Vibration sensing is crucial for machinery health monitoring, but traditional contact sensors face deployment challenges. Recently, millimeter wave (mmWave) radar has emerged as a promising contact-free alternative. However, since radar is mainly sensitive to the vibrations perpendicular to its antennas, existing works can only achieve 1D/2D vibration sensing based on single radar, or 3D sensing by multiple ones. In this paper, we propose 3DVidar, a single-radar 3D vibration sensing system without external reference target. Considering the insufficient information provided by single radar, we introduce a multi-point multi-path multi-antenna signal enhancement strategy to compensate for the lack of 3D vibration information. Furthermore, we develop two dedicated mechanisms to selectively filter the most informative radar signals for subsequent processing. Based on the enhanced signals, we design 3D-VRNet, a deep learning framework that incorporates positional priors and fuses multi-view signals through multi-scale convolutions and an attention mechanism. We implement 3DVidar on a commercial mmWave radar, and the results on two type of vibration targets show that it can accurately reconstruct 3D trajectory across various conditions.

With the proliferation of software-defined radio technology, malicious jamming attacks against wireless communications have become more aggressive and flexible, which could easily create a complex and highly dynamic jamming environment by varying both the jamming parameters and the jamming policies. Such a complex jamming environment makes it challenging for most of deep reinforcement learning (DRL) based anti-jamming schemes in rapidly identifying effective strategies. In this paper, we have developed a dual-tier policy-oriented anti-jamming (DPA) scheme based on DRL to facilitate swift adaptation to the complex jamming environment. Unlike existing works, an upper-tier jamming pattern recognition (JPR) network is introduced to extract underlying jamming policy-related information which serves as a guidance for the lower-tier deep recurrent Q-network on anti-jamming decision-making. The output of the JPR network can enable the sharing of experiences among various jamming patterns originated from the same jamming policy and facilitate more efficient and targeted anti-jamming strategic learning. Extensive experimental results demonstrate that the superiority of our DPA scheme over other DRL-based benchmark schemes in terms of both anti-jamming performance and convergence speed.

Humans achieve contact-rich dexterous grasping through the synergy of visual and tactile information. However, the high-dimensional action space of high DoF multi-fingered hands poses significant challenges to this operation. In this study, we address this complexity by controlling the robotic hand at the reduced dimensional level of individual fingers instead of the entire hand, and develop a finger-based multi-agent deep reinforcement learning strategy by regarding the wrist, arm, and each finger of the hand as intelligent agents. We commence by applying a single-agent reinforcement learning algorithm to guide the whole hand to reach the feasible approaching direction and distance to the object. Then, we develop neuroscience-inspired visuo-tactile fusion networks to train multiple agents to control their assigned fingers by effectively leveraging visual and tactile feedback. This enables dynamic and collaborative adjustments of finger-object interactions, ultimately achieving precise contact with specific areas of the objects. The grasping results on 8 objects show that our approach can achieve stable and compliant grasps. To the best of our knowledge, this is the first work that employs a finger-based multi-agent reinforcement learning approach to control the dexterous grasping process under the guidance of both visual and tactile feedback.

An efficient 3D spatial spectrum prediction method is essential for UAV networks operating in highly heterogeneous spectrum environments, enabling UAVs to proactively navigate toward areas with abundant available spectrum and make decisions to access to idle ones in advance. This paper introduces a novel Multi-Scale Temporal model for 3D spatial spectrum prediction (MST-3DSSP) that comprehensively captures complex correlations across 3D spatial, frequency, and multi-scale temporal domains. Specifically, the proposed model incorporates a 3D Spatial-Frequency Fusion (3DS-FF) module to extract and fuse 3D spatial and frequency features, along with a MultiScale Temporal Extraction (MS-TE) module that combines BiLSTM and Transformer blocks to capture both small scale and large scale temporal dependencies. These two modules enable the model to understand the complex correlations across 3D spatial, frequency, and multi-scale temporal domains, thereby allowing for more accurate spectrum predictions. Extensive experiments on real-world spectrum datasets demonstrate that MST-3DSSP significantly outperforms existing spectrum prediction methods, achieving higher prediction accuracy and reduced errors, thus providing a robust solution for improving spectrum efficiency in UAV networks.

Voice control has become an indispensable interaction method in smart devices. Compared to traditional microphones, mm Wave radar offers a promising solution for speech sensing in noisy environments. However, most current research relies on single-view information, such as vocal cord vibrations or lip movements, to classify speech, which overlooks important details like timbre, speech rate, and intonation, limiting the application of speech sensing. To address these issues, we develop a high-quality speech sensing method based on mm Wave radar, named SpDiff. This method accurately localizes the vocalizing target and, based on the human vocal mechanism, extracts multi-view speech features according to the movement characteristics of the vocal cords, lips, and face. Additionally, to generate high-quality speech signals, we design a conditional latent diffusion model (CLDM), which uses multi-view radar information as conditional guidance, accurately capturing the complex mapping relationships between radar and speech signal distributions. To evaluate the SpDiff method, we build a mmWave system using IWR1443Boost and recruit 14 volunteers to construct a dataset. Experimental results show that SpDiff achieves high standards in speech sensing, with the generated speech directly input into existing recognition models, achieving an average character and word error rate (CER/WER) of only 2.33% and 3.05%.

