Search

This paper investigates the sub-channels and power allocation issue in the multi-carrier NOMA communication system. To maximize the sum rate of the system, we design a fast dynamic resource allocation (FDRA) algorithm. The algorithm can quickly identify the interference nodes with poor channel conditions, and reduces the search complexity of the algorithm. By jointly optimizing sub-carrier and power resource allocation, the proposed scheme can take full advantage of the channel capacity, and maximize the system performance while ensuring the quality of service (QoS) of each user. Simulation results show that when compared with the joint resource allocation (JRA) algorithm and the genetic algorithm (GA), the FDRA algorithm can achieve the best performance on the complexity and the sum rate of the system. Thus, the proposed algorithm can provide a fast and efficient resource allocation scheme for the downlink multi-carrier NOMA systems.

Reliable positioning services are extremely important for users in mountainous environments. However, in such environments, the service reliability of conventional wireless positioning technologies is often disappointing due to frequent non-line-of-sight (NLoS) propagation and poor geometry of available anchor nodes. Hence, we propose a unmanned aerial vehicle (UAV)-enabled positioning system that utilizes UAV’s mobility to overcome the above challenges. In this article, we first analyze and model the major causes of service failures in the proposed system. In particular, a geometry-based NLoS probability model is established based on the digital elevation models (DEMs) of realistic terrain for reliability analysis. Subsequently, we propose a reliability-prediction method and derive the corresponding metric to evaluate the system’s ability to provide reliable positioning services. Moreover, we also develop a voting-based method for the further enhancement of service reliability. Monte Carlo simulations show that in mountainous environments, the proposed reliability-prediction method could achieve a prediction accuracy that is at least 36.8 $\%$ higher than that of the existing technique. In addition, in the experiments conducted in two typical valley scenarios, the proposed reliability-enhancement method improves the service reliability of the proposed system by 23 $\%$ and 29 $\%$ , respectively. These numerical results demonstrate the strong potential of the proposed system and methods for reliable positioning.

In this letter, we put forth a new asymmetric linear PNC (A-LPNC) scheme for the setup where the users are subject to non-identical power constraints and modulation schemes. For deterministic channel, we show that given any pair of average symbol energies, the effective minimum distance of A-LPNC constellation can be made to be identical to the single-user minimum distance, translated into optimal error probability performance at a medium to high SNR. For fading channel, we show that the optimized network coding coefficients can be found and employed such that the effective minimum distance of A-LPNC is equal to the single-user minimum distance lower bound for integer channel gains. For real-valued channel gains, we present the optimal NC coefficients selection that minimizes the error probability of A-LPNC. These results are verified via simulations.

When polar codes are decoded by the successive cancellation (SC) decoding algorithm, erroneously decoded non-frozen bits are caused by either channel noise or error propagation. SC-Flip algorithms aim to improve error performance by first identifying erroneous hard decisions due to channel noise and then flipping them during the decoding process to reduce error propagation. In existing SC-Flip algorithms, cyclic redundancy check (CRC) is used to check the decoded codeword to detect incorrect hard decisions. Differing from this detection approach based on CRC, we propose new SC-Flip decoders that take advantage of both the CRC and distributed parity checks (PCs) to detect, identify and flip erroneously decoded non-frozen bits. The proposed decoders terminate SC decoding early when a distributed PC is not satisfied. In addition, we propose a new metric to help locate the incorrect hard decisions. Finally, simulation results demonstrate that our SC-Flip decoders achieve better performance complexity tradeoffs than prior flipping algorithms, and approach the performance and complexity of the SC-Oracle algorithms.

Positioning services in cellular networks are at risk of being abused by unauthorized users, which may severely damage the interests of operators and legitimate users. In this letter, multiple unmanned aerial vehicles (UAVs) are employed as mobile jammers to disrupt the positioning services at unauthorized users, thereby enhancing the controllability of services at the physical layer. Specifically, we aim to jointly optimize the UAVs’ locations and jamming power to maximize the minimum position error of unauthorized users, while ensuring that the needs of legitimate users are met. To solve this non-convex optimization problem, an iterative algorithm based on the block coordinate descent and successive convex approximation techniques is proposed and applied. Numerical results demonstrate that the proposed scheme could prevent the unauthorized use of positioning services and outperforms the benchmark schemes.

