Carrier Aggregation (CA) is investigated to address spectrum scarcity in 6G Integrated Sensing and Communication (ISAC) systems. However, inter-band phase offsets impose high complexity on achieving coherent gain across multiple bands. Furthermore, typical CA focuses on multi-frequency-band expansion, while multi-time-duration expansion has received limited attention. To address the above issues, this paper proposes a novel high-accuracy Multi-Band Joint Carrier Phase Sensing (MB-JCPS) framework that utilizes carrier phase information from multiple time-frequency bands to improve range and velocity sensing accuracy. A carrier phase extraction method is introduced based on oversampling Range-Doppler phase spectrum. The impact of non-ideal factors—including time, frequency, and phase offsets, as well as phase noise—on carrier phase is modelled, analyzed, and eliminated via an inter-path difference method. A Real-valued Multi-Carrier Ambiguity Resolution (RMCAR) algorithm is designed to resolve integer ambiguities in the range and velocity carrier phase models. The time-frequency selection problem is formulated as a constrained Rayleigh-quotient optimization problem and solved via successive convex approximation. The Cramér-Rao lower bound (CRLB) for the range and velocity estimation under phase offsets is derived, demonstrating the performance gain of CA in both frequency and time domains. Numerical simulations show that the proposed method achieves higher sensing accuracy with lower complexity than benchmark algorithms in 3GPP Uma-AV environments.

Static deep neural network (DNN) watermarking techniques typically employ irreversible methods to embed watermarks into the DNN model weights. However, this approach causes permanent damage to the watermarked model and fails to meet the requirements for integrity authentication. Reversible data hiding (RDH) methods offer a potential solution, but existing approaches suffer from limitations in usability, capacity, and fidelity, hindering their practical adoption. In this paper, we propose a secure static DNN watermarking scheme called Secure Difference Contraction (SDC). Our scheme utilizes a one-dimensional quantizer for watermark embedding and employs dithering to ensure key-dependent security, i.e., the watermark cannot be correctly extracted without the secret key used during embedding. Additionally, we design two schemes to address the challenges of integrity protection and legitimate authentication for DNNs. Simulation results on training loss and classification accuracy demonstrate the feasibility and effectiveness of our proposed methods, highlighting their advantages in capacity and fidelity over existing techniques.

In response to emerging regulations on the “right to be forgotten”, federated unlearning (FU) has been proposed to ensure privacy compliance by efficiently eliminating the influence of specific data from federated learning (FL) models. However, existing FU studies primarily focus on improving unlearning efficiency, with little attention given to the potential privacy risks introduced by FU itself. To bridge this research gap, we propose a novel federated unlearning inversion attack (FUIA) to expose potential privacy leakage in FU. This work represents the first systematic study on the privacy vulnerabilities inherent in FU. FUIA can apply to three major FU scenarios: sample unlearning, client unlearning, and class unlearning, demonstrating broad applicability and threat potential. Specifically, the server, acting as an honest-but-curious attacker, continuously records model parameter changes throughout the unlearning process and analyzes the differences before and after unlearning to infer the gradient information of forgotten data, enabling the reconstruction of its features or labels. FUIA directly undermines the goal of FU to eliminate the influence of specific data, exploiting vulnerabilities in the FU process to reconstruct forgotten data, thereby revealing flaws in privacy protection. Moreover, we explore two potential defense strategies that introduce a trade-off between privacy protection and model performance. Extensive experiments on multiple benchmark datasets and various FU methods demonstrate that FUIA effectively reveals private information of forgotten data.

This paper studies asynchronous repetition slotted ALOHA (a-ReSA) for massive random access. Each user’s packet is repeated and allocated to randomly selected slots. Then, the users transmit simultaneously and arrive at the base station (BS) receiver with different delays, i.e., without user-synchronization. The BS receiver carries out an over-sampling-based operation, yielding an over-sampled signal space. We show that owing to user-asynchrony, the channel state information (CSI) can be acquired even when some users utilize an identical pilot sequence, i.e., pilot collision happens. Further, we develop an iterative soft cancellation detection and decoding that exploits the interference structure of the over-sampled signal for powerful multi-user decoding. Afterwards, packet cancellation for a-ReSA is utilized to solve packet collision. To characterize the performance of a-ReSA, we analyze the achievable channel parameter region (ACPR) and outage probability. Our analysis shows that the ACPR of the user-asynchronous scenario is considerably larger than that of user-synchronous scenarios. Further, we present an asymptotic analysis of the throughput of a-ReSA system. It is demonstrated that the normalized throughput of a-ReSA exceeds that of traditional ReSA by about 20% ~ 80%. We also show that the a-ReSA scheme is more robust than traditional ReSA in imperfect CSI scenarios. Numerical results are verified to agree with the analyzed results.

This paper studies asynchronous repetition slotted ALOHA (a-RSA) for massive random access. Each user’s packet is repeated and allocated to randomly selected slots. Then, the users transmit simultaneously and arrive at the base station (BS) receiver with different delays, i.e., without user-synchronization. The BS receiver carries out an over-sampling-based operation, yielding an over-sampled signal space. We develop an iterative soft cancellation detection and decoding that exploits the interference structure of the over-sampled signal for powerful multi-user decoding. Afterwards, packet cancellation for a-RSA is utilized to solve packet collision. To characterize the performance of a-RSA, we analyze the achievable channel parameter region (ACPR) and outage probability. Further, we present an asymptotic analysis of the throughput of a-RSA system. It is demonstrated that the normalized throughput of a-RSA exceeds that of traditional RSA by about 20% ~ 30%.

Unmanned aerial vehicles (UAVs), serving as aerial base stations (BSs), are able to provide not only the communication services for data collection from sensor nodes (SNs), but also the sensing capabilities for localizing SNs in wireless sensor networks (WSNs). In this article, we propose a novel UAV-assisted WSN framework with integrated sensing and communications (ISAC) functionalities, in which a UAV platform acts as both a mobile data collector and an aerial anchor node to assist WSN in reducing SNs' energy consumption and enhancing SNs' 3D positioning accuracy considering SNs' distributed altitudes. The objective is to minimize the energy consumption across all SNs by jointly optimizing the UAV's 3D trajectory and UAV task allocation schedule over time, while meeting the required SNs' 3D positioning accuracy. This task is formulated as a mixed-integer non-convex optimization problem, and an efficient differential evolution (DE) based iterative optimization method is proposed to solve it. Numerical results indicate that the proposed framework and optimization method achieve significant improvements in both energy-efficient data collection and highaccuracy 3D positioning, comparing to that of UAV 2D trajectory designs or conventional terrestrial sensor networks.

It was shown that by operating over the integer linear combinations (ILCs) of$K$users' messages, lattice-code based multiple-access (LCMA) offers increased system load and improved error-rate performance over non-lattice based schemes. This paper advances the existing LCMA system by designing a new multi-stage LCMA receiver. In each stage, the receiver attempts to compute as many ILCs as possible. Then, from these ILCs, generalized matrix inversion (GMI) is introduced to recover a subset of$K$users' messages. These recovered messages are cancelled from the original received signal, yielding an equivalent system with less users for the next stage. Such operation continues successively until all$K$users' messages are recovered. System loads of up to 400% and near capacity performance are demonstrated for various MA models.

Abstract Depth estimation from stereo images is an important task in computer vision. Despite of the great contributions that are made in this field, most matching‐based methods still face the limitations brought by a pre‐set‐fixed disparity range. Stereo matching is reconsidered using a specially designed dual‐matching method with a cross‐attention mechanism, which liberates the algorithm from manually pre‐specified disparity ranges and the performance is guaranteed without re‐training when the camera rig varies. Moreover, to tackle the mismatches on edges and details, an exquisite module is designed based on left‐right consistency, which further refines the estimated disparity map. The efficient multi‐scale aggregation is done with both 2D and 3D convolutional layers and the proposed method is proved to be competitive and effective by experiments conducted under popular benchmarks.