Integrated sensing and communication (ISAC) has emerged as a promising technology for future millimeter wave (mmWave) networks. However, the susceptibility of mmWave signals to blockages poses considerable challenges for ISAC as it can result in unreliable links and disrupted sensing. As a result, this paper investigates the blockage-resilient ISAC design that leverages the robustness offered by multi-base station (BS) collaboration. Given the dynamic blockages and the fluctuation in the targets’ radar cross section (RCS), the blockage-resilient multi-BS collaborative ISAC design is cast as a chance constrained integer programming (CCIP) by jointly considering the diverse deadlines of different sensing tasks and the spatial/temporal user-target pairing for dual-functional radar and communication (DFRC) waveform scheduling. To facilitate efficient solution finding, we develop a group concatenating assisted reinforcement learning (GCRL) algorithm, where we linearize the chance constraints via variable grouping and concatenation, enabling the RL agent to understand the problem structure with bipartite graphs so as to develop an efficient branching policy. Extensive experiments demonstrate the resilience of the obtained ISAC scheme to dynamic blockages.

Integrated sensing and communication (ISAC) technology has been envisioned to revolutionize the future intelligent vehicle networks (IVNs). Recently, due to the mobility and flexible deployment, unmanned aerial vehicle (UAV) has been regarded as a promising aerial ISAC platform in future IVNs. In this paper, comprehensively considering all the performance on the throughput, sensing accuracy, and sensing rate, we propose a Multi-Agent joint Trajectory control and RadCom task schedule (MA-TRC) scheme for the ISAC-UAV assisted IVN. To make UAVs achieve the optimal decisions autonomously and adaptively, we propose a Game-Embedding Multi-Agent Deep Reinforcement Learning (GE-MADRL) approach. Specifically, considering the complex action space with both discrete and continuous decision variables of UAVs, we develop a multi-agent Parametrized deep Q-network (MAPDQN) based solution, which can help UAVs learn the dynamic and uncertain environment to adaptively obtain the MA-TRC scheme. Furthermore, since UAVs work in a distributed decision making manner, the potential conflicting decisions will impact the network performance. To avoid the decision conflicts among UAVs during the network parameter training, a distributed two-stage Game method is designed as an action adjuster embedded in MAPDQN, by which the learning convergence performance will be further improved and the strategy conflicts can be avoided.

Integrated sensing and communication (ISAC), which integrates sensing capabilities into wireless communication networks, is emerging as a key technology for future millimeter wave (mmWave) communication networks. Given the limited ISAC capability and energy budget of a single base station (BS), this paper studies how to enable energy-efficient sensing and communication via multi-BS collaborative sensing, where each sensing task is served by its most energy-efficient BS as much as possible, with the help of other BSs. Since unregulated multi-BS collaboration may lead to energy wastage and further aggravates the energy consumption in mmWave networks, an energy-efficient collaborative ISAC scheme is proposed, where multi-BS collaborative sensing and dual-functional radar and communication (DFRC) beams are judiciously utilized to reduce the network’s energy consumption. We formulate the design of the energy-efficient collaborative ISAC scheme as a mixed integer nonlinear programming problem by jointly considering task allocation, beam scheduling, and transmit power control. Then, an energy-efficient cooperative beam scheduling (EE-CBS) algorithm is developed for efficient solution finding. Through extensive simulations, the proposed scheme is shown to significantly reduce the network’s energy consumption when compared to the scheme without multi-BS cooperation or the utilization of DFRC waveforms.

Robot grasp dataset is the basis of designing the robot's grasp generation model. Compared with the building grasp dataset for Low-DOF grippers, it is harder for High-DOF dexterous robot hand. Most current datasets meet the needs of generating stable grasps, but they are not suitable for dexterous hands to complete human-like functional grasp, such as grasp the handle of a cup or pressing the button of a flashlight, so as to enable robots to complete subsequent functional manipulation action autonomously, and there is no dataset with functional grasp pose annotations at present. This paper develops a unique Cost-Effective Real-Simulation Annotation System by leveraging natural hand's actions. The system is able to capture a functional grasp of a dexterous hand in a simulated environment assisted by human demonstration in real world. By using this system, dexterous grasp data can be collected efficiently as well as cost-effective. Finally, we construct the first dexterous functional grasp dataset with rich pose annotations. A Functional Grasp Synthesis Model is also provided to validate the effectiveness of the proposed system and dataset. Our project page is: https://hjlllll.github.io/DFG/.

