Wireless and Satellite Systems

Dynamical channel allocation schemes for TDMA based on Media Access Control (MAC) Protocols usually depend on network topology to allocate slot. However, The nodes in the network are allowed to move freely, which causes dynamic changes in the network topology and merging collisions. As the application of wireless ad hoc network in intelligent traffic system (ITS), vehicle in the network has typical characteristics of high-speed movement. Based on the model of vehicle movement, this paper analyzes the collision problem caused by the mobility of vehicle and it’s probability and verifies the correctness of theoretical analysis through simulation. The simulation result shows that a larger access probability or a smaller standard deviation of brings a smaller probability of merging collision.

Unmanned Aerial Vehicles have been widely used in many scenarios, and several kinds of wireless communication methods are adopted for UAV monitoring and remote control, e.g. satellite communication, WiFi communication and cellular communication. During the wireless communication solutions mentioned above cellular method is considered as a potentially dominant solution due to the wide deployment and low cost advantage. In this paper we present the latest industry research and standardization progress in cellular communication solution for UAV application from 3GPP. Two technical issues are observed, i.e. high neighbour cell interference and frequent handover, and based on the analysis three kinds of enhancement for current cellular network are proposed, i.e. flight path information reporting, virtual cell solution and dedicated base station.

With the increasing number of vehicles, many road accidents have occurred. To solve this problem, this paper investigates a security application in vehicle communications where all links require high reliability. We consider that each vehicle-to-infrastructure (V2I) communication shares spectrum resource with multiple vehicle-to-vehicle (V2V) communications. Firstly, we aim to maximize the successful transmission probability (STP) of V2V communications while guaranteeing the reliability of all V2I communications. Then, we formulate the above resource allocation problem as a combinatorial double resource auction (CDRA) problem. In the auction, radio resources occupied by V2I communications are considered as bidders competing for V2V packages. We propose an algorithm to solve the resource allocation algorithm. Finally, simulation results indicate that the proposed scheme outperforms the traditional resource allocations in terms of the reliability.

The problem of interference management and power control in two-tier heterogeneous network is investigated in this paper. Due to the time-varying characteristics of channels, the optimal transmit power changes with time. A hierarchical power control based on Kalman filter is proposed. Using Kalman filter, the current uplink transmit power under the influence of time varying channel gains due to the shadow fading effect is obtained by estimating the power of the last moment. This proposed method follows the slowly varying channel characteristics under the influence of the shadow fading effect in order to obtain an accurate power allocation. The proposed power control method is verified by computer simulations.

The imaging technology is an important part of the ground penetrating radar (GPR) technology research. Traditional ground penetrating radar imaging technology mostly stays in two-dimensional (2-D) imaging. However, when the line of the 2-D section is inclined obliquely to the underground target, the 2-D section will be inconsistent with the actual underground structure, which makes the 2-D imaging technology have inherent defects. Therefore, the research of the 3-D imaging technology has become a hot and difficult problem in current research. In this paper, a 3-D prestack time migration imaging algorithm for expressway detection is proposed. In order to better realize the 3-D migration imaging display, it is necessary to do some preliminary processing on the 3-D ground penetrating radar reflected echo data, including data preprocessing and data processing. Then the 3-D prestack migration imaging technique is applied to the process of the 3-D ground penetrating radar reflected echo data after pre-processing. By rearranging the amplitude of the collected reflected echo signals, the reflected echo energy can be homing to the real position of the space where the initial reflection point is located, thereby the horizontal resolution of the detection area is improved and finally the true outline of the measured target can be reconstructed. The simulation results verify the effectiveness and superiority of the proposed algorithm.

With the recent standardization of Narrow-Band Internet of Things (NB-IoT) as a category of Low Power Wide Area (LPWA) technology by the 3GPP, the endless possibilities it brings for IoT development cannot be overemphasized. Nigeria as a developing nation, has continued to struggle in the development and deployment of Internet of Things (IoT) technology. The approach of this paper is basically to critically analyze the current state and the technological deficiencies of IoT development in Sub-Saharan Africa by using Nigeria as a case study, and to show how the emergence of NB-IoT can help to significantly improve the penetration and optimal development of this relatively modern technology in Nigeria and consequently in Africa, as it is in developed nations. Relevant features of NB-IoT are enumerated, we propose possible NB-IoT use cases that are suitable for the Nigerian eco-system and finally, we give a summarized developmental plan for the mobile network operators (MNOs) in Nigeria.

The underground pipelines of cities are complex and diverse, and they are responsible for important functions such as energy transportation and information transmission. In urban life and construction, it is necessary to grasp the location and depth of underground pipelines. Ground Penetrating Radar (GPR) is a real-time and efficient non-destructive detection technology. It has the advantages of fast detection speed, high resolution, easy operation and wide detection range. Therefore, it is the preferred method for urban underground pipeline detection. Based on the electromagnetic wave reflection mechanism of GPR detection underground pipeline, this paper proposes a new method of non-excavation and non-destructive on-site detection to identify underground pipe diameter, determine the position and radius of underground circular pipeline, and realize the automatic identification to underground pipeline.

In vehicular ad hoc network, the random movement of nodes leads to constant changes in the network topology, in turn causing communication link outage (others is not consider in this paper). This paper will analyze the link outage caused by vehicular motion and the impact on the overall network throughput based on the vehicular movement model and the TDMA dynamic allocation access protocol. And the simulation results show that the increase of velocity standard deviation and the decrease of access probability will increase the probability of vehicle communication link outage, resulting in a decrease in the network throughput.