Wireless communication technology is one of the key technologies to achieve the key requirements of high reliability, low latency and mass connectivity in the Internet of Things (IoT). To meet these requirements, we apply the reconfigurable intelligent surface (RIS) in the wireless IoT networks. Specifically, we study a scheme that combines the joint beamforming design with RIS selection strategy for multi-RIS aided multi-cell wireless networks. In the joint beamforming design, we propose the control mode of “each base station (BS) controls its local RISs” for non-free propagation scenarios in wireless IoT networks, such as smart city and intelligent transportation system. Specifically, we give the details of algorithm for this control mode, and prove that it can achieve the performance comparable to the “all BSs jointly control all RISs” mode in the non-free propagation scenario, with lower algorithm complexity and control signaling overhead. Moreover, based on the joint beamforming design, we further propose a genetic algorithm-based RIS selection strategy to maximize the sum-rate under the constraint of limited RIS deployment. Finally, simulation results show that the proposed RIS selection strategy is effective and outperforms the existing schemes.

Low-earth orbit (LEO) satellite Internet of Things (IoT) has emerged as a promising solution to address the limitations of terrestrial IoT by providing global coverage and seamless connectivity. Among the various techniques enhancing LEO satellite IoT, beam hopping (BH) stands out as an efficient approach that dynamically adjusts beam illumination to match the varying traffic demands of diverse IoT devices. This flexibility enables optimal utilization of limited on-board resources. However, while BH allows adaptive beam illumination planning, it can also introduce severe inter-beam interference, particularly when adjacent beams are simultaneously activated. To address this challenge, we propose a novel rate-splitting multiple access (RSMA)-enabled cluster-based beam hopping (CBH) LEO satellite IoT system. By leveraging RSMA, the proposed framework supports large-scale IoT devices access, and mitigates inter-beam interference introduced by CBH. Within this framework, we introduce a metric—the ratio of offered capacity to traffic demand (ROCD)—to quantify how well the required traffic sum rate aligns with the achievable sum rate for each beam. We then focus on jointly optimizing the precoding vector, common rate allocation, and CBH pattern design to maximize the worst-case ROCD among beams. To solve this problem efficiently, we decompose the original problem into three sub-problems and propose a two-stage algorithm. Numerical results demonstrate that our proposed scheme improves the minimum satisfaction rate by 14.10% and 39.59% compared to the non-orthogonal multiple access and space-division multiple access baselines, achieving effective interference mitigation.

In recent years, space–air–ground integrated networks (SAGIN) have attracted considerable attention for their potential to support various applications in future 6G systems. However, the diversity of tasks generated by different network participants and the heterogeneous distribution of resources in SAGIN pose significant challenges in matching task demands with available resources. To tackle this issue, we propose a multiagent deep deterministic policy gradient (MADDPG) algorithm with centralized training and decentralized execution (CTDE) that jointly addresses offloading decisions and heterogeneous resource allocation. Tasks are categorized into three distinct types with task priorities considered. Low Earth orbit (LEO) satellites equipped with edge computing capabilities are modeled as agents. Each agent makes decisions regarding heterogeneous resource allocation and offloading based on its own observations, while global action and state information are used to update deep neural networks. The CTDE-MADDPG algorithm enables low-latency distributed decision making in complex networks without the need for time-consuming state synchronization and centralized decision making. The simulation results demonstrate that the proposed scheme effectively minimizes energy consumption while ensuring tasks are completed within latency and resource constraints, achieving superior performance compared to the baseline algorithms.

Cooperative positioning (CP) is able to improve the vehicular positioning performance by introducing the data of multiple vehicles into the position estimation. However, CP methods are vulnerable to measurement outliers in dense urban areas. The existing outlier-resistant CP methods are easy to trap in local optimum and may wrongly reject the outliers when the ratio of outliers to inliers is relatively high. To deal with this problem, a factor graph optimization (FGO) based CP method using Graduated Non-Convexity (GNC) Welsch cost is proposed in this paper. The state-of-art FGO algorithm is used to integrate multi-node and multi-epoch measurements including the Global Navigation Satellite Systems (GNSS) pseudoranges, inter-epoch baselines estimated by GNSS time-differenced carrier phase (TDCP), and inter-vehicle ranging measurements in a centralized framework. The least-square cost in traditional FGO is replaced with the GNC-based Welsch cost so as to enhance the robustness of the proposed method to any kind of outliers in our CP system. The use of GNC can reduce the risk of local optimum by gradually increasing the non-convexity of the Welsch cost. The proposed method can de-weight the outliers correctly even if a large number of outliers exist. The experimental results show the superiority of the proposed method over the existing CP methods in resisting multiple outliers.

This paper investigates the performance of simultaneously transmitting and reflecting surface (STARS) assisted semi-grant-free non-orthogonal multiple access network with randomly distributed users. By deploying STARS, the transmit signals of grant-based user (GBU) and grant-free users (GFUs) can be exquisitely adjusted to reduce interference. We propose a maximum channel scheduling (MCS) protocol that allows a GFU to access GBU’s channel with the assistance of STARS. In particular, the impacts of perfect/imperfect successive interference cancellation (pSIC/ipSIC) on MCS protocol are taken into account. To characterize the performance of STARS aided MCS (STARS-MCS) network, we derive the expressions of outage probability for GBU and GFU with pSIC/ipSIC. By applying convolution theorem and Laplace transform, the asymptotic expressions of outage probability and diversity orders for GBU and GFU are attained. We further design a STARS-based power control (SPC) strategy to eliminate the outage probability error floor and improve the outage performance. Numerical results show that: 1) The performance of STARS-MCS outperforms the existing benchmarks in terms of outage probability and system throughput; 2) The SPC strategy can effectively improve the performance of the STARS-MCS network and eliminate the outage probability error floor at high signal-to-noise ratios; and 3) By adjusting reflection and transmission coefficients of STARS, the outage performance of GBU and GFU can be greatly enhanced.

It was shown that by operating over the integer linear combinations (ILCs) ofKusers’ messages, lattice-code based multiple-access (LCMA) offers increased system load and improved error-rate performance over non-lattice based schemes. This paper advances the existing LCMA system in two aspects. 1) We formulate the spreading sequences optimization problem based on the achievable symmetric rate of LCMA. To solve this problem, we develop three new methods, namely target-switching steepest descent (TS-SD), particle swarm (PS) optimization, and Hadamard concatenation (HC). The TS-SD method always targets on the ILC with the lowest computation rate in the SD process. The PS method treats the spreading matrix as a particle and iteratively updates a swamp of particles’ positions and velocities, based on the relative distance to the best position that are currently known. To further reduce the complexity, we first obtain a solution in a lower dimension, and then apply Hadamard concatenation (HC) which yields a solution for the required dimension. The PS and HC methods are shown to approach the capacity of the MA channel. 2) We put forth a new multi-stage LCMA receiver. In each stage, the receiver attempts to compute as many ILCs as possible. Then, from these ILCs, generalized matrix inversion (GMI) is introduced to recover a subset ofKusers’ messages. These recovered messages are cancelled from the original received signal, yielding an equivalent system with less users for the next stage. Such operation continues successively until allKusers’ messages are recovered. System loads of up to 400% and near capacity performance are demonstrated for various MA models.

