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Recently, reconfigurable intelligent surface (RIS) has attracted continuous attention in wireless communications. Because the normal incidence condition of electromagnetic waves cannot always be satisfied in practical applications, the angular insensitivity in RISs has become an important issue. The angular sensitivity in RISs may lead to the failure of RIS-assisted wireless communication networks that rely on the reciprocity of wireless channels. In this work, an angle-insensitive 3-bit RIS meta-atom is proposed. By introducing metallic vias in the substrate of programmable meta-atoms, the interference induced by multiple reflections in the multilayered structure is restrained, and the decoupling between adjacent meta-atoms makes the metasurface insensitive to the incident angle. Hence the angular insensitivity can be achieved. An RIS is fabricated using the proposed angleinsensitive programmable meta-atom with metallic vias, which is capable of maintaining stable phase and amplitude responses at oblique incidences. Both simulated and measured results show stable angle performance. A maximum of 3150 phase range and eight digital coding states with 450 stable interval are obtained for wide incidence angles from 00 to 600. Finally, numerical simulations and experimental results of anomalous reflections are performed to verify the angular reciprocity of the proposed RIS, which is important in wireless communications.

To support more Internet of Things devices, we present a novel blockchain-enabled heterogeneous cellular network (HetNet). In this network, devices store block data to the cloud service provider, offload the proof-of-work mining tasks to base stations (BSs), and associate with the macro-cell BS or small-cell BSs. Then, we analyze the user association problem, computation offloading problem, and block storage problem in the blockchain-enabled HetNet. We also design corresponding algorithms to solve these problems. Furthermore, to tackle the challenge of data congestion of BSs, based on the obtained computation offloading and block storage strategies, we propose a modified user association algorithm. The analysis shows that the proposed blockchain-enabled HetNet can effectively attain computing offloading, block storage, and user association strategies, and more devices can access the blockchain network. Analytical results show that the proposed modified user association algorithm can greatly avoid data congestion of BSs. Numerical results demonstrate the effectiveness of our proposed algorithms for computation offloading and block storage, and the proposed modified user association algorithm has a significantly great advantage compared with the traditional nearest BS association algorithm in terms of avoiding data congestion.

Simultaneous localization and mapping (SLAM) during communication is emerging. This technology promises to provide information on propagation environments and transceivers' location, thus creating several new services and applications for the Internet of Things and environment-aware communication. Using crowdsourcing data collected by multiple agents appears to be much potential for enhancing SLAM performance. However, the measurement uncertainties in practice and biased estimations from multiple agents may result in serious errors. This study develops a robust SLAM method with measurement plug-and-play and crowdsourcing mechanisms to address the above problems. First, we divide measurements into different categories according to their unknown biases and realize a measurement plug-and-play mechanism by extending the classic belief propagation (BP)-based SLAM method. The proposed mechanism can obtain the time-varying agent location, radio features, and corresponding measurement biases (such as clock bias, orientation bias, and received signal strength model parameters), with high accuracy and robustness in challenging scenarios without any prior information on anchors and agents. Next, we establish a probabilistic crowdsourcing-based SLAM mechanism, in which multiple agents cooperate to construct and refine the radio map in a decentralized manner. Our study presents the first BP-based crowdsourcing that resolves the "double count" and "data reliability" problems through the flexible application of probabilistic data association methods. Numerical results reveal that the crowdsourcing mechanism can further improve the accuracy of the mapping result, which, in turn, ensures the decimeter-level localization accuracy of each agent in a challenging propagation environment., Paper accepted for publication in IEEE Transactions on Wireless Communications

In federated learning (FL), model training is distributed over clients and local models are aggregated by a central server. The performance of uploaded models in such situations can vary widely due to imbalanced data distributions, potential demands on privacy protections, and quality of transmissions. In this paper, we aim to minimize FL training delay over wireless channels, constrained by overall training performance as well as each client's differential privacy (DP) requirement. We solve this problem in the framework of multi-agent multi-armed bandit (MAMAB) to deal with the situation where there are multiple clients confornting different unknown transmission environments, e.g., channel fading and interferences. Specifically, we first transform the long-term constraints on both training performance and each client's DP into a virtual queue based on the Lyapunov drift technique. Then, we convert the MAMAB to a max-min bipartite matching problem at each communication round, by estimating rewards with the upper confidence bound (UCB) approach. More importantly, we propose two efficient solutions to this matching problem, i.e., modified Hungarian algorithm and greedy matching with a better alternative (GMBA), in which the first one can achieve the optimal solution with a high complexity while the second one approaches a better trade-off by enabling a verified low-complexity with little performance loss. In addition, we develop an upper bound on the expected regret of this MAMAB based FL framework, which shows a linear growth over the logarithm of communication rounds, justifying its theoretical feasibility. Extensive experimental results are conducted to validate the effectiveness of our proposed algorithms, and the impacts of various parameters on the FL performance over wireless edge networks are also discussed.

Reconfigurable intelligent surface (RIS) has drawn worldwide attention because of its attractive capability to improve the electromagnetic propagation environment and surprising advantages of low cost and low power consumption. Recently, RIS is further advanced to realize the independent control of two orthogonal polarizations in real time. In this paper, we study the dual-polarized RIS-assisted mobile communication system and investigate its performance under practical polarization imperfections. Specifically, we provide a tight upper bound of the ergodic spectrum efficiency. Through the bound, we find that the performance is greatly dependent on the polarization imperfections and RIS phase shifts of the RIS-assisted link, when the number of base station antennas is large enough and the deterministic component exists in the RIS-assisted link. On basis of this tight bound, we propose an approximate optimal RIS phase shift design to maximize the ergodic spectrum efficiency upper bound and emphasize the necessity to make phase adjustment between two polarizations of the RIS. Theoretical analysis further gives guidance for the configuration of RIS. Numerical results demonstrate that the upper bound is very tight and the proposed phase shift design with adjustment is approximate optimal.

