In this paper, we investigate the beamforming and power control issue in cellular-connected unmanned aerial vehicle (UAV) communications under full-duplex (FD). To mitigate the self-interference (SI), we adopt a decoupled uplink (UL)-downlink (DL) association for UAVs to spatially separate the transmit and receive beams. Then, we formulate a joint beamforming and power control problem to maximize the system’s spectral efficiency (SE) while ensuring each UAV meets its UL transmission rate and DL latency requirements. To solve this problem, we design a novel distributed heterogeneous graph neural network (HGNN) architecture for beamforming and power control with a low signaling overhead. Simulation results demonstrate that our proposed scheme outperforms the existing schemes in terms of the total SE, UL transmission rate and DL latency.

Generative semantic communication models are reshaping semantic communication frameworks by moving beyond pixel-wise optimization to align with human perception. However, many existing approaches prioritize image-level perceptual quality, often neglecting alignment with downstream tasks, which can lead to suboptimal semantic representation. This paper introduces an Ultra-Low Bitrate Semantic Communication (ULBSC) system that employs a conditional generative model and a learnable condition codebook. By integrating saliency conditions and image-level semantic information, the proposed method enables high-perceptual-quality and controllable task-oriented image transmission. Recognizing shared patterns among objects, we propose a codebook-assisted condition transmission method, integrated with joint source-channel coding (JSCC)-based text transmission to establish ULBSC. The codebook serves as a knowledge base, reducing communication costs to achieve ultra-low bitrate while enhancing robustness against noise and inaccuracies in saliency detection. Simulation results indicate that, under ultra-low bitrate conditions with an average compression ratio of 0.57 %, the proposed system delivers superior visual quality compared to traditional JSCC techniques and achieves higher saliency similarity between the generated and source images compared to state-of-the-art generative semantic communication methods.

Data privacy protection and secure sharing are the main issues faced by smart healthcare IoT systems. In medical uses, patient health information is frequently kept in the cloud, which limits the user’s ability to entirely control their data. Additionally, standard encryption keys do not sufficiently mitigate the risks posed by malicious entities like compromised cloud service providers. To address these issues, blockchain technology, combined with Internet of Medical Things (IoMT) can securely safeguard patient medical records through a peer-to-peer, secure, and collective ledger. Therefore, we propose a novel IoT-driven architecture that leverages blockchain technology to protect patient medical files from tampering and unauthorized access. This architecture integrates patient medical files with blockchain and is enhanced by a combination of Bidirectional Long Short-Term Memory (BiLSTM) networks and Convolutional Neural Networks (CNN). Utilizing blockchain for the transmission of encrypted data significantly strengthens data security and minimizes the risk of data breaches. The process of generating encryption and decryption keys through a coupled CNN and BiLSTM ensures the robustness and uniqueness of these keys. Additionally, the selection of the best key is performed using the Gradient Descent Optimization Algorithm (GDOA), which demonstrates the effectiveness and efficiency of the encryption and decryption process. We also compare the implementation of our model with existing technologies, assessing its performance based on various metrics, including restoration efficiency, response time, record time, key generation time, encryption time, decryption time, turnaround time, and overall running time. Our proposed method is confirmed to be more effective than current techniques in terms of these performance metrics.

One of the primary challenges in ultra-reliable and low-latency communications (URLLC) is to achieve accurate channel estimation and data detection while minimizing latency. Given the small packet size in URLLC, relying solely on pilot-assisted (PA) coherent detection is almost impossible to meet the seemingly contradictory requirements of high channel estimation accuracy, high reliability, low training overhead, and low latency. In this paper, we explore both frequency domain differential modulation (FDDM) and time domain differential modulation (TDDM), enabling non-coherent short packet URLLC with mini-slot structures. The minimum achievable block error rate and the maximum achievable rate for all three modes (i.e., FDDM, TDDM and PA modes) are derived using non-asymptotic information-theoretic bounds. Furthermore, we show that FDDM can more than compensate for the training overhead inadequacy and performance degradation of PA mode in medium-to-high-mobility scenarios, thereby improving the performance of short packet transmission with mini-slot by dynamically activating FDDM. Simulation results validate the feasibility and effectiveness of the proposed low overhead FDDM mini-slot transmission scheme.

Dynamic reconstruction technology presents significant promise for applications in visual and interactive fields. Current techniques utilizing 3D Gaussian Splatting show favorable results and fast reconstruction speed. However, as scene expanding, using individual Gaussian structure (i) leads to instability in large-scale dynamic reconstruction, marked by abrupt deformation, and (ii) the heuristic densification of individuals suffers significant redundancy. Tackling these issues, we propose a jointed Gaussian representation method named FRPGS, which learns the global information and the deformation using center Gaussians and generates the neural Gaussians around them for local detail. Specifically, FRPGS employs center Gaussians initialized from point clouds, which are learned with a deformation field for representing global relationships and dynamic motion over time. Then, for each center Gaussian, attribute networks generate neural Gaussians that move under the linked center Gaussian driving, thereby ensuring structural integrity during movement within this joint-based representation. Finally, to reduce Gaussian redundancy, a densification strategy is developed based on the average cumulative gradient of the associated neural Gaussians, imposing strict limits on the growing of center Gaussians without compromising accuracy. Additionally, we established a large-scale dynamic indoor dataset at the MuLong Laboratory of ZTE Corporation. Evaluations demonstrate that FRPGS significantly outperforms state-of-the-art methods in both training efficiency and reconstruction quality, achieving over a 50% (up to 74%) improvement in efficiency on an RTX 4090. FRPGS also supports the 4K resolution reconstruction of 60 frames simultaneously.