With the continuous development of remote sensing technology, remote sensing images and other multimedia information in the civil field has become more and more widely used. Unmanned aerial vehicle (UAV)/satellite image recognition and extraction of ground targets has become one of the important means of information acquisition in the civil field. At the same time, intelligent systems (such as mobile phones) have gradually become the core of UAV/nano-satellites. In this paper, through JAVA, the recognition and extraction of static targets in remote sensing images are realized. This method provides a theoretical basis for future image processing in remote sensing applications.

In this paper, a second-order temperature compensated bandgap voltage source based on 0.18 μm standard COMS process with low temperature coefficient (TC) and high power supply rejection ratio (PSRR) was presented. The core structure of the circuit was the improvement of the traditional bandgap reference. The cascade structure was adopted to improve the PSRR and the line sensitivity, and the square of the proportional to absolute temperature current IPTAT2 was utilized to compensate the first order circuit. This circuit constitutes of all-CMOS transistors in order to save the power consumption. The simulation results show that TC of the bandgap reference source in the −25 °C–125 °C temperature range, is 4.5 ppm/°C. At low frequency, the PSRR reaches −45.63 dB@100 Hz, and the power consumption is only 287.2 μW.

In the face of increasingly frequent regional emergency missions, the use of small satellites to obtain spatial information in hotspots has important practical needs. Under the premise of avoiding the short coverage of single satellites and limited access to information, this paper aims at research on the design and optimization of multi-satellite network orbits covered by hotspots regions. Firstly, based on the analysis of satellite coverage characteristics, the satellite coverage model is established, and the coverage calculation and coverage judgment conditions are discussed. Then a genetic algorithm based regional coverage satellite network design and optimization algorithm is proposed, which is designed the coding method, algorithm flow and corresponding constraint test rules in detail. The rationality, feasibility and effectiveness of the algorithm are finally verified by simulation examples. are provided, which provides a useful reference for regional space missions and its certain theoretical significance.

Based on the negative temperature characteristics of threshold voltage and positive temperature characteristics of a multiple of thermal voltage, adding them with proper weight coefficient A voltage reference circuit was proposed with a zero temperature coefficient (TC). The device consists of pure MOSFET operated in subthreshold region and uses no resistors and bipolar transistors. The triple-branch current reference structure is adopted for independence of supply voltage instead of cascade structure and embedded operational amplifier structure with the merit of chip area and power consumption. Simulation results showed that based on standard CMOS 0.18 um process, the circuit can operate at 0.75 V supply voltage with the output voltage only 563 mV. The TC of the voltage was 17.5 ppm/℃ in a range from −40 ℃–125 ℃. The line sensitivity was 569.5 ppm/V in a supply voltage range of 1.2 V–1.8 V, and the power supply rejection ratio (PSRR) was 66.5 dB at 100 Hz. The power dissipation was only 187.4 nW.

How to filter fluctuant RSSI signal has always been a difficult problem in an indoor localization system. This paper provides an efficient indoor localization algorithm in a BLE5.0 based scan-broadcast network by building RSSI path-loss model without a great deal of fingerprints. This method builds a RSSI-Distance fading model between one position node (PN) and one markup node (MN) by maximum likelihood estimation (MLE) based on Gauss distribution of RSSI data. Then the rough fading model about RSSI in data collecting intervals of 1 m will be get. In this paper we reduce the distance intervals in 0.1 m by fitting of path loss model and making discrete samples of confidence intervals to improve the accuracy of localization. Finally, the whole fading regularly will be fixed and the location errors of PN will be determined by centroid model (CM). The results show that sampling interval with high precision can benefit the accuracy performance in an indoor localization environment.

Traditional evolutionary fault-tolerant scheme can effectively repair circuit faults, but for large-scale integrated circuits, the evolution process consumes a lot of time and it is difficult to meet the real-time requirements. In this paper, a real-time system fault-tolerant scheme based on improved chaotic genetic algorithm is proposed. The scheme uses a built-in test detection mechanism with feedback to detect the running state of the circuit in real time. When a fault occurs, normal system operation is maintained by the fault compensation mechanism. At the same time, the system uses the evolution repair mechanism to repair the faulty circuit. Evolution process uses an improved chaotic genetic algorithm, which can quickly converge to obtain a repair circuit through adaptive chaotic crossover and mutation. This paper builds a fault-tolerant system on the FPGA. In the experiment, the fault is randomly injected into circuit so that to simulate the actual circuit fault. The proposed algorithm and fault-tolerant scheme are used to verify the self-repairing ability of the system. The experimental results show that under real-time constraints, the repair rate of the fault circuit reaches 94%.

A pulmonary nodule classification method of Computer Tomography (CT) images based on transfer learning of deep convolutional neural network (CNN) is proposed. Lung CT images with labels are quite limited compared with the large scale image database such as ImageNet. It is easy to produce over-fitting problem when using the limited data to train the deep CNN for classification task. In this paper, in order to overcome this difficulty, the deep CNNs GoogleNet and ResNet are pre-trained on the large scale database ImageNet. The fully connected layers and the classifiers of the pre-trained networks are replaced to complete the classification of CT images of pulmonary nodules. A sub set of the Lung Image Database Consortium image collection (LIDC-IDRI) is used to fine-tune the network and validate the classification accuracy. This is the process of transfer learning. It solves the problem of the deficiency of lung CT images as labeled training data for CNNs. By the knowledge obtained from the pre-trained CNNs which have been trained on ImageNet, the network is easier to converge and the training time is greatly reduced. The classification accuracy of Pulmonary Nodules can be reached up to 71.88% by using the proposed method.