As the increase in satellite number and variety, satellite ground stations should be required to offer user services in a flexible and efficient manner. Network function virtualization (NFV) can provide a new paradigm to allocate network resources on-demand for user services over the underlying network. However, most of the existing work focuses on the virtual network function (VNF) placement and routing traffic problem for enterprise data center networks, the issue needs to further study in satellite communication scenarios. In this paper, we investigate the VNF placement and routing traffic problem in satellite ground station networks. We formulate the problem of resource allocation as an integer nonlinear programming (INLP) model and the objective is to minimize the link resource utilization and the number of servers used. Considering the information about satellite orbit fixation and mission planning, we propose location-aware resource allocation (LARA) algorithms based on Greedy and IBM CPLEX 12.10, respectively. The proposed LARA algorithm can assist in deploying VNFs and routing traffic flows by predicting the running conditions of user services. We evaluate the performance of our proposed LARA algorithm in three networks of Fat-Tree, BCube, and VL2. Simulation results show that our proposed LARA algorithm performs better than that without prediction, and can effectively decrease the average resource utilization of satellite ground station networks., IEEE Transactions on Network and Service Management (2020)

To achieve an efficient and reliable data transmission in time-varying conditions, a novel non-orthogonal multiple access (NOMA) transmission scheme based on rateless codes (NOMA-RC) is proposed in the multicast system in this paper. Using rateless codes at the packet level, the system can generate enough encoded data packets according to users’ requirements to cope with adverse environments. The performance of the NOMA-RC multicast system with delay constraints is analyzed over Rayleigh fading channels. The closed-form expressions for the frame error ratio and the average transmission time are derived for two cases which are a broadcast communication scenario (Scenario 1) and a relay communication scenario (Scenario 2). Under the condition that the quality of service for the edge user is satisfied, an optimization model of power allocation is established to maximize the sum rate. Simulation results show that Scenario 2 can provide better block error ratio performance and exhibit less transmission time than Scenario 1. When compared with orthogonal multiple access (OMA) with rateless codes system, the proposed system can save on the transmission time and improve the system throughput.

For polar codes, the statistical breakdown of belief propagation (BP) decoding errors is firstly proposed by Sun et al. The errors are classified into three categories: unconverged errors, false converged errors and oscillation errors, which are corrected by three different post-processing methods. As for the BP List (BPL) decoding, we discover that one type of error can be transformed to another type by different factor graphs in the failed BP decoding. Besides, false converged error is usually easier to be detected and modified. Therefore, we only target false converged error to detect and correct. In this work, we propose a two-level detection rule for false converged errors and its corresponding post-processing algorithm to modify these errors in order to improve the performance of the BPL decoding. Numerical results show that the error-correction performance of the proposed decoder is more than 0.85dB better than that of the permuted BPL (PBPL) decoder with slight extra computation complexity at the frame error rate (FER) of $10^{-5}$ for 5G (1024,512) polar code.

With the evolution of communication techniques, vehicle-to-vehicle (V2V) communications with low latency and high speed facilitate the study of cooperative positioning (CP) methods for vehicular applications. However, the current CP methods are incapable of bridging the gap between positioning accuracy required for crucial vehicular applications and what CP provides. The CP method with carrier-phase-based Real Time Kinematic (RTK) principle becomes a potential candidate for high-precision positioning. In this letter, we reveal a cooperative navigation filtering (CNF) algorithm fusing Global Navigation Satellite System (GNSS) data and Ultra-wideband (UWB) ranging data to achieve decimeter-level positioning. The main novelty of the proposed CP method is to include centimeter-level GNSS carrier phase information and derive a collaborative ambiguity resolution model. Numerical results and comparisons validate the feasibility and superiority of proposed method.

Most category-level object pose estimation methods are multi-tasking, including instance segmentation, Normalized Object Coordinate Space (NOCS) map estimation and classification. However, previous approaches overlooked the connection between multiple tasks. In this work, we propose an efficient network to make better use of the complementarity between different tasks. Specifically, we propose an external sharing unit (ESU) to promote instance segmentation and NOCS map estimation. In addition, we propose an internal sharing unit (ISU) to improve the NOCS map estimation. The NOCS map head has three branches. And the estimated coordinates of each branch have strong correlation. Extensive experiments on the CAMERA and REAL dataset demonstrate the effectiveness of joint optimization in multi-tasking category-level object estimation. Experimental results also show that the proposed method can improve not only accuracy but also efficiency on several benchmarks.