Recommendation system (RS) is an important information filtering tool in nowadays digital era. With the growing concern on privacy, deploying RSs in a federated learning (FL) manner emerges as a promising solution, which can train a high-quality model on the premise that the server does not directly access sensitive user data. Nevertheless, some malicious clients can deduce user data by analyzing the uploaded model parameters. Even worse, some Byzantine clients can also send contaminated data to the server, causing blockage or failure of model convergence. In addition, most existing researches on federated recommendation algorithms only focus on unimodality learning, ignoring the assistance of multiple modality data to promote recommendation accuracy. Therefore, this article designs an FL-based privacy-preserving multimodal RS framework. To distinguish various modality data, an attention mechanism is introduced, wherein different weight ratios are assigned to various modal features. To further strengthen the privacy, local differential privacy (LDP) and personalized FL strategies are designed to identify malicious clients and bolster the resilience against Byzantine attacks. Finally, two multimodal datasets are established to verify the effectiveness of the proposed algorithm. The superiority of our proposed techniques is confirmed by the simulation results.

Due to the limited energy and computing resources of Internet of Things (IoT) devices, the collaboration of IoT devices and edge servers is considered to handle the complex deep neural network (DNN) inference tasks. However, the heterogeneity of IoT devices and the various accuracy requirements of inference tasks make it difficult to deploy all the DNN models in edge servers. Moreover, a large-scale data transmission is engaged in collaborative inference, resulting in an increased demand on spectrum resource and energy consumption. To address these issues, in this paper, we first design an accuracy-aware multi-branch DNN inference model and quantify the relationship between branch selection and inference accuracy. Then, based on the multi-branch DNN model, we aim to minimize the energy consumption of devices by jointly optimizing the selection of DNN branches and partition layers, as well as the computing and communication resources allocation. The proposed problem is a mixed-integer nonlinear programming problem. We propose a hierarchical approach to decompose the problem, and then solve it with a proportional integral derivative based searching algorithm. Experimental results demonstrate our proposed scheme has better inference performance and can reduce the total energy consumption up to 65.3$\%$, compared to other collaboration schemes.

The deployment of low earth orbit (LEO) satellites megaconstellations presents a promising way for achieving global coverage and service, attributed to their comparatively low round-trip latency and launch costs. However, this surge in LEO satellite launches exacerbates the scarcity of the limited spectrum resources. Spectrum sharing between satellite constellations and terrestrial networks and beam hopping (BH) technology emerge as viable strategies to mitigate this spectrum shortage. To enhance spectrum efficiency and avoid serious inter-system interference, we investigate the beam hopping scheduling of satellites for interference avoidance. The beam hopping scheduling of the integrated satellite-terrestrial wireless networks system is formulated as throughput-driven beam hopping (TDBH) problem and satisfaction-rate-driven beam hopping (SDBH) problem, respectively. In particular, we decompose the TDBH problem into two sub-problems by relaxation, and a genetic algorithm (GA) is introduced to handle the SDBH problem. The impact of channel conditions and traffic load intensity on the satellite system throughput is analyzed in TDBH simulation. As for SDBH optimization problem, the simulation results show that the proposed GA algorithm improves the average traffic satisfaction rate by 16.96% at least, compared with other benchmarks and suits to scenarios with different traffic demands and fading channel conditions.

In space-air-ground integrated networks (SAGINs), the global energy efficiency (GEE) is a crucial metric for balancing the network throughput and energy consumption, and the maximization of GEE requires the optimizations of both user association and power allocation. Most existing methods optimize user association and power allocation separately or successively, relying on instantaneous non-local channel state information (CSI) exchanges. Nevertheless, both the separate and successive methods may fail to find the jointly optimal solution, and acquiring the instantaneous non-local CSI across the SAGINs is challenging due to the long communication distances between the access points (APs) and users. To address these issues, we leverage cloud-edge collaborations and propose an online delayed-interaction collaborative-learning independent-decision multi-agent DRL (DICLID-MADRL) algorithm. With the proposed algorithm, each AP can independently select users and configure transmit power with only local information to enhance GEE. Simulation results demonstrate that the proposed algorithm achieves a higher GEE with reduced time complexity compared to the state of the arts.