In this work, we report high-performance amorphous indium gallium zinc oxide (a-IGZO) thin-film transistors (TFTs) with atomic layer deposited Al₂O₃ dielectric processed at a minimal temperature of 189.6 °C via microwave annealing (MWA). The a-IGZO TFT with MWA exhibits an improvement of 57% in subthreshold swing (SS) compared with the unannealed device; meanwhile, it retains high field-effect mobility of up to 29.2 cm²/(V · s) and large switching ratio of >10⁸. Moreover, the SS of the device was further reduced through a two-step MWA treatment. It is believed that MWA treatment effectively reduces the density of trap states associated with oxygen vacancies, promotes the formation of lattice oxygen, and thus improves the quality of IGZO film. MWA with low thermal budget shows great potential in applications of the back-end of line (BEOL)-compatible oxide devices.

Algorithm unfolding, which provides a systematic connection between conventional model-based algorithms and modern data-based deep learning, has exhibited great empirical success for efficiently balancing the performance and complexity of multiple-input and multiple-output (MIMO) detectors. However, existing unfolding-based MIMO detectors have difficulties adapting to the high discrepancy in channel and noise conditions. In this study, we present a novel unfolding-based framework for MIMO detectors, which can automatically determine internal parameters of an unfolding-based MIMO detector to adapt to the varying conditions. A key part of our approach is to develop a hypernetwork that can effectively learn to generate the internal parameters in the sophisticated expectation propagation-based MIMO detector. In particular, we design long short-term memory-based hypernetwork to ensure the flexibility of the layers of the unfolded algorithm. The proposed framework is also extended to a coded MIMO turbo receiver to adapt to the different feedback beliefs from the decoder. Numerical results demonstrate that the proposed MIMO detectors have excellent adaptation capability to different channel environments and noise levels. Compared with the existing unfolded algorithm that is an optimal reference, the proposed framework avoids frequent retraining and presents the nearly optimal performance in uncoded and coded MIMO systems. An over-the-air platform is presented as well to demonstrate the significant robustness of the proposed receivers in practical deployment.

In current wireless communication and electronic systems, digital signals and electromagnetic (EM) radiation are processed by different modules. Here, we propose a mechanism to fuse the modulation of digital signals and the manipulation of EM radiation on a single programmable metasurface. The programmable metasurface consists of massive subwavelength-scale digital coding elements. A set of digital states of all elements forms simultaneous digital information roles for modulation and the wave-control sequence code of the programmable metasurface. By designing digital coding sequences in the spatial and temporal domains, the digital information and far-field patterns of the programmable metasurface can be programmed simultaneously and instantly in desired ways. For the experimental demonstration of the mechanism, we present a programmable wireless communication system. The same system can realize transmissions of digital information in single-channel modes with beam-steerable capability and multichannel modes with multiple independent information. The measured results show the excellent performance of the programmable system. This work provides excellent prospects for applications in fifth- or sixth-generation wireless communications and modern intelligent platforms for unmanned aircrafts and vehicles.

Recently, deep learning based methods have been widely used for wireless communications. In this paper, different from current researches considering full channel state information (F-CSI) feedback, the eigenvector based CSI feedback with deep learning approach is proposed, referred to as EVCsiNet, where the joint eigenvector concatenated from multiple subbands is compressed and recovered at the encoder and decoder, respectively. Simulation results verify the superiority of our schemes over CSI recovery accuracy and feedback overhead compared with conventional methods using codebooks on link-level channel models in different scenarios.

In adversarial scenarios, it is crucial to timely monitor what tactical messages that opponent transmitters are sending to intended receiver(s), and disrupt the transmissions immediately if in need. The issue becomes more challenging in face of an intelligent transmitter. To address the above-stated issue, a full-duplex (FD) technique is utilized to enable simultaneous jamming and eavesdropping (SJE) at a friendly jammer node. In particular, the “Two Birds with One Stone” strategy is utilized at the jammer node to realize effective rate degradation and information eavesdropping. A confrontation game between an intelligence-empowered FD jammer and its opponent is investigated. Specifically, to capture their adversarial relationship in an environment with incomplete information, a power-domain Bayesian-Stackelberg game is proposed. The existence of a Stackelberg equilibrium (SE) power solution is proved. The semi-closed-form solutions of SE are derived, which are proved to be asymptotically optimal (have a gap of less than 1% with the exact utility), and improves the jammer node 10% utility compared with the Nash equilibrium. Additionally, the SJE strategy outperforms the half-duplex (HD) and other benchmark schemes.

Deploying a mobile edge computing (MEC) server in the mobile blockchain-enabled Internet of things (IoT) system is a promising approach to improve the system performance, however, it imposes a significant challenge on the trust of the MEC server. To address this problem, we first propose an untrusted MEC proof of work (PoW) scheme in mobile blockchain networks where plenty of nonce hash computing demands can be offloaded to the MEC server. Then, we design a nonce ordering algorithm for this scheme to provide fairer computing resource allocation for all mobile IoT devices/users. Specifically, we formulate the user’s nonce selection strategy as a non-cooperative game, where utilities of the individual user are maximized in the untrusted MEC-aided mobile blockchain networks. We also prove the existence of Nash equilibrium and analyze that the cooperation behavior is unsuitable for blockchain-enabled IoT devices by using the repeated game. Finally, we design the blockchain’s difficulty adjustment mechanism to ensure stable block times during a long period of time. Compared with the weighted round-robin algorithm, our proposed nonce ordering algorithm can provide fairer computation resources and optimal nonce selection strategies for all mobile users. Network stability is gained through the proposed blockchain’s difficulty adjustment mechanism. The analysis and optimization results provide valuable design insights for practical mobile blockchain IoT systems.

