two sets of arc‐hook‐shaped branches. The monopolar patch antenna is loaded by a set of conductive shorting vias to achieve a wideband vertically polarized electric field. Two sets of arc‐hook‐shaped parasitic branches connected to the patch and ground plane can generate a horizontally polarized electric field. To further increase the bandwidth of the horizontally polarized electric field, two types of arc‐hook‐shaped branches with different sizes are used, which can generate another resonant frequency. When the parameters of the arc‐hook‐shaped branches are reasonably adjusted, a 90° phase difference can be generated between the vertically polarized electric field and the horizontally polarized electric field, so that the antenna can produce a wideband CP radiation pattern with a conical beam. The proposed antenna has a wide impedance bandwidth (∣S11 | <−10 dB) of 35.6% (4.97‐7.14 GHz) and a 3 dB axial ratio (AR) bandwidth at phi = 0° and theta = 35° of about 30.1% (4.97‐6.73 GHz). Compared with the earlier reported conical‐beam CP antennas, an important feature of the proposed antenna is that the AR bandwidth is completely included in the impedance bandwidth, that is, the overlap bandwidth of ∣S11 | <−10 dB and AR < 3 dB is 30.1%. Moreover, the stable omnidirectional conical‐beam radiation patterns can be maintained within the whole operational bandwidth.

This paper proposes a Markov decision process (MDP)-based pilot placement optimization approach for the radio access in 5G vehicle to everything communications to support Internet of Vehicles applications. The optimal placement problem of pilot symbols is based on a typical pilot-assisted frequency-division multiplexing transmission and simplified to a finite state-space representation. We propose and formulate a finite MDP so as to determine an appropriate pilot pattern from a set of candidate pilot configurations. Also, an enhanced pilot placement scheme is developed to reduce the complexity for solving the formulated MDP problems. Furthermore, we derive analytical expressions of the mutual information, which to some extent allow us to jointly evaluate the dynamics of the channel state in time and frequency domains. Numerical results generated by Monte Carlo simulations show that the proposed pilot optimization policy is capable of improving the channel estimation in fast time-varying vehicular channels, and the mutual information-based measurement criteria can yield more accurate evaluations in fast time-varying vehicular channels than other conventional schemes.

Led by industrialization of smart cities, numerous interconnected mobile devices, and novel applications have emerged in the urban environment, providing great opportunities to realize industrial automation. In this context, autonomous driving is an attractive issue, which leverages large amounts of sensory information for smart navigation while posing intensive computation demands on resource constrained vehicles. Mobile edge computing (MEC) is a potential solution to alleviate the heavy burden on the devices. However, varying states of multiple edge servers as well as a variety of vehicular offloading modes make efficient task offloading a challenge. To cope with this challenge, we adopt a deep Q-learning approach for designing optimal offloading schemes, jointly considering selection of target server and determination of data transmission mode. Furthermore, we propose an efficient redundant offloading algorithm to improve task offloading reliability in the case of vehicular data transmission failure. We evaluate the proposed schemes based on real traffic data. Results indicate that our offloading schemes have great advantages in optimizing system utilities and improving offloading reliability.

In this paper, we investigate a resource allocation scheme for fully loaded distributed antenna system (DAS) with Device-to-Device (D2D) communication. A framework of resource allocation for D2D communications under laying a fully loaded DAS is presented, where the objective is to maximize the throughput of shared spectrum channel. First, we set up DAS with D2D communication model. Then, a dual threshold power control scheme is studied for each D2D pair and its cellular user (CU) partner to maximize the throughput of shared spectrum channel. Finally, the optimal solution is obtained according to the convex optimization theory. Numerical results show that the proposed scheme can significantly improve the throughput and communication quality of communication network system compared to the co- located antenna system (CAS) with D2D communication.

This paper proposes an Markov decision process (MDP) based pilot placement optimization approach for the radio access in 5G vehicle to everything (V2X) communications. The optimal placement problem of pilot symbols is based on a typical pilot-assisted OFDM transmission and simplified to a finite state-space representation. We propose and formulate a finite MDP so as to determine an appropriate pilot pattern from a set of candidate pilot configurations. Additionally, an enhanced pilot placement scheme is developed to reduce the complexity of solving MDP problems. We derive analytical expressions of the mutual information, which to some extent allow us to jointly evaluate the dynamics of the channel state in time and frequency domain. Numerical results show that the proposed pilot optimization policy is capable of improving the channel estimation, and the mutual information based measurement criteria can yield more accurate evaluations in fast time-varying vehicular channels.