In blind detection, both the downlink control information (DCI) format and the physical downlink control channel format are unknown, which results in high detection complexity. If the DCI format can be determined, half of the invalid candidates will be eliminated, which can significantly reduce the detection complexity. To determine the DCI format, this letter designs a DCI metric based on a correction factor of the transition probability, which is capable of measuring the reliability of decoding paths with different number of frozen bits. Furthermore, to reduce the decoding latency, a simplified decoding algorithm is adopted in the proposed blind detection scheme, which shows good adaptivity for a variety of code lengths and code rates. Moreover, an update criterion of the DCI metric based on the simplified decoding method is proposed, which improves the detection accuracy and reduces the decoding latency. Simulation results show the advantages of the proposed method in terms of the detection performance and latency compared with the existing schemes.

Depth map plays an important role in our daily life including automatic driving, intelligent robots, and commercial manufacture. However, existing depth estimation methods are still facing some difficulties in real-life applications. Firstly, using merely point-wise metrics during training usually causes surface distortion due to the lack of geometric restrictions. To tackle this problem, we introduce a normal restriction module to the network to improve the method performance with regards to surface integrity. Secondly, although 3D methods usually achieve higher accuracy by directly concatenating features as cost aggregation, they sacrifice the time and memory efficiency. To make the disparity estimation network light enough for adding the normal restriction, we propose a 2D aggregation method to replace the 3D ones. Experiments prove the effectiveness of our method and we show that our results are competitive to 3D methods under popular benchmarks including KITTI, SceneFlow and Midddlebury.

Rateless spinal codes can achieve reliable transmission with high throughput performance, which is required by some power-constrained applications, such as internet of things (IoT). In this study, the cyclic redundancy check (CRC) is divided into segments. We design the segmented CRC-aided (SCA) spinal codes and propose a novel hybrid decoding algorithm. The decoder can terminate the decoding process earlier when an error decoding is detected in any segment. Moreover, a more targeted symbol transmission strategy after decoding errors occur is provided and we call it as the transmitting redundant symbols for specific segments (RSSS) strategy. The RSSS strategy saves the transmitting symbols by transmitting a variable number of symbols, thus improving the throughput of the system. Furthermore, we design a new tail-biting structure for SCA-spinal codes to compensate for the disadvantage of poor error detection ability for short segment CRC bits. The simulation results show that the proposed SCA-spinal codes can reduce the decoding complexity and improve the throughput of the system. The transmission delay can also be reduced by dividing the information bits and CRC bits into an appropriate number of segments.

In order to reduce memory and latency of the belief propagation (BP) decoding algorithm, we use new constituent codes to simplify the message passing of factor graph for polar codes. Since the left-to-right (right) messages of top level of constituent code are directly updated by the right-to-left (left) messages without visiting its sub-factor graph, which saves the corresponding memory entries and decoding latency. Therefore, based on our proposed constituent codes, we can obtain a memory-efficient and low-latency BP decoding algorithm. Numerical results show that the proposed decoding algorithm saves about 4.6%-17.6% memory entries of the XJ-BP decoding and the latency is reduced by 19%-23.5%.

The commercial deployment of 5G technology has brought new opportunities and challenges to digital broadcasting service. Digital broadcasting, together with other applications, will have to rely on 5G mmWave channel to transmit high quality signals. In mmWave band, channel coding is a key technology to ensure reliable information transmission and quality of service. This paper correspondingly proposes a fast simplified multi-bit successive cancellation list (Fast-SMSCL) decoding method for polar codes, with FPGA implementation details. Simulation study shows that our proposed method makes fully use of consecutive information bits and achieves prominent latency reduction compared to the conventional simplified multi-bit successive cancellation list decoding.

In this paper, we present a graphics processing unit (GPU)-based implementation of a weighted bit-reliability based (wBRB) decoder for non-binary LDPC (NB-LDPC) codes. To achieve coalesced memory accesses, an efficient data structure for the wBRB algorithm is proposed. Based on the Single-Instruction Multiple-Threads (SIMT) programming model, a novel mapping strategy with high intra-frame parallelism is presented to improve the latency and throughput performance. Moreover, by using Single-Instruction Multiple-Data (SIMD) intrinsics, four 8-bit message elements are packed into a 32-bit unit and simultaneously processed. Experimental results show that the proposed wBRB decoder provides good tradeoff between error performance and throughput for the codes with relatively large column degrees or high rates.