Despite the enhanced spectral efficiency brought by the integrated radar and communication technique, it poses significant risks to communication security when confronted with malicious radar targets. To address this issue, a reconfigurable intelligent surface (RIS)-aided transmission scheme is proposed to improve secure communication in two systems, i.e., the radar and communication co-existing (RCCE) system, where a single transmitter is utilized for both radar sensing and communication, and the dual-functional radar and communication (DFRC) system. At the design stage, optimization problems are formulated to maximize the secrecy rate while satisfying the radar detection constraint via joint active beamforming at the base station and passive beamforming of RIS in both systems. Particularly, a zero-forcing-based block coordinate descent (BCD) algorithm is developed for the RCCE system. Besides, the Dinkelbach method combined with semidefinite relaxation is employed for the DFRC system, and to further reduce the computational complexity, a Riemannian conjugate gradient-based alternating optimization algorithm is proposed. Moreover, the RIS-aided robust secure communication in the DFRC system is investigated by considering the eavesdropper’s imperfect channel state information (CSI), where a bounded uncertainty model is adopted to capture the angle error and fading channel error of the eavesdropper, and a tractable bound for their joint uncertainty is derived. Simulation results confirm the effectiveness of the developed RIS-aided transmission scheme to improve the secrecy rate even with the eavesdropper’s imperfect CSI, and comparisons between both systems reveal that the RCCE system can provide a higher secrecy rate than the DFRC system.

Abstract In the context of certain specific digital communication systems, where there are limitations such as spectral resources and energy availability, continuous phase modulation (CPM) emerges as an appealing choice among various modulation methods. Among CPM signals, multi‐h CPM is particularly noteworthy for its ability to address these constraints within the realm of single‐carrier and constant‐envelope waveforms. At the physical layer, the design of a multi‐h CPM receiver necessitates the efficient implementation of timing and frequency synchronization algorithm within a high dynamic environment. So this paper presents an innovative approach for achieving timing and frequency synchronization. To rectify timing offset and mitigate the adverse effects of noise in received signals, a re‐configurable local filter generation method is integrated into the timing synchronization algorithm. Simultaneously, an enhanced least mean square adaptive filter algorithm is applied to address frequency synchronization. A comprehensive series of simulations rigorously evaluates the outcomes of proposed novel synchronization methodology. These analyses demonstrate a notable proximity between the synchronization errors of proposed algorithm in this paper and the performance benchmark set by the modified Cramer–Rao bound. The proposed synchronization technology also exhibits the capability to substantially reduce the bit error rate, thereby effectively enhancing demodulation performance in multi‐h CPM receivers.

With the rapid development of large low earth orbit (LEO) satellite constellations, satellite edge computing is an emerging topic to provide computing services for Internet of Things (IoT) users, which are not in the coverage of terrestrial networks. For computation offloading in satellite edge computing, it is still challenging to allocate the network resources on-demand for IoT users to improve service experience while reducing energy consumption, since user tasks may be offloaded between different satellites by inter-satellite links (ISLs). In this paper, we study the joint optimization problem of computation offloading and resource allocation in cooperative satellite edge computing. Then, a hierarchical dynamic resource allocation (HDRA) algorithm for computation offloading is proposed by introducing breadth first search (BFS) and greedy to tackle the problem, the aim is to minimize service delay and energy consumption jointly. We conduct the experiments to evaluate the performance of the proposed HDRA algorithm, compared with two baselines of BFS-PSO and Gurobi. Experimental results show that the proposed HDRA algorithm can address the formulated problem effectively in satellite edge computing and obtain the results of computation offloading and resource allocation in a low running time.

Model inversion attacks (MIAs) aim to recover private data from inaccessible training sets of deep learning models, posing a privacy threat. MIAs primarily focus on the white-box scenario where attackers have full access to the model’s structure and parameters. However, practical applications are usually in black-box scenarios or label-only scenarios, i.e., the attackers can only obtain the output confidence vectors or labels by accessing the model. Therefore, the attack models in existing MIAs are difficult to effectively train with the knowledge of the target model, resulting in sub-optimal attacks. To the best of our knowledge, we pioneer the research of a powerful and practical attack model in the label-only scenario. In this paper, we develop a novel MIA method, leveraging a conditional diffusion model (CDM) to recover representative samples under the target label from the training set. Two techniques are introduced: selecting an auxiliary dataset relevant to the target model task and using predicted labels as conditions to guide training CDM; and inputting target label, pre-defined guidance strength, and random noise into the trained attack model to generate and correct multiple results for final selection. This method is evaluated using Learned Perceptual Image Patch Similarity as a new metric and as a judgment basis for deciding the values of hyper-parameters. Experimental results show that this method can generate similar and accurate samples to the target label, outperforming generators of previous approaches.

The deployment of satellite mobile edge computing (SMEC) incorporating artificial intelligence (AI) in low Earth orbit (LEO) constitutes satellite edge intelligence (SEI), which is promising to achieve autonomous processing of space missions on board driven by massive data. However, individual satellites with constrained resources and insufficient samples learn inefficiently, while the spatio-temporal constraints of large-scale LEO networks make collaborative training difficult. In this paper, a leader federated learning (FL) architecture for distributed SEI (SELFL) is proposed. By evaluating the connectivity and load of the dynamic constellation, the global and local parameters of the shared AI model are transmitted and updated continuously between the elected leader and other follower satellites based on the established inter-satellite link, which realizes efficient self-evolution of SELFL independent of the ground. Also we introduce a deep reinforcement learning-based resource allocation strategy for SELFL, which leverages the distributed proximal policy optimization (DPPO) to optimize the computing capability and transmit power of satellites for accelerating FL and reducing energy consumption. This method not only updates stably utilizing adaptive learning steps, but also improves sample efficiency with multiple parallel workers. The simulation results demonstrate the proposed SELFL optimization scheme effectively reduces the total energy consumption and training time by ensuring the AI model accuracy, and outperforms the benchmark algorithms.

In this paper, we propose a security-enhanced integrated sensing and communication (ISAC) system enabled by phase-coupled Intelligent Omni-Surfaces (IOS). The IOS, without the need for additional sensors, divides the entire space into two parts: a communication part and a sensing part. The IOS serves multiple users by providing communication services while also creating a virtual line-of-sight (LOS) link for target sensing. Moreover, the IOS is utilized to ensure physical layer security. Due to the possibility of the sensing target intercepting communication information, it is crucial to limit the information leakage rate. To achieve this, we jointly design the communication beamformers, sensing beamformer, and the IOS’s two phase-shift matrices. This joint design maximizes the sensing beam pattern gain towards the target, while meeting the requirements of the minimum signal-to-interference-plus-noise ratio (SINR) for communications and the maximum information leakage to possible malicious targets. To address this problem, we propose two types of alternative algorithms. The first type utilizes an independent phase-controlling model, while the second incorporates a coupled-phase model. Both algorithms aim to optimize the IOS’s phase-shift matrices.

Reconfigurable Intelligent Surfaces (RISs) have been considered as a promising technology for next generation communications. The existing works mainly focused on the single-connected RIS, which is always mathematically characterized as diagonal phase shift matrices, because that each element of traditional RIS separately controls its status and would not like to apply influence to any other elements. Although there exist some research on sub-array RIS, the grouping conception is based on the controlling strategy level but not on the hardware realization level. Fortunately, a new type of beyond diagonal RIS (BD-RIS) is proposed with its mathematical expression is symmetric and not limited to traditional diagonal phase shift matrices. BD-RIS model can fully cover the previous various types of RIS like: reflective RIS, refractive RIS, IOS (Intelligent Omni-Surfaces) or STAR-RIS (Simultaneously Transmitting And Reflecting RIS). In this work, a new model of BD-RIS is proposed to explain its working principle. Then, the quantization strategy for the new model BD-RIS is analyzed. Finally, the optimization algorithms for BD-RIS in single-user multi-input-multi-output (SU-MISO) systems and in multi-user multi-input-multi-output (MU-MISO) systems are proposed. Simulation results verifies the effectiveness of the the algorithms’ performance.