This study aims to determine the 5-year visual field progression and identify the prognostic factors for progression in Malay patients with primary glaucoma. A retrospective cohort record review study was conducted among 222 patients (222 eyes) with primary glaucoma who were selected from a glaucoma research database of a tertiary center in Malaysia. The patients were Malays and diagnosed with primary open-angle glaucoma (POAG) or primary angle-closure glaucoma (PACG). Patients who were followed up regularly for at least 6 months between 1 January 2009 and 31 December 2014 and completed another 1-year follow-up after recruitment (between 1 January 2015 and 31 December 2015) were selected. Multiple prognostic factors that influence visual field progression were identified. Progression of visual field loss was based on the Advanced Glaucoma Intervention Study and Hodapp–Parrish–Anderson scores. Kaplan–Meier survival and Cox proportional hazard regression analyses were performed. Sixty-three patients (28.4%) developed visual field progression after a mean (SD) follow-up of 6.9 (3.3) years. Those with POAG progressed faster (mean time, 10.6 years; 95% confidence interval [CI], 9.3, 11.9) than those with PACG (17.3 years; 95% CI, 14.8, 19.9) but not statistically significant. Disc hemorrhage and history of eye pain increased the risk of progression by 2.8-folds (95% CI, 1.6, 4.8) and 2.5-folds (1.4, 4.4), respectively. The 5-year survival of the Malay primary glaucoma patients with visual field progression was similar with that of other Asian populations. However, aggressive management is required for those with disc hemorrhages and eye pain related to increased intraocular pressure.

Orthogonal frequency division multiplexing (OFDM) has been widely applied in current communication systems. The artificial intelligence (AI)-aided OFDM receivers are currently brought to the forefront to replace and improve the traditional OFDM receivers. In this study, we first compare two AI-aided OFDM receivers, namely, data-driven fully connected deep neural network and model-driven ComNet, through extensive simulation and real-time video transmission using a 5G rapid prototyping system for an over-the-air (OTA) test. We find a performance gap between the simulation and the OTA test caused by the discrepancy between the channel model for offline training and the real environment. We develop a novel online training system, which is called SwitchNet receiver, to address this issue. This receiver has a flexible and extendable architecture and can adapt to real channels by training only several parameters online. From the OTA test, the AI-aided OFDM receivers, especially the SwitchNet receiver, are robust to real environments and promising for future communication systems. We discuss potential challenges and future research inspired by our initial study in this paper., Comment: 30 pages, 12 figures. Copyright may be transferred without notice, after which this version may no longer be accessible

In application scenarios such as Internet of Things, a large number of energy receivers (ERs) exist and line-of-sight (LOS) propagation could be common. Considering this, we investigate wireless energy transfer (WET) in extra-large massive MIMO Rician channels. We derive analytical expressions of the received net energy for different schemes, including 1) training-based WET, where the ER sends beacon signal for channel training and the energy transmitter (ET) uses the channel estimate for energy beamforming, 2) LOS beamforming, where the ET transmits to the LOS direction of the ER, and 3) energy harvesting, which allows an ER to harvest the training energy from the other ERs. We derive a path loss threshold for switching between training and LOS beamforming-based WET. We further show that the WET scheme selection of one ER is not affected by the other ERs, and the energy harvested from training is minimal in practice. With these insights, we propose an algorithm for the multi-ER scenario, which minimizes the power consumption by iteratively updating the WET scheme selection and power allocation for all ERs. Simulations show that the proposed algorithm achieves near-optimal performance as compared to exhaustive searching, while with much lower implementation complexity.

To describe the long-term coupled behaviour of landfilled municipal solid waste (MSW), this paper proposes a numerical model based on the theories of the conservation of mass, momentum and energy of porous media over a three-phase solid–liquid–gas system. Compared with currently available coupled models, the proposed model can simultaneously simulate such physical processes as phase change, dissolution of the gas phase, the diffusion of its components, evaporation in the liquid phase and changes in the pH of leachate, as well as its solute migration. The finite-volume method was used to numerically discretise the governing equations, with a sequentially iterative technique used for the numerical model of the solution. The corresponding computer code was formulated on the open-source platform OpenFoam. The model was then used to simulate the famous MSW degradation consolidating anaerobic reactor experiment conducted at Southampton University, where the results generally agreed well with the experimental data. The proposed model was also applied to capture spatial and temporal variations in the skeleton of MSW, leachate and landfill gas in a simplified two-dimensional landfill. Overall, the work here provides a basic coupled framework for simulating the long-term behaviours of landfilled MSW. It can be improved upon in future research by implementing other, more reasonable, constitutive relations of MSW.

Many types of buildings have been widely constructed in the vicinity of subway lines in China. Normal life and business activities are hampered by excessive subway-induced vibrations. This study aims to determine the influence of structure characteristics on structure-borne vibrations, generally based on experimental results. Vibration measurements were performed in four typical sites in Wuxi, China, involving over-track buildings, along-track buildings, frame structure buildings and a masonry building. Special structure designs like structure transfer floor were also included. Then, the captured data was analyzed in the time domain and the frequency domain. Furthermore, the influence of building location, structure type and structure layout was investigated. Finally, vibrations were evaluated with ISO and Chinese criteria and structure optimization for vibration attenuation was proposed. It is found that over-track buildings are more severely affected than along-track buildings. Higher frequency contents (20–60 Hz) in over-track buildings and lower frequency contents (0–10 Hz) in along-track buildings should be seriously considered in vibration control. Weaker structure member joints and lower material strength would be beneficial to over-track buildings while the inverse situation would be beneficial to along-track buildings. The application of structure transfer floor — generally stiffer structure members and structure discontinuity-is also beneficial.