The integration of multidimensional networks such as space, air and ground is the future trend of the IoT. In this article, we introduce the SAG IoT network paradigm, including its composition and network architecture. Network slicing, the core 5G technology, will be applied to the SAG IoT network. The wide use of mmWave and UAVs is to produce many new scenarios, and it is necessary to study the effects of UAVs on mmWave channels in these new scenarios. In addition, mmWave imaging technology can provide a basis for this research. Machine learning is an important new technology that can be widely used in the SAG IoT. Simulation and measurement are both important means of evaluating the performance of communication systems. To cope with the emerging new applications and technologies, a cloud-based modular simulation system is introduced for 5G and future IoTs; this system is characterized by high efficiency, flexible configuration and high precision.

In this letter, a simplified successive cancellation decoding algorithm of polar codes is proposed, where a hard decision rule is adopted by exploiting the prior information in frozen bits for better decoding performance. Considering the difference of the node reliability, a hypothesis-testing-based strategy is designed to select reliable unstructured nodes for hard decision. Then the reliable nodes are decoded by the proposed hard decision method without serial recursion, which reduces the decoding latency. Simulation results show the advantages of the proposed method in terms of the decoding latency and error-correction ability compared with the existing methods.

This paper investigates the impact of physical layer secrecy on the performance of a unified non-orthogonal multiple access (NOMA) framework, where both external and internal eavesdropping scenarios are examined. The spatial locations of legitimate users (LUs) and eavesdroppers are modeled by invoking stochastic geometry. To characterize the security performance, new exact and asymptotic expressions of secrecy outage probability (SOP) are derived for both code-domain NOMA (CD-NOMA) and power-domain NOMA (PD-NOMA), in which imperfect successive interference cancellation (ipSIC) and perfect SIC (pSIC) are taken into account. For the external eavesdropping scenario, the secrecy diversity orders by a pair of LUs (the n-th user and m-th user) for CD/PD-NOMA are obtained. Analytical results make known that the diversity orders of the $n$-th user with ipSIC/pSIC for CD-NOMA and PD-NOMA are equal to zero/K and zero/one, respectively. The diversity orders of the m-th user are equal to K/one for CD/PD-NOMA. For the internal eavesdropping scenario, we examine the analysis of secrecy diversity order and observe that the m-th user to wiretap the n-th user with ipSIC/pSIC for CD-NOMA and PD-NOMA provide the diversity orders of zero/K and zero/one, respectively, which is consistent with external eavesdropping scenario. Numerical results are present to confirm the accuracy of the analytical results developed and show that: i) The secrecy outage behavior of the $n$-th user is superior to that of the m-th user; ii) By increasing the number of subcarriers, CD-NOMA is capable of achieving a larger secrecy diversity gain compared to PD-NOMA., Comment: 15 pages,10 figures

Deep learning method for 6D object pose estimation based on RGB image and depth (RGB-D) has been successfully applied to robot grasping. The fusion of RGB and depth is one of the most important difficulties. Previous works on the fusion of these two features are mostly concatenated together without considering the different contributions of the two types of features to pose estimation. We propose a selective embedding with gated fusion structure called SEGate, which can adjust the weights of RGB and depth features adaptively. Furthermore, we aggregate the local features of point clouds according to the distance between them. More specifically, the close point clouds contribute a lot to local features, while the distant point clouds contribute a little. Experiments show that our approach achieves the state-of-art performance in both LineMOD and YCB-Video datasets. Meanwhile, our approach is more robust to the pose estimation of occluded objects.

The Internet-of-Things (IoT) will significantly change both industrial manufacturing and our daily lives. Data collection and three-dimensional (3D) positioning of IoT devices are two indispensable services of such networks. However, in conventional networks, only terrestrial base stations (BSs) areused to provide these two services. On the one hand, this leads to high energy consumption for devices transmitting at cell edges. On the other hand, terrestrial BSs are relatively close in height, resulting in poor performance of device positioning in elevation. Due to their high maneuverability and flexible deployment, unmanned aerial vehicles (UAVs) could be a promising technologyto overcome the above shortcomings. In this paper, we propose a novel UAV-assisted IoT network, in which a low-altitude UAV platform is employed as both a mobile data collector and an aerial anchor node to assist terrestrial BSs in data collection and device positioning. We aim to minimize the maximum energy consumption of all devices by jointly optimizing the UAV trajectory and devices’ transmission schedule over time, while ensuring the reliability of data collection and required 3D positioning performance. This formulation is a mixed-integernon-convex optimization problem, and an efficient differential evolution (DE) based method is proposed for solving it. Numerical results demonstrate that the proposed network and optimization method achieve significant performance gains in both energy efficient data collection and 3D device positioning, as compared with a conventional terrestrial IoT network.