Semantic communication (SemCom), as an emerging paradigm focusing on meaning delivery, has recently been considered a promising solution for the inevitable crisis of scarce communication resources. This trend stimulates us to explore the potential of applying SemCom to wireless vehicular networks, which normally consume a tremendous amount of resources to meet stringent reliability and latency requirements. Unfortunately, the unique background knowledge matching mechanism in SemCom makes it challenging to simultaneously realize efficient service provisioning for multiple users in vehicle-to-vehicle networks. To this end, this paper identifies and jointly addresses two fundamental problems of knowledge base construction (KBC) and vehicle service pairing (VSP) inherently existing in SemCom-enabled vehicular networks in alignment with the next-generation ultra-reliable and low-latency communication (xURLLC) requirements. Concretely, we first derive the knowledge matching based queuing latency specific for semantic data packets, and then formulate a latency-minimization problem subject to several KBC and VSP related reliability constraints. Afterward, a SemCom-empowered Service Supplying Solution (S4) is proposed along with the theoretical analysis of its optimality guarantee and computational complexity. Numerical results demonstrate the superiority of S4in terms of average queuing latency, semantic data packet throughput, user knowledge matching degree and knowledge preference satisfaction compared with two benchmarks.

One key feature of ultra-reliable low-latency communications (URLLC) in 5G is to support short packet transmission (SPT). However, the pilot overhead in SPT for channel estimation is relatively high, especially in high Doppler environments. In this paper, we advocate the adoption of differential modulation to support ultra-low latency services, which can ease the channel estimation burden and reduce the power and bandwidth overhead incurred in traditional coherent modulation schemes. Specifically, we consider a multi-connectivity (MC) scheme employing differential modulation to enable URLLC services. The popular selection combining and maximal ratio combining schemes are respectively applied to explore the diversity gain in the MC scheme. A first-order autoregressive model is further utilized to characterize the time-varying nature of the channel. Theoretically, the maximum achievable rate and minimum achievable block error rate under ergodic fading channels with PSK inputs and perfect CSI are first derived by using the non-asymptotic information-theoretic bounds. The performance of SPT with differential modulation and MC schemes is then analysed by characterizing the effect of differential modulation and time-varying channels as a reduction in the effective SNR. Simulation results show that differential modulation does offer a significant advantage over the pilot-assisted coherent scheme for SPT, especially in high Doppler environments.

Recommendation algorithm can effectively reduce the difficulty of proactive edge caching prediction by excavating users’ preferences among the massive contents, which has drawn great attentions from both academia and industry. The effectiveness of prediction models depends on big data analysis of user information, however, traditional methods based on centralized learning become more and more impractical due to the growing concern on privacy data protection. Recently, implementing the recommendation algorithm in a federated learning (FL) manner has emerged as a promising approach. In an FL manner, users are allowed to keep their private data local and upload the model parameters learned by local training to the server for collaborative training. In this work, we propose a proactive caching prediction algorithm for mobile vehicle users based on differential privacy and federate learning. Our proposed algorithm not only predicts the popular contents with a strong protection for users’ private data, but also applies to large-scale networks with massive mobile users. In addition, we also investigate the impact of user mobility on the caching prediction accuracy, and propose an attention-based model aggregation mechanism, which assigns different aggregation weights to each vehicle user and edge server to mitigate the performance degradation caused by user movement. The results show that our proposed model can obtain high caching prediction accuracy and strong privacy protection level in vehicular networks.

To reduce the delay and network congestion for content delivery in wireless networks, proactive caching scheme has attracted lots of attentions from both academia and industry. However, traditional caching prediction methods require to collect user data in a centralized server, which is becoming unreliable and impractical due to regulatory restrictions. To circumvent this issue, deploying caching prediction system in a federated learning (FL) fashion becomes a promising solution. However, there still exist privacy risks, and even worse, the FL is vulnerable to low-cost attacks. To solve this problem, a novel federated prediction system (FPS) is studied to provide high robustness and privacy. Firstly, to keep a balance between further enhancing privacy protection and alleviating the performance degradation caused by additional protection schemes, we propose an edge-assisted, robust and privacy-preserving FPS framework based on the local differential privacy (LDP) scheme. Secondly, to mitigate the impact of heterogeneous data, we add a regularization term to the local loss function. Furthermore, an attention-based aggregation scheme is proposed to defend against Byzantine attacks during the training process. Finally, the experiment results are provided to show the superiority of our proposed algorithm in terms of prediction accuracy and robustness.

Deep learning enhanced Internet of Things (IoT) is advancing the transformation toward smart manufacturing. Intelligent robot guidance is one of the most potential deep learning + IoT applications in the manufacturing industry. However, low costs, efficient computing, and extremely high localization accuracy are mandatory requirements for vision robot guidance, particularly in operational factories. Therefore, in this work, a low-cost edge computing-based IoT system is developed based on an innovative fine-grained attention model (FGAM). FGAM integrates a deep-learning-based attention model to detect the region of interest (ROI) and an optimized conventional computer vision model to perform fine-grained localization concentrating on the ROI. Trained with only 100 images collected from real production line, the proposed FGAM has shown superior performance over multiple benchmark models when validated using operational data. Eventually, the FGAM-based edge computing system has been deployed on a welding robot in a real-world factory for mass production. After the assembly of about 6000 products, the deployed system has achieved averaged overall process and transmission time down to 200 ms and overall localization accuracy up to 99.998%.