Wireless communication involving unmanned aerial vehicles (UAVs) is expected to play an important role in future wireless networks. However, different from conventional terrestrial communication systems, UAVs typically have rather limited onboard energy on one hand, and require additional flying energy consumption on the other hand, which renders energy-efficient UAV communication with smart energy expenditure of paramount importance. In this paper, via extensive flight experiments, we aim to firstly validate the recently derived theoretical energy model for rotary-wing UAVs, and then develop a general model for those complicated flight scenarios where rigorous theoretical model derivation is quite challenging, if not impossible. Specifically, we first investigate how UAV power consumption varies with its flying speed for the simplest straight-and-level flight. With about 12,000 valid power-speed data points collected, we first apply the model-based curve fitting to obtain the modelling parameters based on the theoretical closed-form energy model in the existing literature. In addition, in order to exclude the potential bias caused by the theoretical energy model, the obtained measurement data is also trained using a model-free deep neural network. It is found that the obtained curve from both methods can match quite well with the theoretical energy model. Next, we further extend the study to arbitrary 2-dimensional (2-D) flight, where, to our best knowledge, no rigorous theoretical derivation is available for the closed-form energy model as a function of its flying speed, direction, and acceleration. To fill the gap, we first propose a heuristic energy model for these more complicated cases, and then provide experimental validation based on the measurement results for circular level flight.

To enable more Internet-of-Things (IoT) devices for participating in the Proof-of-Work (PoW) mining process of public blockchains, we propose a cooperative mobile-edge computing (MEC)-aided blockchain network. In the network, devices can offload computation-intensive PoW mining tasks to base stations and store their block data to the cloud service provider. Then, we study the joint computation offloading, block storage, and resource service pricing problem as a three-stage Stackelberg game. We analyze the subgame optimization problem in each stage and propose an iterative algorithm based on backward induction to achieve the Nash equilibrium of the Stackelberg game. Furthermore, we derive the upper bound of the ergodic throughput of the cooperative scheme and the maximum number of devices connected to the network. The analysis shows that the proposed cooperative MEC-aided blockchain network can significantly improve the system throughput, and more devices can access the blockchain network. Analytical results show that the proposed backward induction-based iterative algorithm can efficiently attain the Nash equilibrium of the game. Numerical results show that our proposed backward induction-based iterative algorithm has fast convergence and good stability, and the proposed cooperative scheme can serve more devices in comparison with other noncooperative schemes.

Background Considering the high morbidity and mortality of lung cancer and the high incidence of pulmonary nodules, clearly distinguishing benign from malignant lung nodules at an early stage is of great significance. However, determining the kind of lung nodule which is more prone to lung cancer remains a problem worldwide. Methods A total of 480 patients with pulmonary nodule data were collected from Shandong, China. We assessed the clinical characteristics and computed tomography (CT) imaging features among pulmonary nodules in patients who had undergone video‐assisted thoracoscopic surgery (VATS) lobectomy from 2013 to 2018. Preliminary selection of features was based on a statistical analysis using SPSS. We used WEKA to assess the machine learning models using its multiple algorithms and selected the best decision tree model using its optimization algorithm. Results The combination of decision tree and logistics regression optimized the decision tree without affecting its AUC. The decision tree structure showed that lobulation was the most important feature, followed by spiculation, vessel convergence sign, nodule type, satellite nodule, nodule size and age of patient. Conclusions Our study shows that decision tree analyses can be applied to screen individuals for early lung cancer with CT. Our decision tree provides a new way to help clinicians establish a logical diagnosis by a stepwise progression method, but still needs to be validated for prospective trials in a larger patient population., Considering the high morbidity and mortality of lung cancer and the high incidence of pulmonary nodules, it is of great significance to clearly distinguish between benign and malignant lung nodules at early stages. However, what kind of lung nodules are more prone to lung cancer is still a worldwide problem. In this study, 458 cases with pulmonary nodules data were collected from Shandong, China. We assessed the clinical characteristics and low‐dose computed tomography (LDCT) imaging features among pulmonary nodules in patients who had undergone video‐assisted thoracoscopic surgery (VATS) lobectomy from 2013 to 2018 to determine the machine learning models using multiple algorithms and select the best decision tree model. Our study shows that decision tree analyses can be applied to screen individuals for early lung cancer with CT. Our decision tree provides a new way to help clinicians establish a logical diagnosis by a stepwise progression method, but still needs to be validated for prospective trials in a larger patient population.

As the fifth-generation (5G) mobile communication system is being commercialized, extensive studies on the evolution of 5G and sixth-generation (6G) mobile communication systems have been conducted. Future mobile communication systems are evidently evolving toward a more intelligent and software-reconfigurable functionality paradigm that can provide ubiquitous communication, as well as sense, control, and optimize wireless environments. Thus, integrating communication and localization using the highly directional transmission characteristics of millimeter waves (mmWaves) is a promising route. This approach not only expands the localization capabilities of a communication system but also provides new concepts and opportunities to enhance communication. In this paper, we explain the integrated communication and localization in mmWave systems, in which these processes share the same set of hardware architecture and algorithms. We also provide an overview of the key enabling technologies and the basic knowledge on localization. Then, we provide two promising directions for studies on localization with an extremely large antenna array and model-based (or model-driven) neural networks. We also discuss a comprehensive guidance for location-assisted mmWave communications in terms of channel estimation, channel state information feedback, beam tracking, synchronization, interference control, resource allocation, and user selection. Finally, we outline the future trends on the mutual assistance and enhancement of communication and localization in integrated systems.

Digitally programmable metasurfaces are of potential use in wireless multiplexing techniques because they can encode and transmit information without using traditional radio-frequency components such as antennas or mixers. Space–time-coding digital metasurfaces can, in particular, manipulate the propagation direction and harmonic power distribution of electromagnetic waves, making them suitable for space- and frequency-division multiplexing. However, while digital metasurfaces have been used for wireless communication, these systems could implement signal modulation only in the time domain. Here, we report a wireless communication scheme that uses space–time-coding digital metasurfaces to implement both space- and frequency-division multiplexing. By encoding space–time-coding matrices through multiple channels, digital messages can be directly transmitted to different users at different locations simultaneously, without the need for digital-to-analogue conversion and mixing processes. To illustrate this approach, we have built a dual-channel wireless communication system based on a two-bit space–time-coding digital metasurface and use it to transmit two different pictures to two users simultaneously in real time. Space–time-coding digital metasurfaces can be used to implement secure and low-cost space- and frequency-division multiplexing in a dual-channel wireless communication system.