This paper studies a lattice-code based multiple-access (LCMA) system. In the uplink,$K$users encode their messages with the same 2m-ary ring code mapped to 2m- PAM, belonging to the ensemble of lattice codes. All users transmit simultaneously. The receiver attempts to compute$K$independent streams of integer-combinations (ICBs) of the users' messages. For this, 1) we establish and solve a new “bounded independent vectors problem” (BIVP) which identifies a near-optimal set of coefficient vectors w.r.t. the ICBs, outperforming existing LLL and HKZ lattice reduction methods; 2) we put forth new LCMA soft detection algorithms, which calculate the a posteriori probability w.r.t. the ICB over the lattice. The per-user complexity is of order less than O(K), suitable for massive access of$K$being large. The soft detection outputs are forwarded to$K$ring-code decoders to recover the messages. With our developed techniques, LCMA is shown to support a significantly higher load of users and exhibits an improved frame error rate over state-of-the-art IDMA and SCMA schemes. Such advanced functionality are achieved with just parallel processing and$K$single-user decoding operations.

This paper studies the design of beam hopping (BH) pattern for satellite communication systems with hybrid analog-digital precoding. We investigate the transmit power minimization problem under rate constraints, where the BH pattern is characterized by a matrix with binary entries. We assign weights to cell pairs based on the positional to determine the candidate set of snapshots. Based on that, we proposed to use Viterbi algorithm to identify the optimized beam hopping pattern. Numerical results verify that the proposed algorithm outperforms existing greedy algorithm based method by 2.8 dB.

Enabling a satellite network with edge computing capabilities can complement the advantages further of a single terrestrial network and provide users with a full range of computing service. Satellite edge computing is a potentially indispensable technology for future satellite-terrestrial integrated networks. In this article, a three-tier edge computing architecture consisting of the terminal–satellite–cloud is proposed, where tasks can be processed at three planes and intersatellites can cooperate to achieve on-board load balancing. Facing varying and random task queues with different service requirements, we formulate the objective problem of minimizing the system energy consumption under the delay and resource constraints, and jointly optimize the offloading decision, communication, and computing resource allocation variables. Moreover, the distribution of resources is based on the reservation mechanism to ensure the stability of the satellite-terrestrial link and the reliability of computation process. To adapt to the dynamic environment, we propose an intelligent computation offloading scheme based on the deep deterministic policy gradient (DDPG) algorithm, which consists of several different deep neural networks (DNNs) to output both discrete and continuous variables. Additionally, by setting the selection process of legal actions, the simultaneous decisions on offloading locations and allocating resources under multitask concurrency is realized. The simulation results show that the proposed scheme can effectively reduce the total energy consumption of the system by ensuring that the task is completed on demand, and outperform the benchmark algorithms.

Utilizing the unmanned aerial vehicle (UAV) swarm to realize location awareness of ground users (GUs) is a promising technology in green aerial Internet of Things (IoT) systems. However, a typical UAV-based positioning system is frequently applied in dense urban areas, where the traditional satellite positioning systems are severely impaired. Due to the co-frequency interference and the inevitable defect of limited resources of the positioning anchors, the UAVs positioning ability and system resource utilization suffer serious challenges. In this article, we propose a novel ranging code design for UAV swarm self-positioning, which consists of the code truncation algorithm and Greedy-based code group optimization algorithm, aiming to obtain the code group with the optimal correlation characteristics and shorter code length based on the pseudo noise (PN) code. In particular, the design can flexibly change the truncated length, so as to ensure the self-positioning accuracy with less positioning resource. To evaluate the influence of the proposed design for the self-positioning of UAVs on the aerial IoT, the root-mean-square error (RMSE) of UAVs self-positioning is provided, and based on this, the Cramer–Rao lower bound (CRLB) of GUs location estimate is derived. Numerical results demonstrate that the ranging code design is a superior way for future green aerial IoT in both positioning and resource utilization, as compared with a state-of-the-art approach.

Hand pose is important to various applications and depth information is crucial for a reliable 3D keypoint detection. However, scopes of methods that rely on active depth cameras are limited by the power, volume and illumination. To explore a wider application range, this letter proposes a multi-task method that can detect 3D hand keypoints while estimating dense depth maps from stereo infrared inputs. Based on the inherent encoding-decoding relation of the depth estimation and hand keypoint detection, the proposed network is built with shared intermediate features and separate task branches. To achieve an end-to-end estimation, the hand region is automatically cropped from the depth map. Due to a lack of datasets, a two-step fusion training approach is designed following the transfer learning theory with a self-supervision loss. The proposed method is evaluated under stereo depth datasets and 3D hand keypoints datasets respectively. Meanwhile, a small dataset is also built to test the the overall model performance. Experimental results prove that the proposed method is capable of providing satisfying depth maps along with convincing 3D hand keypoints.

Device positioning has generally been recognized as an enabling technology for numerous vehicular applications in intelligent transportation systems (ITS). The downlink time difference of arrival (DL-TDOA) technique in cellular networks requires range information of geographically diverse base stations (BSs) to be measured by user equipment (UE) through the positioning reference signal (PRS). However, inter-cell interference from surrounding BSs can be particularly serious under poor network planning or dense deployments. This may lead to a relatively longer measurement time to locate the UE, causing an unacceptable location update rate to time-sensitive applications. In this case, PRS muting of certain wireless resources has been envisioned as a promising solution to increase the detectability of a weak BS. In this paper, to reduce UE measurement latency while ensuring high location accuracy, we propose a muting strategy managed by positioning functions that utilizes a combination of optimized pseudo-random sequences (CO-PRS) for multiple BSs to coordinate the muting of PRS resources. The original sequence is first truncated according to the muting period, and a modified greedy selection is performed to form a set of control sequences as the muting configurations (MC) with balance and concurrency constraints. Moreover, efficient information exchange can be achieved with the seeds used for regenerating the MC. Extensive simulations demonstrate that the proposed scheme outperforms the conventional random and ideal muting benchmarks in terms of measurement latency by about 30%, especially when dealing with severe near-far problems in cellular networks.

This paper concerns with efficient communication over Gaussian and fading multiple-access channels (MACs). Existing orthogonal multiple-access (OMA) and power-domain nonorthogonal-OMA (NOMA) cannot achieve all rate-tuples in the MAC capacity region. Meanwhile, code-domain NOMA schemes usually require big-loop receiver-iterations for multi-user decoding, which is subject to high implementation cost and latency. This paper studies a linear physical-layer network coding multiple access (LPNC-MA) scheme that is capable of achieving any rate-tuples in the MAC capacity region without receiver iterations. For deterministic Gaussian MACs with$M$users, we propose to utilize$q$-ary irregular repeat accumulate (IRA) codes over finite integer fields/rings and$q$-ary pulse amplitude modulation ($q$-PAM) as the underlying coded-modulation. The receiver sequentially computes$M$network coded (NC) messages of the$M$users. All users’ messages are then recovered by solving the computed$M$NC messages via the inverse of the NC coefficient matrix. A joint nested code construction and extrinsic information transfer (EXIT) chart based code optimization method is developed, yielding near-capacity performance (within 0.7 and 1.1 dB the capacity limits for two and three users respectively). For fading MAC, we study the symmetric rate of LPNC-MA, and propose a pragmatic method for identifying the mutual information (MI) maximizing network coding coefficient matrix. Numerical results demonstrate that the frame error rate (FER) of the optimized LPNC-MA is within a fraction of dB the outage probability of fading MAC capacity. LPNC-MA remarkably outperforms NOMA-SIC and IDMA while avoiding the big-loop receiver iteration.