As a promising solution to alleviate network congestion, mobile edge caching based on unmanned aerial vehicles (UAVs) has emerged and received intensive research interests, where users could download their desired contents from UAVs with much lower latency. As for the UAV-assisted edge caching, to improve the users’ Quality of Experience while reducing the cost on content updating, how to jointly design the trajectory and caching strategy for UAVs is critical. However, considering the dynamics and uncertainty on the traffic environment, as well as the mutual effect among different UAVs, such joint design is nontrivial. In this article, we propose a collaborative joint trajectory and caching scheme for UAV-assisted networks under the dynamic and uncertain traffic environment. Unlike most existing work relying on model-based or single-agent methods, we develop a multiagent deep reinforcement learning (MADRL) approach to obtain the solution, where the specific content demand model is not needed and each UAV would learn the best decision autonomously based on its local observations. It can achieve the adaptive cooperation among different UAVs, while optimizing the overall network performance. Moreover, standing from the perspective on swarm intelligence, we further develop a dynamic clustering federated learning framework on the MADRL algorithm. By performing parameter fusion, each UAV can improve the learning efficiency.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has emerged to support computation-intensive tasks in 6G systems. Since the battery capacity of a UAV is limited, to serve as many users as possible, a joint design on UAV trajectory and offloading strategy with consideration for service fairness is essential to provide energy-efficient computation offloading to the users in UAV-MEC networks. Unfortunately, such a joint decision-making problem is not straightforward due to various task types required from users and various functionalities of different UAVs enabled by different application programs. Considering the above issues, we take energy efficiency and service fairness as the objective, and propose aMulti-AgentEnergy-Efficient jointTrajectory andComputationOffloading (MA-ETCO) scheme. To adapt to dynamic demands of users, we develop an optimization-embedding multi-agent deep reinforcement learning (OMADRL) algorithm. Each UAV autonomously learns the trajectory control decision based on MADRL to adapt to dynamic demands. Then, it will obtain the optimal computation offloading decision by solving a mixed-integer nonlinear programming problem. The computation offloading result, in turn, will be used as an indicator to guide UAVs’ trajectory design. Compared to relying solely on deep reinforcement learning, such an optimization-embedding way reduces action space dimension and improves convergence efficiency.

Using mmWave radar to conduct gesture recognition is a promising solution for human-computer interaction. Although many studies have shown initial success, two-fold problems still remain unsolved, namely, the high-strength human activity interference and the difficulty in handling similar gestures. In light of these, we develop a robust mmWave radar based gesture recognition system, Rodar, to achieve accurate recognition of similar gestures under high-strength human activity interference, where a Multi-view De-interference Transformer (MvDeFormer) network is proposed. Specifically, to deal with the strong human activity interference, we design a DeFormer module to capture the useful gesture features by learning different patterns between gestures and interference, thereby reducing the impact of interference. Then, we develop a hierarchical multi-view fusion module to first extract the enhanced features within each view, and effectively fuse them across various views for final recognition. To evaluate the proposed Rodar system, we construct a dataset with seven similar gestures under three common human activity interference scenarios. Experimental results show that the accuracy can achieve up to 93.01%. The code implementations are available athttps://github.com/Xlab2024/MvDeFormer.

Device-free vital signs monitoring is an emerging technology that utilizes the unique influence of chest vibrations on surrounding wireless signals to achieve vital signs monitoring in a device-free and contact-free manner. Existing methods could achieve good monitoring performance when high-quality reflected signals can be obtained. However, in daily vital signs monitoring at home, the received reflected signals are often very weak due to factors such as obstruction and attenuation, resulting in a sharp decrease in the monitoring performance. To address the aforementioned challenges, in this paper, we develop a diversity-enhanced robust device-free vital signs monitoring system using mmWave signals. Specifically, inspired by the concept of diversity in the field of communications, we propose a diversity-enhanced wireless sensing strategy that comprehensively utilizes multi-dimensional physical layer resources, including antennas, chirps, and space, to improve the signal-to-noise ratio of vital signs. Additionally, inspired by cameras that achieve clear images by prolonging exposure time, we propose an accumulation-enhanced localization method to lock onto the chest of the human body in complex scenarios. Extensive experiments on a 60 GHz mmWave testbed demonstrate that our developed system could guarantee robust vital signs monitoring performance in various challenging scenarios, even at distances of up to 40 m.

Dynamic spectrum access (DSA) has been regarded as a viable solution to the spectrum shortage problem. To find idle spectrum, partial spectrum sensing could be employed by selecting a suitable sensing window (SW). Since the SW selection determines how many available bands to access, the transmission performance after the access could be used to guide the SW selection. Hence, a sophisticated joint design on spectrum sensing and access is necessary, which, however, is a challenging task when considering the dynamic nature of spectrum environment, and also the mutual impact among different secondary users (SUs). In this paper, we propose a joint partial spectrum sensing and power allocation (PA) scheme to facilitate SUs to make the best decisions on SW and PA to maximize the network throughput with reduced mutual interference. Considering the environmental dynamics and spectrum uncertainty, we develop a viable solution based on hierarchical multi-agent deep reinforcement learning (HMADRL). Our solution enables mutual design with two stages: making each SU learn the best SW and PA strategies autonomously while adapting to the dynamic environment. By using both simulated spectrum data and real spectrum data measured by SAM60-BX, we have demonstrated the effectiveness of our proposed scheme.