To diagnose various autoimmune diseases, the accurate Human Epithelial-2 (HEp-2) cell image classification is a very important step. Automatic classification of HEp-2 cell using microscope image is a highly challenging task due to the strong illumination changes derived from the low contrast of the cells. To address this challenge, we propose a deep residual network (ResNet) based framework to recognize HEp-2 cell automatically. Specifically, a residual network of 50 layers (ResNet-50) with substantial deep layer is adopted to acquire the informative feature for accurate recognition. To further boost the recognition performance, we devise a novel ResNet-based network with deep supervision. The deeply supervised ResNet (DSRN) can address the optimization problem of gradient vanishing/exploding and accelerate the convergence speed. DSRN can directly guide the training of the lower and upper levels of the network to counteract the effects of unstable gradient variations by the adverse training process. As a result, DSRN can extract more discriminative features. Experimental results show that our proposed DSRN method can achieve an average classification accuracy of 93.46% and 95.88% on ICPR20l2 and ICPR20l6- Taskl datasets, respectively. Our proposed method outperforms the traditional methods as well.

In massive MIMO systems, a large number of antennas are difficult to be placed in a limited space, and the antenna space limitation causes a high spatial correlation between the antennas, furthering causes systems performance degradation. In this paper, we implement the multi-polarized antennas in point-to-point massive MIMO systems to reduce the correlation between antennas to enhance the systems performance and realize the space efficiency. Also we establish a 3-D geometrical channel model for the proposed point-to-point multi-polarized massive MIMO systems. The channel is modeled as a Ricean fading channel and the average correlation for the whole systems is defined to indicate the correlation degree of the systems. We compare the performance of multi-polarized massive MIMO systems with uni-polarized massive MIMO systems in different communication scenarios. The achieved results demonstrate that the multi-polarized massive MIMO systems have better performance compared to the uni-polarized massive MIMO systems in many situations.

With the evolutionary development of automobile industry, modern transportation systems cause a series of critical problems, such as increased energy consumption and air pollution. To make green cities a reality, an ever expanding and evolving vehicular heterogeneous network infrastructure is required to enable fine-granularity data collection and reliable service delivery. In this paper, we investigate how to realize energy-efficient vehicular heterogeneous networks for green cities by exploring cooperative two-hop device-to-device-based vehicle-to-vehicle (D2D-V2V) transmission. We propose a two-stage energy-efficient resource allocation algorithm. In the first stage, an auction-matching-based joint relay selection, spectrum allocation, and power control algorithm is derived, which employs an English-auction approach for matching preference updating and conflict avoidance, and optimizes the energy efficiency of two-hop D2D-V2V and cellular links simultaneously in an iterative fashion. In the second stage, a nonlinear fractional programming based power control algorithm is developed to maximize the energy efficiency of the base station. Theoretical properties in terms of convergence, stability, and complexity are analyzed. Finally, the proposed algorithm is evaluated based on real-world road topology and realistic vehicular traffic. Numerical results demonstrate that the proposed algorithm achieves superior performance in terms of energy efficiency and network coverage compared to other heuristic algorithms.

Due to their environment friendliness, electric vehicles (EVs) are anticipated to form a considerable fraction of vehicles for transportation in smart cities. It is essential to design an electricity charging scheme that takes the utilities of both the charging stations and the EVs into consideration. However, the self-interested nature of the EVs together with the information asymmetry between the energy demand and supply sides makes the design a significant challenge. In this paper, we propose a queuing network-based model to characterize the charging process of the multiple EVs in a renewable energy-aided charging station. Based on the model, we adopt a contract theoretic approach to design an optimal charging policy in an information asymmetry scenario. Furthermore, we propose the new contract-based charging rate assignment and admission control schemes that maximize the utility of the charging station under certain charging constraints. To derive the optimal contract, we present a two-step iterative algorithm and prove its convergence. We evaluate the proposed schemes based on the IEEE 69-bus distribution test system. Results indicate that the contract-based charging schemes can effectively benefit both the charging stations and the EVs and concurrently improve the load level of the smart grid.

