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Millimeter wave (mmWave) communications can support huge data rate growth and wide bandwidth in the new 5G era, and a hybrid precoding architecture can effectively handle the high hardware cost and energy consumption problems. A crucial goal is to optimize the system's spectral efficiency (SE) by effectively designing the digital precoder and the analog precoder. In this article, we review the conventional hybrid precoding schemes in mmWave systems, and then investigate several novel systems with index modulation (IM) techniques as well as their hybrid precoder designs. With the advantages of IM, higher SE can be achieved with limited radio frequency chains in mmWave systems. Simulation results demonstrate that two types of IM-aided systems both outperform conventional mmWave systems, which proves that IM is an enabling technique in practical SE-driven mmWave systems. Finally, we discuss the limiting factors and outlook of IM-aided mmWave systems in beyond-5G wireless communications.

To a large extent, the physical layer security (PLS) performance of a visible light communication (VLC) system is affected by light source radiation characteristics. Previous works are limited to well‐known Lambertian beam configurations, which fail to support flexible coordinated coverage. Unlike existing works, this article proposes one secrecy capacity (SC) enhancement scheme based on asymmetric non‐Lambertian optical beam cooperation. Without loss of generality, this article presents a numerical analysis of coordinated and non‐coordinated PLS performance in a typical indoor application scenario. Compared with a non‐coordinated benchmark, the numerical results illustrate that the proposed cooperation‐based scheme could induce about 0.88 bps/Hz average SC gain without a transmission power increase. Therefore, non‐Lambertian beam cooperation could be viewed as one promising secure VLC performance enhancement technology.

Generalized spatial modulation (GenSM) aided millimeter wave (mmWave) multiple-input multiple-output (MIMO) system has been recently considered as a new efficient technique due to the combination of numerous antennas and reduced number of radio frequency (RF) chains. However, the present hybrid precoding schemes for GenSM-aided system are still troubled by high computational complexity and performance loss in spectral efficiency (SE). In this paper we propose low-complexity hybrid precoding schemes based on Martin's expansion for logarithmic determinant (log-det) form in analytical SE lower bound. Digital and analog precoders are herein optimized separately, which means complicated alternating minimization iteration is omitted. Besides, we can adjust algorithmic complexity adaptively corresponding to different demand levels for users. Finally the numerical simulations show that the proposed methods outperform other GenSM-aided mmWave MIMO schemes and can achieve a better tradeoff between complexity and SE.

With LED-based lighting lamps now being utilized all over the world, the concept of the Internet of Lights (IoL) using existing LED illumination networks with information and communications technologies including visible light communications (VLC) was created. IoL improves the lighting efficiency and indoor lighting comfort level, and can support value-added information services by modulating the light intensity. However, further investigation of its impact on human beings is needed. This article first introduces the concept, the requirements, and the system structure of IoL using the Internet of Radio Light system as an example for information service. We investigate the feasibility of regulating human physiological rhythms, especially to alleviate degenerative neurological diseases. This would possibly bring light therapy treatments to patients in a non-intrusive way. From a preliminary set of experiments, it is seen that the hippocampus area of mice clearly reacts to the lighting at low frequency, showing the influence of light intensity variation on degenerative neurological diseases. This suggests that the future standardization of VLC needs to consider not only the system transmission performance but also its potential impact on human beings for indoor applications.

As the most popularly utilized broadcasting network, digital terrestrial television broadcasting (DTTB) can provide multimedia information coverage for the broad audience in a very efficient way because of its characteristic of wide-range coverage and mobile reception ability. After promulgating the first generation DTTB standard, digital terrestrial/television multimedia broadcasting (DTMB), in 2006, China began to research and develop the next generation DTTB standard, namely DTMB-advanced (DTMB-A), aiming to support higher spectrum efficiency and further improve transmission reliability. In 2019, DTMB-A was accepted by ITU as the second generation international DTTB standard (as System C). Similar to DTMB, time-domain synchronous - orthogonal frequency division multiplexing (TDS-OFDM) based multi-carrier modulation scheme is adopted by DTMB-A. Thanks to the more flexible frame structure, advanced error correction coding and improved constellation mapping, DTMB-A offers 30% higher transmission capacity than DTMB under the same transmission conditions. Thus, DTMB-A can support both fixed and mobile reception more efficiently, and provide users with higher quality services such as ultra-high definition television (UHDTV). This paper first gives details of key technologies at the transmitter of DTMB/DTMB-A and introduce core algorithms at the receiver. Both laboratory test and field trial results will then be provided and analyzed, especially for the application of 4K UHDTV and single frequency network (SFN).

In this letter, a novel deep learning-based transmit antenna selection (TAS) scheme for the multiple-input multiple-output (MIMO) with spatial modulation (SM) system is proposed. We formulate the generalized TAS pipeline in both neural networks (NN) and gradient boosting decision trees (GBDT), in which the importance of different features reflecting the different elements from channel state information (CSI) is analyzed regarding to the empirical data as well. Furthermore, the bit error rate (BER) performance and the complexity comparison of two structures is investigated. Simulation results confirm that GBDT can be efficiently implemented for real-time application with near-optimal performance.