Depth recovery for single image is very important to 2D-3D image conversion, which is a challenging problem in computer vision. The focus-defocus as an effective pictorial cue, has been paid more and more attention. In this paper, we reveal the relationship between entropy of DCT coefficient and scale parameter of PSF. Then, a new method to depth recovery for single images using focus-defocus cue is proposed, in which the entropy of DCT coefficient is regarded as the measure of blur, and linear operation mapping the level of blur to depth is adopted. The proposed method, which can generate pixel-level depth map, is unnecessary to select threshold. The experimental results indicate that the new method is reliable and effective.

Diabetes is a chronic disease that seriously endangers human health. Early detection, early diagnosis and early treatment can reduce the possibility of diabetic complications and mortality, which can be solved effectively by prediction model, assisting doctors to make more comprehensive and reliable diagnosis and treatment decisions, and improving diabetes management strategies. Thus, a diabetes prediction model based on Deep Belief Network (DBN) is proposed. Based on the Pima Indians Diabetes data set, the relative strength between the input attributes and the output targets of the model is calculated by using the weight matrix among the layers of the DBN diabetes prediction model. The results showed that plasma glucose concentration, body mass index, diabetic pedigree function, gestational frequency and age are important indexes for diabetes diagnosis. Then, this paper proposes three management strategies, including diabetes prevention education, diabetes individual prevention and diabetes community prevention to improve the management and control of diabetes in China.

Gesture recognition is one of the key technologies in the field of computer vision, and hand gesture recognition can be divided into static hand gesture recognition and the dynamic hand gesture recognition. This paper presents a new static gesture recognition algorithm based on hidden markov model. It uses two kinds of new shape features, the specific angle shape entropy feature and the upper side contour feature. They are firstly used for parameters training of hidden makov model, and then identify gesture categories hierarchically. In order to further improve the recognition effect for those small shape differences gesture, this paper adopts wavelet texture energy feature which can reflect the internal details of the gesture image, and makes the final correction estimation based on minimum total error probability. The experimental results show that the method has good recognition effects for gestures no matter the shape differences are big or not, and it has good real time performance as well.

The image depth estimation problem is the basic issue of computer vision, and extracting the depth information from the two-dimensional image information is a challenge work. Focusing on the issue of extracting the depth information, an algorithm based on Markov Random Field (MRF) model has been proposed to estimate depth from single image. It includes calculating multi-scale texture features using Laws filers to the two-dimensional image, and calculating the probability relationship between texture clues and scene depth according to the texture features at different scales. Then, it establishes MRF probabilistic model and estimate parameters of MRF to get the initial depth image using the least squares method. Finally, an iterating algorithm depending on neighborhood mixing depth information is adopted to further improve the estimation accuracy. The experimental results show that the method performs well both in areas with small range of depth and areas with large range of depth when the texture feature is obvious.

With the continuous development of mobile communication and the Internet, people’s requirements for the speed and quality of digital image processing are increasingly improved. A large amount of high-speed, parallel video stream data needs to be processed in real time, especially in video image processing. The author presents a method of high-definition image transmission and processing system based on FPGA. The system uses FPGA as the main controller, consisting of front-end HDMI video receiving module, image fast median filtering processing module, image ping pong Storage module and HD-SDI video display module through hardware description language programming, effectively realizing real-time video capture, transmission and display. It has been verified that the processing and transmission of digital video signals in this system is stable and reliable. The system also has a series of advantages such as low power consumption, low cost, flexible design, and easy expansion and has been applied in practical engineering.

Image classification has been applied in many fields, which is an important branch of computer vision and pattern recognition. The boosting algorithm which is belong to ensemble learning can integrate several homogeneous classifiers, and combine the output layer’s result of every classifier to improve the final classification accuracy. In this paper, the Adaboost-DBN algorithm is used to combine the four weak classifiers (DBN) and construct a strong classifier. The Adaboost-DBN algorithm is based on the Adaboost M1 algorithm and is used to achieve higher classification accuracy. The proposed algorithm is tested on the Corel-1K data set, and the result of classification is significantly improved comparing to other classifiers.

Diabetes is a chronic disease that seriously endangers human health, which should be early detection, early diagnosis and early treatment by establishing prediction model. With the help of disease auxiliary diagnosis based on machine learning, the process of early diagnosis could be more reliable. Then, the patients have more chances of early treatment. Deep learning technology can take advantage of its own powerful feature learning ability to the application of disease auxiliary diagnosis, and has gained good results. This paper proposes a diabetes prediction model based on Deep Belief Network (DBN). The model is established by using Pima Indians Diabetes data set, combined with cross-validation, setting DBN structure and adjusting DBN network parameters. The experimental results show that the accuracy of the model is as high as 77.60% and the performance is good.

In image retrieval, the shape feature is one of the key features of image content description. At present, most of the widely used Fourier descriptors in shape descriptors are invariant in translation, rotation and scale expansion. But Fourier descriptors are susceptible to the location of the starting point. In this paper, an improved image retrieval method based on Fourier descriptors is proposed. First, the image is preprocessed and the edge of the image is extracted. Secondly, the starting point of the contour is determined by the minimum inertia axis. Then Fourier transform is used to get eigenvectors. Finally, the correlation coefficient is used to calculate the similarity. Experiments show that the Fourier Descriptor Image Retrieval Method based on the minimum inertia axis is more efficient than other methods in Swedish Leaf database.

Spectrum sensing technology is implemented in cognitive radio spectrum, the basis of switching, spectrum management and spectrum sharing is the precondition of effective, reliable, wireless communication, the spectrum sensing algorithm based on sending and have energy detection, matched filtering test and cyclic stationary test three classical algorithms, detailed description of the classical algorithm, and through the simulation to compare the performance of three algorithms, put forward the suitable application scenario, provide some reference for researchers of the algorithm.

