Multi-hop networks are vital for establishing emergency communications, in case the terrestrial communication infrastructures are compromised. Organizing nodes into specific structures can enhance system resilience and efficiency. To this end, we consider using directional antennas to enhance transmission, and the nodes form a Z-chain structure based on the location information provided by the satellite. The core mechanism for performance gain lies in the spatial separation that shifts the dominant interference from the high-gain antenna main lobes to the attenuated side lobes. An exhaustive search demonstrates the optimality of the Z-chain configuration. To combat performance degradation from antenna pointing errors and node drift, we introduce an alternating Newton method (ANM) that jointly optimizes network structure and beam configurations by minimizing single-hop outage probability. Simulations show that the proposed Z-chain structure surpasses existing linear relay designs by converting most intra-chain interference from main-lobe to side-lobe directions, albeit with additional relay nodes. The Z-chain structure offers a promising paradigm for high-performance wireless networks with potential applications in terrestrial, aerial, and space communications.

Directional antennas can synergize with suitable network structure to complement each other in multi-hop wireless networks. This work addresses the combination of directional transmission and network structure to improve the capacity. Firstly, Z-chain structure considering the antenna radiation pattern is proposed, which is consistent with the best chain structure obtained by exhaustive search. Secondly, theoretical analysis indicates that spectral efficiency$\eta_{S}$behaves as a sigmoid function of node density$\rho$, with its asymptotic value increasing as$\Theta\left[\log \left(G_{r} \sin ^{2} \theta\right)\right]\left(\theta \leq \theta^{*}\right)$for large values of$G_{r}$, where$G_{r}$denotes the relative antenna gain,$\theta^{*}$is a function of the antenna beam width and$2 \theta$is the$\mathbf{Z}$-chain link angle. Thirdly, energy efficiency and consumption is assessed from a traffic load perspective. The findings reveal that the Z-chain network can accommodate higher data traffic compared to conventional frequency reuse method and straight structure. Experiments conducted using OMNeT++ validate above conclusions.

Yuhang Zhou, Giannis Karamanolakis, Victor Soto, Anna Rumshisky, Mayank Kulkarni, Furong Huang, Wei Ai, Jianhua Lu. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Providing direct-to-cell (D2C) services via non-geostationary orbit (NGSO) satellites constitutes one of the critical applications in satellite communications for non-terrestrial networks (NTN). To mitigate frequent beam handovers caused by the high mobility of NGSO satellites, an earth-fixed service mode can be employed to maintain beam staring at target areas. Addressing the capacity degradation due to delayed beam updates, we propose a beam tracking method based on dual codebook interpolation. This approach estimates beam update intervals through beam pattern analysis and angular velocity of terminals within satellite’s field of view, while maintaining the codebook. The precoder at any instant is generated through linear interpolation between active and updated codebooks. Simulation results demonstrate that the proposed method adaptively adjusts beam update intervals according to terminal’s position and channel conditions, effectively reducing beam tracking frequency while maintaining system capacity above specified thresholds.

As the adoption of satellite-enabled Internet of Things (IoT) continues to rise, its intricate multi-domain architecture becomes increasingly susceptible to cross-domain cyber threats. Attackers can exploit compromised IoT devices, inject malicious packets into data streams aggregated at the IoT gateway for satellite backhaul, and potentially endanger the satellite network during transmission by exploiting the hardware, software, and protocol vulnerabilities. Compared to single-domain defenses, cooperative defense at the IoT devices, IoT access network, and satellite transmission network provides fine-granularity defense against cross-domain intelligent attacks. However, quantifying cross-domain impacts and tilting incentive misalignment among different participants remain significant challenges, making systematic cooperative defense development a complex task. To address this, we develop a tripartite security game framework to characterize the impacts of attacks and defense methods across both the terrestrial and satellite domains. Leveraging this game model, we devise flow pricing to optimally motivate the IoT Network Operator (IoT-NO) to prevent malicious packet infiltration into the satellite domain. Subsequently, we propose efficient learning algorithms enabling both the IoT-NO to ascertain their ideal flow sampling strategies and the Satellite Service Provider (SAT-SP) to determine optimal flow pricing. The simulation results corroborate the effectiveness of the consolidated game in counteracting cross-domain cyber attacks and facilitating cooperative defense between the IoT-NO and the SAT-SP with non-aligned incentives.

Semantic communications have demonstrated exceptional performance across various tasks, yet they are susceptible to semantic impairments due to the inherent vulnerability of deep neural networks. This paper focuses on semantic impairments in images, particularly those stemming from adversarial perturbations. We introduce a novel metric for quantifying the level of semantic impairment and create a semantic impairment dataset. Furthermore, we propose a deep learning enabled semantic communication system for robust image transmission, termed as DeepSC-RI. The proposed system harnesses a multi-scale semantic extractor with a dual-branch design tailored for extracting semantics with varying granularity, thereby boosting the robustness of the system. The fine-grained branch incorporates a semantic importance evaluation module to identify and prioritize crucial semantics through self-attention score manipulations, while the coarse-grained branch adopts a hierarchical approach for progressively capturing the robust semantics. These two streams of semantics are seamlessly integrated via an advanced cross-attention-based semantic fusion module. Experimental results highlight the superior performance of DeepSC-RI under diverse channel conditions, across various levels of semantic impairment intensity, and in multiple tasks.

Artificial intelligence (AI) is envisioned to be natively integrated into the sixth-generation (6G) mobile networks to support a diverse range of intelligent applications. Federated edge learning (FEEL) emerges as a vital enabler of this vision by leveraging the sensing, communication, and computation capabilities of geographically dispersed edge devices to collaboratively train AI models without sharing raw data. This article explores the pivotal role of FEEL in advancing both the “wireless for AI” and “AI for wireless” paradigms, thereby facilitating the realization of scalable, adaptive, and intelligent 6G networks. We begin with a comprehensive overview of learning architectures, models, and algorithms that form the foundations of FEEL. We, then, establish a novel task-oriented communication principle to examine key methodologies for deploying FEEL in dynamic and resource-constrained wireless environments, focusing on device scheduling, model compression, model aggregation, and resource allocation. Furthermore, we investigate the domain-specific optimizations of FEEL to facilitate its promising applications, ranging from wireless air-interface technologies to mobile and the Internet of Things (IoT) services. Finally, we highlight key future research directions for enhancing the design and impact of FEEL in 6G.

Mobile user equipment (UE) which can connect to satellites directly attracts increasing attention. Power consumption is one of the most significant challenges for mobile UEs. In existing satellite communication systems, beam hopping technology has been utilized to improve the satellite efficiency, but the power consumption of UEs is ignored. Discontinuous reception (DRX) mechanism, wherein UEs turn off their receivers intermittently to save power, has been researched and utilized in terrestrial communication systems. However, the satellite efficiency will decrease if the DRX mechanism is applied to beam hopping satellite systems directly. In this paper, we propose a novel DRX mechanism for beam hopping satellite systems (DRX-BHS) and a new beam hopping operation strategy combining traffic-driven strategy and pre-scheduled strategy. Besides, a new pre-scheduled beam hopping strategy based on UE grouping is proposed, which can overcome the limitation of the satellite efficiency in traditional pre-scheduled beam hopping strategy. A semi-Markov model is established to evaluate the performance of the proposed DRX-BHS mechanism. The numerical results reveal the relationship between the performance and the number of UEs in each group, and show that our proposed DRX-BHS mechanism can balance the satellite efficiency and UE power saving.

The likelihood of encountering scenarios that lead to accidents, namely safety-critical scenarios, is minimal compared to long-term safe driving environments. The generation of repeatable and scalable safety-critical scenarios is essential for the advancement of human and autonomous driving capabilities. Compared with the high complexity and low practicality of existing scenario generation methods, in this paper we propose a real-time approach to automatically generate challenging scenarios and instantiate them in a CARLA-based simulator. First, the safety-critical scenario is decomposed into a perturbed and optimized vehicle trajectory and the remaining reusable Unreal Engine assets based on a hierarchical model. Second, a model that is based on a graph conditional variational autoencoder (VAE) is employed to predict future trajectories and head angles based on past information. Third, the safety-critical scene generation model is used to enhance the diversity of the scene by diversifying the latent variables over a pre-trained trajectory representation model. Finally, the trajectories of real-world vehicles are placed into the simulator by adapting them to enable the generation of safety-critical scenes in a three-dimensional environment. The results demonstrate that the proposed approach generates scenarios that are more plausible than those generated by the baselines, with a performance improvement of over 10% in collision metrics for scenario generation. The research facilitates the simplification of the long-tail scenario construction process for autonomous vehicles, which in turn facilitates the optimization of algorithms such as autonomous trajectory planning.

Motion in-betweening that aims to generate motion transitions between known keyframes plays a significant role in the 3D character animation industry. However, generating long-term transitions is highly challenging due to the non-stationary nature and considerable spatio-temporal uncertainty of motions. Leading transformer-based methods operate at a single temporal scale while they overlook the spatial interactions among joints and temporally hierarchical structure of motions, leading to the generation of over-smoothed and weak transitions. In this paper, we propose a novel spatio-temporal framework for the motion in-betweening task. First, a spatial transformer is introduced to capture the per-frame spatial dependencies among joints, enhancing the capability of the model to generalize across diverse action types. Furthermore, to alleviate over-smoothing, a multi-scale temporal transformer is designed to generate dynamic and realistic transitions by capturing the hierarchical structure of motions, which includes both global motion trends and local subtle variations. Extensive experiments on the LAFAN1 dataset demonstrate that our method achieves state-of-the-art performance compared to existing methods. In addition, the corresponding ablation studies and sensitivity analyses verify the effectiveness of the proposed spatio-temporal framework.

Safety driver vigilance is a prerequisite for the safe operation of autonomous vehicles. In contrast to vehicle behavioral trajectory detection, which suffers from high latency and low accuracy, vigilance detection based on physiological signals is currently the most reliable and accurate method. While vigilance monitoring methods using electroencephalograms (EEG) have made considerable progress in experimental scenarios, they remain a challenging problem in scenario-constrained conditions, such as high-speed moving autonomous vehicles. This is due to the low signal-to-noise ratio in EEG signal acquisition and the difficulty of real-time processing. Moreover, cumbersome data acquisition processes and the challenges of labeling have hindered progress in this area. Given the successful use of EEG for monitoring in experimental settings, we believe that the transfer of knowledge learned from these scenarios to new contexts is reasonably feasible. Thus, this work aims to bridge the domain gap between experimental and real-world scenarios while balancing the number of channels and accuracy. Specifically, we propose a framework for EEG vigilance detection capable ofCross-scenario,Cross-subject, andCross-device, calledCCC. The proposed framework leverages the standard montage structure of EEG channels, reducing the number of channels by considering the common regions of EEG channels across different scenarios. The results show that our proposed model achieves an average accuracy of 86.20% on the SEED-VIG dataset with 12 subjects, which is higher than the 82.21% achieved by state-of-the-art deep learning approaches. Finally, we investigate the role of the attention mechanism and transfer learning, and further attempt to explain the advantages of our proposed approach from a visualization perspective.

The integrated satellite-terrestrial network with cascaded downlinks from satellites to wide-area mobile base stations and subsequently to terrestrial users enables global communication for terrestrial 4G/5G cellular users and is widely used in emergency rescue scenarios. However, in this network, satellites and wide-area mobile base stations are controlled by distinct resource scheduling systems with disparate packet queues, which means resources allocated by the satellite to the wide-area mobile base stations may not match the resources allocated by the wide-area mobile base stations to the terrestrial users, leading to coordination inefficiencies and resource wastage. To tackle this challenge, a resource collaborative scheduling mechanism based on cooperative game theory for cascaded downlinks is established, which effectively adapts to distinct resource scheduling systems with various QoS constraints. Then, the utility function of the Nash product is converted into a max-min problem, and a convex transformation method is proposed for the non-convex optimization problem. Simulation results demonstrate that the proposed collaborative scheduling mechanism effectively improves resource utilization and the transmission rate of cascaded downlinks.

With the rapid development of virtual reality (VR) technology, users are able to access a large number of new applications in their daily lives. VR expands users’ perceptual dimensions, bringing them entirely new experience. However, the user experience assessment for VR videos is still under exploration, which remains an unresolved issue. In such immersive scenarios, the methods based on user scoring require active feedback from users, which will interrupt the immersion experience. Besides, it is difficult to monitor the user experience status in real-time by user scoring. With the development of psychophysiological research, electroencephalographic (EEG) signal measurement is considered to have the potential to non-intrusively obtain the user experience. Hence, this paper employs EEG measurements to capture users’ EEG signals while watching VR videos with varying levels of stuttering, constructing a VR-EEG dataset. Subsequently, we analyze the dataset using time-frequency analysis methods to validate the feasibility of EEG signals reflecting user experience. Finally, we utilize machine learning methods to construct a QoE measurement network capable of analyzing users’ perceptual experience from single-trial EEG signals. Experimental results demonstrate that the proposed method establishes a relationship between brain activities and user experience and can effectively predict QoE scores from EEG signals. It provides a technical means for real-time, non-disturbing measurement of user experience in VR video playback.

Semantic communications have gained significant attention as a promising approach to address the transmission bottleneck, especially with the continuous development of 6G techniques. Distinct from the well investigated physical channel impairments, this paper focuses on semantic impairments in images, particularly those arising from adversarial perturbations. Specifically, we propose a novel metric for quantifying the intensity of semantic impairment and develop a semantic impairment dataset. Furthermore, we introduce a deep learning enabled semantic communication system, termed as DeepSC-RI, to enhance the robustness of image transmission, which incorporates a multi-scale semantic extractor with a dual-branch architecture for extracting semantics with varying granularity, thereby improving the robustness of the system. The fine-grained branch incorporates a semantic importance evaluation module to identify and prioritize crucial semantics, while the coarse-grained branch adopts a hierarchical approach for capturing the robust semantics. These two streams of semantics are seamlessly integrated via an advanced cross-attention-based semantic fusion module. Experimental results demonstrate the superior performance of DeepSC-RI under various levels of semantic impairment intensity.

Adapting large language models (LLMs) to unseen tasks with incontext training samples without fine-tuning remains an important research problem. To learn a robust LLM that adapts well to unseen tasks, multiple meta-training approaches have been proposed such as MetaICL and MetaICT, which involve meta-training pre-trained LLMs on a wide variety of diverse tasks. These meta-training approaches essentially perform in-context multi-task fine-tuning and evaluate on a disjointed test set of tasks. Even though they achieve impressive performance, their goal is never to compute a truly general set of parameters. In this paper, we propose MAML-en-LLM, a novel method for meta-training LLMs, which can learn truly generalizable parameters that not only performs well on disjointed tasks but also adapts to unseen tasks. We see an average increase of 2% on unseen domains in the performance while a massive 4% improvement on adaptation performance. Furthermore, we demonstrate that MAML-en-LLM outperforms baselines in settings with limited amount of training data on both seen and unseen domains by an average of 2%. Finally, we discuss the effects of type of tasks, optimizers and task complexity, an avenue barely explored in metatraining literature. Exhaustive experiments across 7 task settings along with two data settings demonstrate that models trained with MAML-en-LLM outperform SOTA meta-training approaches.