In this work, a wireless broadcast network with a base station (BS) sending random time-sensitive information updates to multiple users with interference constraints is considered. The Age of Synchronization (AoS), namely the amount of time elapsed since the information stored at the network user becomes desynchronized, is adopted to measure data freshness from the perspective of network users. Compared with the more widely used metric---the Age of Information (AoI), AoS accounts for the freshness of the randomly changing content. The AoS minimization scheduling problem is formulated into a discrete time Markov decision process and the optimal solution is approximated through structural finite state policy iteration. An index based heuristic scheduling policy based on restless multi-arm bandit (RMAB) is provided to further reduce computational complexity. Simulation results show that the proposed index policy can achieve compatible performance with the MDP and close to the AoS lower bound. Moreover, theoretic analysis and simulations reveal the differences between AoS and AoI. AoI minimization scheduling policy cannot guarantee a good AoS performance., Comment: accepted and to appear, IEEE Transactions on Wireless Communications

The Fifth generation of mobile broadband (BB) networks, popularly known as 5G, aims to revolutionize different vertical industries, including media broadcasting. Although it remains to be seen the real impact and timescale of the 5G revolution, 5G may represent an opportunity rather than a threat for broadcast (BC), especially when considering new approaches for BC and BB evolving into new converged platforms inter-connecting terrestrial BC and cellular/wired BB systems together [item 1) in the Appendix]. Hybrid BC-BB platforms are already a success in several countries, based, e.g., on HbbTV in EU, hybridcast in Japan and Ginga in Brazil. But the increasing importance of fixed and mobile BB IP delivery for television services, well-illustrated by the new IP-based ATSC 3.0 standard [item 2) in the Appendix] and the on-going DVB-I (Internet) specification, and the fact that the 5G system will support and be interoperable with non-3GPP access technologies, potentially allowing multiple access technologies to be used simultaneously for one or more services, opens the door to new convergence scenarios.

The research about deep learning application for physical layer has been received much attention in recent years. In this paper, we propose a Deep Learning (DL) based channel estimator under time varying Rayleigh fading channel. We build up, train and test the channel estimator using Neural Network (NN). The proposed DL-based estimator can dynamically track the channel status without any prior knowledge about the channel model and statistic characteristics. The simulation results show the proposed NN estimator has better Mean Square Error (MSE) performance compared with the traditional algorithms and some other DL-based architectures. Furthermore, the proposed DL-based estimator also shows its robustness with the different pilot densities.

Monitoring of poultry welfare-related bio-processes and bio-responses is vital in welfare assessment and management of welfare-related factors. With the current development in information technologies, computer vision has become a promising tool in the real-time automation of poultry monitoring systems due to its non-intrusive and non-invasive properties, and its ability to present a wide range of information. Hence, it can be applied to monitor several bio-processes and bio-responses. This review summarizes the current advances in poultry monitoring techniques based on computer vision systems, i.e., conventional machine learning-based and deep learning-based systems. A detailed presentation on the machine learning-based system was presented, i.e., pre-processing, segmentation, feature extraction, feature selection, and dimension reduction, and modeling. Similarly, deep learning approaches in poultry monitoring were also presented. Lastly, the challenges and possible solutions presented by researches in poultry monitoring, such as variable illumination conditions, occlusion problems, and lack of augmented and labeled poultry datasets, were discussed.

In the grant-free massive machine-type communication (mMTC) scenario, a key challenge is the joint device activity detection and data decoding. The sporadic nature of mMTC makes compressed sensing a promising solution to the activity detection problem. However, the typical two-phase coherent transmission scheme, which divides channel training and data decoding into two separate phases, suffers performance losses, especially when only a few bits of data are transmitted by each active device. This paper follows a newly proposed non-coherent transmission scheme in which the data bits are embedded in the pilot sequences and the BS simultaneously detects active devices and decodes the embedded data bits without explicit channel estimation. To exploit statistical channel information and the specific structure of the sparsity pattern introduced by the non-coherent transmission scheme, i.e., only one row in each section can be non-zero, we propose a receiving method based on the approximate message passing (AMP) algorithm with non-separable minimum mean-squared error denoisers specifically designed for the problem. The corresponding state evolution equations, which can be used to predict the section error rate (SER) performance, is obtained and simplified under certain assumptions. We also derive closed-form expressions of the SER performance based on the state evolution results. Finally, numerical simulations are given to validate the accuracy of the performance analysis and to show the superiority of the proposed receiving method over the conventional method based on AMP with separable denoisers in the literature.

Hybrid precoding is a credible solution to address the trade-off between spectral efficiency and power consumption in mmWave massive MIMO systems. Though traditional hybrid precoding with fixed architectures has reached its limits, the flexibility of hybrid precoding has not been fully exploited, which gives insight to improve the performance further. In this paper, we adopt a novel architecture called dynamic-subarray, where the connection between radio-frequency (RF) chains and antennas can be dynamically changed by switches. We propose a low-complexity scheme to design the hybrid precoder with dynamic subarrays. Simulation results demonstrate that our proposed method has an excellent performance on the achievable rate and energy efficiency even with lower complexity.