In this paper, a novel broadband dual-polarized (slant ±45°) base station antenna element operating at 790-960 MHz is proposed. The antenna element consists of two pairs of symmetrical dipoles, four couples of baluns, a cricoid pedestal and two kinds of plastic fasteners. Specific shape metal reflector is also designed to achieve stable radiation pattern and high front-to-back ratio (FBR). All the simulated and measured results show that the proposed antenna element has wide impedance bandwidth (about 19.4%), low voltage standing wave ratio (VSWR219.66 dBi) are also achieved. The proposed antenna element fabricated by integrated metal casting technology has great mechanical properties such as compact structure, low profile, good stability, light weight and easy to fabricate. Due to its good electrical and mechanical characteristics, the antenna element is suitable for European Digital Dividend, CDMA800 and GSM900 bands in base station antenna of modern mobile communication.

Recently, massive multiple-input multiple-output (MIMO) systems have attracted considerable research interest and have been regarded as a candidate technology for the 5th generation (5G) cellular networks. In massive MIMO systems, a base station (BS) is equipped with a large number of antennas which are serving several simultaneous single antenna users. An increasing number of antennas is difficult to be placed in a limited space, and the antenna space limitation causes a high spatial correlation between the antennas, further resulting in systems performance degradation. In this paper, we establish a 3-D geometrical channel model for massive MIMO systems. We focus on the massive MIMO antennas correlation and channel capacity. We consider the far-field effect using the plane wave (PW) and use the spherical wave (SW) to model the near-field effect. We derive average correlation for certain single antenna to describe the single antenna correlation degree in the whole massive MIMO system, and define average correlation for the whole system antennas to describe the whole antennas correlation degree.

This paper proposes a decision-assisted pilot placement optimization method in IEEE802.11p physical layer. The fast time-varying channel is first modeled as a typical Gaussian-Markov process. Under the constraint of state-spaces, the pilot optimization problem is further formulated as constrained Markov decision processes (MDPs). Secondly, for achieving compatibility with existing standards, our goal is to determine the optimal pilot placement and employ only very limited pilot patterns in response to fast varying channels. We develop a channel state matched pilot optimization method, where the optimization procedures focus on how to respond the different channel variations in the time and frequency domains. To jointly evaluate the severity of channel variations in the time and frequency domains, we derive an effective mutual information measurement criterion. Simulation and numerical results show the efficiency of the pilot optimization decision scheme in reducing the channel estimation error, and mutual information measurement can yield an accurate performance evaluation in relatively fast time-varying vehicular communication scenarios.

This work introduces a wideband printed patch antenna design for 60 GHz radio applications. The proposed antenna is a single-layer structure with J-shaped slots loaded on the patch for the impedance bandwidth enhancement. The function of the J-shaped slots provides an additional current path for generating the second resonance of the patch. With this additional resonance, the antenna could find double resonances near the centre frequency of the patch. The antenna is excited by the grounded-coplanar waveguide (G-CPW) to microstrip line. This proposed antenna has an impedance bandwidth of 14.3% (8.6 GHz, 57-64 GHz) and the peak gain of 7.2 dBi. Its array of 4×1 arrangement yields impedance bandwidth of 19.2% (11.5 GHz, 55-65.5 GHz) with respect to the center frequency of 60 GHz, and rewards the maximum gain of 13.5 dBi at the broadside direction. This antenna has simple structure and is easy to fabricate by a conventional PCB technology. The proposed design finds a potential application of microstrip patch antenna in millimeter-wave wireless communications.

Orthogonal Frequency Division Multiplexing (OFDM) is designed to combat the effect of multipath reception, by dividing the wide band frequency selective fading channel into many narrow flat sub- channels, which improves the spectral efficiency and significantly mitigates the intersymbol interference (ISI). However, OFDM can not meet the demand for 5G heterogeneous service scenarios since it has a high out-of-band emission and a large peak-to-average power ratio (PAPR), and it only supports one kind of waveform parameter in the whole bandwidth. The Filtered-OFDM (F-OFDM) is proposed as a candidate technique for 5G high-data rate wireless communication system. This paper proposes a finite impulse response (FIR) digital filter based on window function method to achieve the F-OFDM, and discusses the performance of different window functions implemented in the F-OFDM. Simulation results show that the proposed F-OFDM is easy to be implemented and has a very low out-of-band emission with the same bit error rate (BER) performance compared to the conventional OFDM.