Flexible spot beams have been widely utilized in satellite communication systems to project high power and reuse frequency spectrum. To improve the system efficiency, beams share the same frequency channel and are only steered to the user terminals (UTs) which need to be served. Satellites with numerous beams can serve large numbers of UTs simultaneously, which may lead to severe inter-beam interference because of the non-uniform distributed UTs. Therefore, it is significant to study the effect of beam configuration parameters on the coverage performance to design an efficient satellite communication system. In this paper, we derive analytical expressions for the coverage probability and the average data rate of a flexible beam using stochastic geometry tools. We model the locations of beam centers as a nonhomogeneous spherical binomial point process whose intensity depends on the density of UTs. Meanwhile, the average sum rate of a satellite is derived using two different models of the locations of satellites. From the numerical results, the average sum rate of a satellite first increases but then levels off as the number of beams increases, which may provide insightful design guidelines on satellite communication systems.

In nongeostationary orbit (NGSO) satellite networks, multicast transmission is increasingly important to improve the system performance. Making full use of the beams to cover numerous nonuniform distributed users is a new challenge. User subgrouping techniques are utilized to optimize the coverage of beams and maximize the data rate. However, due to the high-speed movement of NGSO satellites, frequent handovers occur and the user subgroups should be updated frequently, aggravating network control overheads. This article proposes an Earth-fixed multicast user subgrouping scheme for NGSO satellite constellations to reduce network control overheads and improve network throughput (NT). In our proposed scheme, users are partitioned into multiple subgroups according to their geographical locations, and these subgroups remain the same while satellites keep moving along the orbit. First, we formulate a joint user subgrouping and beam optimization problem and decompose it into three subproblems by the approximation of models. An iterative greedy Earth-fixed user subgrouping algorithm is presented to obtain the user subgroups. Each user is chosen into a user subgroup which can maximize the NT, and then the position of each user subgroup is updated according to the positions of users in this subgroup. Next, the subgroup-satellite association relationship is optimized according to the maximum elevation angle criterion. Finally, a simple algorithm is proposed to optimize the direction and beamwidth of the beam serving each subgroup in each satellite. Simulation results demonstrate that our proposed scheme can reduce the update frequency significantly compared with the existing satellite-fixed user subgrouping scheme, and the NT achieved by our proposed scheme is improved by 1~3 times than conventional beam coverage scheme.

Human-oriented image communication should take the quality of experience (QoE) as an optimization goal, which requires effective image perceptual quality metrics. However, traditional user-based assessment metrics are limited by the deviation caused by human high-level cognitive activities. To tackle this issue, in this paper, we construct a brain response-based image perceptual quality metric and develop a brain-inspired network to assess the image perceptual quality based on it. Our method aims to establish the relationship between image quality changes and underlying brain responses in image compression scenarios using the electroencephalography (EEG) approach. We first establish EEG datasets by collecting the corresponding EEG signals when subjects watch distorted images. Then, we design a measurement model to extract EEG features that reflect human perception to establish a new image perceptual quality metric: EEG perceptual score (EPS). To use this metric in practical scenarios, we embed the brain perception process into a prediction model to generate the EPS directly from the input images. Experimental results show that our proposed measurement model and prediction model can achieve better performance. The proposed brain response-based image perceptual quality metric can measure the human brain's perceptual state more accurately, thus performing a better assessment of image perceptual quality.

Semantic communication is increasingly viewed as a promising solution to improve the transmission efficiency. However, semantic communications are susceptible not only to physical channel impairments, but also to semantic impairments, which degrade semantic understanding at the receiver and disrupt the associated downstream tasks. Hence, we focus our attention on the robustness of semantic communications against semantic impairments. Specifically, we first categorize textual semantic impairments into three categories based on their sources. Then, we propose a robust deep learning enabled semantic communication system (R-DeepSC) by introducing a semantic corrector for robust semantic encoding so as to facilitate semantic transmission. Moreover, we develop a non-autoregressive version of R-DeepSC, namely NA-RDeepSC, which offers improved inference speed by relying on a non-autoregressive architecture and an adaptive generator embedded into the semantic decoder. NA-RDeepSC performs semantic decoding in parallel, hence reducing the decoding complexity fromO(n) toO(1) with a comparable performance to that of R-DeepSC. Our experimental results demonstrate the superior robustness of the proposed R-DeepSC and NA-RDeepSC architectures in eliminating semantic impairments, hence highlighting the significance of this work in advancing the development of robust semantic communications.

A programmable packet parser with online configuration, which can identify protocols and extract key fields on demand at runtime, is essential to realize protocol upgrading for satellite networks. Furthermore, low complexity is urgently needed for the packet parser due to the onboard limited resources. However, traditional programmable packet parsers, with large storage redundancy, can only be configured offline. In this paper, we propose a Multi-Table Programmable Parser (MTPP), consisting of five tables and corresponding protocol independent fixed logic to represent a parser graph. Based on MTPP, online configuration is achieved by changing table entries at runtime. The numerical analysis under typical parse graphs demonstrates that MTPP uses less storage resource than that in PISA-based parser. The prototype on Xilinx FPGA shows that the response time of MTPP and the network is 1.635µs and 3.537ms and the size of configuration information is 88.7% less than that of PISA-based parser.

Due to the heterogeneity of Internet of Things (IoT) devices and the diversity of IoT communication protocols, it is challenging to model the communication behaviors of IoT devices to facilitate attack defense. Considering the complex correlation between the IoT device types and the patterns of their communication behaviors, one possible solution is to cluster IoT devices into different types based on the characteristics of their communication behaviors and deal with each type, respectively. However, IoT traffic includes a significant proportion of abnormal traffic, such as attack traffic sourcing from compromised devices, which cannot reflect the behavioral characteristics of the source device. In this article, we propose a Transformer-based IoT device-type identification method to address the above challenges. Specifically, our approach consists of three main components. First, we classify the traffic data from IoT devices into normal and abnormal types by a Transformer-based traffic diagnosis model. Next, another Transformer-based model is adopted on the normal traffic to identify the IoT device type. Finally, considering the immutability of IoT device types, a results-ensemble algorithm is designed to improve the accuracy of IoT device-type identification. Experimental results verify the effectiveness of our method, which brings a noticeable improvement in terms of both accuracy and macro$F1$-score compared to other methods. Moreover, by applying the results-ensemble algorithm in the test phase, we can achieve 100% accuracy under certain conditions.

This article investigates the joint design problem of the hybrid/reflective beamforming for the dual-function radar-communication (DRC) system with the help of an intelligent reflecting surface (IRS). The degrees of freedom for beamformer design are the analog and digital beamformers (DBF) at the base station (BS), and the reflective beamformer (RBF) at the IRS. To begin with, we formulate the beamformer design problem as a weighted sum-rate (WSR) maximization problem subject to the transmit power and similarity between the designed beamformer and the reference one with desired beampattern property. Then, utilizing equivalent reformulation of the preceding design problem, we conceive an alternating direction sequential programming (ADSP)-based algorithm that iteratively updates the following low-complexity subproblems: 1) DBF at the BS through the Powell–Hestenes–Rockafellar (PHR) method; 2) analog beamformer at the BS using the Riemannian conjugate gradient method; and 3) RBF at the IRS by the successive convex approximation (SCA) technique. The convergence is ensured by analytically offering the nondecreasing and boundary of the WSR value. Finally, numerical results verify the effectiveness of the proposed algorithm, which achieves superior WSR performance while ensuring the radar requirement.

Semantic communications has received growing interest since it can remarkably reduce the amount of data to be transmitted without missing critical information. Most existing works explore the semantic encoding and transmission for text and apply techniques in Natural Language Processing (NLP) to interpret the meaning of the text. In this paper, we conceive the semantic communications for image data that is much more richer in semantics and bandwidth sensitive. We propose an reinforcement learning based adaptive semantic coding (RL-ASC) approach that encodes images beyond pixel level. Firstly, we define the semantic concept of image data that includes the category, spatial arrangement, and visual feature as the representation unit, and propose a convolutional semantic encoder to extract semantic concepts. Secondly, we propose the image reconstruction criterion that evolves from the traditional pixel similarity to semantic similarity and perceptual performance. Thirdly, we design a novel RL-based semantic bit allocation model, whose reward is the increase in rate-semantic-perceptual performance after encoding a certain semantic concept with adaptive quantization level. Thus, the task-related information is preserved and reconstructed properly while less important data is discarded. Finally, we propose the Generative Adversarial Nets (GANs) based semantic decoder that fuses both locally and globally features via an attention module. Experimental results demonstrate that the proposed RL-ASC is noise robust and could reconstruct visually pleasant and semantic consistent image in low bit rate condition.

3D human pose estimation has a broad range of applications, including anomaly detection and animation creation. Despite that significant progress on relative research has been made during the past decades, producing precise and smooth estimations for input videos still remains challenging mainly because of its ill-posed attributes. In this paper, we propose HiMoReNet, a post-processing motion refinement neural network based on an elaborate hierarchical architecture. Firstly, we distinguish characteristic motion patterns of joints at different locations by grouping the joints and employing respective spatiotemporal processing modules for each group. In addition, by mimicking interactions among multiple body parts, global context information is leveraged to further guide the motion refinement. Quantitative and qualitative results on the 3DPW dataset demonstrate that our proposed HiMoReNet achieves the state-of-the-art performance, and excels in jitter removal and precise pose estimation.

Safety driver is one of the most important safety precautions to assure road safety in public road tests of autonomous vehicles (AVs), However, the decreasing vigilance of the safety driver has become a major cause of autonomous vehicle accidents in recent years. With the introduction of wireless, wearable Electroencephalography (EEG) devices and the enhanced computational capabilities of AVs, the ubiquitous monitoring of safety drivers as part of the emerging E-Health system has recently attracted great interest from researchers and industry. Unfortunately, we know little about the electrophysio-logical signals that assess safety driver vigilance. In this work, we propose a method for detecting driver vigilance based on EEG signals that combines frequency domain EEG features and a transformer model to maximize the prediction accuracy and recall rate. Experimental results show that trichotomous results based on the proposed model outperform previous dichotomous results, reaching 78% when tested with data from the Sustained-Attention Driving dataset published in Nature scientific data.

With the advent of the 6G era, the concept of semantic communication has attracted increasing attention. Compared with conventional communication systems, semantic communication systems are not only affected by physical noise existing in the wireless communication environment, e.g., additional white Gaussian noise, but also by semantic noise due to the source and the nature of deep learning-based systems. In this paper, we elaborate on the mechanism of semantic noise. In particular, we categorize semantic noise into two categories: literal semantic noise and adversarial semantic noise. The former is caused by written errors or expression ambiguity, while the latter is caused by perturbations or attacks added to the embedding layer via the semantic channel. To prevent semantic noise from influencing semantic communication systems, we present a robust deep learning enabled semantic communication system (R-DeepSC) that leverages a calibrated self-attention mechanism and adversarial training to tackle semantic noise. Compared with baseline models that only consider physical noise for text transmission, the proposed R-DeepSC achieves remarkable performance in dealing with semantic noise under different signal-to-noise ratios.

Unsupervised domain adaptation (UDA) becomes more and more popular in tackling real-world problems without ground truths of the target domain. Though tedious annotation work is not required, UDA unavoidably faces two problems: 1) how to narrow the domain discrepancy to boost the transferring performance; 2) how to improve the pseudo annotation producing mechanism for self-supervised learning (SSL). In this paper, we focus on UDA for semantic segmentation tasks. Firstly, we introduce adversarial learning into style gap bridging mechanism to keep the style information from two domains in a similar space. Secondly, to keep the balance of pseudo labels on each category, we propose a category-adaptive threshold mechanism to choose category-wise pseudo labels for SSL. The experiments are conducted using GTA5 as the source domain, Cityscapes as the target domain. The results show that our model outperforms the state-of-the-arts with a noticeable gain on cross-domain adaptation tasks.

Due to the heterogeneity of Internet of Things (IoT) devices and the diversity of IoT communication protocols, it is challenging to defend against malicious traffic from IoT devices. In this paper, a novel malicious traffic detection method is proposed based on the deep learning method. Specifically, a Transformer-based encoder is designed to automatically select key features of IoT traffic for the detection task, which avoids the cumbersome feature screening process that has been widely used in traditional machine learning methods. To address the complexity of the feature space and improve the efficiency of model training, we exploit the correlation between the characteristics of malicious traffic and the device type of IoT bots to further improve the detection accuracy by introducing a device classification auxiliary loss in the training phase. Experimental results show that our method outperforms the state-of-the-art machine learning-based methods in terms of accuracy, precision, recall and f1-score on real IoT traffic traces. In addition, the benefit of device type information on detection efficiency is verified.

We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9.3B, their subsequent distillation into smaller models ranging from 17M-170M parameters, and their application to the Natural Language Understanding (NLU) component of a virtual assistant system. Though we train using 70% spoken-form data, our teacher models perform comparably to XLM-R and mT5 when evaluated on the written-form Cross-lingual Natural Language Inference (XNLI) corpus. We perform a second stage of pretraining on our teacher models using in-domain data from our system, improving error rates by 3.86% relative for intent classification and 7.01% relative for slot filling. We find that even a 170M-parameter model distilled from our Stage 2 teacher model has 2.88% better intent classification and 7.69% better slot filling error rates when compared to the 2.3B-parameter teacher trained only on public data (Stage 1), emphasizing the importance of in-domain data for pretraining. When evaluated offline using labeled NLU data, our 17M-parameter Stage 2 distilled model outperforms both XLM-R Base (85M params) and DistillBERT (42M params) by 4.23% to 6.14%, respectively. Finally, we present results from a full virtual assistant experimentation platform, where we find that models trained using our pretraining and distillation pipeline outperform models distilled from 85M-parameter teachers by 3.74%-4.91% on an automatic measurement of full-system user dissatisfaction.