In order to further improve the accurate detection signal, reduce interference between signals, this paper designs a new type of signal detection algorithm for satellite communication systems, using stochastic resonance technology improve the signal-to-noise ratio of the input signal, the signal by using energy detection, double threshold, accurate judgment. The first step in the conventional energy of double threshold detection, the second step into the energy detection method based on stochastic resonance detection process. The experimental results show that this algorithm under the condition of low SNR signals effectively detect, promoted the whole satellite communication system performance.

In this paper, the resource allocation problem for collaborative communication of unmanned aerial vehicle network is formulated and analyzed. In our scenario, the unmanned aerial vehicles (UAVs) are uniformly distributed in the network. We consider that multiple UAVs can share one channel resource. First, a system model is established and the resource allocation problem is formulated. Then a resource allocation scheme based on the improved beetle antennae search algorithm is proposed, which finds the optimum solution efficiently. Finally, the simulation results show that the performance of the proposed the improved beetle antennae search algorithm is better than that of random algorithm. This scheme provides an efficient optimization for resource allocation of collaborative communication of UAVs.

Various services of Internet of Things (IoT) require flexible network deployment to guarantee different quality of services (QoS). Aiming at the problem of service function chain deployment, in this paper, we propose the combination of NFV and SDN to optimize resources. Considering forwarding cost and traffic load balance, a joint optimization model of virtual network function (VNF) placement and service function chain routing is given and is proved to be NP-Hard. In order to solve this model, we propose two heuristic algorithms. One is the service function chain deployment algorithm of First Routing Then Placing (FRTP) and the other is the Placing Followed by Routing (PFBR) based on node priority. Simulation results show that the former can reduce forwarding times and bandwidth consumption than the latter. And PFBR algorithm outperforms in balancing network traffic load and improving the acceptance ratio of the chain requests compared with other algorithms.

In this paper, a high-resolution vehicle positioning estimation algorithm based on existing Vehicle to Infrastructure (V2I) communications is proposed to achieve joint estimation of vehicle target’s direction of arrival (DOA) and time of arrival (TOA). We adopt the Estimating Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithm based on total least squares (TLS) to estimate the DOA and TOA, and the vehicle location can be obtained from the estimated parameters. The TLS-ESPRIT algorithm not only has a relatively small amount of computation to meet the real-time requirements of vehicle localization, but also has the advantage of strong anti-noise. We also introduce unscented Kalman filter (UKF) to further improve the localization accuracy of the TLS-ESPRIT algorithm and to reduce the influence of noise interference. The simulation results show that compared with the traditional 2D-ESPRIT parameter estimation methods without UKF and the Global Positioning System (GPS), this method has better performance of positioning parameter estimation.

In this paper, we propose a Device-to-Device (D2D) seed node cluster generation strategy based on coalitional game in social trusted D2D communication system. First, in the premise that the D2D seed node not harm the interests of other nodes and the cooperative power cost considered, a simple distributed algorithm is adopted to form independent and disjoint coalitions to maximize the throughput of the seed node cluster. Then the social trusted framework is introduced to make the segmentation of coalitions effectively meet the requirements of social security. The simulation results show that compared with the node cluster of the traditional cellular network and non-coalitional game, the system throughput of which is maintained at a higher level with social security ensured as well.

In this paper, a puncturing algorithm for mixing 2-kernel and 3-kernel polar codes is presented. The puncturing sequence is generated based on the capacity of channels and the upper bound of minimum block error probability for successive cancellation (SC) decoding. We use the capacity-zero puncturing model, the decoding algorithm of mother codes can still be adopted. An improved greedy algorithm of computing the maximization of the minimum distance is proposed to select the information set. The maximum number of punctured bits is limited to $$[1,{{2}^{n-2}})$$ when the length of subcodes $$M\in (2^{n-2}*3,2^n)$$ . Simulation results show that the block error rate based on the mixing kernels is better than that based on 2-kernel.

The combination of software defined network (SDN) and network function virtualization (NFV) solves some problems in traditional networks, such as service deployment and configuration and management of network resources. However, it also introduces new problems such as network load imbalance. Virtual network function (VNF) migration is an effective way to solve these problems. In this paper, we propose a quick adaptive migration algorithm for VNF, which combines pre-calculation and real-time calculation to reduce the cost of migration. When the node triggers the light-overload-threshold, we perform a pre-calculation of migration for the node and set the result-set. When the node is overloaded, we perform the migration if the result-set is unexpired, otherwise we perform the real-time migration solution. Simulation results show that this algorithm can effectively reduce the number of migration, improve the stability of the system and reduce the overall network migration overhead of the system.

Although Software Defined Networking (SDN) has a lot of advantages, it also leads to some security issues such as DDoS/DoS attacks, unauthorized access, and single point of failure. To improve the security and efficiency of the SDN control plane, we propose a novel consensus algorithm–Simplified Practical Byzantine Fault Tolerance (SPBFT) to transfer messages between controllers and then establish an analysis model to analyze the security and performance of SPBFT based on game theory. In this paper, we apply blockchain technology in SDN to build a readable, addable, and unmodifiable distributed database which maintains a list of updated system activities and time stamps in each controller. The simplified three-step consensus algorithm SPBFT makes the message transfer and verification carry out efficiently in parallel. In addition, we use recovery mechanism and credibility assessment on the primary controller to increase the invulnerability of system. Simulation results show that compared with the PBFT algorithm, the proposed algorithm can significantly improve system performances in terms of security and efficiency.