In this paper, we consider a distributed rate control problem with two selfish users. One user focuses on optimizing the data freshness while the other one aims at optimizing the “power” of throughput and the transmission delay. Two users aim at achieving their own goals by controlling the source sending rate. We formulate the problem as an uncooperative rate control game with two users and show that the Nash Equilibrium (NE) of the game is inefficient compared to the global optimum solution. To improve the efficiency, we propose a linear packet-dropping incentives in the uncooperative rate control game with two users, so that users are encouraged to control their rates near the global optimum sending rates. We show that the proposed incentives can improve the Price of Anarchy (PoA) performance.

Machine vision has become the development trend of strip surface defect detection technology because of its intelligence, real-time, high precision, reliability and other advantages. In this technology, due to the environmental interference of the production line, instability of optical elements and other factors, the surface defect image often with the problems such as uneven illumination, high noise, low contrast between the defect and background, which is not conducive to the subsequent processing. In order to solve these problems, this paper first normalizes the defect image by gray scale, then introduces the adaptive threshold gray transformation into the enhancement of the defect image on the steel surface. Finally, the image is processed by Gabor filter and image segmentation. The experimental results show that the image enhancement algorithm based on adaptive threshold gray transformation can effectively improve the contrast, enhance the image quality and facilitate subsequent processing.

As industrial production becomes more modern and intelligent today, the inspection of product quality of the workshop is becoming more and more accustomed to replacing the old manual visual inspection methods with automated inspection systems. In the welding field, automated welding robots are not only used in traditional large-scale automobile assembly lines. In more general welding work, welding robots also plays an important role. The inspection of the welding quality of the welding robot is mainly to detect the four main types of weld defects. Compared to traditional defect classification based on support vector machines and defect detection based on template matching, this paper uses a welding surface defect detection system designed based on deep learning methods. By working with workshop welding experts, a large-scale image of nearly 5000 pictures is built. Large-scale weld defect datasets, while using the real-time and accuracy of the YOLO series of deep learning object detection frameworks, the weld defects detection model reaches 75.5% mean average precision(mAP) in constructed weld defect data set. In addition, the construction cost of the detection model and the deployment time of the detection system are greatly reduced. During the field test of the system in the workshop, among a batch of welding workpieces provided by the factory, the detection accuracy of weld defects reached 71%, which initially met the requirements of the workshop for an automated defect detection system.

The occurrence of poultry diseases not only affects farm production economics but also leads to poor poultry welfare, food safety concerns, and zoonotic infections. Therefore, timely detection of these diseases is of paramount importance in poultry production. This study proposes a machine vision-based monitoring system for broiler chicken as they walk through a test area. Data were collected from two groups of broilers; control group and treatment group (inoculated intramuscularly with virulent Newcastle disease virus) housed in fully isolated chambers for comparative monitoring. The broilers were monitored by video surveillance for data labelling and depth camera for the automated health status classifier development. Feature variables were extracted based on 2D posture shape descriptors (circle variance, elongation, convexity, complexity, and eccentricity) and mobility feature (walk speed). A statistical analysis of the feature variables established that all investigated features were statistically significant ( p 0.05 ) with time after challenge in the treatment group. The earliest possible infection detection time was on the 4th day based on circle variance and elongation, and the 6th day based on eccentricity and walk speed. However, convexity and complexity could not provide early detection. Two sets of classifiers were then developed based on only the posture shape descriptors, and on all the feature variables, The Support Vector Machine (RBF-SVM) outperformed all the other models with an accuracy of 0.975 and 0.978 respectively. The proposed system can serve as an automatic broiler monitoring system by providing an early warning and prediction of an occurrence of disease continuously and non-intrusively.

Usually, multiple secondary users (SUs) utilize the opportunistic spectrum access (OSA) in cognitive radio networks to communicate with the secondary base station (sBS). In uplink transmission, each SU can only select one channel from ${C}$ primary channels to sense, access, and transmit data packets with the constraints of hardware and energy. Distributed non-orthogonal multiple access-based multi-armed bandit (D-NOMA-MAB) approach is proposed to solve the problem of the channel selection and the power level selection, which alleviates the impact of the collision in the same channel. D-NOMA-MAB approach improves the energy efficiency of the system and the successful transmission probability obviously by introducing 2-D selection for the arms and the factor of energy efficiency in the policy index of the restless MAB problem.