Due to the limited frequency spectrum resources, it is challenging to satisfy the Telemetry, Tracking, and Command (TT&C) requirements of mega-constellations in the near future. An intuitive idea is to compress the telemetry data with high redundancy, but outliers in telemetry data will significantly deteriorate the compression performance. Currently, outlier detection algorithms require a combination of expert experience and physical models, which have limitations in space environments, especially where unexpected failures undergo. To tackle these problems, this paper proposes an outlier detection method for telemetry data based on Kernel based Extreme Learning Machine (KELM). Additionally, to improve outlier detection accuracy, the Improved Sparrow Search Algorithm (ISSA), a novel swarm intelligence optimization technique, is adopted to optimize the algorithm parameters jointly. Take the actual telemetry data of the smart communication satellite of Tsinghua University as a case study to verify the superiority of the proposed algorithm. Also, the simulation results demonstrate that the proposed algorithm can efficiently improve the TT&C capacity of mega-constellations, even without additional ground TT&C stations and Tracking and Data RelaySatellites.

Satellite communications play an important role in future Internet of Things (IoT) networks, and megasatellite constellations can further provide global coverage and high-quality services for IoT communications. In the megaconstellation, large-scale satellites are launched to enhance the capacity. However, the dense distribution of satellites brings intraconstellation interference, limiting the performance. In order to evaluate the restriction of interference caused by system parameters, such as the scale of constellation or the frequency reuse factor, we investigate uplink intraconstellation interference and performance of the megasatellite constellation. First, a multibeam polar constellation with uplink spatial frequency reuse is assumed. Then, the interference model is constructed considering the antenna gain of interfering user terminals and multibeam satellites, where the details of the satellite-fixed frequency reuse scheme and coordinates of co-frequency cells are provided. To evaluate the performance, expressions of outage probability, ergodic capacity, and sum ergodic capacity are driven. The analytical results disclose the impact of system design on the performance, and the accuracy of analysis results is obtained through extensive simulation evaluation. The results show that sum ergodic capacity achieves highest in the case of full frequency reuse for the frequency-limited constellation system, and it gets a linear growth at first but then keeps flat with a trend of fluctuating downward as the scale increases; therefore, the impact of the scale should be considered when constructing megaconstellations.

Network slicing is one the key features of software-defined networks (SDNs) and can be used in next-generation communication networks. Admission control of network slices is the basis of providing the heterogeneous quality-of-service performance guarantee and maximizing the optimization objectives of the network operator. Various admission control mechanisms have been proposed in the literature, including those based on traffic forecasting. However, recurrent neural network-based probabilistic forecasting models have not been given thorough consideration for slice admission control. In this study, the network slicing scheme design problem is formulated mathematically, with an equivalent formulation of the constrained bandwidth-sharing scheme. Then, a DeepAR-based slice admission control mechanism is proposed for sequential decision making for network slice requests in SDN, with the support of the SDN controller. An improved variant is further proposed with a closed-loop parameter update mechanism. The experiments based on real-world historical traffic data validate the effectiveness of the proposed mechanisms, with metrics, including revenue, resource reservation and utilization ratios, and service admission ratio.

The thriving of artificial intelligence (AI) applications is driving the further evolution of wireless networks. It has been envisioned that 6G will be transformative and will revolutionize the evolution of wireless from “connected things” to “connected intelligence”. However, state-of-the-art deep learning and big data analytics based AI systems require tremendous computation and communication resources, causing significant latency, energy consumption, network congestion, and privacy leakage in both of the training and inference processes. By embedding model training and inference capabilities into the network edge, edge AI stands out as a disruptive technology for 6G to seamlessly integrate sensing, communication, computation, and intelligence, thereby improving the efficiency, effectiveness, privacy, and security of 6G networks. In this paper, we shall provide our vision for scalable and trustworthy edge AI systems with integrated design of wireless communication strategies and decentralized machine learning models. New design principles of wireless networks, service-driven resource allocation optimization methods, as well as a holistic end-to-end system architecture to support edge AI will be described. Standardization, software and hardware platforms, and application scenarios are also discussed to facilitate the industrialization and commercialization of edge AI systems.

Facial images are the main focused contents in video conferences and many other applications. Therefore, it turns to a critical issue to measure and maintain the perceptual quality of facial images if transmitted over a bandwidth-limited communication system. In this letter, we propose a regional distortion perceptual threshold measurement model based on electroencephalography (EEG) to establish the relationship between image quality and human perception. Then, a facial image compression method is presented based on the model to improve the perceptual quality. Specifically, we construct a facial image dataset with regional distortion using the better portable graphics (BPG) compression. With the dataset, we design an EEG experiment and collect the brain responses to measure the human perception on regional distortions. By this method, a regional distortion perceptual threshold map (RDPTM) is constructed to guide the data rate allocation process for different regions of facial images. The experimental results show that our method can measure the human perception of regional distortion using EEG and improve the image perceptual quality by data rate allocation based on the RDPTM.

As an alternative to traditional meteorological methods, rain attenuation in satellite-to-Earth microwave communication signals has been used for rainfall reconstruction in recent years. In this article, the existing 2-D rain field reconstruction problem is extended to a 3-D scenario by leveraging the low Earth orbit satellite system. A compressive sensing approach is further proposed to solve the 3-D rain field reconstruction problem. The Starlink system is used as a reference, and two synthetic rain events near the Great Barrier Reef in Australia, which are generated from the weather research and forecasting model, are used to evaluate the reconstruction performance. Simulation results show that the compressive sensing approach performs better than both the traditional least squares and the least absolute shrinkage and selection operator approaches.

With the explosion of multimedia data, quality of experience (QoE) has become a critical metric in multimedia transmission, and therefore, QoE-oriented image quality assess-ment (IQA) turns more important and urgent. However, the performance of the traditional user-based assessment methods is limited by the deviation caused by human cognitive activities. In this paper, we propose a brain-inspired IQA method based on electroencephalography (EEG) feature learning, which is a psychophysiological method for studying human perception for IQA. We first establish the EEG dataset by collecting the corresponding EEG signals when subjects watch distorted facial images and then design a siamese network to extract the EEG features that can distinguish image quality levels and measure user scores. The siamese network establishes the relationship between image quality and QoE that is reflected by the EEG scores. The relationship is then embedded into a prediction network that directly obtains the EEG scores from images with different qualities. In this way, EEG scores can be predicted through end-to-end learning. Experiment results show that our proposed method can not only better evaluate the perceptual quality of facial images and reflect real human perceptions but also achieve better score prediction performance on the facial image datasets.

This paper presents the Generative Adversarial Networks (GANs)-based image semantic coding, the goal of which is semantic exchange rather than symbol transmission. State-of-the-art visually pleasing reconstruction and semantic preserving performance are obtained in extreme low bitrate via a rate-perception-distortion optimization framework. In particular, we investigate convolutional encoder, quantizer, conditional SPADE generator, residual coding as well as perceptual losses. In contrast to previous work, we designed a coarse-to-fine image semantic coding model for multimedia semantic communication system. The base layer of the image is fully generated and preserves semantic information while the enhancement layer restores the fine details. We explore the perception and distortion performance trade-off by tuning the rate of base layer and enhancement layer. Different from the existing methods that adopt pixel accuracy as distortion metric, we train and evaluate the proposed image semantic coding model with multiple perception metrics, in line with the purpose of semantic communications. Experimental results demonstrate that our model could achieve visually pleasant and semantic consistent reconstruction, as well as saving times of bitrate, compared to BPG, WebP, JPEG2000, JPEG, and other deep learning-based image codecs.

With the emergence of more and more latency-sensitive applications and mobile devices pumped to the edge of network, the burden on the backhaul network is getting heavier and heavier due to the limited transmission resources. Mobile edge computing (MEC) considered as a promising technology becomes more and more popular, which can provide services at the edge of the network. In this paper, we take into account the mobility of users and focus on the problem of service migration. Most of existing works modeled a Markov Decision Process (MDP) model with a high-dimensional state space, and have to solve it by deep reinforcement learning. To tackle this issue, we propose a distributed two-layer decomposition model and generate a series of new MDP problem with Low-dimensional in order to replace original High-dimensional MDP. In our model, the size of state space is reduced from M2Nto N × M2 by decomposing the original optimization problem. Simulation results show that the performance of the proposed two-layer decomposition model is better than the baseline models.

With the rapid development of 5G networks, a great amount of Internet of Things (IoT) devices are connected to the Internet. Most of these devices are cost limited and thus are easily compromised by attackers to launch distributed denial of service (DDoS) attacks. The traditional DDoS defense methods at server side can not adapt to this new challenge, thus access-side DDoS detection architecture is urgently needed. In this paper, we propose a deep learning (DL) based IoT device classification method to support fine-grained behavior modeling of malicious traffic and thus enable access-side DDoS detection. Different from traditional studies based on machine learning (ML) which need expertise feature engineering, we propose a time characteristics extraction method based on 1-D convolutional neural network to capture high level time series features automatically for better classification performance. To avoid the feature loss problem, we propose a feature enhancement method based on residual connection module. Experimental results verify the effectiveness of our method, which offers a meaningful gain in terms of both accuracy and macro F1 score over existing approaches.

Structural scene graph based image generation provides a new paradigm for image-oriented semantic communications, whose goal is the semantic level rather than pixel-level reconstruction. The challenges include capturing relationships between objects and producing a reasonable geometric layout for each object accordingly. However, category information alone is not instructive enough for the generation process at the receiver side. Moreover, it is worth effort to extract the spatial dependencies among different objects in an image, therefore determine the object layouts on the whole instead of in an independent manner. In this paper, a deformable geometry framework for scene graph based image generation is proposed, in order to reconstruct images with higher semantic fidelity and visual pleasure. In particular, we introduce shape and appearance information to guide the generation process, from the scope of statistic modeling. Furthermore, we apply a spatial warping network to conduct geometric deformations on the layouts of different objects. Qualitative and quantitative experiments illustrate the superiority of our model compared to the state-of-the-art Sg2im method.

In a heterogeneous multi-core real-time system, once a permanent error occurs, the task cannot be successfully completed before the deadline, which may cause catastrophic consequences. Therefore, the reliability of the real-time system is critical. In this paper, we consider real-time tasks in a heterogeneous system with previous constraints, and explore how to improve the reliability of the system. We propose a new scheduling algorithm-PFTSA, which uses active replication to back up as many tasks on different processors as possible before the deadline, minimizing communication overhead, ensuring that the maximum number of permanent errors can be accommodated before the deadline and providing maximum system reliability. The experimental results show that the reliability of our proposed scheduling algorithm is higher than the existing related algorithms.

Time-sensitive services (TSSs) have been widely envisioned for future sixth-generation (6G) wireless communication networks. Due to its inherent low-latency advantage, mobile-edge computing (MEC) will be an indispensable enabler for TSSs. The random characteristics of the delay experienced by users are key metrics reflecting the Quality of Service (QoS) of TSSs. Most existing studies on MEC have focused on the average delay. Only a few research efforts have been devoted to other random delay characteristics, such as the delay-bound violation probability and the probability distribution of the delay, by decoupling the transmission and computation processes of MEC. However, if these two processes could not be decoupled, the coupling will bring new challenges to analyze the random delay characteristics. In this article, a MEC system with a limited computation buffer at the edge server is considered. In this system, the transmission process and the computation process form a feedback loop and could not be decoupled. We formulate a discrete-time two-stage tandem queueing system. Then, by using the matrix-geometric method, we obtain the estimation methods for the random delay characteristics, including the probability distribution of the delay, the delay-bound violation probability, the average delay, and the delay standard deviation. The estimation methods are verified by simulations. The random delay characteristics are analyzed by numerical experiments, which unveil the coupling relationship between the transmission process and computation process for MEC. These results will largely facilitate the elaborate allocation of communication and computation resources to improve the QoS of TSSs.

With the rapid development of marine activities, there has been an increasing number of Internet-of-Things (IoT) devices on the ocean. This leads to a growing demand for high-speed and ultrareliable maritime communications. It has been reported that a large performance loss is often inevitable if the existing fourth-generation (4G), fifth-generation (5G), or satellite communication technologies are used directly on the ocean. Hence, conventional theories and methods need to be tailored to this maritime scenario to match its unique characteristics, such as dynamic electromagnetic propagation environments, geometrically limited available base station (BS) sites and rigorous service demands from mission-critical applications. Toward this end, we provide a survey on the demand for maritime communications enabled by state-of-the-art hybrid satellite-terrestrial maritime communication networks (MCNs). We categorize the enabling technologies into three types based on their aims: 1) enhancing transmission efficiency; 2) extending network coverage; and 3) provisioning maritime-specific services. Future developments and open issues are also discussed. Based on this discussion, we envision the use of external auxiliary information, such as sea state and atmosphere conditions, to build up an environment-aware, service-driven, and integrated satellite-air-ground MCN.

The existing image compression methods usually choose or optimize low-level representation manually. Actually, these methods struggle for the texture restoration at low bit rates. Recently, deep neural network (DNN)-based image compression methods have achieved impressive results. To achieve better perceptual quality, generative models are widely used, especially generative adversarial networks (GAN). However, training GAN is intractable, especially for high-resolution images, with the challenges of unconvincing reconstructions and unstable training. To overcome these problems, we propose a novel DNN-based image compression framework in this paper. The key point is decomposing an image into multi-scale sub-images using the proposed Laplacian pyramid based multi-scale networks. For each pyramid scale, we train a specific DNN to exploit the compressive representation. Meanwhile, each scale is optimized with different aspects, including pixel, semantics, distribution and entropy, for a good "rate-distortion-perception" trade-off. By independently optimizing each pyramid scale, we make each stage manageable and make each sub-image plausible. Experimental results demonstrate that our method achieves state-of-the-art performance, with advantages over existing methods in providing improved visual quality. Additionally, a better performance in the down-stream visual analysis tasks which are conducted on the reconstructed images, validates the excellent semantics-preserving ability of the proposed method.

Virtual adversarial training (VAT) is a powerful technique to improve model robustness in both supervised and semi-supervised settings. It is effective and can be easily adopted on lots of image classification and text classification tasks. However, its benefits to sequence labeling tasks such as named entity recognition (NER) have not been shown as significant, mostly, because the previous approach can not combine VAT with the conditional random field (CRF). CRF can significantly boost accuracy for sequence models by putting constraints on label transitions, which makes it an essential component in most state-of-the-art sequence labeling model architectures. In this paper, we propose SeqVAT, a method which naturally applies VAT to sequence labeling models with CRF. Empirical studies show that SeqVAT not only significantly improves the sequence labeling performance over baselines under supervised settings, but also outperforms state-of-the-art approaches under semi-supervised settings.

The communication satellite equipped with phased array antennas can produce high power density by narrow spotbeams and thus can achieve high data rates. A large number of narrow spotbeams are required when the satellite provides multicast service for a group of distributed ground users, which limits the system capacity. In this paper, we propose a new satellite multicast scheme as taking advantage of beams with flexible direction and beamwidth generated by phased array antennas. Users are partitioned into multiple groups and an appropriate beam is allocated to each group, where all users in the same group are located in the mainlobe of the beam. It is formulated to jointly optimize user grouping and beamwidth for maximizing the average data rate of satellite and guaranteeing the quality-of-service (QoS) for users. Moreover, we develop an iterative algorithm to solve the problem with low complexity. Finally, simulation results show that our proposed method is superior to other schemes in terms of average data rate.