Mobile video streaming is fundamental to advanced applications in the fifth generation (5G) networks. Millimeter wave (mmWave) communication represents a leading 5G technology, which provides rich bandwidth and, therefore, great potentials for high-quality mobile video streaming. However, mobile video streaming in mmWave 5G networks faces fundamental challenges due to mmWave antenna directivity and high user mobility. As such, users typically have short connection durations and frequent handoffs, making video streaming suffer from long handoff delays and connection latency. In this paper, we tackle the issues by developing a caching-based mmWave framework, which precaches video contents at the base station for handoff users and thus significantly reduces the connection and retrieval delays. As a result, high-mobility users with frequent handoffs can enjoy continuous high-quality video streaming. Specifically, we model the proposed system as a cache management problem and attain optimal video streaming quality by using Markov decision process to dynamically allocate proper cache memory space of each base station to mobile users. A cell-by-cell decomposition method is proposed to solve the dynamic programming problem with significantly reduced computational complexity. Using extensive simulations, we demonstrate that the proposed solution can effectively maintain high-quality mobile video streaming for high-mobility 5G users moving among mmWave small cells with directional antenna.

In this paper, we present a novel wideband circularly polarized (CP) composite, called cavity-backed crossed dipole antenna for 2.45 GHz industrial, scientific, and medical (ISM) band wireless communication. To excite the CP radiation effectively, a curved-delay line providing an orthogonal phase difference among the cross-dipole elements is attached at corners of the sequentially rotated elements. By choosing a proper radius of the curved-delay line, a wide input impedance of the antenna can be realized. Unlike conventional cross-dipole antennas, the proposed cross-dipole antenna is designed with an open stub added to the radiating arms of the dipole so that both impedance and axial ratio bandwidths are enhanced. The antenna is center-fed by a 50-Ω coaxial cable and is placed above a cavity-backed reflector to obtain a directional CP radiation pattern. With the advantage of being center-fed, a symmetric CP radiation pattern can be achieved across the entire operating bandwidth. To further improve the directivity and the radiation pattern, a rectangular cavity-backed reflector is used. Simulated and measured results confirm that the proposed antenna has good CP characteristics. The proposed antenna obtains a broad 3-dB axial ratio bandwidth of 49% (1.20 GHz, 1.96–3.16 GHz) and an impedance bandwidth of 67.7% (1.66 GHz, 1.69–3.35 GHz) for reflection coefficient (S11) ≦ −10 dB. It also yields an average CP gain of 9.2 dBic across the operating bandwidth and a peak CP gain of 10 dBic. Copyright © 2016 John Wiley & Sons, Ltd.

This paper proposes a novel method for ergodic sum rate analysis of spatial modulation (SM) systems with maximum likelihood receiver. This method is developed based on the MT-ary symmetric channel, where MTis the number of transmit antennas. The probability of antenna detection error is approximated by the pair-wise error probability. Then, an approximation to the ergodic sum rate of information transmission via SM with maximum likelihood receiver is computed. It is demonstrated via simulation that the proposed analysis method is able to provide an excellent approximation to the ergodic sum rate of SM.

In this paper, we design and analyze three kinds of doubly-generalized low-density parity-check (D-GLDPC) codes. We employ single parity-check (SPC) codes as super variable nodes (SVNs) and two-dimensional (2-D) single parity-check product codes (SPC-PCs) as super check nodes (SCNs). Three kinds of different SPC codes as super variable nodes (SVNs) are used in the systematic (5) form, the cyclic (C) form and the anti-systematic (A) form. Because the minimum distance of SPC codes is 2, they can meet upper bound of D-GLDPC codes. The performance of the proposed D-GLDPC codes is investigated over the AWGN channel with the extrinsic information transfer (EXIT) charts. The SPC codes in different forms have an effect on stability bound of D-GLDPC codes and also make a difference in decoding threshold. Simulation results show that D-GLDPC codes with C-form SPC codes as SVNs have the best stability bound and the minimum decoding threshold, the second best is D-GLDPC codes with S-form SPC codes as SVNs, and the worse one is D-GLDPC codes with A-form SPC codes as SVNs.