Digital terrestrial television multimedia broadcasting-advanced (DTMB-A), the new digital terrestrial television broadcasting (DTTB) system proposed by China, has been accepted by International Telecommunications Union as the international DTTB standard in July, 2015. By adopting several advanced technologies, such as near-capacity channel coding and modulation as well as improved frame structure, DTMB-A can provide faster system synchronization, higher receiving sensitivity, better performance against multipath effect, higher spectrum efficiency, and the flexibility for future extension, compared with its previous generation, DTMB system. This paper provides the technical analyses on the physical layer specifications of DTMB-A system in detail, together with the discussion on the key technologies adopted. Laboratory test and field trial results are also given which demonstrate that DTMB-A can offer excellent overall system performance for both fixed and mobile receptions under complicated terrestrial broadcasting conditions, and is capable of supporting multiple services with different quality of service requirements.

As the bandwidth resource in TV white space (TVWS) is permitted to be used for the secondary users, multiple secondary networks coexisting in the same geographical area can provide multiple services without interfering with the primary service, such as the TV broadcasting. To satisfy the minimum rate requirements and maximise the sum rate of the multiple services, the problem of resource allocation can be modelled as a mixed integer non-linear programming (MINLP). According to this model, the joint bandwidth and power allocation (JBPA) method is proposed to solve the resource allocation for multiple services. The JBPA method is sub-optimal, which can approximate the performance of the optimal two-step exhaustive search (TSES) method very well, especially when the number of services is larger than the number of idle channels. Compared with TSES, the JBPA method decreases the complexity from exponential level to polynomial level. Besides, compared with one channel occupied by single service, the JBPA can serve more services and provide stable performance when the number of services varies dynamically. Compared with the algorithm random bandwidth allocation, the JBPA can approximate the optimal performance provided by TSES better with the time complexity of the same order.

In this letter, we study the outage achievable rate region of non-orthogonal multiple access (NOMA), which is defined as the achievable rate region when all users are required to satisfy their outage probability constraints. First, we formally define the outage achievable rate region of NOMA assuming a slow fading channel model. Then, we introduce a successive interference cancelation (SIC) order under the assumption of statistical channel information. Under this assumption, we find that the outage capacity region of broadcast channel (BC) is also ${\epsilon }$ -achievable by NOMA, which requires the use of SIC, though the definition of outage in NOMA is different from that in BC. Combining the converse result, we get a full characterization of the outage achievable rate region of NOMA, which also shows the optimality of the SIC order used here. Then, we give some discussions about power allocation and the practical insights derived from the characterization. Finally, numerical simulations are provided.

With the rise of intelligent and connected vehicles (ICVs), new vehicle applications continue to emerge, while the computing capability of vehicles remains limited. Mobile edge computing (MEC) is considered to be the most effective technique for mitigating vehicle computing pressure, with computation offloading being a key technology for MEC. To solve the problem of excessive task processing delay and energy consumption due to the vehicle-limited computing power in the vehicular network, we consider the tasks and the characteristics of MEC, and divide the tasks into indivisible tasks and divisible tasks according to the size of data (that is, whether it affects functionality after segmentation). Then, two computation offloading algorithms are proposed named binary offloading and partial offloading separately. The binary offloading unloads the task to the mobile edge computing server as a whole and selects only an optimal offloading site; thus, an improved upper confidence bound algorithm is adopted. The partial offloading divides the complex tasks with large data volumes through time slots processed by different MEC servers, and uses the Q-learning algorithm to find the most effective offloading strategy. The simulation results show that the total cost of delay and energy consumption of the binary offloading algorithm is lower when processing computationally intensive tasks. When addressing divisible and complex tasks, the partial offloading algorithm improves the real-time performance of the tasks significantly and conserves the energy of the vehicle terminal.

Fusion algorithm for foggy image enhancement based on transmittance weight factor is proposed. Firstly, the rough transmittance image of foggy image is obtained by the dark channel prior dehazing algorithm, and then the fine transmittance image is obtained by the guided filter. The dehazing image is obtained by the dark channel prior dehazing method based on the guided filter, and the enhanced image is obtained by histogram equalization based on the HSI color model. Finally, the weight factor is constructed by the fine transmittance, the above dehazing image and enhanced image are processed by weighted fusion. The algorithm gives full play to the respective advantages of two kinds of algorithms in foggy image processing, and significantly improves the fog removal and enhancement effect of foggy image.

Focused on the issues that the sparseness of the data obtained after factorization is reduced and with the increasing of training samples, the computing scale increases rapidly, an incremental learning algorithm based on graph regularized non-negative matrix factorization with sparseness constraints was proposed. It not only considers the geometric structure in the data representation, but also introduces sparseness constraint to coefficient matrix and combines them with incremental learning. Using the results of previous factorization involved in iterative computation with sparseness constraints and graph regularized, the cost of the computation is reduced and the sparseness of data after factorization is highly improved. Experiments on both ORL and PIE face recognition databases demonstrate the effectiveness of the proposed method.

At present, the development of IoT has been highly concerned by the domestic and even the world. The IoT has become a new engine of economic growth. The emergence and development of 5G technology have widely expanded the application fields of IoT technology. The intelligent feeding system is one of the application fields of IoT technology. The current intelligent feeding system is characterized by providing services based on multiple systems. In order to expand the range of services and enable multiple intelligent feeding systems to accept the same services, a standardized platform is needed and services are provided on that platform. This paper considers providing a service layer using a platform based on the oneM2M standardization protocol. Based on this platform, an intelligent feeding system is designed. In addition, we implement the intelligent pets feeding system based on the oneM2M, and use this system as an example.