Image captioning is a challenging problem owing to the complexity of image content and the diverse ways of describing the content in natural language. Although current methods have made substantial progress in terms of objective metrics (such as BLEU, METEOR, ROUGE-L and CIDEr), there still exist some problems. Specifically, most of these methods are trained to maximize the log-likelihood or objective metrics. As a result, these methods often generate rigid and semantically incomplete captions. In this paper, we develop a new model that aims to generate captions conforming to human evaluation. The core idea is to use local-to-global semantic supervised learning by introducing the two-level optimization objective functions. At the word level, we match each word to the image regions using the local attention objective function; at the sentence level, we align the entire sentence and the image using the global semantic objective function. Experimentally, we compare the proposed model with current methods on MSCOCO dataset. We show that either local attention supervision or global semantic supervision is the necessary component for the success of our model through ablation studies. Furthermore, combining these two supervision objective functions achieves state-of-the-art performance in terms of both standard evaluation metrics and human judgment.

Audio quality greatly affects users evaluation of multimedia communication, especially when the communication signal is disturbed, the noise in audio and video will decrease the quality of user experience. Psychophysiological indicators have high time resolution and precision, which can be used as important quality of experience characteristics. In this paper, electroencephalography is used as a psychophysiological method to assess brain connectivity in response to perceive the noise under different scenario. Specifically, we first record the response of the subjects' brainwaves to the audio quality using a high resolution electroencephalogram. Then, directed transfer function is used to analyze the directional information flow intensity between channels in the frequency domain, and 10% directed transfer function value are selected to construct the edge set of the directed graph to obtain the brain connectivity graph. Finally, the human perception to audio noise is obtained by using the weighted degree clustering method. In addition, the effectiveness of above results is verified by the small-world network coefficients experiment.

Automated maritime object detection is a significant research challenge in intelligent marine surveillance systems for the Internet of Things (IoT) and smart ocean applications. In particular, ship detection is recognized as one of the core research issues of these IoT-driven intelligent marine surveillance systems. Traditional methods based on machine learning have made some achievements in detection tasks for specific objects. However, the ship objects are relatively small, and they are usually not accurately detected. In this article, we propose an electroencephalography (EEG)-based maritime object detection algorithm for IoT-driven surveillance systems in the smart ocean. For this purpose, we conduct experiments to record the EEG signals of subjects when they are watching the maritime image scenes. With the feature analysis of EEG signals, the event-related potential (ERP) components associated with detecting objects are induced, such as the$P3$and$N2$components. Employing classification based on linear discriminant analysis (LDA), the area under curve (AUC) of the receiver operating characteristic (ROC) is used to evaluate the detection accuracy. We use this novel method to determine and identify essential objects and areas from IoT devices, such as digital camera imaging sensors. Our proposed method can not only help to detect small objects accurately using fewer samples but can also be used to reduce the data volume needed to be stored and transmitted in IoT-driven marine surveillance systems.

Quantum secure direct communication (QSDC) is capable of direct confidential communications over a quantum channel, which is achieved by dispensing with the key agreement channel of the well-known quantum key distribution (QKD). However, to make QSDC a practical reality, we have to mitigate its reliance on quantum memory, its immediate communication interruption caused by eavesdropping and its low transmission reliability due to the heavy qubit losses. Hence a new QSDC protocol is proposed based on a sophisticated coded single-photon DL04 QSDC protocol to tackle the open challenges. In particular, quantum memory is dispensed with and a high-accuracy secrecy capacity estimate is derived for this protocol by conceiving dynamic joint encryption and error-control (JEEC) coding. We demonstrate that this quantum-memory-free DL04 QSDC (QMF-DL04 QSDC) protocol inches closer to the quantum channel's capacity and significantly improves the original DL04 QSDC's robustness. Moreover, a rate-compatible low-rate JEEC coding scheme is designed for the proposed framework, and the JEEC code advocated is shown to approach the secrecy capacity, despite tolerating an extremely high loss of qubits in the time-varying wiretap channel. Our simulations and experimental results demonstrate that the QMF-DL04 QSDC scheme significantly increases both the secure information rate and the communication distance of the original DL04 protocol.

Compressing large images with a generative model goes beyond typical image encoding standards under a notably low bitrate. In this paper, we step toward practical generative compression systems based on recent advances. Specifically, we show that the channel redundancy of the latent representation produced by an autoencoder network can be effectively compressed via mask compression. The mask compression performs quantization on the channel variance of latent representation instead of original values. Instead of training multiple models, changing the mask leads to a simple and efficient variable rate compression scheme. Then, we estimate the relative bitrate by measuring the L1 norm of the channel variance and hence obtain the rate-distortion formulation. The L1 regularizer assumes a Laplacian prior on the channel variance, through which model we develop corresponding methods to produce approximate images at a target bitrate. This eliminates the need for manually searching hyperparameters for our variable-rate compression. We conduct exhaustive experiments to demonstrate the advanced performance of the proposed method in preserving image quality and semantics.

Deep learning has been successfully introduced for 2D-image denoising, but it is still unsatisfactory for hyperspectral image (HSI) denosing due to the unacceptable computational complexity of the end-to-end training process and the difficulty of building a universal 3D-image training dataset. In this paper, instead of developing an end-to-end deep learning denoising network, we propose a hyperspectral image denoising framework for the removal of mixed Gaussian impulse noise, in which the denoising problem is modeled as a convolutional neural network (CNN) constrained non-negative matrix factorization problem. Using the proximal alternating linearized minimization, the optimization can be divided into three steps: the update of the spectral matrix, the update of the abundance matrix and the estimation of the sparse noise. Then, we design the CNN architecture and proposed two training schemes, which can allow the CNN to be trained with a 2D-image dataset. Compared with the state-of-the-art denoising methods, the proposed method has relatively good performance on the removal of the Gaussian and mixed Gaussian impulse noises. More importantly, the proposed model can be only trained once by a 2D-image dataset, but can be used to denoise HSIs with different numbers of channel bands.

Human perception sensitivity to video distortion is vital for visual quality assessment (VQA). Different from the perception mechanism of image distortion that has been thoroughly studied, the perception of video distortion is inevitably influenced by motion of dynamic content due to the characteristics of the human visual system (HVS). In this paper, electroencephalography (EEG) is used as a novel psychophysiological method to study the human perception sensitivity to quantification-aroused video distortion under various content motion conditions. For this purpose, we conduct experiments to record the EEG signals of the subjects when they are watching distorted videos. According to the feature analysis of EEG data, the P300 component aroused by human perception of video quality change is selected as the indicator of human perception of distortion. By the means of classification based on linear discriminant analysis (LDA), it is found that the separability of the P300 component, which is measured by the area under curve (AUC) of the receiver operating characteristic (ROC), is positively correlated with the perceptibility of distortion. The correlation provides a valid psychophysiological method, which is exempt from being influenced by subjective bias due to human high-level cognitive activities, for evaluating distortion perceptibility. In addition, the regression analysis results demonstrate a sigmoid-typed quantitative relation between the perceptibility of distortion and separability of the P300 component. Based on such relation, the perceptibility thresholds of distortion corresponding to various content motion speeds are calibrated by EEG signals and it is found that the content motion speed has a significant impact on distortion perceptibility.

To meet the ever-increasing demands for mobile video streaming, proactive caching over the network edge has been proposed as a promising solution for next generation wireless networks. In this paper, we consider the trace-driven cache-enabled video streaming design in the scenario of a metropolis to boost the spectral efficiency on the system side and the quality of experience (QoE) on the user side. A novel scheme to jointly provide proactive caching, power allocation, user association and adaptive video streaming is designed via the formation of a QoE-aware throughput maximization problem. Specifically, the caches are refreshed in the content placement phase according to the resource status and expected traffic, which is obtained by exploring the traces collected over a big city. In addition, users need to be associated with a proper small base station (SBS) in the content delivering phase to provide the highest attainable rate. We demonstrate the effectiveness of the proposed scheme via experiments conducted over real user trace datasets.

Random access is a key technology in satellite communication, as a large number of machine- type communication (MTC) terminals accessing the satellite makes it difficult to guarantee the access quality. Irregular repetition slotted ALOHA (IRSA) is one random access protocol relying on transmitting irregular number of replicas in multiple time slots, achieving a peak throughput at 0.8 in practical implementations. However, the probability of sending a certain number of replicas stays the same when given degree distribution, without considering the effects of different loads, which means there are extra useless packets sent and brings power waste in IRSA. Therefore, enhanced irregular repetition slotted ALOHA (EIRSA) based on tracking degree distribution control (TDDC) algorithm is proposed in this paper with adaptive degree distribution adjustment scheme to reduce the number of replicas while maintaining the same access performance with adaptation. Simulation results show that proposed protocol can achieve higher performance at the same power level and it is adaptive to load change.

Heterogeneous networks (HetNets) can improve resource efficiency and coverage range in cellular networks to meet the growing demand for wireless data rate. The main challenges faced by HetNets are load balancing and interference coordination, which needs to be addressed by effective user association and resource allocation (UARA) methods. In this paper, we propose a mobility- aware centralized reinforcement learning (MCRL) framework in order to achieve global optimality of dynamic resource allocation. A centralized agent is defined to select the values of the hyper parameters for UARA according to the real-time status of all users in HetNets. Besides, the state of the art Actor-Critic technique is employed in the training process to guarantee the convergence and performance of the agent's policy. Simulation results demonstrate the effectiveness of the proposed method and show the performance gain under different user distributions.

In this paper, the geometry-aware GAN is proposed to address the issue of facial attribute transfer with unpaired data. To tackle the unpaired training sample problem, the CycleGAN architecture is applied, where the bilateral mappings between the source and target domains are learned. The deformation flow is learned to capture the geometric variation between two domains. We first warp the source face into desired pose and shape according to the flow. Then, the transfer sub-network is designed to refine the results by hallucinating new components on the warped image. The attribute is removed by the reconstruction sub-network, coupled with the warping process. Experiments on benchmark demonstrate the advantages of our method compared to baselines.

Recently, deep neural network (DNN)-based image compression methods have achieved impressive results. These methods generally use thumbnail images or crop small patches from high-resolution images to train their networks. Instead of using patch-based training mode, we propose a novel DNN-based image compression framework in this paper. We apply the Laplacian pyramid to construct a multi-scale image representation. By learning the increasingly detailed representations, the proposed method is able to progressively restore an image. Furthermore, we use the adversarial networks for training to encourage the perceptual quality of the reconstructed image. Particularly at low bitrates, our model can only store the global semantics of an image and automatically synthesize the texture to achieve high subjective quality. Experimental results on demonstrate that our method achieves state-of-the-art performance, with advantages over existing methods in terms of visual quality.

The unmanned aerial vehicle (UAV) is a promising enabler of the Internet of Things (IoT) vision, due to its agile maneuverability. In this paper, we explore the potential gain of UAV-aided data collection in a generalized IoT scenario. Particularly, a composite channel model, including both large-scale and small-scale fading is used to depict typical propagation environments. Moreover, rigorous energy constraints are considered to characterize IoT devices as practically as possible. A multiantenna UAV is employed, which can communicate with a cluster of single-antenna IoT devices to form a virtual MIMO link. We formulate a whole-trajectory-oriented optimization problem, where the transmission duration and the transmit power of all devices are jointly designed to maximize the data collection efficiency for the whole flight. Different from previous studies, only the slowly varying large-scale channel state information is assumed available, to coincide with the fact that practically it is quite difficult to predictively acquire the random small-scale channel fading prior to the UAV flight. We propose an iterative scheme to overcome the nonconvexity of the formulated problem. The presented scheme can provide a significant performance gain over traditional schemes and converges quickly.

3-D reconstruction is at the core for many driving applications of Internet of Intelligent Vehicles. Previous works on reconstruction of a 3-D point shape commonly use a two-step framework. Precisely localizing a series of feature points in an image is performed on the first step. Then the second procedure attempts to fit the 3-D data to the observations to get the real 3-D shape. Such an approach has high time consumption, and easily gets stuck into local minimum. To address this problem, we propose a method to jointly estimate the global 3-D geometric structure of car and localize 2-D landmarks from a single viewpoint image. First, we represent the 3-D shape with a set of predefined shape bases, while parametrizing it by the coefficients of the linear combination of them. Second, we adopt a cascaded regression framework to regress the global shape encoded by the prior bases, by jointly minimizing the appearance and shape fitting differences. The position fitting item can help cope with the description ambiguity of local appearance, and provide more information for 3-D reconstruction. We apply the proposed approach on a multiview car dataset. Experimental results demonstrate favorable improvements on pose estimation and shape prediction, compared with some previous methods.

Pricing is an effective mechanism for network resource allocation that can be used to achieve a desirable balance between efficiency and fairness. However, sophisticated utility models are needed to guarantee the performance of price-based resource allocation, especially with regard to video transmission. Among various performance indices of video transmission, rebuffering is an important one that influences user quality of experience (QoE). Therefore, a price-based bandwidth allocation scheme for a dynamic adaptive streaming over hypertext transfer protocol (DASH) system is proposed for mitigating the effect of rebuffering on QoE. The utility model of the proposed scheme considers the relationship between the allocated bandwidth and the rebuffering length, as well as the effect of rebuffering length on QoE. Specifically, electroencephalography (EEG) experiments are conducted, and the distribution of the subjects' time limits at which rebuffering arouse negative emotions is used for calibration. Based on this model, the DASH server collects the buffer state information of all DASH clients periodically to adjust its bandwidth allocation. Assuming the server protects itself from congestion by pricing the clients' requested bandwidth, a Stackelberg game is formulated to study the joint utility maximization on the revenue of the server and the utility of the clients. The Stackelberg equilibrium of the game is characterized, and an efficient searching algorithm is proposed for its solution. EEG experiments are repeated on another group of subjects to verify the generalization ability of the results, and simulation results are presented to validate the effectiveness of the proposed algorithm. The proposed algorithm is shown to have low complexity and outperforms both traditional price-based scheme and QoE maximized scheme in terms of rebuffering.