Unmanned Aerial Vehicle (UAV)-assisted mobile edge computing (MEC) has emerged as a promising solution to support the computation-intensive tasks in the Internet of Things (IoT) networks. As for the operation of UAV-assisted MEC, jointly design of the UAV trajectory control and computation offloading strategies becomes the key for achieving high offloading efficiency, which is extremely challenging due to the uncertain and dynamic demands in the network. In this paper, aiming at maximizing the offloading task amount, we propose an Multi-Agent joint TrAjectory and Computation Offloading (MA-TACO) scheme, where all related factors including task type variety, quality of service (QoS) guarantee, and service fairness are taken into account. To facilitate each UAV to obtain the best joint strategy under dynamic network environment, considering the complex decisions with both continuous and discrete variables, we develop an Optimization-oriented Multi-Agent Deep Reinforcement Learning approach (OMADRL), where each UAV could autonomously learn the trajectory decision to adapt to the dynamic demands, and the offloading decision would be made by solving a mixed-integer programming problem based on the observations, which would be utilized to guide the trajectory learning. Comparing with solely relying on learning, such an optimization-oriented way could reduce the action space dimension and make each UAV achieve the best strategy faster. The simulation results indicate the effectiveness of the proposed scheme.

To relieve the high backhaul load and long transmission time caused by the huge mobile data traffic, caching devices are deployed at the edge of mobile networks. The key to efficient caching is to predict the content popularity accurately while touching the users’ privacy as little as possible. Recently, many studies have applied federated learning in content caching to improve data security. However, they still give away some privacy of participants, especially ignoring the private data leakage in the trained model. To solve this problem and further improve the cache hit ratio, we propose an efficient content popularity prediction of privacy-preserving (CPPPP) scheme based on federated learning and Wasserstein generative adversarial network (WGAN), which achieves a high cache hit ratio. Benefited by the Federated-WGAN and the generated fake samples, the private data, the content preferences of individual users, and so on are well protected. In particular, gradient clipping and model parameter limitation are introduced in the model training, and the security of the modified model is greatly improved compared with the original model. Results show that the proposed scheme has a higher cache hit ratio than the existing federated learning-based methods while limiting the privacy leakage caused by the trained model to a quite low level.

The emerging Internet of Things (IoT) paradigm makes our telecommunications networks increasingly congested. Unmanned aerial vehicles (UAVs) have been regarded as a promising solution to offload the overwhelming traffic. Considering the limited spectrums, cognitive radio can be embedded into UAVs to build backhaul links through harvesting idle spectrums. For the cognitive UAV (CUAV) assisted network, how much traffic can be actually offloaded depends on not only the traffic demand but also the spectrum environment. It is necessary to jointly consider both issues and co-design the trajectory and communications for the CUAV to make data collection and data transmission balanced to achieve high offloading efficiency, which, however, is non-trivial because of the heterogeneous and uncertain network environment. In this paper, aiming at maximizing the energy efficiency of the CUAV-assisted traffic offloading, we jointly design the Trajectory, Time allocation for data collection and data transmission, Band selection, and Transmission power control ($\text{T}^{\mathrm{ 3}}\text{B}$) considering the heterogeneous environment on traffic demand, energy replenishment, and spectrum availability. Considering the uncertain environmental information, we develop a model-free deep reinforcement learning (DRL) based solution to make the CUAV achieve the best decision autonomously. Simulation results have shown the effectiveness of the proposed DRL-$\text{T}^{\mathrm{ 3}}\text{B}$strategy.

To support the emerging applications with the coming of the Beyond-5G (B5G) era, e.g., Ultra Reliable Low Latency Communications (URLLC) services, our telecommunications networks have witnessed a serious spectrum shortage problem. According to our spectrum measurement campaign, we note that many bands are actually extremely under-utilized, even for the operators’ ones, e.g., LTE spectrums. Thus, it is expected to share the idle spectrums for the B5G services. Nevertheless, how to determine an effective sharing strategy is non-trivial. It is necessary to jointly consider the spectrum requirement of primary networks and the traffic demand of secondary networks when making the sharing decision, which, however, are both uncertain and hardly known precisely in advance. In this paper, taking the uncertain network environment into account, we propose a Proactive Dynamic Spectrum Sharing (PDSS) scheme to employ the under-utilized LTE spectrums for URLLC service provisioning. We take the long-term overall utility as the objective to achieve a trade-off between two networks to avoid the performance degradation of primary networks, while fulfilling as many URLLC services as possible with quality of service (QoS) guarantee. To deal with the environment uncertainty, we develop a model-free deep reinforcement learning (DRL) based solution, which can proactively capture the feature of the uncertain environment and achieve the best sharing decision autonomously. Based on the real spectrum data, simulation results have shown the effectiveness of the proposed DRL based PDSS scheme.

To deal with the ever increasing wireless traffic, we have recently designed a vehicular cognitive capability harvesting network (V-CCHN) architecture to leverage vehicles as an alternative “transmission medium” (i.e., an opportunistic data carrier), besides the wireless spectrum, to effectively transport data from the location where it is collected to the place where it is consumed or utilized in a smart city environment. In the V-CCHN, cognitive radio technologies are utilized so that a large amount of data can be exchanged between vehicles and roadside infrastructure through short-range high-speed transmissions. Considering the limited contact duration and the uncertain activities of primary users, how to facilitate efficient data exchange between vehicles and roadside infrastructure is very challenging. This problem is further complicated by the fact that the mobility of vehicles might not be accurately predicted. In this paper, we propose a probabilistic data prefetching (PDP) scheme for the V-CCHN to address these challenges. By considering the conditional value at risk, we formulate the PDP schematic design as an optimization problem which allows us to obtain the corresponding PDP scheme. Finally, we have conducted extensive study to evaluate the performance of the obtained PDP scheme under various parameter settings.