In this paper, a method of parameter estimation based on Non-Circular Signal via two-dimensional ESPRIT algorithm for MIMO radar is proposed. The algorithm considers the characteristic of the maximum non-circularity signal, and utilizes not only the covariance matrix but also the pseudo-covariance matrix to obtain the data matrix of the non-circular signal. This algorithm expands the received signal data matrix by data recombination, which increases the number of effective array elements of MIMO radar and improves the utilization of echo information. Then the parameter estimates are obtained by using the two-dimensional ESPRIT algorithm. Compared with the ESPRIT algorithm developed in other references, this algorithm has higher accuracy of parameter estimation in the case of lower signal-to-noise ratio or fewer array elements. Meanwhile, the algorithm can improve the accuracy of parameter estimation when the targets are close together. Simulation results indicate that the proposed algorithm improves the performance significantly.

This paper deals with multi-target localization in statistical MIMO radar. An improved target locating algorithm is proposed which combines Kalman filtering with fuzzy C clustering. The Kalman filter is utilized to acquire the information of target location and fuzzy C clustering is used for data fusion as there are multiple receivers in radar. For target locating in MIMO radar, we first utilize the maximum likelihood estimation algorithm to estimate the parameters of targets. To eliminate the influence of noise on the parameter estimation, we take advantage of the gliding property of Kalman filter to process the result of parameter estimation. All these processing data from different receivers is fused by fuzzy C cluster to obtain the parameters estimation of all targets. We give scenarios including MIMO radar and targets to analyze the performance of this target location algorithm. With considering the effects of noise, the position of receivers and transmitters and the moving of targets, the analysis is carried out by evaluating the location accuracy of the algorithm. The simulation result shows that the proposed method can locate multiply targets effectively and improves the location accuracy.

In this paper, resource allocation problem for ultra-dense D2D communications is studied. In ultra-dense scenarios, the number of D2D user equipments (DUEs) is far bigger than the number of cellular user equipments (CUEs). The dense user equipments (UEs) increase the complexity of resource allocation problem. Firstly, the system model of ultra-dense D2D communications is described. Then the resource allocation problem of ultra-dense D2D communications is formulated. Next a fast resource allocation algorithm based on predatory search algorithm is proposed and analyzed. Finally, the analysis and simulation results validate that the performance of proposed scheme is very efficient and has a low algorithmic complexity. This scheme can be applied into the ultra-dense D2D communication networks.

In large-scale multiple-input multiple-output (LS-MIMO) systems, singular value decomposition (SVD) or eigenvalue decomposition (EVD) are common channel estimation schemes. However, the computational complexity of two estimators limits the application in LS-MIMO systems. Motivated by this, in order to reduce the complexity, a novel method that combines fast single compensation approximated power iteration (FSCAPI) algorithm with iterative least square with projection (ILSP), FSCAPI-ILSP, is proposed in this paper, In the proposed method, the received signals subspace is estimated by the FSCAPI algorithm firstly, then the initial channel estimation is obtained by the pilot signals. Finally, we combine it with the ILSP algorithm to improve the accuracy of the channel estimation. Compared with the conventional methods, the proposed scheme degrades the computational complexity significantly. Simulated results indicate the provided method is better than its counterparts and improves the channel estimation accuracy effectively.

Massive multiple input multiple output (MIMO) is a promising technology that has been proposed to meet the requirement of the fifth generation wireless communications systems. For uplink massive MIMO systems, the typical linear detection such as minimum mean square error (MMSE) shows near-optimal performance. However, due to the direct matrix inverse, the computational complexity of the MMSE detection algorithm is too high, especially when there are a large number of users. Thus, in this paper, we propose an improved Gauss-Seidel algorithm by utilizing delayed over relaxation (DOR) scheme, which is named as delayed over relaxation Gauss-Seidel (DRGS) algorithm. The basic idea of the DOR scheme is to combine the predicted iterative step (n + 1) with the iteration of step (n − 1). The scheme can provide a significant improvement of the convergence speed for iterative algorithm. The theoretical analysis of DRGS algorithm shows that the proposed algorithm can reduce the computational complexity from O (K3) to O (K2), where K is the number of users. Simulation results verify that the DRGS algorithm can achieve almost the same BER performance as that of MMSE detection with a small number of iterations.

Nowadays, the fast development of the digital circuits results in a more and more high digital level of radar system. Especially, the development of the solid-state active module, the high-speed multi-digital A/D convertor, the direct digital synthesizer (DDS), and universal use of the high-speed digital signal processor provide an outstanding basis of the radar communication integration. Minimum Shift Keying Linear Frequency Modulation (MSK-LFM) is a novel multifunctional radar waveform. For all above, this paper proposes an integrated working mode based on positive and negative frequency modulation in radar communication integration. In the mode, the radar main station transmits positive linear modulation frequency signals, however, the communication affiliated station transmits negative linear modulation frequency signals. Their orthogonality causes less interference. Through the derivation, the method for the orthogonality improvement is obtained. The effectiveness of this working mode is proved by the simulations.

A multi-temporal kernel density regression (KDR) method is proposed in this paper for reflectance restoration. Kernel density regression perform optimization to search the best regression coefficients. The proposed method is applied on the Landsat-8 dataset, and shows a better estimation of the true pixel value from the contaminated images.

To overcome the problems caused by conventional ground routing protocols applied in the satellite communication network, a novel switching architecture is proposed. The proposed architecture employs layer-2 switching for same port and IP routing for different ports. Furthermore, the onboard IP switching process is well designed. OPNET is applied to build a satellite network simulation environment based on onboard IP switching. Simulation results demonstrate that the switching architecture meets the requirements of onboard IP data packet switching for both inter-beam, between beams and between satellites.