Quality of experience (QoE) serves as a direct evaluation of users' experiences in mobile video transmission and thus essential for network management, such as network optimization. In this paper, we propose a deep learning-based QoE prediction approach with a large-scale QoE dataset for mobile video transmission. Specifically, we develop a mobile phone application for collecting user QoE data when viewing videos transmitted over the mobile internet in a practical environment. Then, we construct a large-scale dataset by collecting over 80000 piece of data with four kinds of subjective scores and 89 network parameters. Each QoE metric is related to only some of the 89 network parameters. Therefore, we apply the feature selection method to find the feature parameters related to user scores. Additionally, the boxplot method is used to clean the raw data by removing outliers. Finally, a deep neural network (DNN) is developed to learn the relationships between the network parameters and the subjective QoE scores. The proposed DNN can also be seen as a data-driven objective QoE prediction approach for mobile video transmission, which can be used to predict the user QoE scores. The experimental results show that the proposed approach can effectively remove most features irrelevant to QoE prediction. Moreover, the performance of QoE prediction by the proposed model outperforms other state-of-the-art approaches.

Pushing contents from content providers (CPs) directly to user equipments (UEs) during off-peak hours can significantly reduce the incurring traffic during peak hours. Both wired and wireless transmission costs are considerable in this scenario, especially in current cellular networks where the backhaul is regarded as a bottleneck of transmission. In order to alleviate the traffic pressure over the network without loss of users' quality of experience, this paper presents a joint wired and wireless transmission scheme which considers grouping, subchannel allocation, wired routing, and wired bandwidth allocation. An iterative algorithm is proposed to achieve the tradeoff between those two kinds of traffic. The users in the same group are served by a single multicast transmission from a base station (BS), while the BSs which multicast the same content form a multicast tree in backbone wired network. Compared with traditional unicast scheme, our approach can reduce the wired, wireless, and total traffic by 23%, 46%, and 34% at most according to the simulation results.

Although the recent success of convolutional neural networks (CNNs) greatly advance the semantic segmentation of the natural images, few work has focused on the remote sensing images, especially the synthetic aperture radar (SAR) images. Specifically, the existing methods do not consider the speckle noise of the SAR images and the multi-scale characteristics contained in the SAR images. In this paper, we propose a multiscale convolutional neural network (CNN) model for SAR image semantic segmentation. The multi-scale CNN model includes noise removal stage, convolutional stage, feature concatenation stage and classification stage. In particular, we construct a sparse representation loss function to obtain a clear SAR image in noise removal stage. Then, the multi-scale convolutional stage is employed to learn the multi-scale deep features. The concatenation stage is used to connect the features with different scales and depths. Finally, softmax classifier is developed to obtain the labels of the SAR images with the multi-scale CNN model being trained in an end-to-end way. The experimental results on synthetic and real SAR images demonstrate the effectiveness of the proposed method.

In this paper, we present the boundary objectness network (BON), an effective convolutional neural network (CNN) for object detection. Its core contribution is to accurately localize the objects. Generally, the CNN-based localizers predict four bounding box coordinates by learning a regression function. This method shows a low Intersection-of-Union (IoU) with the ground truth box. In our work, the localization is formu-lated as a probabilistic problem. Specifically, the deep features inside the candidate proposal are mapped into a row and a column feature vector, which are called boundary object-ness. The boundary objectness indicates the existence of an object in the horizontal and vertical direction of the proposal, enabling us to elaborately localize the object. Moreover, the modules of object detection share the common convolution-al layers. Meanwhile, a multi-task loss function is designed for joint training strategy. Experimental results on the PAS-CAL VOC datasets demonstrate the competitive performance of our method. For the VGG16 model, we achieve 77.6 % mAP at a speed of 4 frame per second (FPS), thus having the potential for real-time processing.

In this paper, we propose an architecture of cloud based integrated terrestrial-satellite networks, in which satellite and terrestrial networks that belong to the same operator cooperatively provide seamless coverage for mobile users. Meanwhile, a resource pool at the cloud acts as the integrated resource management and control center of the entire network. Then, based on the delay constraint of users, we formulate the resource allocation problem for the operator to minimize the energy consumption. By decomposing the optimization problem into two subproblems and utilizing the theory of multidimensional knapsack problem, we eventually obtain the optimal resource allocation strategies for the operator. Furthermore, numerical results are provided to evaluate the performance of the proposed strategies.

Conditional random filed (CRF) model relaxes the conditional independence of the observed data and simultaneously captures the spatial contextual information. However, the single spatial contextual model is difficult to describe the heterogeneous structures of the synthetic aperture radar (SAR) images. This paper propose an semantic conditional random field (SCRF), which integrate the semantic space and pixel space for object-based SAR image segmentation. Specifically, the SAR image is divided into aggregated, structural and homogeneous subspaces by using the hierarchical semantic model. Then, we design gaussian kernel function, geometric kernel function and uniform kernel function to adaptively describe the spatial contextual constraints in the different subspaces. These kernel functions are incorporated into the pairwise potential of CRF model to improve the ability of the model. Afterwards, the piecewise training and Bayesian inference are proposed to achieve the object-based segmentation. Experiments on the synthetic and real SAR images demonstrate the effectiveness of the proposed method in the semantic consistency and detail preservations.

State-of-the-art semantic segmentation methods adopt fully convolutional neural networks (FCNs) to solve this dense prediction problem. However, replacing fully connected layers with the standard 2D convolution layer is straightforward yet not optimal in generating segmentation results. In this paper we develop a dense convolution scheme that is more suitable for semantic segmentation. Instead of generating a single output, dense convolution produces the same number of output as its input and introduces spatial overlaps into current convolutions. Then each activation is obtained from multiple overlapped dense convolutions with learnable weights. Such dense convolution helps to reinforce local connections between activations and provide more flexible receptive fields for predictions. Experiments on benchmark dataset demonstrate the effectiveness of the proposed approach in semantic segmentation tasks.

Hyperspectral image (HSI) denoising plays an important role to enhance the image quality for subsequent applications. This paper proposes a novel denoising framework for the HSI, in which the denoising issue is modeled as a convolutional neural network (CNN) constrained non-negative matrix factorization problem. Then, by adopting the proximal alternating linearized minimization, the proposed approach can be decomposed into two iterative steps: In the first step, the spectral matrix is updated using a proximal operator to guarantee the non-negativity; in the second step, the abundance matrix is updated using a designed CNN. Finally, we propose a transfer learning scheme to train the design CNN with a natural gray image dataset. Exhaustive experiments show that the proposed approach outperforms the comparison state-of-the-art methods on two different test HSI datasets.

Digital channelizers have been considered as the key technique for extraction and recon struc tion of multi-band signals, in which the complex-exponential modulated filterbank (CEMFB) is corroborated to be an effi cient implementation structure. Previous works for designing filterbanks were generally difficult to adapt to the high-order scenarios or simply assumed the perfect reconstruction (PR) condition which resulted in high implementation costs. Considering these problems, this paper proposes an efficient design method of CEMFB based channelizers with adjustable reconstruction performance. We reanalyze the PR constraints and formulate an optimization problem based on the improved quadratically constrained quadratic programming (QCQP) model. Simulation results indicate that the proposed method can effectively achieve arbitrarily adj usta ble reconstruction performance, as well as high channel resolution and high stopband attenuation. Therefore, the filter length can be reduced significantly, which brings remarkable reduction in implementation costs.

Wireless on-board bus system can not achieve the same performance of transmission delay and th roug hput when compared to the wired one. In this paper a multi-channel media access control (MAC) protocol is proposed. The protocol takes advantage of the coexistence of the multiple chan nels pro vided by the wireless standard. Three consecutive session, request session, schedule session and transmission session, make up the superframe in the star topology. In order to handle pac kets of different real-time requirements efficiently, packets produced in each slave node are divided into three queues of different quality of service $(~\mathrm {Q}\mathrm {o}\mathrm {S})$. During the schedule session, the earliest deadline first (EDF) scheduling based on the global queue achieves the better channel utilization when it is only ex ecuted on the lo cal one. Simulation results indicate that the proposed multi-channel MAC protocol achieves better performance of transmission delay and throughput.

Brain-Inspired Systems (BIS) are an emerging field of brain and intelligence sciences that studies natural intelligence models of AI and cognitive systems in one direction, and the formal models of the brain simulated by computational intelligence in another direction. A typical BIS is the cognitive robots that mimic and implement the brain through all cognitive levels. BIS provides insights for brain-machine interfaces (BMI), which may lead to novel man-machine interactions and hybrid intelligent systems. BIS may also advance classic computers from dada processors to the next generation of knowledge processors mimicking the brain. BIS will underpin a wide range of engineering paradigms such as cognitive systems, cognitive computers, cognitive robots, machine learning systems, semantic comprehension systems, big data systems, unmanned systems, self-driving vehicles and hybrid man-machine systems.

In this paper, we investigate the downlink cooperative multigroup multicast transmission in the integrated terrestrial-satellite network, in which base stations (BSs) and the satellite provide the multicast service for ground users in a cooperative manner while reusing the entire bandwidth. For both terrestrial BSs and the satellite, multiantennas are equipped and beamforming techniques are utilized for improving the system performance. Based on the architecture, we formulate a weighted max-min fair (MMF) beamforming design problem to jointly optimize the beamforming vectors of BSs and the satellite, which is solved based on the relation between the quality of service problem and the MMF problem. When it comes to the large scale case, where large numbers of BSs are distributed within the coverage of the satellite, we propose a time division cooperative multigroup multicast scheme consisting of two phases for the BSs and the satellite. Then, an iterative algorithm is proposed to solve the weighted MMF problem in the time division case. Finally, numerical results are provided to evaluate the cooperative multicast schemes as well as the proposed algorithms.

Estimating 3D shape of an object from 2D observations in a monocular image is fundamentally an inverse problem due to the ambiguity of the projection from 3D to 2D and becomes more challenging when there are undesirable outliers in the observations. In this paper, we develop a robust model to estimate 3D shape from 2D landmarks with an unknown camera pose. The 3D shape of the object is assumed as a linear combination of a group of prior shape bases. At the same time, we explicitly model the outliers as sparse noises to handle severely contaminated observations. The objective function is nonconvex and nonsmooth constrained on Stiefel manifold, where the coupling of underdetermined shape representation coefficients and camera pose makes it more difficult to solve. We first propose a numerical algorithm based on alternating direction method of multipliers for the no-outlier case. We set the orthogonality constraints into the smooth subproblem, which admits a closed-form solution, and the other subproblems are all well known and can be easily solved. We then extend this algorithm to the proposed robust model. The proposed algorithms can achieve convergence rapidly. The experimental results on both synthetic data and real data show that the proposed method outperforms the other methods.

This paper presents a latency-efficient mobile video streaming design in the context of metropolis by incorporating caching. It shows that video traffic can be substantially offloaded from backhaul by caching predictable demands in the network edge. Notably, exploiting the spatial and temporal characteristics of video popularity, we focus on two sub problems: how to cache the content and how to associate users. Firstly, we investigate cache deployment strategy based on clients' viewing behavior in both downtown and suburb. The proposed hybrid collaborative filtering (CF)-based scheme guarantees high hit rate utilizing the available storage capacity in small base stations (SBS). Further, we formulate the dynamic user equipment and SBS (UE- SBS) optimal association problem into a convex optimization problem, so as to maximize the sum transmission rate of SBSs under the resource and quality-of-service constraint. Performance evaluation of real trace data demonstrates the significant advantage of our proposed framework.

Nonparametric Bayesian models have been implemented in dictionary learning. However, for signal samples from multiple subspaces, existing methods only learn one uniform dictionary and thus are not optimal for representing the subspace structures. To address this issue, we first utilize a combination of Dirichlet process and hierarchical Beta process as priors to infer the latent subspace number and dictionary dimension automatically; second, to derive tractable variational inference, we modify the priors with the Sethuraman's construction and further employ the multinomial approximation. Experimental results indicate that our approach can achieve a set of nonparametric subspace dictionaries, while showing performance enhancements in the tasks of image denoising.

In data relay satellite (DRS) systems, the performance of tasks scheduling is influenced by the variation of task and resources, which degrades the processing capacity of relay satellites. Considering this problem, we investigate the dynamic scheduling in the application of DRS. To achieve the efficient resource utilization and reliable data transfer, the strategies of task preemptive switching and decomposition are designed. Based on the initial scheme, we construct a dynamic scheduling model with multiple objectives, including maximizing the total weight of scheduled tasks, minimizing the change of scheduling scheme and minimizing the number of decomposed subtasks. Meanwhile, a preemptive dynamic scheduling algorithm (PDSA) is designed to solve the proposed model. Explicitly, our simulation results show that PDSA is superior to the whole rescheduling algorithm (WRA) in quantities of completed tasks, rescheduling rate of scheme and processing time, which can efficiently improve the performance of dynamic scheduling in DRS systems.

The conventional sparsity-based compressive sensing (CS) has been extended to a more general framework based on the broad class of manifold models. Although some existing manifold-based CS methods use a mixture of factor analyzers to discover the low-dimensional geometric structures of the signals, they have two issues that may limit their practical use: First, the signal representation using manifolds assumes that the mixture components are independent and thus misses the potential overlapping structure of the factors; Second, the mixture model is not analytically tractable and requires a time-consuming stochastic technique. In this paper, we address these issues by: 1) capturing the correlation structure of mixture components via a hierarchical Beta process which is built with the Sethuraman's stick-breaking construction in the nonparametric Bayesian manner; 2) deriving an efficient variational inference for the modified model with the assistant of multinomial approximation. Experimental results on real dataset indicate that our proposed approach can outperform state-of-the-art manifold-based inversion algorithms in the application of CS reconstruction, while exhibiting satisfying time consumption compared to the stochastic sampling schemes.

This paper investigates a multimedia multicast beamforming scheme in the integrated terrestrial-satellite networks, where base stations (BSs) and the satellite work cooperatively provide ubiquitous services for ground users. Due to the contents diversity of multimedia services, users that request the same contents can be served as a group using multicasting. By utilizing multiple transmission antennas, multicast beamforming is performed among groups while reusing the entire bandwidth, which, however, can inevitably cause the co-channel interference among users. Taking both system performance and user fairness into account, we optimize the total system capacity performance under the satellite capacity constraint and derive the optimal power allocation schemes. Numerical results are presented in the end to evaluate the effectiveness of the proposed scheme compared with the greedy and suboptimal searching strategies.

The increasing traffic demand in both ground and satellite communication systems will lead to increasing spectrum demand. Spectrum sharing would become a challenging issue in future between terrestrial and satellite systems with frequency reusing, as well as the interference management. Upon this, we propose the concept of the Cloud Based Integrated Terrestrial-Satellite Network (CTSN), where both base stations of the cellular networks and the satellite are connected to a cloud central unit and the signal processing procedures are executed centrally at the cloud. By utilizing the channel state information (CSI), the interference from the mixed signal can be mitigated. When it comes to the case of imperfect CSI, we propose a resource allocation scheme in respect to subchannel and power to maximize the total capacity of the terrestrial system while limiting the total interference to the satellite. The optimization problem is solved by means of the dual decomposition method. Simulation results are provided to evaluate the effectiveness of the algorithm.