Actual vehicle-to-vehicle (V2V) channel measurements have shown that the wide-sense stationary (WSS) modeling assumption is valid only for very short time intervals. This fact motivates us to develop non-WSS V2V channel models. In this paper, we propose a novel three-dimensional (3D) theoretical non-WSS regular-shaped geometry-based stochastic model (RS-GBSM) and the corresponding sum-of-sinusoids (SoS) simulation model for non-isotropic scattering wideband multiple-input multiple-output (MIMO) V2V fading channels. The movements of the transmitter (Tx), scatterers, and receiver (Rx) result in the time-varying angles of departure (AoDs) and angles of arrival (AoAs) that make our models non-stationary. The proposed RS-GBSMs, combining line-of-sight (LoS) components, a two-sphere model, and multiple confocal elliptic-cylinder models, have the ability to study the impacts of vehicular traffic density (VTD) and non-stationarity on channel statistics, and jointly consider the azimuth and elevation angles by using the von Mises Fisher (VMF) distribution. The proposed RS-GBSMs are sufficiently generic and adaptable to model various V2V scenarios. Based on the proposed 3D non-WSS RS-GBSMs, important local channel statistical properties are derived and thoroughly investigated. The impacts of VTD and non-stationarity on these channel statistical properties are investigated by comparing them with those of the corresponding WSS model. The proposed non-WSS RS-GBSMs are validated by measurements in terms of the channel stationary time. Finally, numerical and simulation results demonstrate that the 3D non-WSS model is more practical to characterize real V2V channels.

Errors encountered in digital wireless channels are not independent but rather form bursts or clusters. Error models aim to investigate the statistical properties of bursty error sequences at either packet level or bit level. Packet-level error models are crucial to the design and performance evaluation of high-layer wireless communication protocols. This paper proposes a general design procedure for a packet-level generative model based on a sampled deterministic process with a threshold detector and two parallel mappers. In order to assess the proposed method, target packet error sequences are derived by computer simulations of a coded enhanced general packet radio service system. The target error sequences are compared with the generated error sequences from the deterministic process-based generative model using some widely used burst error statistics, such as error-free run distribution, error-free burst distribution, error burst distribution, error cluster distribution, gap distribution, block error probability distribution, block burst probability distribution, packet error correlation function, normalized covariance function, gap correlation function, and multigap distribution. The deterministic process-based generative model is observed to outperform the widely used Markov models. Copyright © 2013 John Wiley & Sons, Ltd.

This paper analyzes the capacity of the finite scatterer (FS) multiple-input multiple-output (MIMO) channel model with finite angular resolution. The rank of the channel is bounded by not only the number of antennas but also the finite angular resolution of antenna arrays. The probability mass function (PMF) of rank is studied and derived based on the Stirling number of the second kind. As a result, the closed-form expression of the capacity of a FS MIMO channel model is obtained. Furthermore, asymptotic analyses such as the large-scale system analysis, infinite scatterer analysis, and the multiplexing gain analysis are investigated.

The sidelobe level of a base station antenna is one of the important parameters to describe the performance of an antenna array. Given a required value of the sidelobe level, we can obtain a set of initial phases, and then further get a set of cable lengths. However, a tolerance (or error range) associated with manufacturing techniques will introduce an error in each cable length, thereby influencing the sidelobe level. This paper uses the knowledge of probability and mathematical statistics to make a statistical analysis for the reliability of the first sidelobe of the antenna array based on Monte Carlo simulations. We also obtain a distribution curve of reliabilities of the first sidelobe versus phase tolerances, which can bring great convenience for practical applications.

Spatial modulation (SM) is a relatively new multiple-input multiple-output (MIMO) technology that can provide high data rate with reasonable spectral efficiency. In this paper, the bit error rate (BER) performance of SM systems under vehicle-to-vehicle (V2V) channel models is investigated. The theoretical BER expression is given. The impact of some V2V channel model parameters on the underlying space-time correlation function (STCF) and the BER performance of SM systems are also studied. Simulation results indicate that modulation schemes, maximum Doppler frequency, the distance between the transmitter (Tx) and receiver (Rx), and antenna element spacings can affect the performance of SM systems.