Metasurface has great capabilities in tailoring the electromagnetic (EM) waves in a controllable way owing to the carefully designed and arranged subwavelength meta-atoms. In this review paper, the digital version of metasurface, information metasurface (IM), is systematically introduced with its application in wireless communications. IM extends the research scope of metasurface from physics to the information perspective, in which the meta-atoms are represented by using digital codes “0” and “1” to characterize the EM responses. It can achieve programmable controls of EM waves in real time by integrating with the field-programmable gate array (FPGA). On the other hand, the digital coding strategy bridges the physical world and digital world, and further make the metasurface realize direct information processing. With the outstanding advantages, IMs have shown great potentials to bring revolutionary applications in wireless communications. In this article, the basic concepts and principles of IMs and their programmable features are presented first. Then it focuses on the IM-based new architecture transmitters, as well as the IM-integrated wireless networks in more detail. Finally, the current progress and the future directions of IMs in the wireless communications are respectively summarized.

Sparse signal recovery problems from noisy linear measurements appear in many areas of wireless communications. In recent years, deep learning (DL) based approaches have attracted interests of researchers to solve the sparse linear inverse problem by unfolding iterative algorithms as neural networks. Typically, research concerning DL assume a fixed number of network layers. However, it ignores a key character in traditional iterative algorithms, where the number of iterations required for convergence changes with varying sparsity levels. By investigating on the projected gradient descent, we unveil the drawbacks of the existing DL methods with fixed depth. Then we propose an end-to-end trainable DL architecture, which involves an extra halting score at each layer. Therefore, the proposed method learns how many layers to execute to emit an output, and the network depth is dynamically adjusted for each task in the inference phase. We conduct experiments using both synthetic data and applications including random access in massive MTC and massive MIMO channel estimation, and the results demonstrate the improved efficiency for the proposed approach., Comment: IEEE Journal on Selected Areas in Communications (JSAC), accepted

Defensive and offensive capabilities are both significant in communication confrontation games. By exploiting the above two capabilities, a new confrontation mechanism in the spectrum domain between two opposing teams denoted as the blue team (BT) and red team (RT), is designed. The basic idea is that by sacrificing parts of ally performance to severely deteriorate the opponent side communications. Specifically, a friendly and smart jammer (assuming in the BT) is deployed to weaken opponent (i.e., members in the RT) communications without causing great damages to other BT members, while the smart RT members try to evade the jamming and alleviate mutual interference. The interactions among the friendly jammer and other nodes are modeled as a Stackelberg game, with each player seeking for their respective utility maximization. We prove that each sub-game is an exact potential game. To efficiently search for the equilibrium solutions, a parallel log-linear learning algorithm is proposed, based on which each user intelligently decides their spectrum access policies. Numerical results demonstrate that: 1) RT communications are effectively suppressed; meanwhile, mutual interference among ally BT communication pairs is significantly alleviated; 2) the proposed algorithm achieves a close-to-optimal solution; 3) compared with the current state of solutions, i.e., random selection, stochastic learning automata, our algorithm performs better in terms of both utility and convergence.

Synaptic devices that merge memory and processing functions into one unit have broad application potentials in neuromorphic computing, soft robots, and human-machine interfaces. However, most previously reported synaptic devices exhibit fixed performance once been fabricated, which limits their application in diverse scenarios. Here, we report floating-gate photosensitive synaptic transistors with charge-trapping perovskite quantum dots (PQDs) and atomic layer deposited (ALD) Al2O3 tunneling layers, which exhibit typical synaptic behaviors including excitatory postsynaptic current (EPSC), pair-pulse facilitation and dynamic filtering characteristics under both electrical or optical signal stimulation. Further, the combination of the high-quality Al2O3 tuning layer and highly photosensitive PQDs charge-trapping layer provides the devices with extensively tunable synaptic performance under optical and electrical co-modulation. Applying light during electrical modulation can significantly improve both the synaptic weight changes and the non-linearity of weight updates, while the memory effect under light modulation can be obviously adjusted by the gate voltage. The pattern learning and forgetting processes for “0” and “1” with different synaptic weights and memory times are further demonstrated in the device array. Overall, this work provides synaptic devices with tunable functions for building complex and robust artificial neural networks.

Constant envelope (CE) precoding design is of great interest for massive multiuser multi-input multi-output systems because it can significantly reduce hardware cost and power consumption. However, existing CE precoding algorithms are hindered by excessive computational overhead. In this letter, a novel model-driven deep learning (DL)-based network that combines DL with conjugate gradient algorithm is proposed for CE precoding. Specifically, the original iterative algorithm is unfolded and parameterized by trainable variables. With the proposed architecture, the variables can be learned efficiently from training data through unsupervised learning approach. Thus, the proposed network learns to obtain the search step size and adjust the search direction. Simulation results demonstrate the superiority of the proposed network in terms of multiuser interference suppression capability and computational overhead.

In this letter, we present a novel framework of 3D scene based beam selection for mmWave communications that relies only on the environmental data and deep learning techniques. Different from other out-of-band side-information aided communication strategies, the proposed one fully utilizes the environmental information, e.g., the shape, the position, and even the materials of the surrounding buildings/cars/trees that are obtained from 3D scene reconstruction. Specifically, we build the neural networks with the input as point cloud of the 3D scene and the output as the beam indices. Compared with the LIDAR aided technique, the reconstructed 3D scene here is achieved from multiple images taken offline from cameras and thus significantly lowers down the cost and makes itself applicable for small mobile terminals. Simulation results show that the proposed 3D scene based beam selection can outperform the LIDAR method in terms of accuracy.