Requirements for higher resolution remote sensing images lead to rapid increase of data amount in space communications. However, since satellite communications capacity is suffering from great pressure, seeking for more effective compression scheme is supposed to solve existing conflict between tremendous data and limited bandwidth. For this reason, this paper proposes a prior-information-based remote sensing image compression scheme. We firstly utilize prior information contained in historical remote sensing images for incremental image extraction, which is assumed to have removed redundant information possessed both on the satellite and ground. Moreover, Bayesian dictionary serves to sparsely represent the incremental image, generating finite number of representation coefficients in place of numerous pixels. Finally, quantization and encoding schemes are further designed for efficient data transmission. Experimental results show that the proposed scheme is competitive to existing general image compression schemes.

A class of check-hybrid low-density parity-check (CH-LDPC) codes is designed and implemented to mitigate the problem of periodical signal blockage over the helicoptersatellite channels. The CH-LDPC code is derived by replacing one layer check nodes of a quasi-cyclic LDPC (QC-LDPC) code by simplex code constraints or first-order Reed-Muller (RM) code constraints, based on the property that the check matrix of the QC-LDPC code has a layered structure. Moreover, extrinsic information transfer (EXIT) functions are established to analyze the iterative decoding performance of the CH-LDPC codes. Simulation results show that, compared with the QCLDPC coding scheme, the CH-LDPC coding scheme designed in this paper achieves more than 25% bandwidth efficiency improvement over the helicopter-satellite channels.

This work investigates a statistical technique for high performance remote-sensing imagery compression. By exploiting existing remote-sensing data sets, useful structural and texture prior information can be learned. The main methodologies are Bayesian dictionary learning and stochastic approximation. A Bayesian network simulating the generation mechanism of remote- sensing images is modelled. The whole compression scheme is established. And the corresponding inference algorithm using Gibbs sampling is given, where the inference is realized in an online way. The performance of the proposed compressing scheme is evaluated over a high-resolution remote-sensing image data set captured by TH-1 series satellites. Experiment results have shown that our compression scheme outperforms JPEG-2000 by 3dB on average with same bits-per-pixel performance, and that Bayesian learning can provide a dictionary with high expressiveness for remote-sensing images. In addition, with online learning skills our proposed compression scheme can scale up to very large-scale training data.

Millimeter-wave (mmWave) communication is envisioned to provide orders of magnitude capacity improvement. However, it is challenging to realize a sufficient link margin due to high path loss and blockages. To address this difficulty, in this paper, we explore the potential gain of ultra-densification for enhancing mmWave communications from a network-level perspective. By deploying the mmWave base stations (BSs) in an extremely dense and amorphous fashion, the access distance is reduced and the choice of serving BSs is enriched for each user, which are intuitively effective for mitigating the propagation loss and blockages. Nevertheless, co-channel interference under this model will become a performance-limiting factor. To solve this problem, we propose a large-scale channel state information (CSI)-based interference coordination approach. Note that the large-scale CSI is highly location-dependent, and can be obtained with a quite low cost. Thus, the scalability of the proposed coordination framework can be guaranteed. Particularly, using only the large-scale CSI of interference links, a coordinated frequency resource block allocation problem is formulated for maximizing the minimum achievable rate of the users, which is uncovered to be an NP-hard integer programming problem. To circumvent this difficulty, a greedy scheme with polynomial-time complexity is proposed by adopting the bisection method and linear integer programming tools. Simulation results demonstrate that the proposed coordination scheme based on large-scale CSI only can still offer substantial gains over the existing methods. Moreover, although the proposed scheme is only guaranteed to converge to a local optimum, it performs well in terms of both user fairness and system efficiency.

In this paper, we investigate the downlink transmission of a non-orthogonal multiple access (NOMA)-based integrated terrestrial-satellite network, in which the NOMA-based terrestrial networks and the satellite cooperatively provide coverage for ground users while reusing the entire bandwidth. For both terrestrial networks and the satellite network, multi-antennas are equipped and beamforming techniques are utilized to serve multiple users simultaneously. A channel quality-based scheme is proposed to select users for the satellite, and we then formulate the terrestrial user pairing as a max-min problem to maximize the minimum channel correlation between users in one NOMA group. Since the terrestrial networks and the satellite network will cause interference to each other, we first investigate the capacity performance of the terrestrial networks and the satellite networks separately, which can be decomposed into the designing of beamforming vectors and the power allocation schemes. Then, a joint iteration algorithm is proposed to maximize the total system capacity, where we introduce the interference temperature limit for the satellite since the satellite can cause interference to all base station users. Finally, numerical results are provided to evaluate the user paring scheme as well as the total system performance, in comparison with some other proposed algorithms and existing algorithms.

This paper focuses on fusing hyperspectral and multispectral images with an unknown arbitrary point spread function (PSF). Instead of obtaining the fused image based on the estimation of the PSF, a novel model is proposed without intervention of the PSF under Bayesian framework, in which the fused image is decomposed into double subspace-constrained matrix-factorization-based components and residuals. On the basis of the model, the fusion problem is cast as a minimum mean square error estimator of three factor matrices. Then, to approximate the posterior distribution of the unknowns efficiently, an estimation approach is developed based on variational Bayesian inference. Different from most previous works, the PSF is not required in the proposed model and is not pre-assumed to be spatially invariant. Hence, the proposed approach is not related to the estimation errors of the PSF and has potential computational benefits when extended to spatially variant imaging system. Moreover, model parameters in our approach are less dependent on the input data sets and most of them can be learned automatically without manual intervention. Exhaustive experiments on three data sets verify that our approach shows excellent performance and more robustness to the noise with acceptable computational complexity, compared with other state-of-the-art methods.

Hyper-spectral image fusion has been a hot topic in medical imaging and remote sensing. This paper proposes a Bayesian fusion model which combines the panchromatic (PAN) image and the low spatial resolution hyper-spectral (HS) image under the same framework. Sparsity constraint is introduced as double "spike-and-slab" priors, and anisotropic Gaussian noise is adopted for accuracy. To achieve reduction in computational complexity, we turn the anisotropic Gaussian distribution into isotropic one with modified linear transformation and propose a variational Bayesian expectation maximization (EM) algorithm to calculate the result. Experiment results show that our solution can achieve comparable performance in pan-sharpening to other state-of-art algorithms while largely reducing the computational complexity.

This paper studies distributionally robust chance-constrained minimum variance beamforming. In contrast to deterministic modeling of the steering vector, our approach models the uncertainty statistically via distributions. We select the weights that minimize the combined output power subject to the distributionally robust chance constraint that for all distributions in the uncertainty set, the gain should exceed unity with high probability. Our discussion begins with the simplest case where the distributional set contains only Gaussian distribution; then we derive the robust weights for three distributional sets, namely, the set of (central symmetric) distributions with known mean and covariance, and a distributional model where the mean is known, the components are independent and belong to some known bounded intervals. It can be seen that these four robust beamformers provide statistical interpretation for the deterministic counterpart. Finally, we demonstrate the performance of these robust beamformers via several numerical examples.

High resolution hyper-spectral imaging works as a scheme to obtain images with high spatial and spectral resolutions by merging a low spatial resolution hyper-spectral image (HSI) with a high spatial resolution multi-spectral image (MSI). In this paper, we propose a novel method based on probabilistic matrix factorization under Bayesian framework: First, Gaussian priors, as observations' distributions, are given upon two HSI-MSI-pair-based images, in which two variances share the same hyper-parameter to ensure fair and effective constraints on two observations. Second, to avoid the manual tuning process and learn a better setting automatically, hyper-priors are adopted for all hyper-parameters. To that end, a variational expectation-maximization (EM) approach is devised to figure out the result expectation for its simplicity and effectiveness. Exhaustive experiments of two different cases prove that our algorithm outperforms many state-of-the-art methods.

Most recently, there has been a growing interest in the study of dictionary learning based (DL-based) image compression, which has potential in relieving the bandwith-hungry bottleneck of visual communication. All existing DL-based image compression approaches mainly focus on the effective representation of images, thus losing sight of two basic elements of image compression, i.e., quantization and entropy coding. For this reason, this paper proposes a quantization and entropy coding scheme for DL-based image compression. In our scheme, the proposed Partition-Interval K-means (PIK) quantizer adaptively maps continuous coefficients to discrete values. The arithmetic coding, combined with differential coding technique, is applied to encode the indices of nonzero coefficients as well as the labels of quantization values. In our experiments, the proposed scheme is verified to be more effective than other quantization and entropy coding schemes for DL-based image compression.

With the ever-increasing energy consumption in transmissions of explosive growing mobile data, energy-efficient solutions are needed to be integrated into the future mobile networks. The upcoming 5G networks support device-to-device (D2D) communication underlaying the cellular networks, which enables proximity cellular users to communicate directly with high data rate and low transmit power. In this paper, targeting the energy-aware D2D communications underlaying cellular system, we investigate the fundamental problems of what is the potential gains of D2D communications for energy saving, which are the fundamental reasons to decrease the energy consumption, and how about the tradeoffs between energy consumption and other network factors of available bandwidth, buffer size and service delay in large scale D2D communication networks. To answer the above challenging problems, we utilize a dynamic graph approach to model the system with human mobility for a realistic D2D communication scenario. Specifically, by formulating a mixed integer linear programming problem that minimizes the energy consumption for data transmission from the cellular base stations to the receivers through any possible ways of transmissions, we obtain the theoretical performance lower bound of system energy consumption, which shows that cellular D2D communications decrease the energy consumption about 65% averagely under the realistic scenario. Furthermore, the obtained fundamental tradeoffs reveal that energy consumption for large bandwidth can be kept at a low level with increase of the buffer size and service delay.

Motivated by the attention on salient objects, conventional region-of-interest (ROI)-based image coding approaches attempt to assign more bits to ROIs and fewer bits to other regions. Thus, the perceptual quality of salient object regions is improved by sacrificing the quality of non-ROI regions with unpleasant artifacts. To address this issue, we concentrate on the efficient compression of object-centered images by encoding salient objects and background features separately. To fully recover the object and background, we propose a hierarchical exemplar-based matching-synthesis (HEMS) approach to reconstruct the image from exemplars. In the proposed framework, once the salient object regions are encoded, only the quantized color features and local descriptors of the background are kept, achieving bit-rate reduction. To make it possible and practical to reconstruct background regions, the hierarchical framework is designed in three layers, including relevant image search, patch candidates matching, and distortion optimized image synthesis. In the hierarchical framework, firstly, image search from an external database returns relevant images, limiting the search space to a feasible number of patch candidates. Secondly, patches are matched by color features to select the appropriate candidates. Finally, the distortion optimized image synthesis further makes it possible to automatically choose the most suitable texture sample, and seamlessly reconstruct the image. Compared to the conventional ROI-based image coding schemes, the proposed approach can achieve better visual quality on both ROI and background regions.

In this paper, we address the design of message-passing receiver for massive multiple-input multiple-output orthogonal frequency division multiplex (MIMO-OFDM) systems. With the aid of the central limit argument and Taylor-series approximation, a computationally efficient receiver that performs joint channel estimation and decoding is devised by the framework of expectation propagation. In particular, the local belief defined at the channel transition function is expanded up to the second order with Wirtinger calculus, to transform the messages sent by the channel transition function to a tractable form. As a result, the channel impulse response between each pair of antennas is estimated by Gaussian message passing. In addition, a variational expectation-maximization-based method is derived to learn the channel power-delay profiles. The proposed scheme is assessed in 3D massive MIMO-OFDM systems with spatially correlated channels, and the empirical results corroborate its superiority in terms of performance and complexity.

The massive multiple input multiple output (MIMO) technique is a promising candidate to enormously increase the capacity of wireless networks. In a massive MIMO system, coordinated signal processing among different antennas is crucial to cope with the inevitable co-channel interference. However, it is normally difficult to perform perfect coordination in practical applications, due to the challenging requirement of global channel state information at the transmitter (CSIT). To solve this problem, this paper considers a massively distributed MIMO model, and presents dynamic-cell (DC)-based macro coordination, which requires only the instantaneous intra-DC CSIT and the slowly-varying large-scale inter-DC CSIT. Particularly, we first divide the system into a number of coupled user-centric DCs. Perfect coordination in the form of maximum ratio transmission (MRT) is adopted locally within each DCs based on instantaneous intra-DC CSIT. Then, we propose an inter-DC coordination approach termed as enhanced MRT to mitigate the inter-DC interference. The coordination is designed on the basis of large-scale inter-DC CSIT, which thus is referred to as macro coordination. Simulation results demonstrate that the proposed DC-based macro coordination can achieve a satisfactory performance gain in terms of system sum rate, while requiring much less CSIT than traditional schemes.

The broad class of manifold models are considered for extending the conventional compressive sensing (CS) to a more general framework. However, although the manifold-based CS approaches using a mixture of factor analyzers can learn latent geometric structures of high-dimensional signals, they have two issues that potentially limit their practical use: First, the manifold modeling does not take account of sharing factors among mixture components and thus misses the chance to enhance statistical strength; Second, introducing correlation constraints may cause intractable model inference. In this paper, we address these issues by: (1) depicting the mixture correlations with a hierarchical Beta process whose upper-layer process is an Indian buffet process in nonparametric Bayesian manner; (2) deriving a combination of collapsed Gibbs sampler and auxiliary-variable-based slice sampler to obtain the model accurate solutions. Experimental results on real dataset demonstrate that our proposed method provides significant performance improvements compared to the sparsity-based CS algorithms, while outperforming the state-of-the-art manifold-based inversion strategies for image reconstruction.

Space-memory-memory (SMM) Clos-network switch is an attractive alternative to an onboard switch due to its features of non-blocking, path diversity, distributed scheduling, and low implement cost. In this paper, in order to resist serious crosspoint faults induced by the harsh space radiation environment, we propose a fault-tolerant desynchronized static round-robin (FT-DSRR) cell dispatching algorithm for onboard SMM Clos-network switch. Different from previous schemes without fault tolerance, the cells in the proposed FT-DSRR can bypass the faulty paths and be evenly dispatched to the fault-free paths. Theoretical analysis demonstrates that, in the worst case, 100% throughput can be achieved under any admissible traffic when any (m-n) crosspoint faults occurring in an input/output module or in all central modules, where m and n are the number of inputs and outputs of input module, respectively. Simulation results indicate that, in the case of faults occurring randomly, FT-DSRR exhibits a good performance in terms of throughput and average cell delay under different traffic scenarios.