Indoor positioning system (IPS) plays an important role in the applications of Internet of Things (IoT), including intelligent hospital, logistics, and warehousing. Ultrawideband (UWB)-based IPS has shown superior performance due to its strong multipath resistance and high temporal resolution. However, the non-line-of-sight (NLOS) situations noticeably degrade both the positioning accuracy and the communication reliability. To address this issue, we first propose a support vector machine (SVM)-based channel detection method to distinguish the line-of-sight (LOS) and NLOS conditions. Then, one base station (BS)-based distance and angle positioning algorithm with extended Kalman filter (DAPA-EKF) in NLOS environment is proposed. For the LOS environment, least squares (LSs) with EKF processing of acceleration (LS-AEKF) and velocity (LS-VEKF) are developed. To further improve the performance, the combination of time difference of arrival (TDOA) and KF in LOS environment is proposed. Simulation results show that the positioning accuracy of the proposed algorithm is improved in various environments. Finally, validated using more than 1000 testing positions, the positioning accuracy of LS-AEKF is 73.8%–74.1% higher than that of LS-VEKF among the two proposed algorithms in terms of three or four BSs metrics.

Stream media content caching is a key enabling technology to promote the value chain of future urban vehicular networks. Nevertheless, the high mobility of vehicles, intermittency of information transmissions, high dynamics of user requests, limited caching capacities and extreme complexity of business scenarios pose an enormous challenge to content caching and distribution in vehicular networks. To tackle this problem, this paper aims to design a novel edge-computing-enabled hierarchical cooperative caching framework. Firstly, we profoundly analyze the spatio-temporal correlation between the historical vehicle trajectory of user requests and construct the system model to predict the vehicle trajectory and content popularity, which lays a foundation for mobility-aware content caching and dispatching. Meanwhile, we probe into privacy protection strategies to realize privacy-preserved prediction model. Furthermore, based on trajectory and popular content prediction results, content caching strategy is studied, and adaptive and dynamic resource management schemes are proposed for hierarchical cooperative caching networks. Finally, simulations are provided to verify the superiority of our proposed scheme and algorithms. It shows that the proposed algorithms effectively improve the performance of the considered system in terms of hit ratio and average delay, and narrow the gap to the optimal caching scheme comparing with the traditional schemes.

Computation offloading via device-to-device communications can improve the performance of mobile edge computing by exploiting the computing resources of user devices. However, most proposed optimization-based computation offloading schemes lack self-adaptive abilities in dynamic environments due to time-varying wireless environment, continuous-discrete mixed actions, and coordination among devices. The conventional reinforcement learning based approaches are not effective for solving an optimal sequential decision problem with continuous-discrete mixed actions. In this paper, we propose a hierarchical deep reinforcement learning (HDRL) framework to solve the joint computation offloading and resource allocation problem. The proposed HDRL framework has a hierarchical actor-critic architecture with a meta critic, multiple basic critics and actors. Specifically, a combination of deep Q-network (DQN) and deep deterministic policy gradient (DDPG) is exploited to cope with the continuous-discrete mixed action spaces. Furthermore, to handle the coordination among devices, the meta critic acts as a DQN to output the joint discrete action of all devices and each basic critic acts as the critic part of DDPG to evaluate the output of the corresponding actor. Simulation results show that the proposed HDRL algorithm can significantly reduce the task computation latency compared with baseline offloading schemes.

Personalized Federated Learning (PFL) is a new Federated Learning (FL) approach to address the heterogeneity issue of the datasets generated by distributed user equipments (UEs). However, most existing PFL implementations rely on synchronous training to ensure good convergence performances, which may lead to a serious straggler problem, where the training time is heavily prolonged by the slowest UE. To address this issue, we propose a semi-synchronous PFL algorithm, termed as Semi-Synchronous Personalized FederatedAveraging (PerFedS2), over mobile edge networks. By jointly optimizing the wireless bandwidth allocation and UE scheduling policy, it not only mitigates the straggler problem but also provides convergent training loss guarantees. We derive an upper bound of the convergence rate of PerFedS2 in terms of the number of participants per global round and the number of rounds. On this basis, the bandwidth allocation problem can be solved using analytical solutions and the UE scheduling policy can be obtained by a greedy algorithm. Experimental results verify the effectiveness of PerFedS2 in saving the training time as well as guaranteeing the convergence of training loss, in contrast to synchronous and asynchronous PFL algorithms.

Image compression and bit transmission are con-ducted separately in most existing methods for image trans-mission, leading to possible transmission failure or a waste of communication resource for a time-varying channel condition. This paper proposes a neural network-based image transmission system trained by generative adversarial networks (GANs) aiming to achieve robust transmission. Specifically, the deep semantic of an input image is extracted and represented as bit streams at the transmitter, and the receiver reconstructs the original image based on possible bit error and the same background knowledge as the transmitter. Experimental results show that the proposed robust transmission system trained by GAN can adapt to the current communication condition, and achieve a high-quality reconstruction even with a high transmission error rate and a smaller transmission data size than engineered codecs such as JPEG.