Hyperspectral image (HSI) anomaly targets detection is always applied for timeliness and onboard mission. For high detection accuracy, deep learning based HSI anomaly detectors (ADs) are widely employed in recent researches. However, their huge network scale for high-level representation ability leads to great computation burden for the onboard computation system. To decrease the computation complexity of the detector, a lightweight network is expected for the HSI AD. In this paper, by creating a multiobjective optimization with nondominated sorting genetic algorithm II (NSGA-II), an automatic evolution based deep learning network HSI AD (Auto-EDL-AD) is proposed to explore a lightweight network. The experimental results on an HSI dataset show that the proposed Auto-EDL-AD can generate an optimal network for the HSI anomaly detection which reaches up to 170% speedup without any detection accuracy loss.

In this paper, we investigate spectrum sensing relying on multiple satellites, which can achieve global seamless spectrum sensing, due to the feature of wide coverage. We conceive a pair of satellite based spectrum sensing schemes, namely hard combination oriented energy detection based spectrum sensing (HC-EDSS) and semisoft-combination double-threshold oriented energy detection based spectrum sensing (SD-EDSS). In the HC-EDSS scheme, secondary users send their decision results to fusion center in order to get the final decision. By contrast, secondary users not only send their decision results, but also send some individual information to fusion center in the SD-EDSS scheme. We also derive the closed-form of the probability of detection over satellite fading channel. In our performance evaluations, the conceived HC-EDSS and SD-EDSS schemes outperform the conventional single user oriented energy detection based spectrum sensing (SU-EDSS) in terms of its probability of detection. Moreover, the SD-EDSS scheme achieves the best probability of detection among them, demonstrating the advantage of increasing the accuracy of spectrum sensing.

Satellites of the third generation BeiDou Navigation Satellite System (BDS) will be equipped with Ka-band antennas and communicate through point-to-point Inter-Satellite Links (ISLs). Link assignment and routing algorithms of other Global Navigation Satellite Systems (GNSSs) are not suitable for BDS ISL due to its unique communication system. This article mainly focuses on inter-satellite link assignment and routing problem in BDS ISL network which is a new challenge and has no mature algorithm proposed specially for it before. Firstly, time slot matrix assigning algorithm is put forward to solve link assignment problem based on characteristic of BDS ISL. Performance of this algorithm is simulated and ISL network topology designed by this algorithm is proved to be good in aspects of average number of links, average position dilution of precision (PDOP) and other important indicators. Secondly, on the basis of determined network topology, this paper provides a heuristic route planning strategy for information transmission in BDS ISL network. Path Combinational Optimization algorithm based on Simulated Annealing (PCO-SA) is proposed and elaborated in this paper. By combining PCO-SA and adjusted Contact Graph Routing (CGR) algorithm, a common solution for routing problem of navigation related data can be achieved. Simulation results compared with other route planning methods show that PCO-SA can not only significantly increase success ratio of route planning, but also optimize average time delay and hop of planned path.

Along with the development of spacecraft technology, the demands for intelligence and autonomy of future satellites are promoting. However, the operational capability of processor and transmittability of bus are mainly the factors against the promotion in existing satellite avionics system. Moreover, the efficient allocation method for computing and storage resource based distributed architecture is also the valid measures to improve the efficiency of satellite avionics system. In this paper, we focus on the resource limitation of processing units, and mention the resource allocation based auction game of satellite avionics system, advance the use ratio of free resource and optimize the power consumption.

In this paper, we investigate the outage probability (OP) of a hybrid satellite and terrestrial cooperative network (HSTCN) with the terrestrial relay having multiple antennas. Here, it is assumed that the satellite channel undergoes the shadowed-Rician fading, while terrestrial channel follows correlated Rayleigh fading. By supposing that statistic channel state information (CSI) of relay-destination link is available at the relay, we first obtain the end-to-end output signal-to-noise ratio (SNR) expression of the HSTCN. Then, the closed-form expressions of the outage probability for the considered system are derived, where two combining schemes, namely, selection combing (SC) and maximal-ratio combining (MRC) protocols are utilized at the destination to combine signals form the satellite and relay. Finally, numerical results are given to validity of the OP analysis, and reveal the performance difference of the two combining schemes.

UAV tracking is aimed to infer the location of the object from the videos captured by an aerial viewpoint. The challenges mainly focus on fast motion, scale variation and aspect ratio variation. The region proposal in image detection can detect the object candidates in the image, which can be leveraged to find the optimal location of the object. In this paper, a tracking algorithm using Farneback optical flow is proposed to provide object proposals for correlation filter for robust tracking under aerial scenarios. The Farneback flow estimates the motion of the object between adjacent frames and an improved FAST detector is adopted to detect the keypoints that contain the local patterns of the object from the last frame. The object proposal is obtained by computing translations of the keypoints. The final proposal is determined by computing the bounding box that encloses the keypoints. A correlation filter from KCF is used to detect the object on the proposal. The quantitative evaluation results on OTB100 show the advantage of the proposed tracker to state-of-the-art trackers in accuracy, especially under fast motion.

A main obstacle to limit the capacity of next generation Terabit/s broadband satellite communication is the limited spectrum available in the Ka band. A feasible solution is to move the feeder link to the higher Q/V band, where spectrum is more available. However, the Q/V band is sensitive to rainfall attenuation. Compensating for the falling caused by rainfall, gateway diversity is considered to ensure the required feeder link availability. So far, many strategies for gateway diversity have been proposed, and each of them has its own advantages, so it is imperative to research gateway switching strategies. In this paper, a modified switch and stay combining strategy named TH-SSC is proposed for a Q/V band feeder link, which is suitable for three gateways to switch and we make an analysis and simulations about its performance. It can be seen that this modified TH-SSC strategy is pragmatic and has better performance in some aspects.