This paper investigates distributionally robust minimum variance beamforming under first-order moment uncertainty. In contrast to deterministic modeling of the array response, our approach employs a distributional set to describe the uncertainty. The distributional set we introduce consists of two constraints: the probability measure constraint and a first-order moment constraint. The weights are selected to minimize the combined output power, subject to the modified distortionless response constraint that the expected real part of the array gain exceeds unity for all distributions in the uncertainty set. We begin our discussion by revealing the intrinsic connection between the distributionally robust minimum variance beamformers (DRMVB) and the robust minimum variance beamformer (RMVB). Then for the sample space described by a union of ellipsoids, the DRMVB is reformulated as the optimal solution of a semidefinite program (SDP). Finally, we demonstrate the performance of the DRMVB via several numerical examples.

In this paper, we present a face video database that contains 31,500 videos of 100 individual volunteers. The primary purpose of building this database is to serve as a standardized test video sequences for any research related to video-conferences. Each of the volunteers was filmed by 9 groups of synchronized webcams under 7 illumination conditions, and was requested to complete a series designated actions. Thus, face variations on lip shape, occlusion, illumination, pose, and expression are presented in each video clip. Compared to the existing databases, THU face database provides multi-view video sequences with strict temporal synchronization, enabling evaluations on gaze-correction methods. Besides, based on our database, three well-known methods were tested, demonstrating the numerical performances under different circumstances. Free samples of this database can be downloaded at www.facedbv.com.

As an important computer graphics technique, parameter-driven animation has seen increasing deployment on mobile platforms, where both communication bandwidth and device storage are subjected to stringent constraints. We propose an efficient rate-distortion optimized inter-frame compression scheme for parameter-driven animation capable of finding optimal bit-allocations for any given bit-rate. Experiments conducted on face animation based on active appearance models demonstrate that with the proposed method, the transmission and storage requirements of parameter-driven animation can be significantly reduced.

This paper proposes a Bayesian learning approach to structured sparse image reconstruction. In contrast to conventional paradigms which convert images into high-dimensional vectors and thus are impractical for recovering large-scale images, we formulate columns of image matrices into a multiple-measurement-vector (MMV) model to reduce the problem dimension. Besides, we simultaneously exploit the tree structure of image wavelet coefficients and the column correlations of image matrices in wavelet domain as two prior structured constraints to improve reconstruction accuracy. Experimental results reveal that our method significantly outperforms other MMV-based strategies in terms of reconstruction error and provides a practical and efficient alternative to large-scale structured sparse image reconstruction.

The integrated and/or hybrid satellite and terrestrial network has become more and more important because of its broad application prospect and has received considerable attention. At the same time, the integrated network also brings many challenges, especially the problem of interference. Due to the lack of frequency spectrum, frequency reuse is considered in the satellite network and the terrestrial network for enhancing spectral efficiency. However, this will cause considerable Co-Channel Interference (CCI) and thus interference coordination is imperative. In this paper, we propose an interference coordination scheme for the integrated satellite and terrestrial network. The satellite sends pilots for channel estimation at terrestrial base-stations, and transmits the received data to the terrestrial gateway. Then interference coordination is performed at the terrestrial gateway, where the interference channel is updated according to both the estimated information and the predicted change based on the positions. Furthermore, based on the scheme, we analyze the precision that needs to be reached and obtain a direct view on how the precision may influence the system performance.

This paper addresses the problem of dynamic resource control and scheduling algorithms in multi-hop wireless networks with power constrained nodes and time-varying channels. Subject to different per-flow end-to-end delay requirements, a new queue management policy is designed and then, a delay parameter is introduced by combining both delay constraints and path length to destination nodes. By taking advantage of the delay parameter and the queue management policy, this paper proposes a distributed congestion control, routing and scheduling back-pressure algorithm. The proposed algorithm not only achieves high throughput, but also adaptively selects favorable routes according to per-flow delay constraints. Simulation results show our algorithm effectively achieves well- performed delay performance while ensuring the throughput.

Multibeam satellites allow for significant boost in capacity by reusing the available spectrum and regularized zero-forcing (RZF) precoding promises to be one efficient technique to manage the inter-beam interference in the forward link. In this paper, the RZF precoding problem is first cast within an equivalent Bayesian inference framework. Then, we propose two kinds of message passing based precoding approaches, namely variational message passing based RZF (VMP-RZF) and approximate message passing based RZF (AMP-RZF). Compared with evaluating RZF directly, our proposed methods circumvent the matrix inversion operation and thus enjoy a much lower complexity. Simulation results demonstrate that the normalized mean square errors of both VMP-RZF and AMP-RZF with respect to the true RZF are negligible while AMP-RZF converges faster than VMP-RZF.

Iterative decoding of orthogonal convolutional code is widely used for its excellent performance. However, the iterations lead to high complexity and long decoding delay, which is unsuitable for low- rate voice service in mobile satellite communications with on-board processing. In this paper, a novel scheme of orthogonal convolutional coding as well as an associated non-iterative joint decoding algorithm based on factor graph are proposed. Due to its non-iterative nature, this novel scheme has low decoding complexity and short latency, which indicates its potential on-board use in the low-rate satellite voice service, or other kinds of services with a high bit error rate (BER) tolerance and a tight delay requirement. Simulation results demonstrate the efficacy of the proposed novel coding scheme and non-iterative decoding algorithm in both AWGN and Rician channels.

In frequency-hopping systems, the multiple-access interference (MAI) occurs when more than one user appear on the same frequency at the same time. In this paper, we address the multi-user detection (MUD) problem for slow frequencyhopping (SFH) system with binary phase shift keying (BPSK). We first built a MAI model taking the hopping patterns and the phase offset into account, whereby the MAI is considered as independent yet unnecessarily identically distributed random variable. Under the proposed model, we further simplify the MAI by exploiting the Lyapunov central limit theorem and propose an iterative per-chip multiuser detection (PC-MUD) algorithm. By simulations, it's verified that the performance of proposed algorithm is near to the performance of single user bound in additive white Gaussian noise environment.

An alternative derivation for the well-known approximate message passing (AMP) algorithm proposed by Donoho is presented in this letter. Compared with the original derivation, which exploits central limit theorem and Taylor expansion to simplify belief propagation (BP), our derivation resorts to expectation propagation (EP) and the neglect of high-order terms in large system limit. This alternative derivation leads to a different yet provably equivalent form of message passing, which explicitly establishes the intrinsic connection between AMP and EP, thereby offering some new insights in the understanding and improvement of AMP.

Principal component analysis (PCA) is widely used to extract features and reduce dimensionality in various computer vision and image/video processing tasks. Conventional approaches either lack robustness to outliers and corrupted data or are designed for one-dimensional signals. To address this problem, we propose a robust PCA model for two-dimensional images incorporating structured sparse priors, referred to as structured sparse 2D-PCA. This robust model considers the prior of structured and grouped pixel values in two dimensions. As the proposed formulation is jointly nonconvex and nonsmooth, which is difficult to tackle by joint optimization, we develop a two-stage alternating minimization approach to solve the problem. This approach iteratively learns the projection matrices by bidirectional decomposition and utilizes the proximal method to obtain the structured sparse outliers. By considering the structured sparsity prior, the proposed model becomes less sensitive to noisy data and outliers in two dimensions. Moreover, the computational cost indicates that the robust two-dimensional model is capable of processing quarter common intermediate format video in real time, as well as handling large-size images and videos, which is often intractable with other robust PCA approaches that involve image-to-vector conversion. Experimental results on robust face reconstruction, video background subtraction data set, and real-world videos show the effectiveness of the proposed model compared with conventional 2D-PCA and other robust PCA algorithms.

The known tree-structure of wavelet transform coefficients is considered for conventional sparse image reconstruction to enhance performance. However, although existing Bayesian approaches can learn the latent structures, they have two issues that potentially limit their applications to high resolution images: First, treating the wavelet coefficients of large-scale images as high-dimensional vectors leads to impractical problem dimension; Second, Bayesian learning methods based on Markov chain Monte Carlo can guarantee global optima, but they require infinite stochastic samplings and thus are of high time complexity. In this paper, we address these issues by: 1) representing the wavelet transform images as multiple-measurement vectors (MMV) to reduce the memory complexity; and 2) modifying the structured priors for the Bayesian model to derive an analytical variational Bayesian strategy which can converge with much less time complexity. Experimental results demonstrate that our proposed method provides a practical alternative to large-scale sparse image recovery with low memory and computational requirements, while exhibiting close reconstruction accuracy compared to the time-consuming exact solutions.

We propose a message-passing algorithm of joint channel estimation and decoding for OFDM systems, where expectation propagation is exploited to deal with channel estimation. Specially, the message updating is formulated into a recursive form. As a result, for system with K subcarriers and L channel taps, only O(K + L) messages need to be tracked, and meanwhile they can be efficiently calculated using FFT with complexity O(K|A| + K log2K), where |A| denotes the constellation size. Numerical experiments show that our algorithm achieves BER performance within 0.5 dB of the known-channel bound.

In the implementation of distributed antenna systems (DASs), the antenna elements in some cases may only be deployed in the form of distributed antenna-clusters (ACs), due to various practical limitations. Consequently, correlation usually exists among the antenna elements within each AC. In contrast to most of the previous work that focused on the antenna correlation in a single-cell environment, this paper investigates the impact of the antenna correlation in a more general multi-cell scenario, where the inter-cell interference becomes the key challenge. We formulate a joint multi-cell input covariance optimization problem, accounting for the transmit antenna correlation, the propagation path-loss and the shadow fading. We show that the problem is a complicated non-convex problem. Moreover, the objective function, i.e., the ergodic sum capacity, is found difficult to be expressed in a straightforward form. After mathematical simplification, we propose an iterative inter-cell coordination scheme by adopting the successive approximation method. Simulation results demonstrate that, thanks to much more effective adaptation to the antenna correlation, the proposed scheme outperforms the existing ones and can significantly improve the system performance of a DAS with highly-correlated ACs.

Quality of service (QoS) guarantees play a significant role in next-generation wireless networks and the concept of effective capacity is proposed to characterize the maximum arrival rate that a time-varying fading channel can support under a delay-QoS constraint. However, the probability density function (PDF)-based analysis of effective capacity over generalized fading channels suffers from mathematical intractability in some situations of interest. In this paper, an approach based on the moment generating function (MGF) is introduced, which is suitable to deduce the closed-form expression for effective capacity computation. We start with the general case of adaptation transmission policy under the assumption of channel side information (CSI) only at the receiver, and then extend it to other adaptation policies. In addition, we derive unified and concise expressions of effective capacity by means of Fox's H function, which can be easily calculated, accommodating a wide variety of fading scenarios. Finally, the formulas of effective capacity are verified by numerical and simulation results.

In this paper, we propose a cognitive transmission scheme for Amplify-and-Forward (AF) two-way relay networks (TWRNs) and investigate its joint sensing and transmission performance. Specifically, we derive the overall false alarm probability, the overall detection probability, the outage probability of the cognitive TWRN over Rayleigh fading channels. Furthermore, based on these probabilities, the spectrum hole utilization efficiency of the cognitive TWRN is defined and evaluated. It is shown that smaller individual or overall false alarm probability can result in less outage probability and thus larger spectrum hole utilization efficiency for cognitive TWRN, and also produce more interference to the primary users. Interestingly, it is found that given data rate, more transmission power for the cognitive TWRN does not necessarily obtain higher spectrum hole utilization efficiency. Moreover, our results show that a maximum spectrum hole utilization efficiency can be achieved through an optimal allocation of the time slots between the spectrum sensing and data transmission phases. Finally, simulation results are provided to corroborate our proposed studies.

Energy efficiency has currently become one of the central topics in today's wireless communication industry. In this paper, we focus on the energy-efficient design in downlink multi-user MIMO-OFDM systems, so as to optimize the energy efficiency with users' quality of service (QoS) guarantees. Particularly, an optimization problem concerning power allocation is formulated, where the objective is the energy efficiency measured by “Bit per Joule” and the constraints are users' QoS demands. Based on a mathematical equivalence and Lagrange duality, we propose an effective algorithm, named QoS-Guaranteed Energy-Efficient Power Allocation, to address the problem and achieve the optimal energy efficiency. Simulation results reveal that our proposed algorithm brings about remarkable gains on energy efficiency and simultaneously satisfies users' QoS requirements.

This paper investigates QoS-aware relay selection and subcarrier assignment in cooperative OFDMA networks. In contrast to some existing works, which solve the sum-rate maximization problem directly, we first simplify it by exploiting the structure of the optimal solution, then we solve the simplified problem. To characterize the optimal network sum-rate performance, we solve the simplified problem optimally by the branch-and-cut method. In addition to the sum-rate maximization problem, we demonstrate that its two variants can also be similarly simplified and then be solved optimally by the branch-and-cut method. The optimal method serving as the benchmark is particularly suitable for moderate scale problems. Simulations show that the branch-and-cut method can locate the optimal solution effectively, which may in turn provide some insights into the performance of the heuristic method.

Because of the high system capacity and excellent capability against narrowband interference (NBI), Direct Sequence-Code Division Multiple Access (DS-CDMA) is widely used in Geosynchronous Earth Orbit satellite systems. However, due to the existence of the uncertain non-cooperative external interference, traditional colored noise suppression methods cannot achieve high performance in DS-CDMA systems. In this paper, based on spectrum shaping, combining with the feature analysis of the external interference, an eigen-based spreading sequences design framework for CDMA satellite systems is proposed. In this proposal, by the uniform orthogonal transformation (UOT), the eigen-based spreading sequences can combat not only the multiple access interference (MAI) but also the external interference and support multiple users' performance fairness. Furthermore, the design physical significance is analyzed. By simulations, it's verified that both MAI and the external interference can be eliminated by the proposed eigen-based spreading sequences and the fairness of different users can be efficiently guaranteed.

This paper investigates a variational Bayesian expectation maximization (VBEM) scheme to reconstruct structured sparse signals. To fully exploit available signal priors, the structured sparsity combines sparsity prior and structure prior together. In contrast to recent studies, inferring the unobserved variables via Markov chain Monte Carlo (MCMC) which demands infinite iterations to converge, this work employs probabilistic graphic models (PGMs) to factorize the complex joint distributions of signal models and utilizes VBEM algorithm to optimize the lower bound of the model marginal likelihood. In this way, analytical posterior distributions of signal coefficients and model parameters can be obtained by a two-step iterative algorithm. The proposed method reduces computational time consumption with little reconstruction performance loss compared to long-time MCMC, and outperforms state-of-art recovery algorithms. Thus, it offers a practical and stable approach to large-scale Bayesian recovery applications.