Recent years have witnessed a blooming of new applications that demand different network services. Network slicing is advocated by the research community to simultaneously support multiple services on a common physical infrastructure. Federated network slicing, which involves multiple operators, further generalizes the concept to cover a broader range. Existing federated slicing systems advocate the master-slave architecture among untrusted operators, which brings some centralization concern, making operators hesitate to join the system. Recently, blockchain shows great power to build trust in decentralized environments. Besides, artificial intelligence (Ai), especially reinforcement learning, is envisioned with the potential to develop more efficient optimization algorithms. Motivated by innovations in blockchain, smart contract, and Ai, this article proposes a decentralized federated slicing architecture that is trustful and efficient. We systematically discuss the design principles and key challenges in realizing the blockchain-enabled architecture. With these principles and challenges in mind, we develop a general architecture for multiple operators and cloud providers, with a new proof of business consensus protocol to ensure incentive and fairness. To further enhance its efficiency, we utilize reinforcement learning to accelerate optimizations in the resource allocation. Benefits of the Ai accelerated optimizer are demonstrated in simulations.

Background With an aging population, China is facing a huge burden of elderly patients with drug resistant tuberculosis (DR-TB), which has become a significant obstacle for the global TB control. There is still little study on DR-TB in the elderly in China so far. Thus, more research on the epidemiological characteristics and trend of primary DR-TB among the elderly will be necessary. Methods A retrospective study was conducted in Shandong, China from 2004 to 2019. We collected 12,661 primary TB cases, of which 4368 elderly (≥60 years) primary TB cases were involved. Clinical characteristics including age, sex, cavity, smoking, drinking, comorbidity and drug susceptibility data were collected from 36 TB prevention and control institutions of Shandong Province. Sputum samples were collected by each surveillance site, and examined in the TB Reference Laboratory of SPCH. Descriptive statistical analysis, chi-square and linear regression were used for analyzing. Results Among 4368 elderly patients with primary TB, the DR-TB and multi-resistant tuberculosis (MDR-TB) accounted for 17.19% and 2.29%, respectively. During 2004-2019, the proportions of MDR-TB, polydrug resistant tuberculosis (PDR-TB), rifampin (RFP)-resistance increased by 160.00%, 18.18%, 231.82%, respectively and the rate of DR-TB among elderly patients with primary cavitary TB increased by 255%. Among the elderly with primary DR-TB during 2004-2019, the proportion of male (from 85.19 to 89.06), cavity (from 7.41 to 46.88), RFP-resistance (from 3.70 to 21.88), and streptomycin (SM)-resistance (from 37.04 to 62.5) increased significantly (P

Reconfigurable intelligent surface (RIS) is a new paradigm that has great potential to achieve cost-effective, energy-efficient information modulation for wireless transmission, by the ability to change the reflection coefficients of the unit cells of a programmable metasurface. Nevertheless, the electromagnetic responses of the RISs are usually only phase-adjustable, which considerably limits the achievable rate of RIS-based transmitters. In this paper, we propose an RIS architecture to achieve amplitude-and-phase-varying modulation, which facilitates the design of multiple-input multiple-output (MIMO) quadrature amplitude modulation (QAM) transmission. The hardware constraints of the RIS and their impacts on the system design are discussed and analyzed. Furthermore, the proposed approach is evaluated using our prototype which implements the RIS-based MIMO-QAM transmission over the air in real time., This paper aims to investigate the feasibility of using RIS for MIMO wireless transmission for higher-order modulation by presenting an analytical modeling of the RIS-based system and providing experimental results from a prototype which has been built

Vitamin C has shown benefits in patients with sepsis in addition to standard therapy recently. However, further evidence is required to verify the efficacy of vitamin C in clinical practice. This study aimed to investigate the effect of adjunctive intravenous high-dose vitamin C treatment on hospital mortality in patients with sepsis. One hundred seventeen patients with sepsis in our department from June 2017 to May 2019 were randomly divided into two groups: the control group (56 cases) and the vitamin C group (61 cases). The control group was treated by the routine and basic therapy with intravenous drip of 5% dextrose and placebo (100 ml/time, 2 times/day), while the vitamin C group was administered intravenously by 3.0 g vitamin C dissolved into 5% dextrose (100 ml/time, 2 times/day) based on the control group. The mortality and efficacy were statistically analyzed and compared between the two groups. The 28-day mortality differed significantly between the control group and the vitamin C group (42.97% vs. 27.93%) (p

The modulation classification problem can be effectively handled by modern machine learning techniques, however, the available data for training is usually limited due to costly measurements in practice. Concerning the problem of insufficient training data, we propose a conditional variational auto-encoder (CVAE)-enhanced learning model for modulation classification. Different from the conventional data augmentation approaches where the data is independently generated, we introduce a feedback unit from the classifier to the CVAE generative network, to ensure that the generated data helps improving the classification accuracy. A two-stage generating-training algorithm is proposed. Via both simulation and practical implementation on a universal software radio peripheral (USRP) platform, it is shown that the proposed method effectively improves the classification accuracy in realistic propagation environment.

Background Tuberculosis (TB) is one of the major infectious diseases that seriously endanger people’s health. In Shandong province, the relationship between the level of economic development and TB incidence has not been studied. This study aims to provide more research basis for the government to prevent and control TB by exploring the impact of different economic factors on TB incidence. Methods By constructing threshold regression model (TRM), we described the extent to which different economic factors contribute to TB registered incidence and differences in TB registered incidence among seventeen cities with different levels of economic development in Shandong province, China, during 2006–2017. Data were retrieved from the China Information System for Disease Control and Prevention. Results Per capita medical expenditure (regression coefficient, -0.0314462; SD, 0.0079305; P > |t|, 0.000) and per capita savings (regression coefficient, 0.0001924; SD, 0.0000566; P > |t|, 0.001) passed the significance test at the level of 1%.They are the two economic indicators that have the greatest impact on TB registered incidence. Through the threshold test, we selected the per capita savings as the threshold variable. In the three stages of per capita savings (33,835.5391 CNY), rural per capita income always has a significant negative impact on the TB registered incidence (The regression coefficients are − 0.0015682, − 0.0028132 and − 0.0022253 respectively. P is 0.007,0.000 and 0.000 respectively.).In cities with good economies, TB registered incidence was 38.30% in 2006 and dropped to 25.10% by 2017. In cities with moderate economies, TB registered incidence peaked in 2008 at 43.10% and dropped to 27.1% by 2017.In poorer cities, TB registered incidence peaked in 2008 at 56.30% and dropped to 28.9% in 2017. Conclusion We found that per capita savings and per capita medical expenditure are most closely related to the TB incidence. Therefore, relevant departments should formulate a more complete medical system and medical insurance policy to effectively solve the problem of “difficult and expensive medical treatment”. In order to further reduce the TB incidence, in addition to timely and accurate diagnosis and treatment, it is more important for governments to increase investment in medicine and health care.