To preserve participants' privacy, Federated Learning (FL) has been proposed to let participants collaboratively train a global model by sharing their training gradients instead of their raw data. However, several studies have shown that con-ventional FL is insufficient to protect privacy from adversaries, as even from gradients, useful information can still be recovered. To obtain stronger privacy protection, Differential Privacy (DP) has been proposed on the server's side and the clients' side. Although adding artificial noise to the raw data can enhance users' privacy, the accuracy performance of the FL is inevitably degraded. In addition, although the communication overhead caused by the FL is much smaller than that of centralized learning, it still becomes a bottleneck of the learning performance and utilization efficiency due to its frequent parameters exchange. To tackle these problems, we propose a new FL framework via applying DP both locally and centrally in order to strengthen the protection of par-ticipants' privacy. To improve the accuracy performance of the model, we also apply sparse gradients and Momentum Gradient Descent on the server's side and the clients' side. Moreover, using sparse gradients can reduce the total communication costs. We provide the experiments to evaluate our proposed framework and the results show that our framework not only outperforms other DP-based FL frameworks in terms of the model accuracy but also provides a more powerful privacy guarantee. Besides, our framework can save up to 90% of communication costs while achieving the best accuracy performance.

Positioning technology is essential for promoting intelligence in many residential, commercial, and industrial application scenarios. To improve the accuracy of indoor positioning, researchers have proposed many localization schemes based on the fusion of sensors. However, most existing methods focus on integrating more sensors instead of further extracting the original data. In this paper, we propose an efficient cooperative positioning algorithm for None-Line-of-Sight (NLOS) environments. Firstly, a multi-scenarios NLOS detection approach is introduced based on the channel impulse response of ultra-wideband. Secondly, a cycle least-squares positioning algorithm is proposed to maximize the utilization of the original ranging information. Thirdly, we propose a cooperative positioning algorithm based on location information sharing to minimize the impact of NLOS propagation. The simulation results demonstrate that our method outperforms all baseline methods with a large margin in terms of both stability and accuracy.

Grant-free (GF) access is expected to support low-latency services in fifth-generation (5G) systems, while non-orthogonal multiple access (NOMA) has been proposed to enable massive connectivity in cellular networks. However, the performance analysis for the GF access mode based on NOMA is not trivial, especially for large-scale multi-cell networks due to the inherent random near-far phenomenon. In this paper, we exploit tools from stochastic geometry to develop a tractable framework for analysing uplink performance in large-scale multi-cell networks under GF NOMA and short packet transmission. To make the framework tractable, we further transform the intra- and inter-cell interference to an equivalent interference model. The URLLC performance of GF NOMA networks is derived under the assumption of perfect successive interference cancellation (SIC) and short packet transmission. Numerical results obtained from theoretical calculations and Monte Carlo simulations verify the correctness of our analysis.

Cache-enabled device-to-device (D2D) communication is a potential approach to tackle the resource shortage problem. However, public concerns of data privacy and system security still remain, which thus arises an urgent need for a reliable caching scheme. Fortunately, federated learning (FL) with a distributed paradigm provides an effective way to privacy issue by training a high-quality global model without any raw data exchanges. Besides the privacy issue, blockchain can be further introduced into the FL framework to resist the malicious attacks occurred in D2D caching networks. In this study, we propose a double-layer blockchain-based deep reinforcement FL (BDRFL) scheme to ensure privacy-preserved and caching-efficient D2D networks. In BDRFL, a double-layer blockchain is utilized to further enhance data security. Simulation results first verify the convergence of the BDRFL-based algorithm, and then demonstrate that the download latency of the BDRFL-based caching scheme can be significantly reduced under different types of attacks when compared to some existing caching policies.

Deep learning-enhanced Internet of Things (IoT) plays a pivot role in advancing the transformation toward smart manufacturing, and an essential component in many smart manufacturing IoT systems is the quality inspection. However, challenges, such as expensive data labeling, innumerable types of defects, and high costs for iterative optimization, hinder the industrial applicability of previous visual surface quality inspection methods. In this article, we present an edge-computing-enabled IoT system based on an innovative hybrid learning method for visual surface quality inspection using only few labeled data and minimum iterative optimization efforts. Our hybrid learning method first employs a deep neural network to synthesize global representations of real-world industrial images, which are subsequently analyzed via an unsupervised clustering algorithm for anomaly detection. Besides, enhancement strategies, such as fine-tuning and data augmentation, are proposed to improve the robustness against the noisy data set and support low-cost inference in multiple edge devices for manufacturing operation. On a holdout data set collected from real-world factories, our method achieves classification accuracies between 90% and 98%, outperforming the benchmark method by 7%–12%. Moreover, this hybrid learning method demonstrates the effectiveness in detecting new types of surface defects and achieves test recalls between 86% and 97%, outperforming the benchmark method by 11%–34%.

High-precision location information formulates the basis of the modern Internet of Things (IoT). However, since the navigation signals from the global navigation satellite systems (GNSSs) are frequently attenuated or blocked in urban areas, reliable and high accuracy positioning alternatives are thus required for ground devices (GDs). Due to the advantages of their flexible deployment and extensive coverage, unmanned aerial vehicles (UAVs) show significant potential in this ground localization enhancement system. In this article, we propose a UAV aided positioning (UAP) system for GDs, where the UAVs provide valuable flying Line of Sight (LoS) observations. Specifically, we first give the fundamental limits of the proposed UAP system in terms of the Cramer–Rao low bound (CRLB), where the UAVs are treated as “agents” with unknown positions instead of anchors. Then, we formulate a general UAP method using the nonparametric belief propagation (NBP)-based probabilistic framework, to jointly positioning UAVs and GDs simultaneously. Moreover, a two-step clustering-based solution is given to tackle the data association challenge in the multi-UAV scenarios. We also show that proper data feedback could achieve additional performance advantages without any extra measurements. The optimal multi-UAV deployment strategy is then proposed, by which the potential of the UAP system could be fully characterized. Last but not least, we verify our solutions via numerical simulations and practical experiments, which provide meaningful insights and performance evaluations to the system design and implementations.