We consider updating strategies for a local cache which downloads time-sensitive files from a remote server through a bandwidth-constrained link. The files are requested randomly from the cache by local users according to a popularity distribution which varies over time according to a Markov chain structure. We measure the freshness of the requested time-sensitive files through their Age of Information (AoI). The goal is then to minimize the average AoI of all requested files by appropriately designing the local cache's downloading strategy. To achieve this goal, the original problem is relaxed and cast into a Constrained Markov Decision Problem (CMDP), which we solve using a Lagrangian approach and Linear Programming. Inspired by this solution for the relaxed problem, we propose a practical cache updating strategy that meets all the constraints of the original problem. Under certain assumptions, the practical updating strategy is shown to be optimal for the original problem in the asymptotic regime of a large number of files. For a finite number of files, we show the gain of our practical updating strategy over the traditional square-root-law strategy (which is optimal for fixed non time-varying file popularities) through numerical simulations., To appear ITW2020

In this paper, we consider a scenario where the base station (BS) collects time-sensitive data from multiple sensors through time-varying and error-prone channels. We characterize the data freshness at the terminal end through a class of monotone increasing functions related to Age of information (AoI). Our goal is to design an optimal policy to minimize the average age penalty of all sensors in infinite horizon under bandwidth and power constraint. By formulating the scheduling problem into a constrained Markov decision process (CMDP), we reveal the threshold structure for the optimal policy and approximate the optimal decision by solving a truncated linear programming (LP). Finally, a bandwidth-truncated policy is proposed to satisfy both power and bandwidth constraint. Through theoretical analysis and numerical simulations, we prove the proposed policy is asymptotic optimal in the large sensor regime.

Aimed at the problems of traditional data feature recognition schemes such as feature extraction difficulties, poor classification and recognition effects, and high sensitivity, a data feature recognition scheme based on deep learning is proposed to realize feature extraction and classification of the data information automatically. By analyzing the characteristics of the data and the problems of the deep neural network, the attention mechanism is introduced in the convolutional neural network to extract more important networks, and the long and short-term memory network is improved, reducing the complexity of the model, and avoiding the problem of information loss caused by long sequences.

In massive multiple-input multiple-output (MIMO) system, channel state information (CSI) is essential for the base station to achieve high performance gain. Recently, deep learning is widely used in CSI compression to fight against the growing feedback overhead brought by massive MIMO in frequency division duplexing system. However, applying neural network brings extra memory and computation cost, which is non-negligible especially for the resource limited user equipment (UE). In this paper, a novel binarization aided feedback network named BCsiNet is introduced. Moreover, BCsiNet variants are designed to boost the performance under customized training and inference schemes. Experiments shows that BCsiNet offers over 30$\times$ memory saving and around 2$\times$ inference acceleration for encoder at UE compared with CsiNet. Furthermore, the feedback performance of BCsiNet is comparable with original CsiNet. The key results can be reproduced with https://github.com/Kylin9511/BCsiNet., 6 pages, 5 figures, 4 tables. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice

As one of the key features in 5G network, Millimeter wave (mmWave) technology can provide the ultra-wide bandwidth to support higher data rate. However, for high frequency band, mmWave signal still suffers from the high pathloss, the multipath fading and the signal blockage issue, especially in the indoor environment. For different application scenarios, the channel conditions and quality of services (QoS) are quite different. Therefore, it is essential to investigate the impact of the mmWave channel on the system performance. This paper investigates and measures the performance of a 60GHz mmWave module that is exploited for the downlink and uplink high data rate transmission in the Internet of Radio-Light (IoRL) project. The coverage area and the throughput of the mmWave module is estimated by measuring the error vector magnitude (EVM) of received signals with different transmitter (TX) and receiver (RX) angles and at different locations in a laboratory. In this paper, the measurement environment and system setup are introduced. After that, the waveform design for the measurement is also discussed. The measurement results show that this 60GHz mmWave module can provide an acceptable performance only in some cases, which restricts its application scenarios.

In this work, we consider a scenario where a time sensitive source is broadcasted to multiple receivers by a base station (BS) over unilateral networks. A recently proposed metric-the Age of Information (AoI) is adopted to measure data freshness from the perspective of receiver. Unlike previous work, we consider that the BS receives no feedback from receivers and thus broadcasts every fixed interval. We derived the optimum fixed interval such that the average AoI can be minimized. Our work suggests that, when the transmission delay is highly random, the optimum fixed interval is larger than the expected transmission delay so that success delivery to more users can be guaranteed. Both theoretical analysis and simulation results show that without feedback, the average AoI performance following the proposed policy is near to transmission policy that utilizes receiver feedback.