Satellite communication which is a crucial part in wireless communication field faces the spectrum scarcity problem. Therefore, exploring a suitable spectrum sharing mechanism has become a key issue in ensuring the full utilization of satellite users while improving the spectrum utilization of existing spectrum. Cognitive communication is an emerging solution to solving spectrum problems in wireless systems. An important part of cognitive radio is spectrum awareness, which is used for acquiring information about the spectral opportunities. One of the spectrum sensing methods is spectrum sensing, which utilizes spectrum holes in multiple fields to sense the presence or absence of primary users by using signal processing techniques. This paper studies some cognitive scenarios and systems for satellite communication, and then proposes a spectrum sensing algorithm based on the channel states information to solve the problem of large transmission loss in satellite cognitive scenarios.

Given to the performance of the resource utilization in the cooperative communication systems, the paper proposed a novel power allocation scheme in the Decode and Forward cooperative communication scenario. Based on the analysis of the system model and the fading channel model, the paper proposed an optimized power allocation scheme using optimization theory to minimize the outage probabilities of the communication system, for the accuracy and reliability of each link’s transmission. Simulation results are presented to illustrate that optimized power allocation in terms of minimum outage probabilities offers better outage performance than common power allocation in cooperative diversity systems.

In this paper, a cluster-based energy-efficient Cognitive Internet of Things (CIoT) is proposed, which can harvest the radio frequency (RF) energy of the primary user (PU) to supply energy consumption of spectrum sensing. A joint optimization problem of time and node is presented to maximize the spectrum access probability of the CIoT. The simulations show that there are optimal resource allocations to improve both spectrum efficiency and energy efficiency of CIoT.

With the approaching of Internet of Things (IoT), non-orthogonal multiple access technology was proposed in the fifth generation (5G) mobile communication system to improve the system capacity and meet the needs of massive connectivity. Multi-User Shared Access (MUSA) technology is a non-orthogonal multiple access technology of code domain. MUSA receiver adopts multi-user detection algorithm, mainly using interference cancellation based on linear detection. This paper proposes the successive-parallel interference cancellation multi-user detection algorithm for the shortage of typical multi-user detection algorithms of MUSA uplink receiver, and gives the comparison results of the proposed algorithm and typical algorithms. Compared with parallel interference cancellation detection algorithm, the proposed algorithm improves the detection performance greatly. Compared with successive interference cancellation detection algorithm, the proposed algorithm reduces the processing time delay effectively.

Contention resolution diversity slotted ALOHA (CRDSA) with packet repetition and iterative interference cancellation (IIC) has been proven that achieves $$48\%$$ improvement in terms of throughput than pure slotted ALOHA which merely has a theoretical throughput upper bound of 0.36. So far, optimizations of such random access scheme have been proposed in the literature called irregular repetition slotted ALOHA (IRSA) and coded slotted ALOHA (CSA) which both targeted the collision channel model. In this paper, the environment of LEO satellite communication and capture effect at the satellite receiver are considered. Meanwhile, due to the inherent propagation feature of LEO satellite, capture effect can be enhanced through separating LEO footprint into districts. Under a setting of finite frame length, this separating scheme is analyzed via Monte Carlo simulation combining with optimized power control. Numerical results are provided, which prove the stability of proposed scheme when channel load exceeding 1.

5G research gradually focus on non-orthogonal multiple access technology, owing to huge traffic, more mobile terminals, and explosive growth of throughput capacity in recent years. Sparse code multiple access (SCMA) is a multi-dimensional codebook-based non-orthogonal multiplexing technique proposed to address the above requirements. This paper investigates the Message Passing Algorithm (MPA) in the receiver of SCMA and proposes two multi-user detection algorithms with low computational complexity in uplink. Improved Variable Message Passing Algorithm (IVMPA) reduces computational complexity compared to Variable Message Passing Algorithm (VMPA) by changing the users iteration order. Incomplete Iterative Message Passing Algorithm (IIMPA) is proposed to reduce the number of iteration for users with high signal-to-noise ratio (SNR) and reduces computational complexity compared to MPA.

The typical method adopted in the decoding of linear network codes is Gaussian Elimination (GE), which enjoys extreme low policy complexity in determining actions, i.e., the XORing operation executed upon the decoding matrix and the coded packets. However, the amount of the total required actions is quite large, which makes the overall decoding complexity high. In this paper, we consider the problem of minimizing the decoding complexity of binary linear network codes. We formulate the decoding problem into a special shortest path problem where the weight of each edge consists of: (1) a const weight due to the execution of the action; (2) a variable weight due to the adopted policy in determining the action. The policy is formulated as an optimization problem that minimizes a particular objective function by enumerating over a certain action set. Since finding the optimal policy is intractable, we optimize the policy in dual directions. At one hand, we guarantee the objective function and the action set are similar to the optimal policy that minimizes const weight summation; at the other hand, we guarantee that the objective function have simple structure and the action set is small, so that the variable weight summation is also small. Simulation results demonstrate that our proposed policy can significantly reduce the decoding complexity compared with existing methods.