Simultaneously transmitting/refracting and reflecting reconfigurable intelligent surface (STAR-RIS) has been introduced to achieve full coverage area. This paper investigate the performance of STAR-RIS assisted non-orthogonal multiple access (NOMA) networks over Rician fading channels, where the incidence signals sent by base station are reflected and transmitted to the nearby user and distant user, respectively. To evaluate the performance of STAR-RIS-NOMA networks, we derive new approximate expressions of outage probability and ergodic rate for a pair of users, in which the imperfect successive interference cancellation (ipSIC) and perfect SIC (pSIC) schemes are taken into consideration. Based on the asymptotic expressions, the diversity orders of the nearby user with ipSIC/pSIC and distant user are achieved carefully. The high signal-to-noise ratio slopes of ergodic rates for nearby user with pSIC and distant user are equal to $one$ and $zero$, respectively. In addition, the system throughput of STAR-RIS-NOMA is discussed in delay-limited and delay-tolerant modes. Simulation results are provided to verify the accuracy of the theoretical analyses and demonstrate that: 1) The outage probability of STAR-RIS-NOMA outperforms that of STAR-RIS assisted orthogonal multiple access (OMA) and conventional cooperative communication systems; 2) With the increasing of reflecting elements $K$ and Rician factor $\kappa $, the STAR-RIS-NOMA networks are capable of attaining the enhanced performance; and 3) The ergodic rates of STAR-RIS-NOMA are superior to that of STAR-RIS-OMA., Comment: 16 pages, 12 figures

In isolated regions, utilizing the unmanned aerial vehicle (UAV) as an aerial anchor node is a promising technique to enable location awareness of ground terminals (GTs). In this letter, considering a UAV swarm-enabled localization for a group of distributed GTs, we aim to minimize the maximumCramer-Rao lower bound (CRLB) for position estimates with anchor uncertainty. Then, an efficient differential evolution (DE)-based method is proposed to find a sub-optimal solution. In particular, the rigidity of the UAV swarm is recognized as a critical constraint in the problem formulation to provide a unique swarm coordinate configuration and to maintain a prescribed flight formation. A gradient-based local optimization for rigidity is then proposed and embedded in the DE algorithm. Numerical results demonstrate that our proposed designs can reach better performance in localization accuracy while ensuring the rigidity of the UAV swarm, as compared with a random approach.

As the cost and technical difficulty of jamming devices continue to decrease, jamming has become one of the major threats to positioning service. Unfortunately, most conventional wireless positioning technologies are vulnerable to jamming attacks due to inherent shortcomings like weak signal strength and unfavorable anchor geometry. Thanks to their high operational flexibility, unmanned aerial vehicles (UAVs) could be a promising solution to the above challenges. Therefore, in this article, we propose a UAV-assisted anti-jamming positioning system, in which multiple UAVs first utilize time-difference-of-arrival (TDoA) measurements from ground reference stations and double-response two-way ranging (DR-TWR) measurements from UAV-to-UAV links to perform self-localization as well as clock synchronization, and then act as anchor nodes to provide TDoA positioning service for ground users in the presence of jamming. To evaluate the feasibility and performance of the proposed system, we first derive the Cramer-Rao lower bound (CRLB) of UAV self-localization. Then, the impacts of UAV position uncertainty and synchronization errors caused by jamming on positioning service are modeled, and the theoretical root-mean-square error (RMSE) of user position estimate is further derived. Numerical results demonstrate that the proposed system is a promising alternative to existing positioning systems when their services are disrupted by jamming. The most notable advantage of the proposed system is that it is fully compatible with existing user equipment terminals and positioning methods.

In this letter, we propose an efficient blind detection scheme of polar codes compliant with the 5G standard. In the proposed scheme, the lengths and locations of the downlink-control information (DCI) are detected separately in two stages. Before entering the second stage that is dedicated to the detection of locations, at least half of the candidates are eliminated through the detection of the DCI lengths in the first stage. Specifically, a novel detection metric is used in the first stage to detect the lengths of the DCI and further exclude invalid candidates. Simulation results show that the proposed scheme outperforms the existing schemes in terms of detection accuracy and latency.