The emerging Internet of Things (IoT) era has stimulated many new computation-intensive applications. To support them, mobile edge computing (MEC) is a promising solution that allows users to offload their heavy computing tasks to nearby edge servers. Taking such computation offloading as the service, application service providers (ASPs) can rent resources from mobile network operators for MEC service provisioning. However, it is challenging for ASPs to determine how many resources to rent at different regions and times due to the uncertain user demand. When making an investment strategy, it is crucial to maximize the profit with the consideration on the Quality of Service (QoS), where a joint scheduling on both communication and computing resource under the uncertain demand is needed. To deal with the uncertainty, the probability distribution information is usually employed, which, unfortunately, might be hardly obtainable in practice. Therefore, in this article, we propose a data-driven risk-averse MEC resource investment (DRAI) strategy, where the demand uncertainty issue is particularly addressed. Specifically, we formulate the DRAI strategy into a stochastic optimization problem, which can achieve the expected optimal profit under the QoS guarantee statistically from a risk-averse perspective. To solve it, instead of relying on specific distribution models, we construct an ambiguity set based on the statistical characteristics derived from the historical data that contains all possible distributions, and develop a data-driven distributionally robust solution, aiming at achieving the best strategy under the worst case to make it trustworthy. Simulation results illustrate the effectiveness of the proposed DRAI strategy.

Power efficiency is a big concern in the Internet of Underwater Things (IoUT). The power consumption of underwater acoustic communications is typically in the scale of watts, which may drain the battery of underwater devices quickly. Whereas, the power consumption of underwater magnetic induction (MI) wireless communications is in the scale of milliwatt. Therefore, this article devotes to combine the underwater MI and acoustic communications to form a power-efficient underwater hybrid wireless network. Specifically, we investigate the power-efficient autonomous underwater vehicle (AUV) data collection schemes in an underwater MI and acoustic hybrid sensor network. We propose an alternating anchor nodes selection and flow routing (AANSFR) AUV data collection method, which alternately optimizes the AUV path planning and network data flow routing. The simulation results show that the proposed hybrid data collection scheme can significantly prolong the lifespan of underwater sensor networks.

Unmanned aerial vehicle (UAV) assisted edge caching has been emerged as a promising solution to alleviate network congestion, which can provide users with their desired contents with reduced latency. For achieving effective UAV-assisted edge caching, how to jointly design the trajectory and caching strategy is critical, which, however, is not straightforward due to the heterogeneous and uncertain demand in the network. In this paper, aiming at maximizing the reduced delay brought by the UAV-assisted caching, we propose a proactive joint strategy on trajectory and caching for the UAV, where the demand uncertainty is particularly studied. Specifically, by regarding the demand on each content as a random variable, we formulate the strategy design as a risk-averse stochastic optimization problem to make the network performance guaranteed under certain confidence level. Different from most existing works assuming the perfect distributional information is available to deal with the uncertainty, we develop a data-driven approach based on the first and second order statistics to achieve a distributionally robust (DR) solution, which can make the strategy trustworthy with guaranteed network performance even though the specific distributional information is unknown. Simulation results have demonstrated the effectiveness of the proposed DR strategy.

The dramatic proliferation on emerging Internet-of-Things (IoT) makes our telecommunications networks more and more congested. Due to the flexible deployment and spectrum supplement capabilities, cognitive radio based unmanned aerial vehicles (CUAVs) have been regarded as a promising solution to help the network offload the overwhelming traffic. For the CUAV-assisted network, how to offload as much traffic as possible is significant. It is necessary to jointly consider both sides on data collection and data transmission, which, however, is a very challenging problem due to the heterogeneous and uncertain environment on both traffic demand and spectrum availability. In this paper, aiming at maximizing the offloaded traffic, we propose a joint strategy on trajectory design, time division, and spectrum access. Considering the unobtainable environmental information on both traffic demand and spectrum availability, we further develop a model-free deep reinforcement learning (DRL) based solution for the T2S joint strategy, so that the CUAV could make the best decisions autonomously under the uncertain environment. Simulation results have shown the effectiveness of the designed DRL solution and also the offloading efficiency of the proposed T2S strategy.

Multiaccess edge computing (MEC) empowers service providers (SPs) to run applications on the shared edge platforms in close proximity to mobile users, enabling ultralow latency access to a wide variety of cloud services. However, how to decide the amount of edge computing resources to rent for mobile service provisioning poses great challenges as the service demand is unknown to SPs a priori and may vary across the geographically distributed edge sites spatially and temporally. The resource rental decision also significantly affects SPs' deploying profits since it is critical for service deployment and workload assignment. This article investigates the service provisioning problem in a cooperative edge computing system under service demand uncertainty. We develop a holistic solution to make two-timescale decisions on edge resource rental and workload assignment to maximize SP's deploying profits. Briefly, we exploit historical service demand traces at the edge sites to characterize the uncertainty in a data-driven manner and formulate the edge service provisioning problem into a two-stage risk-averse optimization. To solve the formulated problem without compromising the data privacy, we propose an algorithm integrating Benders decomposition (BD) and alternating direction method of multipliers (ADMMs), which enables each edge site to keep the historical traces locally and participate in the optimization process. Based on real-world data sets, extensive simulations are conducted to validate the efficacy of our scheme.