Network slicing has been widely recognized as the architectural technology for 5G and beyond wireless network systems to provide tailored service for diverse applications by flexibly splitting and allocating various heterogeneous resources. However, it is still challenging to meet the strict delay requirements of a large number of delay-sensitive applications under traditional slicing architectures. One potential way to tackle this issue is to build network slicing upon Mobile Edge Computing (MEC) systems, where both communication and computing resources are integrated for providing customized service. As such, in this paper, we propose a framework, to jointly optimize communication and computing resources under the scenario of multi-service coexistence, with the objective to minimize the system cost while meeting the diverse QoS requirements. To make the original optimization problem more tractable, we decompose it into two convex sub-problems first. Then we obtain the optimal solutions of the two sub-problems respectively, and finally derive the optimal communication and computing resource allocation scheme based on the optimal solutions of these two sub-problems. Simulation results show that our proposed scheme significantly saves the system cost under various scenarios compared with other benchmarks.

Mobile-edge computing (MEC) is considered as a promising technology to reduce the energy consumption (EC) and task accomplishment latency of smart mobile user equipments (UEs) by offloading computation-intensive tasks to the nearby MEC servers. However, the Quality of Experience (QoE) for computation highly depends on the wireless channel conditions when computation tasks are offloaded to MEC servers. In this article, by considering the imperfect channel-state information (CSI), we study the joint offloading decision, transmit power, and computation resources to minimize the weighted sum of EC of all UEs while guaranteeing the probabilistic constraint in multiuser MEC-enabled Internet-of-Things (IoT) networks. This formulated optimization problem is a stochastic mixed-integer nonconvex problem and challenging to solve. To deal with it, we develop a low-complexity two-stage algorithm. In the first stage, we solve the relaxed version of the original problem to obtain offloading priorities of all UEs. In the second stage, we solve an iterative optimization problem to obtain a suboptimal offloading decision. As both stages include solving a series of nonconvex stochastic problems, we present a constrained stochastic successive convex approximation-based algorithm to obtain a near-optimal solution with low complexity. The numerical results demonstrate that the proposed algorithm provides comparable performance to existing approaches.

The notion of age-of-information (AoI) is investigated in the context of large-scale wireless networks, in which transmitters need to send a sequence of information packets, which are generated as independent Bernoulli processes, to their intended receivers over a shared spectrum. Due to interference, the rate of packet depletion at any given node is entangled with both the spatial configurations, which determine the path loss, and temporal dynamics, which influence the active states, of the other transmitters, resulting in the queues to interact with each other in both space and time over the entire network. To that end, variants in the packet update frequency affect not just the inter-arrival time but also the departure process, and the impact of such phenomena on the AoI is not well understood. In this paper, we establish a theoretical framework to characterize the AoI performance in the aforementioned setting. Particularly, tractable expressions are derived for both the peak and average AoI under two different transmission protocols, namely the first-come-first-serve (FCFS) and the last-come-first-serve with preemption (LCFS-PR). Additionally, our analysis also accounts for the effects of channel access controls such as ALOHA on the AoI. The accuracy of the analysis is verified via simulations, and based on the theoretical outcomes, we find that: i) networks operating under LCFS-PR are able to attain smaller values of peak and average AoI than that under FCFS, whereas the gain is more pronounced when the infrastructure is densely deployed, ii) in sparsely deployed networks, ALOHA with a universally designed channel access probability is not instrumental in reducing the AoI, thus calling for more advanced channel access approaches, and iii) when the infrastructure is densely rolled out, there exists a non-trivial ALOHA channel access probability that minimizes the peak and average AoI under both FCFS and LCFS-PR.

In this paper, we consider the interference detection and resource allocation issue in Long-Term Evolution Unlicensed (LTE-U) system with carrier aggregation (CA). First, to avoid the co-channel interference between the WiFi and LTE-U users, we adopt the logistic regression method to train a classifier model for the base stations (BSs) to find the users that are susceptible to the interference from the WiFi. Then, we formulate the optimization problem with the goal to maximize the downlink (DL) throughput while guaranteeing the quality-of-service (QoS) for each user. To make the original problem more tractable, we first split it into two sequential subproblems and then propose a dual decomposition method to solve them efficiently. The numerical results show that the proposed schemes can significantly improve the overall throughput and outperform the existing schemes.