Compared with the non-prediction algorithm, the prediction-based symbol flipping decoding algorithm of non-binary, low-density, parity-check codes over GF( $q$ ) can significantly improve the error performance. To escape from undesirable local maximum, this paper proposes a self-adjustment strategy to redesign the flipping metric. Combined with the prediction mechanism, our proposed algorithm provides a good tradeoff between the complexity and the error correction performance. Simulation results for the additive white Gaussian noise channel and the binary symmetric channel verify the effectiveness of the proposed algorithm.

In this study, a segment-selected mapping (SSLM) scheme is proposed to reduce the peak-to-average power ratio (PAPR) of the filter bank multi-carrier (FBMC) signal in satellite communications. The standard selected mapping (SLM) scheme is suggested to reduce the PAPR of the traditional multi-carrier signals. However, it cannot be directly applied to the FBMC signal due to the overlapping nature of the FBMC signal. In the proposed method, each symbol in the FBMC signal is divided into several segments to solve the overlapping problem. Each segment is regarded as an independent unit, and rotates a suitable phase to minimise the PAPR in the frequency domain. The authors simulated the FBMC signal in the satellite communications by introducing the influence of the high power amplifiers and the Doppler frequency shift. The simulation results from complementary cumulative distribution function of the PAPR show that the proposed scheme can achieve about 0.3 dB PAPR performance compared to the existing dispersive SLM scheme, with lower complexity.

In order to facilitate reliable and efficient data transmission in time-varying environments, a block dynamic decoding algorithm for spinal codes is proposed in this paper. First, the code tree is divided into several decoding units to scatter the decoding complexity. According to the results of the last decoding, the decoder will modify the scope of nodes accessed with dynamic parameters, which improves the execution efficiency of the algorithm. Furthermore, the complexity and related parameters of the algorithm are analyzed and verified by some simulations. The results show that the proposed algorithm has the ability to enhance the bandwidth efficiency (or rate ) performance, and to reduce both complexity and frame error rate.

Impulsive noises severely degrade the performance of a communication system. This study deals with the performance of the soft decision decoder for low-density parity-check codes over impulsive noise channels. To simplify the calculation of log likelihood ratio (LLR) and cooperate with the soft decision decoder, a new non-linear approximation named inverse demapper of LLR over the impulsive noise is proposed. Without carrying the noise statistics, the inverse demapper performs close to the optimal demapper. In addition, the density evolution is employed to obtain optimal parameters of the inverse demapper. Then, the extrinsic information transfer chart analysis and simulation results are presented to verify the effectiveness of the authors' proposed demapper.

Successive cancellation list (SCL) decoder provides a good trade-off between error-correcting performance and complexity. To simplify the decoding algorithm with negligible performance loss, a path splitting selecting (PSS) strategy is proposed based on analyzing the reason why the SCL decoder fails. On the basis of the PSS strategy, two schemes are proposed to find erroneous information bits more accurately. Simulation results show that the proposed strategy based on the search set and decision function is a good solution to reduce the complexity with almost no performance loss.

Successive cancellation-based decoding algorithm and corresponding polarization theorems for polar codes over channels with deletions have been proposed recently. In that decoding algorithm, each node in the conventional successive cancellation decoding trellis is divided into many different scenarios according to different deletion patterns. The number of scenarios increases with the square of the number of deletion errors $d$ which results in high decoding complexity. In this paper, to reduce the decoding complexity, we propose the scenario-simplified successive cancellation decoding algorithm for the polar codes over the deletion channel. In the proposed decoding algorithm, we use exact upper and lower bounds to identify the feasible scenarios of each node in the decoding trellis and avoid calculating the impossible scenarios. And by rearranging the scenario index table, the operations of calculating indices of scenarios can be simplified. We also investigate the joint-weight for each scenario. By setting a threshold $\tau $ to prune the scenarios with low joint-weight probabilities, the complexity can be reduced further. For polar codes of length $N=512$ and $d = 10$ , we can reduce 42.5% stored scenarios and 46.8% computed scenarios when $\tau = 10^{-5}$ with a negligible performance loss.