This paper introduces a 8K ultra-high definition digital television transmission system based on DTMB-A. The system transmits 8K digital TV programs through two DTMB-A signals, and the data rate can reach 80 Mb/s. The bit error rate of the system is measured to be zero, and digital TV signals with 8K resolution can be transmitted normally without mosaic.

MmWave MIMO communications has been recognized as one revolutionary new technology in wireless communication area, which can support wide ban width and high data rate transmission compared with conventional communications. Recently, hybrid precoding is widely utilized to reduce the number of RF chains and power consumption, which has perfect performance on spectral efficiency (SE) and energy efficiency (EE). In this paper a new GenSM-aided low-complexity hybrid precoding algorithm is proposed, which enjoys higher spectral efficiency and lower complexity compared with the existed low-complexity methods.

With the development of artificial intelligence, convolutional neural networks (CNN) have been widely used in image processing and other aspects due to their excellent performance. However, as a computationally intensive algorithm, CNN face huge challenges in the realization of mobile devices. FPGA have the advantages of high performance, reprogramming, and low power consumption, and have becoming suitable choices for CNN deployment. Compared with various CNN algorithms, the YOLO algorithm regards target detection as a regression problem. It is a one-step algorithm with fast execution speed and small amount of calculation. It is suitable for implementation on FPGA hardware platforms. This paper proposes an improved algorithm for deep learning YOLO network based on Xilinx ZYNQ FPGA. By optimizing the YOLO network model and fixed-point, etc., the problem of large computational of CNN and limited resources on FPGA chips is solved, and the parallelism of FPGA is used to accelerate the CNN. Experimental results show that the method proposed in this paper has greatly improved the operation rate while maintaining accuracy, and has important practical value in the realization of mobile terminals of CNN and real-time computing.

We consider a scenario where a power constrained transmitter delivers randomly arriving packets to the destination over Markov time-varying channel and adapts different transmission power to each channel state in order to guarantee successful transmission. To minimize the expected average transmission delay of each packet, we formulate the problem into a constrained Markov decision process (CMDP). We reveal the queue-length threshold structure of the optimal policy, i.e., the transmitter sends packets if and only if the queue length surpasses a threshold and obtain the optimal cross-layer scheduling strategy through linear programming (LP). Numerical results validate the performance of the proposed strategy and illustrate a delay-power tradeoff in such scenario.

In order to improve the network learning effect, based on the existing target learning framework MobileNet-V2, an ELU function is inserted as an activation function. Firstly, use the expansion convolution to increase the number of channels to get more features to activate and output through the ELU function, which can alleviate the disappearance of the gradient of the linear part, the nonlinear part is more robust to the noise of the input change. Then, the way of the residual connection combine high-level features with low-level features and then output. Finally, output to softmax using global pooling. The experimental data shows that compared with the current mainstream lightweight deep learning target recognition algorithm,E-MobileNet has improved the accuracy of recognition and the frame rate per second in the same test environment of the same test set. The experimental data fully demonstrates that the use of the ELU activation function and the global pooling layer reduces the number of parameters, enhances the generalization ability of the model, and improves the robustness of the algorithm. On the basis of ensuring the light weight of the neural network model, the recognition accuracy of the target is effectively improved.

We study a network with a central controller collecting random updates from power-limited sensors. The time-varying channels between sensors and the central controller are modeled as ergodic Markov chains while packet-loss may happen due to decoding error. We measure the data freshness from the central controller by the metric Age of Synchronization (AoS), i.e., the average time elapsed since information about a sensor becomes desynchronized. To minimize the average AoS under all aforementioned bandwidth and power constraints, we first relax the hard bandwidth limit and decouple the multi-sensor problem into a single-sensor constrained Markov decision process (CMDP), which is then solved through linear programming (LP). We then propose an asymptotic optimal scheduling policy to solve the original hard-bandwidth-constrained problem. It is revealed that sensors are more likely to send updates under better channel states and higher AoS to save energy and avoid packet-loss.

In the future, Drones or general Unmanned Aerial Vehicles(UAVs) need to implement online real-time object detection, but their memory and computing power are limited. An improved YOLOv3 is adopted in this paper, making full use of the lightweight network instead of the feature extraction network of YOLOv3 to achieve high-efficiency object detection for remote sensing images. In the case of similar detection accuracy, parameters and FLOPs (floating point operations) of the improved model are 2.5 times and 3.3 times smaller than YOLOv3, respectively. In addition, an IoU K-medians algorithm is proposed, which improves mAP by 7.0% on YOLOv3 and by 2.3% on the improved YOLOv3.Experiments show that the detection speed of the improved YOLOv3 is 101 frame/s at the fastest, and it is still 1.6 times faster than that of YOLOv3 when its mAP is 6% higher than that of YOLOv3. The efficient remote sensing object detection method proposed in this paper lays the foundation for future UAVs.