In this paper, we firstly introduce theory of magnetic coupling. Then, the shielding-effectiveness based electromagnetic shielding theory is studied, and the effectiveness of shielding materials to the coupling process is analysed in mobile payment systems. Simulation results show that when the feed frequency is low, plastic and lithium cell cause almost no attenuation to the magnetic field. If a shielding material is common metal, the magnetic field intensity is reduced to 20%-70% of the original. In the low frequency condition, the time-varying magnetic field produced by the transmitting coil is very stable and can resist the external interference. A convenient simulation software to calculate magnetic field coupling with shielding materials is also developed.

This paper proposes a novel theoretical non-stationary three dimensional (3-D) wideband twin-cluster channel model for massive multiple-input multiple-output (MIMO) communication systems with carrier frequencies on the order of gigahertz (GHz). As the dimension of antenna arrays cannot be ignored for massive MIMO, near field effects instead of far field effects are considered in the proposed model. These include the spherical wavefront assumption and a birth-death process to model non-stationary properties of clusters such as cluster appearance and disappearance on both the array and time axes. Their impacts on massive MIMO channels are investigated via statistical properties including correlation functions, condition numbers, and angular power spectra. Additionally, the impact of elevation angles on correlation functions is discussed. A corresponding simulation model for the theoretical model is also proposed. Finally, numerical analysis shows that the proposed channel models are able to serve as a design framework for massive MIMO channel modeling.

A new UHF RFID tag antenna used on the surface of a metallic object is proposed in this paper. In practical RFID applications, metals appear in many occasions. However, the UHF band is very sensitive to metal. When the tag antenna is near a metal, its impedance matching, radiation efficiency and directivity change and the reading distance is reduced so quickly that it cannot work. Therefore, it's necessary to employ some special handling or use a special tag so that it can be applied on the metal surface. After several rounds of design optimization and experimental validation, the designed tag antenna can work in the 920 MHz frequency band and is able to maintain a good performance on the metal surface. What's more, it has some advantages such as planar structure, small size and low cost. The proposed tag antenna provides an effective solution for metal identification applications in the UHF frequency band, which is validated by simulation results and practical test.

A radio frequency identification (RFID) tag antenna mountable on metal surfaces is designed. It operates in the UHF frequency band (usually called UHF RFID tag) and is matched to the Alien Higgs-3 chip (a kind of RF IC chip). Based on the special structure of the microstrip antenna-two conducting surfaces: the antenna plane and the ground plane, we embed a pair of E-type slot to obtain broadband characteristics and use a short-circuited stub feed in the antenna plane. The antenna impedance is conveniently matched to the complex conjugate of the RF IC chip impedance so as to achieve good power transfer from the antenna to the RF IC chip. The simulation and practical measurement results show that the proposed anti-metal tag antenna structure is simple and the antenna bandwidth reaches 250 MHz when the return loss S11 is less than 10 dB. Also, the proposed antenna has good radiation pattern and gain.

A dual-band tag antenna for UHF RFID systems is proposed. The tag antenna is composed of a zigzag main body and a zigzag loop, which is an inductively coupled feed structure antenna. The proposed structure is simple, which is not only easy to adjust the matching impedance but also to reduce the size of the antenna. Compared with the same type of antennas, this antenna structure is more compact and the length of antenna is reduced by at least 18%. Using the Alien's Higgs-3 tag chip, the proposed antenna operates at 860 MHz and 920 MHz, and the impedance match is achieved in a wide frequency band. We also measure the reading distances of the tag. Simulation and measurement results show that the proposed antenna has good performance and is very practical.

ABSTRACT Low‐density parity‐check (LDPC) codes are very powerful error‐correction codes with capabilities approaching the Shannon's limits. In evaluating the error performance of an LDPC code, the computer simulation time taken becomes a primary concern when tens of millions of noise‐corrupted codewords are to be decoded, particularly for codes with very long lengths. In this paper, we propose modeling the parity‐check matrix of an LDPC code with compressed parity‐check matrices in the check‐node domain (CND) and in the bit‐node domain (BND), respectively. Based on the compressed parity‐check matrices, we created two message matrices, one in the CND and another in the BND, and two domain conversion matrices, one from CND to BND and another from BND to CND. With the proposed message matrices, the data used in the iterative LDPC decoding algorithm can be closely packed and stored within a small memory size. Consequently, such data can be mostly stored in the cache memory, reducing the need for the central processing unit to access the random access memory and hence improving the simulation time significantly. Furthermore, the messages in one domain can be easily converted to another domain with the use of the conversion matrices, facilitating the central processing unit to access and update the messages. Copyright © 2011 John Wiley & Sons, Ltd.