Recently, deep learning has been widely applied in binocular stereo matching for depth acquisition, which has led to an immense increase of accuracy. However, little attention has been paid to the structured light field. In this letter, a network for structured light is proposed to extract effective matching features for depth acquisition. The proposed network promotes the Siamese network by considering receptive fields of different scales and assigning proper weights to the corresponding features, which is achieved by combining pyramid-pooling structure with the squeeze-and-excitation network into the Siamese network for feature extraction and weight calculations, respectively. For network training and testing, a structured-light dataset with amended ground truths is generated by projecting a random pattern into the existing binocular stereo dataset. Experiments demonstrate that the proposed network is capable of real-time depth acquisition, and it provides superior depth maps using structured light.

Radio Frequency (RF) technology represents a high-precision relative navigation solution that has significant potential for application to earth-orbiting satellites. In precision applications, multipath errors dominate the total error because observables, which are used to estimate carrier-phase integer ambiguity, are not always subject to a Gaussian distribution when dual-frequency ambiguity estimation methods are used in the presence of multipath. As it has been shown that ranging observables obey a Gaussian mixture distribution, this study proposes improvements to the accuracy of estimation based on multipath mitigation founded on the Gaussian mixture model. To this end, such a model is created for integer ambiguity resolution in the presence of multipath, using which the theoretical error in dual-frequency ambiguity estimation is derived. Expectation Maximization (EM), which aids dual-frequency ambiguity estimation, is subsequently proposed to reduce the effect of multipath errors. Finally, two experimental scenarios are implemented to test the performance of the proposed method. The results show that EM-aided dual-frequency ambiguity estimation reduces the range error to approximately 20% in comparison with simple dual-frequency ambiguity estimation. Therefore the proposed technique is effective for multipath mitigation in RF relative measurement. Keywords: Dual-frequency ambiguity estimation, Gaussian mixture model, Multipath mitigation, RF relative measurement, Satellite navigation

Skyrmion racetrack memory (Sk-RM) is a new storage technology in which skyrmions are used to represent data bits to provide high storage density. During the reading procedure, the skyrmion is driven by a current and sensed by a fixed read head. However, synchronization errors may happen if the skyrmion does not pass the read head on time. In this paper, a polar coding scheme is proposed to correct the synchronization errors in the Sk-RM. Firstly, we build two error correction models for the reading operation of Sk-RM. By connecting polar codes with the marker codes, the number of deletion errors can be determined. We also redesign the decoding algorithm to recover the information bits from the readout sequence, where a tighter bound of the segmented deletion errors is derived and a novel parity check strategy is designed for better decoding performance. Simulation results show that the proposed coding scheme can efficiently improve the decoding performance.

In this letter, the design of a low-complexity receiver which is robust to orthogonal frequency division multiplex (OFDM) signals with nonlinear clipping distortion is investigated. An approximation of the probability density function (PDF) of the received signals with nonlinear distortion is derived. Based on iteration-dependent approximation of the mean and variance of the clipped signal, a fast generalized approximate message passing (F-GAMP) algorithm is proposed. The proposed F-GAMP achieves bit error rate (BER) performance gain over the conventional OFDM receiver due to the F-GAMP's ability to recover inter-carrier interference (ICI) and exploit frequency diversity induced by the spectral growth of a clipped signal. With almost the same BER performance as the conventional GAMP algorithm, the proposed F-GAMP reduces the computational complexity by a factor of about 3000! This work was supported by Beijing Natural Science Foundation (L202003); Chinese Scholarship Council Grant (201806020012); and National Natural Science Foundation of China (No. 31700479).

Satellite networks, as a supplement to terrestrial networks, can provide effective computing services for Internet of Things (IoT) users in remote areas. Due to the resource limitation of satellites, such as in computing, storage, and energy, a computation task from a IoT user can be divided into several parts and cooperatively accomplished by multiple satellites to improve the overall operational efficiency of satellite networks. Network function virtualization (NFV) is viewed as a new paradigm in allocating network resources on-demand. Satellite edge computing combined with the NFV technology is becoming an emerging topic. In this paper, we propose a potential game approach for virtual network function (VNF) placement in satellite edge computing. The VNF placement problem aims to maximize the number of allocated IoT users, while minimizing the overall deployment cost. We formulate the VNF placement problem with maximum network payoff as a potential game and analyze the problem by a game-theoretical approach. We implement a decentralized resource allocation algorithm based on a potential game (PGRA) to tackle the VNF placement problem by finding a Nash equilibrium. Finally, we conduct the experiments to evaluate the performance of the proposed PGRA algorithm. The simulation results show that the proposed PGRA algorithm can effectively address the VNF placement problem in satellite edge computing.