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.

This paper studies a linear physical-layer network coding multiple access (LPNC-MA) scheme that is capable of achieving any rate-tuples in the MAC capacity region without receiver-iterations or time-sharing. We propose to utilize q-ary irregular repeat accumulate (IRA) codes over finite integer field-s/rings and q-PAM as the underlying coded-modulation. The receiver sequentially computes M network coded (NC) message sequences, where the previously computed message sequence is used as side information in computing subsequent ones. All users’ messages are then recovered by solving the computed M NC messages via the inverse of the NC coefficient matrix. A joint nested code construction and EXIT chart based code optimization method is developed, yielding near-capacity performance (within 1.1 dB the capacity limit for three users). For fading MAC, we propose a pragmatic method for identifying the network coding coefficient matrix that maximizes the mutual information. Numerical results demonstrate that the frame error rate (FER) of LPNC-MA is within a fraction of dB the outage probability of fading MAC capacity. For a relatively large number of users, it is shown that LPNC-MA remarkably outperforms NOMA-SIC and IDMA in the high spectral efficiency regime, while avoiding the big-loop receiver iteration.

An integrated space-terrestrial network based on the ultradense low-earth-orbit (LEO) satellite constellations has been envisioned in both 5G and beyond 5G (B5G) networks. This approach is a powerful solution to some key challenges from Internet of Things (IoT) services, such as the lack of link capacity to deal with large data transfer or coverage in the remote areas. This article focuses on the beamforming design for the transmissions from multiple LEO satellites, equipped with massive phased array antenna, to a large number of heterogeneous terrestrial terminals. Superposition coding-based beamforming is efficient in dealing with the receiver heterogeneity, but at the cost of higher computational complexity. Based on the dual decomposition theory as well as deep neural networks (DNNs), this article proposes to combine the nonlinear approximation ability of DNNs with distributed algorithms. This combination not only supports advanced nonorthogonal beamforming algorithms for achieving superior throughput performance, but also keeps the overall computational complexity low and enables the beamforming process to be speed up dramatically through parallel computing.

In this paper, a simplified successive cancellation (SC) decoding algorithm for polar codes on insertion/deletion error channels is proposed. First, the SC decoding is designed to decode polar codes on insertion/deletion channels and the joint weight distribution is derived to measure the occurrence probability of different scenarios. Some scenarios with small occurrence probability can be pruned to obtain lower decoding complexity with negligible performance loss. Inspired by this, a fixed pruning strategy (FPS) is proposed to reduce the decoding complexity, which can prune as many scenarios as possible with the given performance requirement. By exploiting the periodicity of the joint weight distribution, the upper bound of the block error rate of the pruned SC decoding is derived. Furthermore, according to the convergence of the upper bound, a dynamic self-adjusting pruning strategy is designed to further reduce the decoding complexity and improve the flexibility of the pruning algorithm. Simulation results show that the decoding complexity of the proposed pruning-based decoding algorithms is significantly reduced compared to the state-of-the-art scenario simplified SC decoding algorithm.

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 an 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 minimize the deployment cost for each user request, furthermore, we consider that a satellite network should provide computing services for as many user requests as possible. We formulate the VNF placement problem as a potential game to maximize the overall network payoff 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.

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 paper, we investigate the service chaining placement problem for user requests in satellite ground station networks with minimum energy cost, which consists of server energy, switch energy, and link energy. We build the Server-Switch-Link energy model and formulate the energy optimization problem as an integer nonlinear programming problem. To address this problem, we implement a prediction-aided Greedy (PA-Greedy) algorithm depending on satellite mission planning in satellite control centers. We conduct the experiments to evaluate the proposed energy model and PA-Greedy algorithm in Fat-Tree networks, and compare the performance with the baseline Greedy algorithm and two energy models of Server-Link and Server. In a Fat-Tree network with 16 servers, the proposed Server-Switch-Link energy model with the PA-Greedy algorithm can reduce energy consumption by 17.28% when compared with the baseline Greedy algorithm, and outperform these Server-Link and Server energy models by 47.10% and 62.91%, respectively.

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.

Intelligent transport systems demand the provision of a continuous high-accuracy positioning service. However, a vehicle positioning system typically has to operate in dense urban areas where conventional satellite-based positioning systems suffer severe performance degradation. 5G technology presents a new paradigm to provide ubiquitous connectivity, where the vehicle-to-everything (V2X) communication turns out to be highly conducive to enable both accurate positioning and the emerging Internet of Vehicles (IoV). Due to the high probability of Line-of-Sight (LoS) communication, as well as the diversity and number of reference stations, the application of ultradense networks (UDN) in the vehicle-to-infrastructure (V2I) subsystem is envisaged to complement the existing positioning technologies. Moreover, the cooperative determination of location information could be enhanced by the vehicle-to-vehicle (V2V) subsystem. In this article, we propose a V2X-integrated positioning methodology in UDN, in which the V2I, V2V, and inertial navigation systems (INSs) are unified for data fusion. This formulation is an iterative high-dimensional estimation problem, and an efficient multiple particle filter (MPF)-based method is proposed for solving it. In order to mitigate the non-LoS (NLoS) impact and provide a relatively accurate input to the MPF, we introduce an advanced anchor selection method using the geometry-based${K}$-means clustering (GK) algorithm based on the characteristics of network densification. Numerical results demonstrate that utilizing the GK algorithm in the proposed integrated positioning system could achieve 18.7% performance gains in accuracy, as compared with a state-of-the-art approach.

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 ethods under popular benchmarks including KITTI, SceneFlow and Midddlebury.

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 article, 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.

In cloud computing, an important concern is to allocate the available resources of service nodes to the requested tasks on demand and to make the objective function optimum, i.e., maximizing resource utilization, payoffs, and available bandwidth. This article proposes a hierarchical multi-agent optimization (HMAO) algorithm in order to maximize the resource utilization and make the bandwidth cost minimum for cloud computing. The proposed HMAO algorithm is a combination of the genetic algorithm (GA) and the multi-agent optimization (MAO) algorithm. With maximizing the resource utilization, an improved GA is implemented to find a set of service nodes that are used to deploy the requested tasks. A decentralized-based MAO algorithm is presented to minimize the bandwidth cost. We study the effect of key parameters of the HMAO algorithm by the Taguchi method and evaluate the performance results. The results demonstrate that the HMAO algorithm is more effective than two baseline algorithms of genetic algorithm (GA) and fast elitist non-dominated sorting genetic algorithm (NSGA-II) in solving the large-scale optimization problem of resource allocation. Furthermore, we provide the performance comparison of the HMAO algorithm with two heuristic Greedy and Viterbi algorithms in on-line resource allocation.

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.

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.

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.

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.

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.

This paper proposes a new approach, Cooperator Resource Allocation Algorithm (CoopeRA), to improve the radio resources allocation scheme and reduce the impact of congestion in LTE Heterogeneous network (HetNets) and 5G networks by using Device-to-Device (D2D) communications and by borrowing some adjacent Macro-cell resources from the neighboring nodes that are belonging to the same WLAN network but not to same Macro-cell. Our solution is based on cluster construction and D2D transmissions in Local Area Networks (LAN) to relay the traffic of users belonging to congested LTE cells. Our new algorithm increases the number of new calls in the congested Macro-cell, even if the resources are limited. The simulation validates this study when the results shows the performance of our mechanism.