Machine-to-machine (M2M) communications, which aims to enable autonomous communication between machine devices with minimum human intervention, have experienced remarkable growth over the past few years. In massive M2M communications, random access (RA) collision is one of the critical problems at the network entry stage. To alleviate the channel contention during the network access stage, most existing RA solutions for M2M communications need the base station (BS) to determine backoff parameters, whose optimal values are hard to obtain and would also incur substantial signaling overhead, especially in dense scenarios. To address this issue, a distributive approach is highly desirable. In this paper, a new method for distributive estimation of optimal backoff parameters is proposed. Specifically, each machine type device (MTD) only needs to observe their own successful and total transmissions of access requests during an estimation interval, after which each MTD can obtain an estimation of the optimal backoff parameters according to an explicit expression of the observed statistics. It is found that the estimation interval determines the performance of the proposed scheme. When the number of MTDs in the network does not change or varies slowly, the network throughput can be maximized if the estimation interval is chosen properly. Compared to existing centralized methods, the proposed method can achieve similar performance with much lower signaling overhead. The proposed scheme can be applied for both homogeneous and heterogeneous scenarios and provide a practical access design method for M2M communications.

This paper proposes a model-driven deep learning-based downlink channel reconstruction scheme for frequency division duplexing (FDD) massive multi-input multi-output (MIMO) systems. The spatial non-stationarity, which is the key feature of the future extremely large aperture massive MIMO system, is considered. Instead of the channel matrix, the channel model parameters are learned by neural networks to save the overhead and improve the accuracy of channel reconstruction. By viewing the channel as an image, we introduce You Only Look Once (YOLO), a powerful neural network for object detection, to enable a rapid estimation process of the model parameters, including the detection of angles and delays of the paths and the identification of visibility regions of the scatterers. The deep learning-based scheme avoids the complicated iterative process introduced by the algorithm-based parameter extraction methods. A low-complexity algorithm-based refiner further refines the YOLO estimates toward high accuracy. Given the efficiency of model-driven deep learning and the combination of neural network and algorithm, the proposed scheme can rapidly and accurately reconstruct the non-stationary downlink channel. Moreover, the proposed scheme is also applicable to widely concerned stationary systems and achieves comparable reconstruction accuracy as an algorithm-based method with greatly reduced time consumption.

Artificial intelligence (AI) has shown great potential in wireless communications. AI-empowered communication algorithms have beaten many traditional algorithms through simulations. However, the existing works just use the simulated datasets to train and test the algorithms, which can not represent the power of AI in practical communication systems. Therefore, Peng Cheng Laboratory holds an AI competition, National Artificial Intelligence Competition (NAIC): AI+wireless communications, in which one of the topics is AI-empowered channel feedback system design using practical measurements. In this paper, we give a baseline neural network design, QuanCsiNet, for this competition, and the details of the channel measurements. QuanCsiNet shows excellent performance on channel feedback and the complexity of the neural networks is also given.

The interplay between artificial intelligence (AI) and fog radio access networks (F-RANs) is investigated in this work from two perspectives: how F-RANs enable hierarchical AI to be deployed in wireless networks and how AI makes F-RANs smarter to better serve mobile devices. Due to the heterogeneity of processing capability, the cloud, fog, and device layers in F-RANs provide hierarchical intelligence via centralized, distributed, and federated learning. In addition, cross-layer learning is also introduced to further reduce the demand for the memory size of the mobile devices. On the other hand, AI provides F-RANs with technologies and methods to deal with massive data and make smarter decisions. Specifically, machine learning tools such as deep neural networks are introduced for data processing, while reinforcement learning (RL) algorithms are adopted for network optimization and decisions. Then, two examples of AI-based applications in F-RANs, i.e., health monitoring and intelligent transportation systems, are presented, followed by a case study of an RL-based caching application in the presence of spatio-temporal unknown content popularity to showcase the potential of applying AI to F-RANs.

In recent years, China has been increasingly witnessed as a major global outward investor, especially since the launch of the Belt and Road Initiative (BRI) in 2013. The question of whether and if yes how the BRI reshapes firm outward investment motives remains under-researched. Using a project-level database of China’s Outward Direct Investment from the Ministry of Commerce from 2010 to 2015, this paper investigates the changing investment motives of state-owned and private-owned enterprises (SOEs and POEs) before and after the implementation of the BRI in two periods, namely 2010–2013 and 2014–2015. Our conditional logit models show that (1) market-seeking is one of the key motives for both POEs and SOEs; (2) POEs pursued natural resources in ASEAN based on geographical and relational proximity in the pre-BRI period while SOEs are directed to exploit natural resources in ASEAN besides remoter destinations after the launch of the BRI; (3) POEs are risk-taking in both periods, which runs counter to conventional expectations. This can be explained by the long-term investment tradition of POEs in ASEAN in which POEs are attracted predominantly by socio-economic factors and often less sensitive to variegated host institutions among ASEAN countries and (4) the BRI promotes Chinese OFDI in ASEAN through increased senior leader visits and enhanced diplomatic relations.