Face liveness detection is the first stage of the whole face detection technology, and it is of great significance to the system security using face recognition technology. In this paper we use convolutional neural networks to extract facial features. Compared to ResNet, SqueezeNet and VGG Face networks, the network structure is lighter and the model training takes less time. After testing, it achieved an accuracy of 99.5% on the Nanjing University of Aeronautics and Astronautics (NUAA) face detection data set of China Southern Airlines. In order to improve the robustness of the model, we built our own dataset through collecting pictures from internet and camera shooting, and horizontally compared the accuracy of the above several models on the self-built dataset. Finally the model proposed in this paper achieved the highest 99.02% accuracy and achieved the best results in actual testing.

This work is motivated by the need of collecting fresh data from power-constrained sensors in the industrial Internet of Things (IIoT) network. A recently proposed metric, the Age of Information (AoI) is adopted to measure data freshness from the perspective of the central controller in the IIoT network. We wonder what is the minimum average AoI the network can achieve and how to design scheduling algorithms to approach it. To answer these questions when the channel states of the network are Markov time-varying and scheduling decisions are restricted to bandwidth constraint, we first decouple the multi-sensor scheduling problem into a single-sensor constrained Markov decision process (CMDP) through relaxation of the hard bandwidth constraint. Next we exploit the threshold structure of the optimal policy for the decoupled single sensor CMDP and obtain the optimum solution through linear programming (LP). Finally, an asymptotically optimal truncated policy that can satisfy the hard bandwidth constraint is built upon the optimal solution to each of the decoupled single-sensor. Our investigation shows that to obtain a small AoI performance: (1) The scheduler exploits good channels to schedule sensors supported by limited power; (2) Sensors equipped with enough transmission power are updated in a timely manner such that the bandwidth constraint can be satisfied., accepted and to appear, IEEE JSAC. arXiv admin note: substantial text overlap with arXiv:1908.01334

A low-complexity fully connected hybrid precoding design is proposed for multiuser massive MIMO systems over frequency-selective fading channels. Digital precoding and analog precoding are adaptively designed based on instantaneous channel state information (CSI) and statistical CSI to maximize transmission efficiency. Furthermore, a dynamic partially connected structure and a novel beamforming algorithm are proposed to reduce the power consumption. Our simulation shows a satisfying performance compared with other proposed schemes.

International audience; An interacting multiple model based on unscented Kalman filter (IMM-UKF) is widely applied to positioning and tracking targets in various tracking scenarios. At the same time, visible light positioning (VLP) is developing rapidly due to the low cost and accuracy. Therefore, indoor positioning and tracking based on VLP combined with the IMM-UKF has attracted considerable interest. However, existing algorithms work on the assumption that the light-emitting diodes used for tracking are all point light sources, which ignores the geometry of these transmitters and results in low tracking accuracy. To overcome this problem, this paper proposes an innovative tracking algorithm based on VLP. This algorithm considers the shapes of the lights in combination with existing tracking algorithms, such as IMM-UKF. Simulation results show that in a standard Gaussian noise environment, the larger the transmitter is, the more meaningful the proposed algorithm is. INDEX TERMS Target positioning and tracking, visible light positioning (VLP), interacting multiple model (IMM), unscented Kalman filter (UKF), shapes of transmitters.

In this paper, we introduce spatial modulation (SM) in the layered division multiplexing (LDM) system for digital TV scenarios. To analyze the achievable spectral efficiency (SE) performance of the proposed SM-LDM system, we calculate its mutual information (MI) through the Monte Carlo method. Considering both Gaussian and finite alphabet inputs, we derive the MI’s computational-efficient lower bounds as well as their asymptotic values. Simulation results confirm both the advantages and our SE analysis of the proposed SM-LDM system.

The IEC standard WIA-PA is a communication protocol for industrial wireless sensor networks. Its special features, including a hierarchical topology, hybrid centralized-distributed management and packet aggregation make it suitable for large-scale industrial wireless sensor networks. Industrial systems place large real-time requirements on wireless sensor networks. However, the WIA-PA standard does not specify the transmission methods, which are vital to the real-time performance of wireless networks, and little work has been done to address this problem. In this article, we propose a real-time aggregation scheduling method for WIA-PA networks. First, to satisfy the real-time constraints on dataflows, we propose a method that combines the real-time theory with the classical bin-packing method to aggregate original packets into the minimum number of aggregated packets. The simulation results indicate that our method outperforms the traditional bin-packing method, aggregating up to 35% fewer packets, and improves the real-time performance by up to 10%. Second, to make it possible to solve the scheduling problem of WIA-PA networks using the classical scheduling algorithms, we transform the ragged time slots of WIA-PA networks to a universal model. In the simulation, a large number of WIA-PA networks are randomly generated to evaluate the performances of several real-time scheduling algorithms. By comparing the results, we obtain that the earliest deadline first real-time scheduling algorithm is the preferred method for WIA-PA networks.