In this paper, one of the constituent codes, Rate-1 node, is used to simplify Successive-Cancellation List (SCL) decoding of non-binary polar codes for reducing the decoding complexity. First, we derive the Logarithmic Likelihood Ratio based (LLR-based) path metric of non-binary polar codes in SCL decoding. Then we propose that the path metric only depends on the LLR value at the top of Rate-1 node tree, which avoids traversing a complete decoding tree in non-binary SCL decoding. Finally, we design a novel reliability metric, which is used to select the unreliable symbols from the LLRs at the top of Rate-1 node tree. By the proposed metric, we select the unreliable symbols to generate the candidate paths, which avoids splitting paths for all symbols of Rate-1 node in the conventional SCL decoding. Simulation results show that the proposed non-binary SCL decoding reduces significantly the computation and time complexity without the performance loss.

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.

Nonorthogonal multiple access (NOMA) has been identified as a promising technology in millimeter-wave (mmWave) multiple-input-multiple-output (MIMO) communication networks for Internet-of-Things (IoT) applications, which has the advantages of both massive connectivity and high spectral efficiency. However, few researchers have considered the probability of introducing NOMA to a positioning system. In this article, a novel mmWave MIMO-NOMA-based positioning system is proposed, which is capable of meeting the requirements of IoT applications. We establish a NOMA-based positioning model from the perspective of the system level, along with the design of a transmission strategy. To characterize the positioning performance, the position error bound (PEB) is selected as evaluation criteria and theoretical expressions of the PEB are provided. Simulations of comparing localization performance between NOMA and conventional orthogonal multiple access (OMA) are conducted by using the theoretical analysis. The numerical results show that the application of NOMA to localization is a viable way to reduce the PEB compared to OMA. This article further shows under what circumstances can NOMA outperform OMA in terms of localization performance and the corresponding parameter settings.

The Internet of Things (IoT) will significantly change both industrial manufacturing and our daily lives. Data collection and 3-D positioning of IoT devices are two indispensable services of such networks. However, in conventional networks, only terrestrial base stations (BSs) are used 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 technology to overcome the above shortcomings. In this article, 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 3-D positioning performance. This formulation is a mixed-integer nonconvex optimization problem, and an efficient differential evolution (DE)-based method is proposed for solving it. Numerical results demonstrate that the proposed network and the optimization method achieve significant performance gains in both energy-efficient data collection and 3-D device positioning, as compared with a conventional terrestrial IoT network.

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 PDNOMA 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.PD-NOMA.

Belief Propagation (BP) decoding provides soft outputs and features high-level parallelism. In this paper, we propose an optimized software BP decoder for polar codes on graphics processing units (GPUs). A full-parallel decoding architecture for codes with length n ≤ 2048 is presented to simultaneously update n/2 processing elements (PEs) within each stage and achieve high on-chip memory utilization by using 8-bit quantization. And, for codes with length n > 2048, a partial-parallel decoding architecture is proposed to partly update PEs of each stage in parallel and coalesced global memory accesses are performed. Experimental results show that, with incorporation of the G-matrix based early termination criterion, more than 1 Gbps throughput for codes n ≤ 1024 can be achieved on NVIDIA TITAN Xp at 5 dB while the decoding latency is less than 1 ms. Compared with the state-of-the-art works, the proposed decoder achieves throughput speedups from 2.59× to 131× and provides good tradeoff between error performance and throughput.

In this paper, we consider the problem of estimating the number of unrecovered active users in a coded random access systems over a collision channel with both packet erasures and slot erasures. Each user transmits multiple packet replicas in random slots of the frame and successive interference cancellation (SIC) is exploited at the receiver. Each packet replica is transmitted over an erasure channel. Moreover, the whole signal received in a slot may be erased, making the slot content useless. The developed algorithm can track over SIC iterations the probability distribution of the sought number of users, given all information that it is possible to gather through SIC iterations. The algorithm is validated by numerical simulation, showing that it can considerably reduce the mean square error of the estimation process.

This paper investigates the impact of physical layer secrecy on the performance of a unified non-orthogonal multiple access (NOMA) framework. The spatial locations of legitimate users (LUs) and eavesdroppers are governed by invoking stochastic geometry. To characterize the secrecy performance of the unified framework, new exact and asymptotic expressions of secrecy outage probabilities are derived, in which both imperfect successive interference cancellation (ipSIC) and perfect SIC (pSIC) schemes are taken into account. The secrecy diversity orders achieved 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 secrecy diversity orders of LUs with ipSIC/pSIC for CD/PD-NOMA are equal to zero/K and zero/one, respectively. Finally, numerical results are present to confirm the accuracy of the developed analytical results.

One of the available capacities of satellite application is applying in Earth Observation (EO) video capture satellites. On the one hand, the governing equations of satellite motion in orbit indicate the fact that a satellite moves in a very predictable manner and stays on schedule. On the other hand, the superiority of Intra and Inter codings, drive video compressors to reduce the spatial redundancy and data rate. This advantage stems from the flexible coding structure and high density of angular prediction modes, in all video compression versions. How to combine these facts to achieve better performance in industrial satellite applications is our aim in this article. In this study, a novel architectural approach for EO video capture satellites that have taken the new demands of the next commercial market has been proposed. Here after looking at video coding parameters, technical EO satellite essentialities, and proving the proposed hypothesis by simulation and evidence, a preliminary configuration has been described. The Attitude and orbit control systems (AOCS) are responsible for keeping the satellite position in the required condition contributing to Onboard Data Handling (OBDH). The exact parameters of direction are key factors that can be used besides video prediction vector data to reduce calculation load in video compression algorithms that particularly explained.

In this letter, we propose a segmented CRC-aided spinal code (SCA-spinal code) where an encoding structure is designed based on a novel sliding window decoding algorithm. The proposed algorithm reduces the decoding computation significantly by terminating the decoding process earlier when the segment decoding results fail to pass the corresponding CRC check. The decoding results are checked once a segment decoding is complete. Furthermore, we propose a transmission strategy after the decoding process fails to save the transmitting symbols and therefore improve the spectral efficiency. The numerical results show that our proposed SCA-spinal code reduces decoding complexity and improves spectral efficiency.

Spinal codes are a new kind of rateless codes and can achieve the shannon capacity over binary symmetric channel (BSC) and additive white gaussian noise (AWGN) channel. This paper describes a self-adaptive wireless communication system for spinal codes. The key points to our design include the integrated methods of coding and mapping for spinal codes, transmission protocols and feedback frame. Finite feedback transmission protocol for the cumulative probability distribution function (CDF) decoding and multi-frame aggregation protocol are introduced to improve the rate of transmitted data for the system. We also propose a mechanism of interference cancellation (IC) to handle the problem of packet loss. Finally, we design and implement this system by using IEEE 802.11. The results of our simulation show that our system is able to operate adaptively to copying with time-varying channel and provide a good performance.

This paper proposes a non-orthogonal multiple access system based on rateless codes to improve anti-jamming performance. Firstly, each user's data is encoded by rateless codes. Then all the data is superimposed to form some composite signals, which are broadcast to every user. The power factors of each user and the superimposed signals are adjusted by the transmitter according to feedback provided by the users. This paper also analyzes the packet loss ratio, the frame error rate (FER) and the decoding time. The simulation results show that the proposed scheme not only reduces the FER, but also improves the system throughput.

Device positioning has generally been recognized as an important service of LTE Advanced pro and the upcoming fifth generation (5G) mobile communication. The Observed Time Difference of Arrival (OTDoA) technique that utilizes cellular signals for positioning is considered as a promising candidate technology capable of meeting users' growing demands for high-precision and ubiquitous device positioning. The application of ultra-dense network (UDN) in 5G systems brings new opportunities and challenges to device positioning. However, existing researches mainly focus on the communication characteristics of UDN, while its positioning characteristics have not been analyzed thoroughly. In this study, a software simulation platform of OTDoA positioning in 5G UDN is first established based on the current 5G standards. Then, the performance of OTDoA technique in UDN and its influencing factors are comprehensively analyzed by heat map method over our simulation platform. The simulation results demonstrate that in 5G UDN, the unsatisfactory geometric distribution quality of base stations used for positioning and the non-line-of-sight (NLoS) propagation of cellular signals are the main factors that cause the performance degradation of OTDoA positioning. Moreover, based on the above analysis, we also provide some meaningful guidance for the development or improvement of OTDoA positioning technique used in 5G UDN.