Mobile crowdsensing (MCS) has become a new sensing and computing paradigm due to the proliferation of global positioning system (GPS) enabled mobile devices. There are three parties in the MCS, the MCS server, task requesters and workers. The MCS server needs to collect workers' location information to optimize the task allocation problem. However, during the location data collection process, workers' location privacy might be disclosed without their knowledge. It is challenging to preserve workers' location privacy while effectively and efficiently selecting proper workers to fulfill an MCS task. In this work, we propose a novel differentially private geocoding (DPG) mechanism to preserve workers' location privacy. Specifically, instead of reporting the exact latitude and longitude to the server, workers can use obfuscated geocode to describe their locations, since geocodes can provide an intuitive visualization of workers' spatial information to the MCS server. Based on the workers' obfuscated geocodes, we also formulate a travel distance minimization problem in MCS into an integer linear programming problem. We leverage conditional value at risk (CVaR) to characterize the uncertainty brought by the obfuscated geocodes, and develop feasible solutions to the formulated optimization problem. We conduct simulations with a real-world taxi dataset and verify the effectiveness of the proposed mechanism.

As a promising solution of spectrum shortage, spectrum sharing has received tremendous interests recently. However, under different sharing policies of different licensees, the shared spectrum is heterogeneous both temporally and spatially, and is usually uncertain due to the unpredictable activities of incumbent users. In this paper, considering the spectrum uncertainty, we propose a spectrum sharing based delay-tolerant traffic off-loading (SDTO) scheme. To capture the available heterogeneous shared bands, we adopt a mesh cognitive radio network and employ the multi-hop transmission mode. To statistically guarantee the end-to-end (E2E) session request under the uncertain spectrum supply, we formulate the SDTO scheme into a stochastic optimization problem, which is transformed into a mixed integer nonlinear programming (MINLP) problem. Then, a coarse-fine search based iterative heuristic algorithm is proposed to solve the MINLP problem. Simulation results demonstrate that the proposed SDTO scheme can well schedule the network resource with an E2E session guarantee.

The fast development on emerging services makes our telecommunications networks witness two key problems. One is the flexibility to fulfill the diverse service requests and the other is the shortage on spectrum. Network slicing and spectrum sharing have been regarded as two prominent solutions, which, however, are barely jointly studied. When taking the shared spectrum into account, its unique feature of heterogeneity and uncertainty will bring new challenges for the slicing. In this paper, we propose a service-oriented spectrum-aware RAN-slicing trading (SSRT) scheme with a comprehensive consideration on both aspects. For the SSRT scheme, we jointly slice three kinds of resources, namely, time, spectrum (including both licensed one and shared one), and network facilities, according to the diverse traffic requests, which are classified into delay-tolerant (DT) ones and delay-sensitive (DS) ones, as well as the willing payments from different service providers (SPs). To achieve both inter-slice and intra-slice isolation, we construct a three-dimensional (3D) conflict graph and formulate the SSRT scheme into a mixed-integer nonlinear programming (MINLP) problem with a cross-layer spectrum-aware resource allocation and a hybrid transmission mode (including both single-hop and multi-hop). Since finding all the maximum independent sets (MIS) for the 3D conflict graph is an NP-hard problem, we further develop an iterative heuristic algorithm for the MIS determination.

Intelligent reflecting surface (IRS) is considered as an enabling technology for future wireless communication systems since it can intelligently change the wireless environment to improve the communication performance. In this paper, an IRS-enhanced wideband multiuser multi-input single-output orthogonal frequency division multiplexing (MU-MISO-OFDM) system is investigated. We aim to jointly design the transmit beamformer and the reflection of IRS to maximize the average sum-rate over all subcarriers. With the aid of the relationship between sum-rate maximization and mean square error (MSE) minimization, an efficient joint beamformer and IRS design algorithm is developed. Simulation results illustrate that the proposed algorithm can offer significant average sum-rate enhancement, which confirms the effectiveness of the use of the IRS for wideband wireless communication systems.

The explosive growth on emerging Internet-of-Things (IoT) applications makes our telecommunications networks confront twofold challenges. One is to provide sufficient flexibility for service diversity. The other is the shortage on spectrum. Network slicing and spectrum sharing have been deemed as two prominent solutions, which, however, are barely jointly studied in the literature. In this article, standing on both aspects, we propose a service-oriented spectrum-aware RAN-slicing trading (SSRT) scheme to achieve a dynamic on-demand RAN slicing under the spectrum sharing scenario. For the SSRT scheme, we jointly slice multidimensional resources, including heterogeneous spectrums (licensed and shared), time, and network facilities (nodes, radios, and powers). In particular, considering the uncertainty of shared spectrums, we distinguish them from the traditional licensed ones to fulfill different types of sessions, which are classified into delay tolerant and delay sensitive. To achieve an effective isolation, we construct a 4-D conflict graph and formulate the slice generation problem into a mixed-integer nonlinear programming (MINLP) problem, where a cross-layer resource allocation based on a hybrid transmission mode is designed for the customization. To cope with the difficulties when solving the problem, we employ the column generation algorithm to obtain the final slicing result over all the resources. The simulation results have shown the effectiveness of the proposed scheme.