With the increasing application of deep learning (DL) algorithms in wireless communications, the physical layer faces new challenges caused by adversarial attack. Such attack has significantly affected the neural network in computer vision. We chose DL-based analog channel state information (CSI) to show the effect of adversarial attack on DL-based communication system. We present a practical method to craft white-box adversarial attack on DL-based CSI feedback process. Our simulation results showed the destructive effect adversarial attack caused on DL-based CSI feedback by analyzing the performance of normalized mean square error. We also launched a jamming attack for comparison and found that the jamming attack could be prevented with certain precautions. As DL algorithm becomes the trend in developing wireless communication, this work raises concerns regarding the security in the use of DL-based algorithms., 12 pages, 5 figures, 1 table. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Extremely large-scale massive multiple-input multiple-output (MIMO) has shown considerable potential in future mobile communications. However, the use of extremely large aperture arrays has led to near-field and spatial non-stationary channel conditions, which result in changes to transceiver design and channel state information that should be acquired. This letter focuses on the channel estimation problem and describes the non-stationary channel through mapping between subarrays and scatterers. We propose subarray-wise and scatterer-wise channel estimation methods to estimate the near-field non-stationary channel from the view of subarray and scatterer, respectively. Numerical results demonstrate that subarray-wise method can derive accurate channel estimation results with low complexity, whereas the scatterer-wise method can accurately position the scatterers and identify almost all the mappings between subarrays and scatterers.

Atomic layer deposition (ALD) can be used for wafer-scale synthesis of 2D materials. In this paper, a novel, reliable, secure, low-cost, and high-efficiency process for the fabrication of MoS2 is introduced and investigated. The resulting 2D materials show high carrier-mobility as well as excellent electrical uniformity. Using molybdenum pentachloride (MoCl5) and hexamethyldisilathiane (HMDST) as ALD precursors, thickness-controlled MoS2 films are uniformly deposited on a 50 mm sapphire and a 100 mm silica substrate. This is done with a high growth-rate (up to 0.90 A/cycle). Large-scale top-gated FET arrays are fabricated using the films, with a room-temperature mobility of 0.56 cm2/(V·s) and a high on/off current ratio of 106. Excellent electrical uniformity is observed in the whole sapphire wafer. Additionally, logical circuits, including inverters, NAND, AND, NOR, and OR gates, are realized successfully with a high-k HfO2 dielectric layer. Our inverters exhibit a fast response frequency of 50 Hz and a DC-voltage gain of 4 at VDD = 4 V. These results indicate that the new method has the potential to synthesize high quality MoS2 films on a large-scale, with hypo-toxicity and enhanced efficiency, which can facilitate a broader range of applications in the future.

In this paper, we investigate the multiple input multiple output (MIMO)-non-orthogonal multiple access (NOMA) transmission with the aid of location information. We first consider two users separated in both the distance and angle domains. With different access distances, NOMA could be used to serve the near-user and the far-user simultaneously, whereas spatial division multiple access (SDMA) would be applied if the two users have largely-separated angles of departure (AOD) that guarantees spatial orthogonality. Comparing the ergodic sum rates of these two multiple access (MA) schemes, we first characterize the preferable MA regions in the angle-distance plane. Analytical expression of the region boundary between NOMA and SDMA is derived. Moreover, NOMA-preferable regions are expressed in terms of the maximum distance difference and the minimum angle difference between the two users, respectively. On basis of these results, we further propose a location-based low-complexity user pairing algorithm for the general multiuser scenario. Numerical results confirm the accuracy of the derived region boundaries, and the simulations show that the proposed user pairing algorithm can effectively improve the resource utilization rate, compared to the conventional MA and user pairing schemes.

Massive connectivity is a key requirement for the Internet of Things (IoT). In practice, the network should be capable of accommodating thousands of devices and meeting their traffic demands. In this article, we consider the access phase for IoT in a mixed-analog-to-digital converter distributed massive multiple-input–multiple-output system, in which users are classified into light-load users and heavy-load users depending on their traffic load requirements. To meet the low-latency and low-cost demands in IoT, the access scheme for both types of users are designed in a grant-free fashion. For users with light-load traffic demands, by formulating the user activity detection (UAD) and channel estimation (CE) into a compressed sensing problem, we provide a low-complexity algorithm solver which requires no prior information. The simulation results verify that the proposed algorithm can effectively detect user activity and estimate channel state information (CSI) between the users and access points (APs). To satisfy the throughput requirements of heavy-load users, after UAD and CE, a two-step dynamic clustering is proposed for coordinated multipoint transmission using the large-scale fading (LSF) information. The impact of quantization noise on LSF estimation is investigated, as well as, a corresponding compensation method and accuracy bound. By detecting the clustering behavior among users in the first step, the complexity of the joint user and AP clustering is substantially reduced. The numerical results reveal that the proposed algorithm can offer significant performance gains in various scenarios with fast convergence.

Metasurfaces are well-designed artificial periodic or quasiperiodic structures to achieve customized electromagnetic responses. Based on the time-domain digital coding metasurface (TDCM) with dynamic reflection or transmission characteristics, one can realize direct information modulation on the metasurface, which has been used in wireless communications. In comparison with the classical architectures of wireless communication systems, the TDCM has advantages for simple architecture, easy manufacture, and low cost. However, the wireless communication systems based on the TDCM are still limited by low conversion efficiency, spectrum pollution, and hard to implement high-order modulation schemes. To solve these limitations, we propose a new route to achieve multi-modulation schemes in wireless communications by regulating the reflection phases of the TDCM in a nonlinear way. By carefully designing the regulation voltages applied to the diodes on TDCM, arbitrary constellation diagrams are successfully synthesized. The novel systems can implement various modulation schemes such as quadrature phase shift keying (QPSK), 8PSK, and 16QAM with good communication quality, high stability, and free scheme switching. The presented method may find important applications in modern wireless communication technologies.