The emerging Internet of Things (IoT) applications, such as smart manufacturing and smart home, lead to a huge demand on the provisioning of low-cost and high-accuracy positioning and navigation solutions. Inertial measurement unit (IMU) can provide an accurate inertial navigation solution in a short time but its positioning error increases fast with time due to the cumulative error of accelerometer measurement. On the other hand, ultrawideband (UWB) positioning and navigation accuracy will be affected by the actual environment and may lead to uncertain jumps even under line-of-sight (LOS) conditions. Therefore, it is hard to use a standalone positioning and navigation system to achieve high accuracy in indoor environments. In this article, we propose an integrated indoor positioning system (IPS) combining IMU and UWB through the extended Kalman filter (EKF) and unscented Kalman filter (UKF) to improve the robustness and accuracy. We also discuss the relationship between the geometric distribution of the base stations (BSs) and the dilution of precision (DOP) to reasonably deploy the BSs. The simulation results show that the prior information provided by IMU can significantly suppress the observation error of UWB. It is also shown that the integrated positioning and navigation accuracy of IPS significantly improves that of the least squares (LSs) algorithm, which only depends on UWB measurements. Moreover, the proposed algorithm has high computational efficiency and can realize real-time computation on general embedded devices. In addition, two random motion approximation model algorithms are proposed and evaluated in the real environment. The experimental results show that the two algorithms can achieve certain robustness and continuous tracking ability in the actual IPS.

Direct Acyclic Graph (DAG)-based ledger and the corresponding consensus algorithm has been identified as a promising technology for Internet of Things (IoT). Compared with Proof-of-Work (PoW) and Proof-of-Stake (PoS) that have been widely used in blockchain, the consensus mechanism designed on DAG structure (simply called as DAG consensus) can overcome some shortcomings such as high resource consumption, high transaction fee, low transaction throughput and long confirmation delay. However, the theoretic analysis on the DAG consensus is an untapped venue to be explored. To this end, based on one of the most typical DAG consensuses, Tangle, we investigate the impact of network load on the performance and security of the DAG-based ledger. Considering unsteady network load, we first propose a Markov chain model to capture the behavior of DAG consensus process under dynamic load conditions. The key performance metrics, i.e., cumulative weight and confirmation delay are analysed based on the proposed model. Then, we leverage a stochastic model to analyse the probability of a successful double-spending attack in different network load regimes. The results can provide an insightful understanding of DAG consensus process, e.g., how the network load affects the confirmation delay and the probability of a successful attack. Meanwhile, we also demonstrate the trade-off between security level and confirmation delay, which can act as a guidance for practical deployment of DAG-based ledgers.

This paper investigates multi-antenna beamforming and time allocation to maximize the network energy-efficiency (EE) in a wireless powered communication network (WPCN). Since the EE optimization problem has an inherent fractional form, it is difficult to obtain the optimal value directly due to the lack of convexity in the objective function. To overcome this challenge, we first convert the original problem into a more tractable one by the fractional programming. Then, two schemes are proposed to find the optimal value. In the first scheme, the iterative value is updated according to the EE based on energy beamforming and time allocation derived in the current iteration. In the second one, the optimal value is obtained by consecutively shrinking the region in which it is located. Simulation results show that the proposed two schemes can improve the network EE significantly compared with the algorithm that only pursues high throughput. In addition, it is shown that the two schemes have simlilar performance in the network EE. However, the computation complexity of the first one is lower than that of the second one.

In this paper, we study resource allocation in full-duplex OFDMA networks. We explore the joint optimization of subcarrier assignment, uplink-downlink user pairing, and power allocation to maximize the overall throughput with consideration of self-interference and inter-node interference. By using the dual method, we can decompose the original optimization problem into a primal problem and a dual problem. We adopt the concave-convex procedure to transform the primal problem into a tractable form through sequential convex approximations while we utilize the sub-gradient method to solve the dual problem. Simulation results show that the proposed algorithm can always achieve better throughput in comparison with the existing algorithms.

With device-to-device (D2D) communications, a user terminal can be used as a relay node to support multi-hop transmission, so that cell-edge or deeply faded users can obtain a better connective experience. In this paper, we investigate energy-efficient mobile association in D2D-enabled heterogeneous networks. We consider joint access point selection, mode switching, D2D relay node (DRN) selection, and power control to maximize the energy efficiency (EE) of uplink transmission while guaranteeing the quality-of-service requirement of users. The optimization problem can be decomposed into three subproblems: access point selection, power control, and joint mode switching and DRN selection. The joint mode switching and DRN selection problem is a 0-1 integer optimization problem, whose optimal solution can be found by the brute-force searching method that is complexity-prohibitive when the number of DRNs is large. To reduce the complexity involved in computation, channel estimation, and feedback, we develop a distance-based mobile association (DMA) algorithm, which only operates based on the location information of users and DRNs. Simulation results demonstrate that the proposed DMA algorithm can achieve a good tradeoff between the EE and the complexity.

In this paper, we address the power control problem in an interference network with multiple users transmitting simultaneously on the same channel. We aim at achieving the energy efficiency (EE) balance among difference users. First, a multi-objective optimization problem is formulated, which maximizes the EE of each individual user while guaranteeing their minimum data rate requirements. To find its solution, we adopt two different scalarization methods to combine multiple objectives into a single one, namely, the weighted-sum method and the weighted Tchebycheff method. The problem in the weighted-sum method turns out to be a non-concave sum of- ratios optimization and an effective algorithm is developed based on the concave-convex procedure (CCCP) method. On the other hand, the problem in the weighted Tchebycheff method becomes a generalized fractional programming and we utilize the Dinkelbach method and the CCCP method to solve it. Through numerical simulation, we find that both methods can effectively obtain the Pareto optimal solutions to the multiobjective optimization problem and achieve the EE balance among users as well.