This paper firstly introduced some principles of the phase-shifting network of an electronically-steered smart antenna for TD-SCDMA systems. Secondly, we described the principle of a phase-shifter. The proposed phase-shifter is used in TD-SCDMA systems and is simulated by the Ansoft HFSS software. Simulation results show that the proposed phase-shifter can obtain better angle of phase-shifting. The phase-shifting network based on the proposed phase- shifter can also attain efficient downtilt angle.

In this paper, we propose an iterative data detection scheme to combat unknown narrowband interference for orthogonal frequency division multiplexing (OFDM) systems. The key idea of the proposed scheme is to joint channel estimation, noise plus interference power estimation and decoding together to enhance both the estimation accuracy and bit error ration (BER) performance. We derive the Cramér-Rao Bounds (CRB) for mean square errors of channel estimation and noise plus interference estimations, and the CRBs can be achieved via the proposed scheme with very few iteration numbers, hence, the complexity of the proposed scheme is very low. Moreover, simulation results show that the decoding performance of the proposed scheme substantially approaches to that of the maximum likelihood decoder with perfect channel estimation and full knowledge of interference distribution.

In this paper, we propose a cross-layer design (CLD) scheme combining rate adaptation and four types of hybrid automatic repeat request (HARQ) for multiband orthogonal frequency division multiplexing (MB-OFDM) ultra wideband (UWB) systems following the ECMA-368 standard. The time varying property is incorporated into standard UWB channel models for the purpose of investigating rate adaptation. To accurately accommodate fast time-varying channel conditions, we propose to embed the selected rate information into the acknowledgement (ACK) frames. It is shown that the proposed CLD scheme combining rate adaptation and HARQ can provide higher throughput than the HARQ-only schemes. Among the four types of HARQ schemes, HARQ Type-III has the best throughput performance while Type I has the worst.

In this paper, we attempt to evaluate irregular LDPC code performance at the high SNR region using the importance sampling (IS) approach in conjunction with primary- trapping-set identification. Results have indicated that our proposed IS scheme can produce speed-up gains up to 3.9 x 109times compared with Monte Carlo simulations.

Study of complex networks has been conducted across many fields of science, including computer networks, biological networks and social networks. Characteristics of different types of complex networks such as random networks, regular-coupled networks, small-world networks and scale-free networks have been discovered by researchers. Application of such network properties to solve engineering problems, however, is still at the infancy stage. In this study, we make one of the first attempts in applying complex network theories to communications engineering. In particular, inspired by the shortest-average-path-length property of scale-free networks, we design short-length low-density-parity-check (LDPC) codes with an aim to shortening the average distance between any two variable nodes. We will also compare the error performance, both theoretically and by simulations, of the proposed codes with those of other well-known LDPC codes.

To enable coexistence between an OFDM based opportunistic spectrum access system and a primary user, we proposed two novel methods called EAIC (Extended Active Interference Cancellation) and EAIC-H (EAIC-Hybrid) for spectral sculpting of OFDM signal. In EAIC and EAIC-H, cancellation signals are added to OFDM signal to cancel interference in target spectrum band caused by data tones, so that interference perceived by primary user can be limited. The cancellation signal (EAIC tones) has longer time duration than that of OFDM symbol, which enables a better notching capability than that of most existing methods. Optimal weights of EAIC tones have been analyzed and given in this paper. Simulation results show that the proposed methods can obtain very deep spectral notches of about 80 dB. Although the EAIC tones cause certain interferences to OFDM data tones, the SNR degradation of OFDM system is very limited and it can be acceptable for high order modulation such as 64 QAM. Compared with EAIC, EAIC- H provides a better tradeoff between notching performance and SNR degradation for high order modulation.

Space-time block code (STBC) provides full diversity gain but does not achieve any coding gain. To provide a significant coding gain, STBC needs to be concatenated with an outer code. Turbo product code (TPC) is a kind of high-efficient coding scheme with low latency decoding. Moreover, TPC does not have an error floor. In this paper, we investigate the performance of a TPC-based STBC coded MIMO-OFDM system under several IMT2000 channel environments.