With the rapid growth on data traffic, spectrum shortage becomes increasingly serious, leading to the paradigm shift in spectrum usage from an exclusive mode to a sharing mode. However, how to utilize shared spectrums effectively for service provisioning is not straightforward due to its uncertain availability, known as spectrum uncertainty. In this paper, we propose a new metric to evaluate the achievable rate of a link on a share band under a confidence level, called probabilistic link capacity, which offers us an effective way to guarantee the quality of service statistically when using the shared spectrum for service delivery. Different from most existing works where the distributional information is explicitly given based on certain structural assumption, we develop a data-driven distributionally robust approach by using the first and second order statistical information. To achieve the result, we formulate it into a tractable semidefinite programming problem based on the worst-case of conditional-value-at-risk. Finally, as a use case, we design a service-based spectrum-aware transmission scheme, so that different kinds of spectrums (licensed and shared) can be efficiently utilized to satisfy the diverse service requirements.

In this paper, we introduce a network entity called point of connection (PoC), which is equipped with customized powerful communication, computing, and storage (CCS) capabilities, and design a data transportation network (DART) of interconnected PoCs to facilitate the provision of Internet of Things (IoT) services. By exploiting the powerful CCS capabilities of PoCs, DART brings both communication and computing services much closer to end devices so that resource-constrained IoT devices could have access to the desired communication and computing services. To achieve the design goals of DART, we further study the spectrum-aware placement of edge computing services. We formulate the service placement as a stochastic mixed-integer optimization problem and propose an enhanced coarse-grained fixing procedure to facilitate efficient solution finding. Through extensive simulations, we demonstrate the effectiveness of the resulting spectrum-aware service placement strategies and the proposed solution approach.

This paper designs a novel geometry-conformal antenna for Magnetic Induction (MI)-based subsea wireless communications for autonomous underwater vehicles (AUV). The designed tri-directional antennas can be wrapped directly on the surface of AUVs, such that the AUVs fluid dynamics are well maintained to ensure power efficiency of the vehicles. In addition, ferrite materials are added between the MI antenna and the metallic body surface of the AUVs to overcome the shielding effect and enhance the MI signal strength. The designed MI communication system is implemented in hardware and the effectiveness of the geometry-conformal MI antenna is demonstrated through COMSOL simulations and lab experiments.

Due to its promising application in the field of human- machine interaction, letter recognition in the air has drawn considerable attention in recent years. Compared with traditional sensor-based and camera-based methods, letter recognition in the air using channel state information (CSI) is more user-friendly and easy-to-deploy. Unfortunately, due to the limited range of the moving hand and the similarity of different letters, it is difficult to extract discriminative writing patterns for different letters from the noisy environment. In this paper, we design LetFi, a high accuracy letter recognition in the air system, which could detect and recognize the letter written by a user by analyzing its influence on surrounding WiFi signals. Specifically, we design a differential method to extract robust CSI measurements, develop a variance based scheme to detect the start and the end points of the letter writing activity, and propose a coherence histogram based multi-domain feature extraction strategy to extract discriminative features from not only the time domain and frequency domain, but also the spatial structural domain. Extensive experimental results show that the proposed LetFi system could achieve a recognition accuracy of 95% when recognizing the 26 capital letters.

With the growth of smartphone sales and app usage, fingerprinting and identification of smartphone apps have become a considerable threat to user security and privacy. Traffic analysis is one of the most common methods for identifying apps. Traditional countermeasures towards traffic analysis includes traffic morphing and multipath routing. The basic idea of multipath routing is to increase the difficulty for adversary to eavesdrop all traffic by splitting traffic into several subflows and transmitting them through different routes. Previous works in multipath routing mainly focus on Wireless Sensor Networks (WSNs) or Mobile Ad Hoc Networks (MANETs). In this paper, we propose a multipath routing scheme for smartphones with edge network assistance to mitigate traffic analysis attack. We consider an adversary with limited capability, that is, he can only intercept the traffic of one node following certain attack probability, and try to minimize the traffic an adversary can intercept. We formulate our design as a flow routing optimization problem. Then a heuristic algorithm is proposed to solve the problem. Finally, we present the simulation results for our scheme and justify that our scheme can effectively protect smartphones from traffic analysis attack.

Communication technologies supply the blood for smart city applications. In view of the ever-increasing wireless traffic generated in smart cities and our already congested radio access networks (RANs), we have recently designed a data transportation network, the vehicular cognitive capability harvesting network (V-CCHN), which exploits the harvested spectrum opportunity and the mobility opportunity offered by the massive number of vehicles traveling in the city to not only offload delay-tolerant data from congested RANs but also support delay-tolerant data transportation for various smart-city applications. To make data transportation efficient, in this paper, we develop a spectrum-aware (SA) data transportation scheme based on Markov decision processes. Through extensive simulations, we demonstrate that, with the developed data transportation scheme, the V-CCHN is effective in offering data transportation services despite its dependence on dynamic resources, such as vehicles and harvested spectrum resources. The simulation results also demonstrate the superiority of the SA scheme over existing schemes. We expect the V-CCHN to well complement existing telecommunication networks in handling the exponentially increasing wireless data traffic.