A high precision method for position and attitude determination is presented. One of the difficulties in pose determination is the lack of reliable visual features. As a result, we exploit a novel convolutional neural network for stereo matching, so as to solve the problem of image feature extraction. The network is based on a siamese network and incorporates dense block into each branch. There is occlusion problem in the close-range phase of measuring object. In order to solve the occlusion, a multi-view system is established. The measurement results meet the accuracy requirements of pose determination and verify the feasibility of the method.

Mobile terminal positioning has recently been recognized as an indispensable functionality for the upcoming Fifth Generation (5G) wireless networks. However, in harsh environments like urban and indoors, multipath caused by signal reflection or diffraction still severely affects the performance of 5G positioning. Aiming to reduce the positioning errors caused by multipath, we propose a novel multipath mitigation method called Two-Stage Multipath Estimating Delay Lock Loop (TS-MEDLL) by appropriately modifying the structure of conventional MEDLL. The experimental results demonstrate that in 5G positioning, the proposed TS-MEDLL outperforms the existing methods and has a 60% improvement in the medium delay multipath mitigation performance compared with the conventional MEDLL.

Depth estimation has widely demands in autopilot and scene reconstruction. Although depth estimation has been greatly improved by deep learning, there is still some room for improvement. Usually, stereo-matching-based depth estimation matches low-resolution features and then up-samples depth map to full-resolution. Such methods suffer from low accuracy because of information loss in low-resolution features. To solve this problem, a depth estimation method via multi-resolution gradual-refining stereo matching is proposed. As with classic methods, this method first extracts pyramid features by convolution network and estimates low-resolution depth maps. Then, as an innovation, stereo-matching at each pyramid feature is successively executed using the low-resolution maps as initial depth, which limits search range of stereo-matching for accuracy and efficiency. Results of stereo-matching are used as residuals to refine the depth maps. Experimental results demonstrate that accuracy of depth estimation by the proposed method is significantly improved, but computational complexity does not much increase compared with classic methods.

Stereo matching is a challenging yet important task to various computer vision applications, e.g. 3D reconstruction, augmented reality, and autonomous vehicles. In this paper, we present a novel image-based convolutional neural network (CNN) for dense disparity estimation using stereo image pairs. In order to achieve precise and robust stereo matching, we introduce a feature extraction module that learns both local and global information. These features are then passed through an hour-glass structure to generate disparity maps from lower resolution to full resolution. We test the proposed method in several datasets including indoor scenes and synthetic scenes. Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods in several datasets.

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.

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.

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.

We develop a maximum a posteriori detector for the number of unrecovered users in coded random access systems based on the irregular repeat slotted Aloha (IRSA) protocol, where each user transmits in random slots multiple packet replicas whose number (known as the user degree) is drawn according to a given probability mass function and where successive interference cancellation (SIC) is exploited at the receiver. The detector is capable to track over SIC iterations the a posteriori probability distribution of a vector whose elements are the numbers of not yet recovered active users of the various degrees. Numerical results are provided to illustrate the capability of the detection algorithm to considerably reduce the mean square error on the number of active users, taking advantage of the iterative SIC process.

This paper addresses the problem of detecting the number of unrecovered active users in a coded random access system with successive interference cancellation (SIC) at the receiver. The considered transmission protocol is contention resolution diversity slotted Aloha, where each user generates and transmits in random slots multiple packet replicas. A maximum a posteriori detector, capable to track the a posteriori probability mass function of the number of not yet recovered active users over SIC iterations, is developed for the case of availability of a priori information on the active users' distribution. Numerical results are provided, illustrating the detection algorithm capability to achieve low mean square error values taking advantage of new observations gathered throughout the SIC process.

Summary Nonbinary low‐density parity‐check (NB‐LDPC) codes are excellent error correcting codes and outperform their binary counterparts under the same code length. NB‐LDPC decoders are based on Belief Propagation Algorithm, which demands intensive message‐passing computation. Recently, to achieve both flexibility and good throughput performance, NB‐LDPC decoders have been ported from dedicated hardware solutions to multi/many‐core systems. In this paper, we propose an FFT‐based q‐ary Sum‐Product Algorithm (QSPA) decoding architecture for NB‐LDPC codes with layered and flooding schedules on a graphics processing unit (GPU). To improve the throughput performance of the proposed decoder, four optimization methods are presented to not only accelerate the decoding kernel execution but also improve the data transfer efficiency. The experiments are mainly accomplished on NVIDIA GTX580 and GTX Titan X. Throughputs up to 63 Mbps over GF(16) and 7.65 Mbps over GF(256) are achieved on GTX580 when executing 5 layered decoding iterations. Throughputs can reach up to 139 Mbps over GF(16) and17 Mbps over GF(256) on GTX Titan X. Experimental results show that the speedups of the decoding throughputs range from ×1.7 to ×16.8 by comparison with the existing FFT‐based QSPA decoders on GPU.

Depth images extracted by light‐coding‐based depth cameras are widely used to reconstruct three‐dimensional scenes in recent years. However, the retrieved depth accuracy greatly influences the reconstruction quality. Here, the authors present an appropriate depth extraction method based on subpixel matching to improve the depth accuracy. The proposed method utilises nearest neighbour interpolation to the projector's image plane to obtain depth values at subpixel accuracy, thereby better preserving the important depth information without changing any inner structure of the depth camera. Experimental results show that the proposed method improves the depth images both on image quality and depth accuracy.

This paper tackles the problem of channel estimation and decoding in environments that exhibit time-varying block-memoryless impulsive noise. In order to perform the estimation in continuous value space and the decoding in discrete value space jointly, a novel message passing framework is proposed that combines the sampling-importance resampling (SIR) estimator and the LDPC sum-product decoder in an iterative manner. The resampling process within the framework is further improved based on the asymptotic performance analysis for mismatched decoding. The simulation results prove the validity of the enhanced SIR estimator in the proposed method and demonstrate our method can achieve better performance than the LDPC decoder with quantile estimator.

Channel asymmetry and channel correlation are frequently encountered in wireless communication systems. Orthogonal transmission schemes are usually inefficient in dealing with these problems. In this paper, in order to boost the throughput performance for multiple-input multiple-output broadcast communications in the presence of channel asymmetry and/or channel correlation, we study optimization algorithms for multi-user superposition coding beamforming (SCBF). Starting with solving the minimum power optimization problem for the two-user case, we derive the optimal solution structure of the problem and two types of dedicated algorithms that could efficiently find the optimal solutions with all parameter setups. Extensions are then made to the same problem with the signals of more than two users multiplexed in the power domain as well as to the rate region computation problem. Finally, to adapt our algorithms to more general cases, novel hybrid precoding schemes are proposed, where certain user grouping strategy is used to combine zero-forcing beamforming and SCBF. Numerical simulations are provided to show that with our algorithms, a considerable performance gain is achieved by SCBF compared to the other orthogonal transmission methods.