The terrestrial return channel provides interactive services in digital television terrestrial broadcasting systems to meet various consumers’ demands around the world. The extension of non-orthogonal multiple access and generalized spatial modulation multiple-input multiple-output to the terrestrial return channel improves both the spectral and energy efficiencies of the system, but it puts forward detection challenges. In this paper, a joint user activity and signal detection scheme based on the block-sparse compressive sensing (BS-CS) method in the terrestrial return channel is proposed, in which the generalized spatial modulation technology is used. By exploiting the structure and sparsity of the multi-user generalized spatial modulation signal, we formulate the detection problem into a block-sparse recovery problem. Then a BS-CS-based detection algorithm, enhanced structured block-sparse compressive sampling matching pursuit (ESB-CoSaMP), is proposed to detect the active users and transmitted data efficiently. Moreover, the information of active antennas at each user is exploited in ESB-CoSaMP to further improve the accuracy. Simulations show that the proposed detection scheme outperforms the conventional CS and BS-CS-based schemes.

As a combination of spatial modulation (SM) system and massive multiple-input multiple-output (MIMO) system, massive SM aided MIMO (SM-MIMO) system is recently proposed. In broadband scenarios, single-carrier (SC) modulation is introduced to massive SM-MIMO system, thus massive SC-SM MIMO system is proposed for uplink multi-user transmission over frequency-selective fading channels. In this study, the uplink spectral efficiency (SE) of multi-cell massive SC-SM MIMO system is analysed, meanwhile the pilot contamination effect is taken into consideration. A tight SE lower bound of multi-cell massive SC-SM MIMO system is proposed with maximum ratio (MR) combining, which also takes into account the imperfect channel estimation, transmit antenna correlation and path loss. The tightness of the authors' proposed closed-form SE lower bound is shown via simulation results. The optimal pilot reuse factor can be determined with different system configurations by simulations, and the pilot reuse factor that is larger than one is more suitable for less TAs and user equipments.

Recently the combination of massive multiple-input multiple-output (MIMO) and spatial modulation (SM), has been considered as a promising concept for uplink transmission, in which each user equipment (UE) uses SM for uplink transmission and base station (BS) is equipped with massive antennas. In this paper, we evaluate a massive single-carrier (SC) SM-MIMO system with frequency domain equalization, where SC transmission is combined with SM (SC-SM) to combat the negative impact of broadband frequency-selective fading, and frequency domain equalization is utilized to mitigate the intersymbol-interference with a low complexity. With frequency domain processing, a framework is proposed to analyze the achievable uplink spectral efficiency (SE) of single-cell massive SC-SM MIMO systems. Based on this framework, the closed-form SE lower bound of frequency domain maximum ratio combining is derived, and both the derivation of framework and SE lower bound are much more complicated than those of systems with time domain combining. Monte Carlo simulations verify the tightness of proposed SE lower bound, and show that massive SC-SM MIMO systems can outperform the SE of conventional single transmit antenna (TA) massive MIMO systems. The systems can even have a better SE performance than massive MIMO systems with spatial multiplexing UEs in a low signal-to-noise ratio. Finally, the SE gain is found to be mainly dependent on the specific number of UE's TAs, which facilitates an SE maximization via optimizing the number of TAs.

Conventional non-orthogonal multiple access (NOMA) schemes assume perfect channel state information (CSI) at the transmitter side, which is nearly impractical for many communication scenarios. Instead, this letter considers a practical downlink NOMA system in the Nakagami- ${m}$ fading channels, of which the transmitter only knows the statistical CSI associated with each user. We analyze the outage performance of the proposed system and derive a closed-form formulation of each user’s outage probability. Moreover, based on statistical CSI, the transmitter can optimize the system’s sum throughput over power allocation for different users. The accuracy of our outage analysis and the efficacy of the proposed optimization algorithm are both confirmed by simulation results.

The valley-to-peak difference in power consumption is a crucial problem in load regulation and control for a power grid. By allowing electric vehicles (EVs) to charge during off-peak hours and feed power back into the grid during peak hours, Vehicle-to-Grid (V2G) technology can help to shave the power peak. Long-distance communication is essential for data exchange between dispersed EVs and charging stations for the realization of V2G systems. However, because of the high mobility of EVs, the highvolume data transmission required and the limitations of the third-party infrastructure, it is challenging to achieve efficient and effective communication. To address these challenges, we propose a new V2G network architecture based on software-defined networking (SDN) technology. (1) We use an IEEE 802.11 WiFibased long-distance (WiLD) network with the TDMA scheme as the backhaul network, and (2) we partially replace the road side units (RSUs) with some of the WiLD nodes to provide access for, and to rapidly broadcast data to, EVs. In addition, we propose: (3) a two-stage flow table mechanism and a double roaming mechanism to address the mobility demands of V2G network terminals; and (4) a rapid data transmission scheme for communication from charging stations to EVs. A testbed was built to validate the proposed network architecture. Experimental results show that the communication time delay is in the order of milliseconds and that the reliability is higher than 99.9%.

The Stirling engine is a promising device to efficiently utilize external heat sources for various purposes. The understanding of the thermodynamic cycle of the gas parcels present in the Stirling engine is vital to its design and optimization. In this paper, a one-dimensional transient numerical model for Stirling engines is developed. A system for a β-type prototype was built and investigated by using both experimental and numerical methods. The relative error between the experimental and theoretical results measures