Acoustic communication is the most versatile and widely used technology for underwater wireless networks. However, the frequencies used by current acoustic modems are heavily overlapped with the cetacean communication frequencies, where the man-made noise of underwater acoustic communications may have harmful or even fatal impact on those lovely marine mammals, e.g., dolphins. To pursue the environmental friendly design for sustainable underwater monitoring and exploration, specifically, to avoid the man-made interference to dolphins, in this paper, we propose a cognitive acoustic transmission scheme, called dolphin-aware data transmission (DAD-Tx), in multi-hop underwater acoustic networks. Different from the collaborative sensing approach and the simplified modeling of dolphins' activities in existing literature, we employ a probabilistic method to capture the stochastic characteristics of dolphins' communications, and mathematically describe the dolphin-aware constraint. Under dolphin-awareness and wireless acoustic transmission constraints, we further formulate the DAD-Tx optimization problem aiming to maximize the end-to-end throughput. Since the formulated problem contains probabilistic constraint and is NP-hard, we leverage Bernstein approximation and develop a three-phase solution procedure with heuristic algorithms for feasible solutions. Simulation results show the effectiveness of the proposed scheme in terms of both network performance and dolphin awareness.

Auction-based secondary spectrum market provides a platform for spectrum holders to share their under-utilized licensed bands with secondary users (SUs) for economic benefits. However, it is challenging for SUs to directly participate due to their limited battery power and capability in computation and communications. To shift complexity away from users, in this paper, we propose a novel multi-round service-oriented combinatorial spectrum auction with two-tier framework support. In Tier I, we introduce several secondary service providers (SSPs) to provide end-users with services by using purchased licensed bands even if the end-users do not have cognitive radio capability. When an SU submits its service request with certain bidding allowance to its SSP, the SSP will help find out which bands within its area are available and bid for the desired ones from the market in Tier II. Specifically, we formulate the bidding process at the SSP as an optimization problem by considering interference management, spectrum uncertainty, flow routing, and budget allowance. In Tier II, considering two possible manners of the seller, we propose two social-welfare-maximizing auction mechanisms accordingly, including the winner determination based on weighted conflict graph and the Vickrey-Clarke-Groves-styled price charging mechanism. Extensive simulations have been conducted and the results have demonstrated the higher revenue of the proposed scheme compared with the traditional commodity-oriented single-round truthful schemes.

Spectrum trading benefits primary users (PUs) by monetary gains and secondary users (SUs) by spectrum accessing opportunities in cognitive radio networks (CRNs). Unfortunately, most existing spectrum trading designs only focus on the guarantee of economic properties, but forget the wireless transmission nature, especially for multi-hop cognitive radio (CR) communications. In this paper, we propose an economic-robust session based spectrum trading, which has a joint consideration of economic properties such as incentive compatibility, individual rationality, and budget balance, and the end-to-end performance for multi-hop communications. Considering two bidding manners, i.e., bidding for the whole session and unit rate bidding, we formulate the spectrum trading optimization problems under multiple economic and multi-hop CR transmission constraints, design two pricing mechanisms to charge the winning spectrum bidders, and further mathematically prove the economic- robustness of the proposed spectrum trading schemes. Through extensive simulations, we show the proposed schemes are economic-robust and effective in improving spectrum utilization.

Simultaneous wireless information and power transfer (SWIPT) and interference alignment (IA) are two emerging techniques in energy harvesting and interference management for the next generation wireless networks, respectively. Although many studies have focused on SWIPT and IA, the conjunction of these two techniques is largely ignored, which should be noted to reuse the interference as energy for harvesting. In this paper, we jointly study SWIPT and IA in the multiuser MIMO system to realize energy harvesting and interference management simultaneously and effectively. Specifically, antenna selection (AS) based SWIPT scheme is proposed and analyzed for IA networks. Furthermore, power allocation (PA) for multiple data streams is designed to further improve its performance, and the closed-form solution can be obtained by Lagrange duality method. In addition, given the constrained number of antennas, another scheme called power splitting (PS) based SWIPT is utilized, where PA is also considered and formulated as a joint optimization problem. Simulation results are presented to show the superiority of the proposed schemes.

Interference alignment (IA) is a promising technique that can eliminate the interferences in wireless networks effectively, and has been applied to cognitive radio (CR). However, the quality of desired signal may be poor when the interferences are aligned in the direction similar to that of the desired signal. Thus, we propose a novel IA scheme based on antenna selection to improve the performance of CR networks. In the proposed scheme, multiple antennas are equipped at each secondary receiver, and we choose some of them that have the optimal channel coefficients according to a certain objective function. Furthermore, we also consider the condition of imperfect channel state information (CSI), and an efficient antenna selection IA algorithm based on discrete stochastic optimization is proposed. Simulation results show that the proposed schemes can improve the performance of IA-based CR networks significantly.