Combining the polar coded hybrid automatic repeat request (HARQ) with higher-order modulation has been proposed recently. To further improve the throughput of HARQ, a novel polar coded HARQ cheme with higher-order modulation is proposed. Wherein the proposed HARQ scheme, the unequal rror protection (UEP), caused by bit-mapping, in bit- interleaved polar coded modulation (BIPCM) is considered. Based on UEP, a non-linear integer programming (NLIP) problem is constructed to determine the optimal retransmitted sequence. Furthermore, a low-complexity suboptimal algorithm is proposed to solve the resulting NLIP problem. Simulation results show that the throughput of the proposed HARQ scheme outperforms the existing polar coded HARQ for higher-order modulation; in particular, our scheme easily accommodates arbitrarily puncturing methods. This makes it possible to use recently proposed puncturing methods for polar codes to further improve the performance of our HARQ scheme.

This paper investigates the impact of relay selection (RS) on the performance of cooperative non-orthogonal multiple access (NOMA), where relays are capable of working in either full-duplex (FD) or half-duplex (HD) mode. The locations of relays considered in the networks are modeled using stochastic geometry. The single-stage relay selection (SRS) scheme is proposed. In order to characterize the performance of SRS scheme, new closed-form expressions for both exact and asymptotic outage probabilities are derived. Based on the analytical results, the diversity orders achieved by the SRS scheme for FD/HD cooperative NOMA are obtained. It is confirmed that the FD-based SRS schemes obtain a zero diversity order, while the HD-based RS is capable of achieving a diversity order of K. Simulation results show that the outage performance of FD-based RS scheme outperforms HD-based RS scheme in the low signal-to-noise radio (SNR) region rather than in the high SNR region.

Structured light systems have become an effective tool for reconstructing three‐dimensional models of objects due to the advent of low‐price, high‐speed depth cameras such as Kinect. However, this kind of active depth sensor extracts low‐quality depth maps because of its inaccurate image matching process. This study proposes a depth extraction method based on image rectification for accurate image matching. Due to the sizes of the projected patterns and the captured images are usually different, a virtual camera is defined, through which the rectified images are generated to match the images in the real camera at pixel level. Experiments on simulated and hardware platforms demonstrate that the proposed method achieves efficient rectification and obtains better‐quality depth maps.

Using multiple depth cameras can extend the field of view, which is beneficial to three-dimensional (3D) reconstruction. However, multiple active depth cameras would interfere with each other, which degrades the quality of depth images significantly. In this paper, we first present a depth extraction method based on sampling of the space at camera viewpoints to address the interference problem. Compared with the previous plane-sweeping method based on structured-light stereo and multiview stereo, depth images with less depth errors and better shape of objects are generated. Then, we analyze factors affecting depth accuracy for light-coding-based depth cameras and demonstrate the proposed depth extraction method also improves the accuracy of depth images by utilizing multiple projected patterns. Based on the analysis, subpixel-based matching (SPM) is finally applied to further improve the depth accuracy obtained by the depth extraction method. In the image matching process of our method, nearest neighbor interpolation is utilized to each projector's image plane to increase their resolution, which leads to the improved depth accuracy at subpixel level and avoids the drawbacks of changing parameters of light-coding-based depth cameras. Experimental results under simulated and real-world examples validate our analysis and show that the proposed method not only mitigates the impacts of interference effectively but also further improves the accuracy of depth images by using SPM.

In this paper, we propose a simplified multi-bit successive cancellation list (SMSCL) decoding method for polar codes. In the proposed SMSCL decoding, frozen bits are ignored, and multiple information bits are intermediately decoded at each decision step. In addition, a modified two-stage pruning network is proposed to reduce the complexity of path pruning in SMSCL decoding. Compared to the existing multi-bit SCL decoders, the SMSCL decoder can achieve significant latency and complexity reduction over a wide range of code rates.

In this paper, a random access with priority protocol named irregular repetition slotted ALOHA with priority (PIRSA) is introduced. By optimizing the packets repetition and using successive interference cancellation (SIC), irregular repetition slotted ALOHA (IRSA) has achieved a high throughput. Contention resolution diversity slotted ALOHA with access control (AC-CDRSA) enhances both throughput and efficiency under high traffic load. In P-IRSA, priority is considered in order to handle traffics from the users who demands enhanced access and throughput. Access control is added before the traffic, and users with high priority gain higher probability to access the channel under high traffic load. Further more, performance of the proposed P- IRSA in terms of the packet loss rate, throughput and channel efficiency are verified by simulations and are compared with IRSA and AC-CDRSA protocols. Simulation results show that the throughput of users with high priority can be enhanced under high traffic load with the proposed protocol.

Polar codes achieve the rate-distortion bound for nonuniform sources and/or asymmetric distortion measures. However, the performance is not always near optimal for finite code length, especially for short code length. In this paper a new scheme for lossy source coding is proposed. In addition to polar coding, arithmetic coding is applied in the scheme. The source is first encoded by polar coding for lossy compression, then it is further compressed losslessly by arithmetic coding. It is shown that the scheme achieves the rate-distortion bound asymptotically with a good empirical performance. It is also shown that the distortion of the scheme has a better second-order exponent than those of the other polar coding schemes.

A Luby transform (LT) coding scheme providing unequal error protection (UEP) is proposed. As an enhanced method, the proposed UEP‐LT coding scheme can be easily applied to the existing UEP rateless codes to achieve further bit error rate (BER) performance improvement. In the proposed UEP‐LT encoder, a number of the so‐called fixed symbols are added to the original source symbols. The level of importance of them can be assigned as that of any subset of the original source symbols. When the encoded symbols are generated, the proposed encoder only XOR the original source symbols. Therefore the efficiency of the UEP‐LT codes remains unchanged. It is found that this can benefit the belief propagation decoding. Using the And–Or tree analysis technique, the asymptotic BER performance of the proposed method is analysed in detail. Compared with some existing UEP rateless codes, the asymptotic BERs of the proposed UEP‐LT codes are much lower when the parameters are properly chosen. Besides, the proposed method needs less decoding iterations to converge, which means the source symbols can be recovered earlier. Simulation results show that the BER performances are improved when the proposed scheme is applied to the existing UEP rateless codes.

In this letter, we propose a low-complexity discrete cosine transform (DCT)-based distributed source coding (DSC) scheme for hyperspectral images. First, the DCT was applied to the hyperspectral images. Then, set-partitioning-based approach was utilized to reorganize DCT coefficients into waveletlike tree structure and extract the sign, refinement, and significance bitplanes. Third, low-density parity-check-based Slepian-Wolf (SW) coder was adopted to implement the DSC strategy. Finally, an auxiliary reconstruction method was employed to improve the reconstruction quality. Experimental results on Airborne Visible/Infrared Imaging Spectrometer data set show that the proposed paradigm significantly outperforms the DSC-based coder in wavelet transform domain (set partitioning in hierarchical tree with SW coding), and its performance is comparable to that of the DSC scheme based on informed quantization at low bit rate.

In this paper, we present a distributed video coding scheme based on zero motion identification at the decoder and constrained rate adaptive low density parity check (LDPC) codes. Zero-motion-block identification mechanism is introduced at the decoder, which takes the characters of video sequence into account. The constrained error control decoder can use the bits in the zero motion blocks as a constraint to achieve a better decoding performance and further improve the overall video compression efficiency. It is only at the decoder side that the proposed scheme exploits temporal and spatial redundancy without introducing any additional processing at the encoder side, which keeps the complexity of the encoding as low as possible with certain compression efficiency. As a powerful alternative to Turbo codes, LDPC codes have been applied to our scheme. Since video data are highly non-ergodic, we use rate-adaptive LDPC codes to fit this variation of the achievable compression rate in our scheme. We propose a constrained LDPC decoder not only to improve the decoder efficiency but also to speed the convergence of the iterative decoding. Simulation demonstrates that the scheme has significant improvement in the performances. In addition, the proposed constrained LDPC decoder may benefit other application.