In view of the increasing scale and poor concealment of existing electronic reconnaissance satellites, this paper proposes a passive direction finding algorithm based on baseline selection of communication load phased array. The algorithm performs direction finding by selecting part of array elements in the phased array to form a two-dimensional interferometer. Then the direction finding result of interferometer is used as the basis to determine the spatial domain search range of the MUSIC algorithm. Finally the fast and high precision estimation of elevation angle and azimuth angle is completed by MUSIC algorithm. The algorithm can realize fast and high-precision direction finding under communication concealment. At the same time, multi-scale direction finding results can guide the beamforming of phased array and enhance the communication. This algorithm provides a new solution for the integrated payload of miniaturized concealed electronic reconnaissance and communication.

In this paper, we consider the problem of DOA estimation for a mix of incoherent and coherent targets by using the monostatic co-prime MIMO array with N sparse transmitting sensors and $$2M-1$$ sparse receiving sensors. The co-prime MIMO array generates a non-redundant and uniform sub sum co-array with $${\text {O}}(MN)$$ contiguous sensors using only $${\text {O}}(M+N)$$ physical sensors. Based on the concept of sum co-array equivalence, we can obtain different configurations of virtual MIMO arrays with $${\text {O}}(MN)$$ contiguous virtual sensors, and then construct the corresponding virtual data matrices, which provides different tradeoffs between the number of resolvable targets and the maximum number of mutually coherent targets that can be resolved. On the basis of the virtual data matrix and the conventional DOA estimation approaches such as MUSIC, $${\text {O}}(MN)$$ mixed coherent and incoherent targets can be resolved only with $${\text {O}}(M+N)$$ physical sensors, namely the number of resovable targets exceeds the limitation of the number of physical sensors. Finally, simulation results demonstrate the effectiveness of the proposed DOA estimation method with the monostatic co-prime MIMO array in the presence of both the coherent and incoherent targets.

In this paper, a DOA estimation method based on co-prime array is proposed to resolve the coherent and incoherent hybrid sources. Firstly, with respect to the difference co-array of co-prime array, the desired units with corresponding contiguous intervals in the correlation matrix are extracted and rearranged into an augmented correlation matrix. Then we decorrelate the augmented correlation matrix by reconstructing matrix algorithm, forward spatial smoothing and forward-backward spatial smoothing algorithm. Finally, through MUSIC spatial spectrum searching on the basic of the decorrelated correlation matrix, DOA estimation towards sources is obtained. The simulation results show that the proposed method can achieve DOA estimation of the coherent and incoherent hybrid sources with more number than physical array. Through comparison, it can be concluded that the reconstructing matrix algorithm obtains a larger number of distinguishable sources and the error performance of which is better under low SNR. However, the spatial smoothing algorithms have a better estimation error performance in the case of low snapshot.

High resolution image can be obtained with backprojection (BP) algorithm, but at the same time, significant grating lobes will be brought in radar image and reduce the quality of image. This paper presents a through-the-wall radar (TWR) imaging method based on deep learning to improve the quality of radar image. A convolutional neural network was designed for TWR imaging, the radar image can be obtained as the output of neural network. The simulation and real data experiments demonstrate the effectiveness of proposed method.

High precision localization is a kind of promising technology in industry and everyday life. In this paper, ultra-wide band (UWB) signals are employed to make use of its high time resolution to obtain high precision ranging results. In dense multipath indoor environment, an entropy based first path detection is proposed to take advantage of the essential features of noise and UWB signals. Furthermore, when the number of anchor nodes is less than three, some of the indoor users are employed as auxiliary anchor nodes to obtain high precision localization result. The lease square error method is employed to get the localization result. Simulation results show that the entropy based first path detection algorithm can get the ranging result in a high accuracy. Besides, when there are not enough anchor nodes, the proposed cooperative localization scheme can help localization with high precision.

The number of Beidou intelligent terminal is dramatically increasing recently. Only during 2017, there have been More than 200 mobile communication terminals supporting BDS applied for telecom equipment access to network license. It’s important to evaluate the Beidou antenna performance of these intelligent terminals but currently there is neither specification nor common methods. Over-The-Air (OTA) test evaluates three-dimension (3D) radiated antenna performance in anechoic chamber to approach the real user experience. In this paper, an over the air test method for Beidou intelligent terminal is proposed, including standalone mode and communication assisted mode. The test scenarios and test procedures for radiated 3D Carrier-to-Noise (C/N0) pattern measurement and radiated sensitivity measurement are described. An over the air test system is developed in China Telecommunication Technology Lab (CTTL) and a number of intelligent terminals supporting Beidou are tested. The results indicate that although large numbers of terminals are claimed supporting Beidou, its Beidou antenna performance is relatively poor and when Global Position System (GPS) is not available, Beidou positioning cannot meet the user requirements currently. Thus, it is urgent to standardize the Beidou OTA technical requirements and test methods of intelligent terminals.

An intelligent wireless channel allocation algorithm for HAPS 5G systems based on reinforcement learning was proposed. Q-learning reinforcement learning algorithm and the back-propagation neural network were combined, which made HAPS 5G systems autonomous learn according to the environment and allocate channel resources of the system efficiently.

In order to improve shielding effectiveness of optoelectronic instrumental windows, a filtering method is proposed using a transparent mesh PET film consisting of flexible PET film and conductive mesh film. And then an analysis model is built based on optical characteristic transfer-matrix theory of multi-layer optical films. Simulation and analysis indicate that shielding effectiveness can be improved by optimizing thickness of flexible PET films to make corresponding quarter-wavelength frequency move to low one in frequency-band of 10–20 GHz. Optimization results show that shielding effectiveness of optimized optoelectronic instrumental windows utilizing a transparent mesh PET film is higher than 16.8 dB by optimizing the thickness of a flexible PET film of 200 μm. So it can be concluded that the proposed filtering method utilizing a transparent mesh PET film can be used to improve shielding effectiveness of optoelectronic instrumental windows.