With device-to-device (D2D) communications, a user terminal(UT) can naturally be used as a relay node (RN) and thus inherently support multi-hop transmission. Thus, cell-edge or deeply faded users can obtain a more uniform connectivity experience. In this paper, we investigate mobile association for the UT with the capability of D2D communications in heterogeneous networks (HetNets). We will develop a framework on joint mobile association and transmission mode switching between the direct and the D2D relay modes to improve the system spectrum efficiency (SE) and energy efficiency (EE). We first formulate the optimization problems, and then obtain closed-form solutions. Simulation results show that with the proposed schemes, both SE and EE of the network can be significantly improved compared to the traditional solutions without D2D communications. We also discuss the trade-off between the minimum rate requirement and EE of a network.

This paper investigates energy-efficient device-to-device (D2D) communications in cellular networks. We aim to maximize the overall energy-efficiency (EE) of D2D users and regular cellular users (RCUs) while considering the circuit power consumption and the quality-of-service (QoS) requirements for both types of users as well as power constraints. Three transmission modes, namely, dedicated mode, reusing mode, and cellular mode, are considered for D2D users to share spectrum with RCUs. Parametric Dinkelbach method and concave-convex procedure (CCCP) are adopted to transform the original optimization problems into more tractable forms through sequential convex approximations. Then, interior point method is exploited to obtain the optimal solution. Simulation results show that system EE can be improved significantly with the proposed mode switching algorithm compared with the single mode transmission. Besides, it is also shown that the reusing mode is more preferred in the EE based mode switching while it is the dedicated mode in the spectrum-efficiency (SE) based mode switching in most situations.

Device-to-device (D2D) communications have been recently proposed as an effective way to increase both spectrum and energy efficiency for future cellular systems. In this paper, joint mode selection, channel assignment, and power control in D2D communications are addressed. We aim at maximizing the overall system throughput while guaranteeing the signal-to-noise-and-interference ratio of both D2D and cellular links. Three communication modes are considered for D2D users: cellular mode, dedicated mode, and reuse mode. The optimization problem could be decomposed into two subproblems: power control and joint mode selection and channel assignment. The joint mode selection and channel assignment problem is NP-hard, whose optimal solution can be found by the branch-and-bound method, but is very complicated. Therefore, we develop low-complexity algorithms according to the network load. Through comparing different algorithms under different network loads, proximity gain, hop gain, and reuse gain could be demonstrated in D2D communications.

In this paper, we investigate optimal resource allocation for device-to-device (D2D) communication underlaying cellular network in fading channels. We consider a scenario that the instantaneous channel power gain of interference links from regular cellular users (CUs) to D2D users are unknown at base station (BS) since obtaining the channel-state-information (CSI) in this case is difficult and requires high overhead. We assume that BS provides guaranteed quality-of-service (QoS) in terms of signal-to-interference-plus-noise-ratio (SINR) for CUs and outage probability for D2D pairs, respectively. Based on the assumptions, we first propose a probabilistic access control for D2D pairs to satisfy all the QoS requirements and power constraints. We then derive joint power and channel allocation to maximize the overall throughput of the CUs and admissible D2D pairs. Through simulation, we show the effectiveness of the proposed probabilistic strategy and there exists an optimal threshold of the targeted outage probability with respect to D2D access rate and overall network throughput.

In device-to-device (D2D) communications underlaying uplink (UP) cellular networks, the channel state information (CSI) of interference links between regular cellular users (CUs) and D2D receivers is necessary to provide guaranteed quality-of-service (QoS) to D2D users. However, getting the CSI is very difficult and requires high overhead. In this paper, we propose a selected-K maximum distance ratio (MDR) feedback scheme (KMDR) to reduce feedback overhead, in which each D2D receivers only needs to feedback CSI of K CUs with the largest MDR metric. Simulation results show that up to 80% feedback can be reduced at D2D receivers by KMDR while still providing a near optimal performance. We also study the effect of side information at the D2D receivers. It is shown that it is possible to further reduce the feedback information when full side information is known at the D2D receivers.

In cellular networks, proximity users may communicate directly without going through the base station, which is called Device-to-device (D2D) communications and it can improve spectral efficiency. However, D2D communications may generate interference to the existing cellular networks if not designed properly. In this paper, we study a resource allocation problem to maximize the overall network throughput while guaranteeing the quality-of-service (QoS) requirements for both D2D users and regular cellular users (CUs). A three-step scheme is proposed. It first performs admission control and then allocates powers for each admissible D2D pair and its potential CU partners. Next, a maximum weight bipartite matching based scheme is developed to select a suitable CU partner for each admissible D2D pair to maximize the overall network throughput. Numerical results show that the proposed scheme can significantly improve the performance of the hybrid system in terms of D2D access rate and the overall network throughput. The performance of D2D communications depends on D2D user locations, cell radius, the numbers of active CUs and D2D pairs, and the maximum power constraint for the D2D pairs.