Environmental backscattering technology is a new technology for creating wireless communication in the Internet of Things by reflecting radio signals that already exist in the environment, such as TV broadcast signals, mobile phone signals, WI-FI, etc., and has the functions of energy harvesting and signal transmission. This paper designs a new type of dualantenna structure system for the problem of low reliability and insufficient reading distance for traditional device identification in the power IoT scenario based on unified identification. The structure proposed in this paper adopts the omnidirectional reception and directional transmission methods to achieve the purpose of comprehensive coverage of the power IoT application scenarios, which has the advantages of clean, low-carbon, efficient and practical.

Massive Multiple-Input Multiple-Output (m-MIMO) is a promising technology for improving the capacity of fifth generation (5G) wireless networks. However, m-MiMo suffers from the cost and complexity of the radio frequency (RF) chain. One solution to solve this problem is the method of antenna selection. However, this method requires information on the channel state (CSI) in order to select the most efficient subset, which is impossible in the presence of the pilot contamination. In addition, the exhaustive search method, used in conventional MiMo for selecting a subset of antennas, is ineffective for the massive MIMO system because it adds complexity to the processing and requires high energy consumption. For this purpose, a model using a water-filling algorithm and Low-Density Parity-Check (LDPC) decoded symbols is proposed in this article. This method exploits the characteristic of the Physical Layer to address the problem. It does not require supplementary chain, and it does not use pilot symbols to estimate the channel, which makes this proposed model frugal in term of energy consumption and processing resources comparing to the exhaustive method. Simulation results show that the proposed solution attains the same optimal values when the Exhaustive search method is used.

In this paper, we address the problem of placing the unmanned aerial vehicles (UAVs) in an area covered by a congested or damaged base station (BS). We aim to cover the affected users while maximizing the operator's profit. We proposed to adopt the k-means method associated with the pattern search technique to find the optimal UAVs 3D locations. We show in the simulations, that our approach achieves higher profit and a higher load balancing index among UAVs.

To achieve efficient and reliable data transmission in a communication system in time varying conditions, a downlink non-orthogonal multiple access system based on rateless codes (NOMA-RC) is proposed in this paper. The NOMA-RC system can continuously send superimposed signals to the users according to the decoding results within a limited time. After a user decodes the signals successfully, it will send an acknowledgement to the transmitter. Then the system may adjust the message to be transmitted to improve the decoding probability for the remaining users. The performance of the NOMA-RC system with delay constrains is analyzed over Nakagami-m fading channels. The simulation results show that the NOMA-RC system is capable of reducing the transmission time and improving system efficiency compared to orthogonal multiple access system based on rateless codes.

Mobility load balancing (MLB) in small cell networks transfers the load from an overloaded small cell to under-loaded neighbouring small cells by adjusting the mobility parameters. Consequently, a greater number of handovers is required. This is one of the costs for the MLB operation that may affect the network performance severely. Furthermore, frequent handovers may occur if the handed over user equipment (UE) is moving fast across the small cell network. The traditional MLB considers only the edge-UEs (Edge-UEs) for the offloading process. In this paper, we presented a Utility-based Mobility Load Balancing algorithm with Handover minimization (UMLB-HO) by considering not only the edge-UEs but also the non-edge-UEs for overloaded small cells. The overloaded cells determine the fast-moving non-edge-UEs and transfer them to the underloaded Macrocell. Moreover, the small cells determine the fast-, slow or very slow-moving edge-UEs during the MLB and transfer them to either under loaded neighbour small cell or Macrocell. Four terms are defined to determine whether the UE is fast-, slow or very slow-moving to determine the best handover decisions for each UE. Simulation results show that the proposed UMLB-HO algorithm has the minimum number of handovers for a minimum standard deviation with an enhanced level of throughput.

Wireless Sensor Networks (WSNs) are attracted great attention in the past decade due to the unlimited number of applications they support. However, security has always been a serious concern for these networks due to the insecure communication links they exploit. In order to mitigate the possible security threats, sophisticated key management schemes must be employed to ensure the generating, distributing and revocation of the cryptographic keys that are needed to implement variety of security measures. In this paper, we propose a novel decentralized key management scheme for hierarchical grid organized WSNs. The main goal of our scheme is to reduce the total number of cryptographic keys stored in sensor nodes while maintaining the desired network connectivity. The performance analysis shows the efficiency of the proposed protocol in terms of communication overhead, storage cost and network connectivity.