In this paper, we propose an expectation propagation based iterative multi-user detection algorithm for multiple input multiple output interleave-division multiple access (MIMO-IDMA) systems with high-order modulation. The proposed detector can be well integrated into the traditional structure of turbo receivers for MIMO-IDMA systems. By formulating a scalar factor graph representation of the multi-user detector and choosing Gaussian distribution as the projection set for the symbol belief, the overall detection complexity can be reduced to scaling linearly with the number of users, the number of receive antennas and transmit antennas. Numerical results for coded MIMO-IDMA systems with 16-QAM modulation show that our proposed algorithm outperforms the factor graph based detection with Gaussian approximation in terms of the bit error rate (BER) performance with lower complexity.

It has been long believed that the turbo equalizer leveraging a soft input/soft output (SISO) detector is effective for system performance enhancement in terms of bit error rate (BER). In practical applications, the implementation of SISO detection is usually challenging, due to its high computational-complexity. To address this issue, this paper proposes a low complexity SISO detector based on QR decomposition (QRD) and the M-search algorithm for single carrier block transmission systems. Benefiting from two unique properties called the aggregation property and the natural ordering property obtained by applying the QRD method into single carrier block transmission systems, the QRD-M based SISO detection algorithm can be dramatically simplified. Detailed analysis shows a linear growing computational-complexity. The extrinsic information transfer (EXIT) chart analysis tool is used to illustrate the performance of the proposed scheme. Both EXIT analysis and simulation results reveal that the turbo equalization with the proposed QRD-M detection algorithm could achieve a sub-optimal system performance close to the BER-optimal maximum a posteriori (MAP) detector at each iteration with a much lower computational-complexity.

For the spatially correlated multiuser MIMO-OFDM channels, the conventional iterative MMSE-SIC detection suffers from a considerable performance loss. In this paper, we use the factor graph framework to design robust detection algorithms by clustering a group of symbols to combat the spatial correlation and using the principle of expectation propagation to improve message passing. Furthermore, as the complexity of detection becomes one of the issues in the design of large-scale multiuser MIMO-OFDM systems, we propose a low-complexity approximate message-passing algorithm by opening the channel transition node, which eliminates the expensive matrix inversions involved in the MMSE-SIC based algorithms. Finally, numerical results are presented to verify the proposed algorithms.

In this brief we propose a multisample sparse representation (MSR)-based online dictionary-learning approach to encode images more efficiently. To minimize the reconstructed error while handling a variety of image samples, we develop a multisample sparse representation method capable of obtaining sparser coefficients combined with learning dictionaries on-the-fly. With a well-learned dictionary, we further derive an MSR-based image coding approach to encode the quantized sparse coefficients with reduced reconstructed errors. Experimental results demonstrate rapid convergence of the proposed dictionary-learning algorithm and improved rate-distortion performance over other competitive image compression schemes both subjectively and quantitatively, validating the effectiveness of the proposed approach.

This paper proposes a compressibility constrained sparse representation (CCSR) approach to low bit-rate image compression using a learnt over-complete dictionary of texture patches. Conventional sparse representation approaches for image compression are based on matching pursuit (MP) algorithms. Actually, the weakness of these approaches is that they are not stable in terms of sparsity of the estimated coefficients, thereby resulting in the inferior performance in low bit-rate image compression. In comparison with MP, convex relaxation approaches are more stable for sparse representation. However, it is intractable to directly apply convex relaxation approaches to image compression, as their coefficients are not always compressible. To utilize convex relaxation in image compression, we first propose in this paper a CCSR formulation, imposing the compressibility constraint on the coefficients of sparse representation for each image patch. In addition, we work out the CCSR formulation to obtain sparse and compressible coefficients, through recursively solving the \(\ell _{1}\) -norm optimization problem of sparse representation. Given these coefficients, each image patch can be represented by the linear combination of texture elements encoded in an over-complete dictionary, learnt from other training images. Finally, low bit-rate image compression can be achieved, owing to the sparsity and compressibility of coefficients by our CCSR approach. The experimental results demonstrate the effectiveness and superiority of the CCSR approach on compressing the natural and remote sensing images at low bit-rates.

Noise uncertainty, which is unavoidable in practice, can severely limit the detection performance of cooperative spectrum sensing. Derived from the conventional hard combination scheme, the improved one-out-of-N rule is presently the most effective algorithm to reduce the influence of noise uncertainty. However, in this algorithm the abandonment of local test statistic when it falls in the uncertainty region may lose some useful information. In this paper, first we analyze the optimality of the improved one-out-of-N rule and indicate that there should be an optimal number of cooperative users that minimizes the total error rate. Then, a new weighted hard combination scheme which makes full use of the uncertainty region is further proposed. The optimizations of weight coefficient and user number in this new scheme are also investigated. Numerical results show that the minimal total error rate and its corresponding user number of this scheme are both lower than those of the improved one-out-of-N rule.

Most existing works on scheduling for cooperative OFDMA systems have focused on homogeneous users with the same service and demand. Although scheduling for service differentiated cooperative OFDMA systems have been discussed in some previous works, their schedule problems do not take the joint optimization of base-station and relay power into account. In this paper, we investigate the optimization problem for joint base-station and relay power allocation, relay selection and subcarrier assignment with QoS guarantees and service support. We formulate the QoS provisioning joint schedule problem as a Mixed Binary Integer Program (MBIP). By introducing power and QoS prices, this MBIP is transformed into its corresponding dual problem, and a two-level dual decomposition method is proposed to solve the problem. Simulation results demonstrate that our proposed method outperforms previous works in terms of spectrum efficiency and QoS satisfaction.

To extract two-dimensional principal components from image samples while being insensitive to outliers, we propose a robust model for two-dimensional principal component analysis (robust 2D-PCA) by regularizing sparse penalty term. Moveover, we develop a novel iterative algorithm for robust 2D-PCA via alternating optimization, learning the projection matrices by bi-directional decomposition. To further speed up the iteration, we develop an alternating greedy approach, minimizing over the low-dimensional feature matrix and the sparse error matrix. Experimental results on dynamic background subtraction are evaluated to show the effectiveness of the proposed model, compared with conventional 2D-PCA and robust PCA algorithms.

In deep space communications, the received signals are always highly dynamic and very weak, which makes it quite challenging to achieve reliable data reception. To tackle these problems, a robust algorithm which joins tracking and code‐aided synchronisation is proposed in this study. The frequency lock loop‐assisted phase lock loop is used as the tracking loop, which integrates the characteristics of both outstanding dynamic performance and precise measurement. The code‐aided synchronisation is adopted as the demodulation and decoding loop, which utilises the relationship among the coded symbols to assist signal demodulation under the low signal‐to‐noise ratio condition. It is worth pointing out that the power tradeoff between the main carrier and subcarrier signals which operate in the two loops mentioned above is also optimised to minimise the total transmitting power. Simulation results showed that the proposed scheme with code rate 1/5 low‐density parity‐check codes could almost eliminate the effect of these factors, with the excellent performance, which closely approximates the ideal decoder in additive white Gaussian noise channel.

The virtual multiple input multiple output (MIMO) technique can dramatically improve the performance of a multi-cell distributed antenna system (DAS), thanks to its great potentials for inter-cell interference mitigation. One of the most challenging issues for virtual MIMO is the acquisition of channel state information at the transmitter (CSIT), which usually leads to an overwhelming amount of system overhead. In this work, we focus on the case that only the slowly-varying large-scale channel state is required at the transmitter, and explore the performance gain that can be achieved by coordinated transmissions for virtual MIMO with large-scale CSIT. Aiming at maximizing the achievable ergodic sum rate, the input covariances for all the mobile terminals (MTs) are jointly optimized, which turns out to be a complicated non-convex problem with a non-closed-form objective function. Further analysis reveals that the coordinated transmission problem can be recast as a Max-Min problem with a closed-form objective function and linear constraints. Then, by appealing to the successive approximation method and the saddle-point theory of concave-convex functions, we propose an iterative algorithm for coordinated transmissions with large-scale CSIT and establish its convergence. Simulation results corroborate that the proposed scheme converges quickly, and it yields significant performance gains compared to the existing schemes. Moreover, it is observed that the proposed scheme can achieve a nearly globally-optimal point under the diagonal input covariance constraint. Since the acquisition of large-scale CSIT is far less demanding than that of full CSIT, we believe that the proposed coordinated transmissions with large-scale CSIT in DASs shed some light on virtual MIMO in the making.

This paper presents a theoretical boundedness and convergence analysis of online gradient method for the training of two-layer feedforward neural networks. The well-known linear difference equation is extended to apply to the general case of linear or nonlinear activation functions. Based on this extended difference equation, we investigate the boundedness and convergence of the parameter sequence of concern, which is trained by finite training samples with a constant learning rate. We show that the uniform upper bound of the parameter sequence, which is very important in the training procedure, is the solution of an inequality regarding the bound. It is further verified that, for the case of linear activation function, a solution always exists and, moreover, the parameter sequence can be uniformly upper bounded, while for the case of nonlinear activation function, some simple adjustment methods on the training set or the activation function can be derived to improve the boundedness property. Then, for the convergence analysis, it is shown that the parameter sequence can converge into a zone around an optimal solution at which the error function attains its global minimum, where the size of the zone is associated with the learning rate. Particularly, for the case of perfect modeling, a strong global convergence result, where the parameter sequence can always converge to an optimal solution, is proved.

Energy efficiency has currently become one of the central issues in today's wireless communication industry. In this paper, we focus on the optimal energy efficiency in a multiuser cellular system and investigate its achievable upper bound from the perspective of radio resource available. Particularly, an optimization problem is formulated concerning radio resource scheduling, and an analytical optimal solution is obtained via mathematical analysis. Furthermore, an energy-efficient radio resource scheduling (EE-RRS) algorithm is proposed for numerical applications. Simulation results show that our proposed EE-RRS algorithm can realize significant improvements on energy efficiency with the optimal solution, and there is always a tradeoff between energy efficiency and transmission capacity.

This paper investigates the interference-aware linear precoder design with finite-alphabet inputs. It maximizes the mutual information between the transmitter and intended receiver while controlling the interference power caused to unintended receivers. For this nonconcave problem, this work proposes a global optimization approach, which is based on two key observations: 1) the interference-aware precoding problem can be reformulated to the problem minimizing a function with bilinear terms over the intersection of multiple co-centered ellipsoids; 2) these bilinear terms can be relaxed by their convex and concave envelopes. In this way, the global optimal solution is obtained by solving a sequence of relaxed problems over shrinking feasible regions. The proposed algorithm calculates the optimal precoder and the theoretical limit of the transmission rate with interference constraints. Thus, it offers an important benchmark for performance evaluation of interference constrained networks.

This paper proposes a sparse representation based approach for low bit-rate image compression using the learnt over-complete dictionary of texture patches. We first propose to compress each patch of the image with sparse and compressible linear combinations (via nonzero coefficients) of texture patterns encoded in a dictionary for image patches. Then, we find out that the compressibility and sparsity of coefficients can be achieved by the proposed recursive procedure of solving ℓ1optimization problem of sparse representation. Moreover, rather than transform-based patterns (e.g. DCT), we explore the basic texture patterns from other training images with a learning algorithm based on the gradient descent, to form the over-complete dictionary. The experimental results demonstrate the effectiveness of the proposed approach.

This paper proposes a novel approach, based on unequal error protection, to enhance rateless codes with progressive recovery for layered multimedia delivery. With a parallel encoding structure, the proposed Progressive Rateless codes (PRC) assign unequal redundancy to each layer in accordance with their importance. Each output symbol contains information from all layers, and thus the stream layers can be recovered progressively at the expected received ratios of output symbols. Furthermore, the dependency between layers is naturally considered. The performance of the PRC is evaluated and compared with some related UEP approaches. Results show that our PRC approach provides better recovery performance with lower overhead both theoretically and numerically.

Quality-of-Service (QoS) aware scheduling in cooperative orthogonal frequency division multiple access (OFDMA) systems has long been a critical yet challenging research topic for achieving full system utilization. The optimal performance of such systems can only be achieved by joint optimization of various resources (power, bandwidth and etc). In this paper, we investigate the optimization of base station power allocation with QoS requirements in cooperative OFDMA systems. We formulate the QoS aware resource allocation problem and exploit its structure to derive an efficient solution. By employing Lagrangian relaxation, the problem decouples on each subcarrier into a hierarchy of subproblems. Combined with an iterative procedure, some separable and parallel structures could be exploited to obtain an efficient solution. Simulation results demonstrate that our proposed method outperforms previous works in terms of spectrum efficiency and QoS satisfaction.

In this paper, we develop a general transmission scheme for bi-directional communications by using eigenmode sharing, where existing two-way relaying protocols can be viewed as a special case of the proposed transmission scheme. In addition, the proposed bi-directional scheme can also be applied to more challenging scenarios with more than one source pair. Asymptotical behavior of the outage probability achieved by the proposed transmission protocol is studied in order to obtain insightful understandings for the fundamental limits of the proposed scheme. Our developed results show that the proposed bi-directional transmission scheme can realize larger system throughput than time sharing based approaches, and serving more than one pair at the same time is more beneficial than simple two-way relaying in terms of multiplexing gains.

This paper investigates the linear precoder design for spectrum sharing in multi-antenna cognitive radio networks with finite-alphabet inputs. It formulates the precoding problem by maximizing the constellation-constrained mutual information between the secondary-user transmitter and secondary-user receiver while controlling the interference power to primary-user receivers. This formulation leads to a nonlinear and nonconvex problem, presenting a major barrier to obtain optimal solutions. This work proposes a global optimization algorithm, namely Branch-and-bound Aided Mutual Information Optimization (BAMIO), that solves the precoding problem with arbitrary prescribed tolerance. The BAMIO algorithm is designed based on two key observations: First, the precoding problem for spectrum sharing can be reformulated to a problem minimizing a function with bilinear terms over the intersection of multiple co-centered ellipsoids. Second, these bilinear terms can be relaxed by its convex and concave envelopes. In this way, a sequence of relaxed problems is solved over a shrinking feasible region until the tolerance is achieved. The BAMIO algorithm calculates the optimal precoder and the theoretical limit of the transmission rate for spectrum sharing scenarios. By tuning the prescribed tolerance of the solution, it provides a trade-off between desirable performance and computational complexity. Numerical examples show that the BAMIO algorithm offers near global optimal solution with only several iterations. They also verify that the large performance gain in mutual information achieved by the BAMIO algorithm also represents the large gain in the coded bit-error rate.

This paper considers the precoder design for dual-hop amplify-and-forward relay networks and formulates the design from the standpoint of finite-alphabet inputs. In particular, the mutual information is employed as the utility function, which, however, results in a nonlinear and nonconcave problem. This paper exploits the structure of the optimal precoder that maximizes the mutual information and develops a two-step algorithm based on convex optimization and optimization on the Stiefel manifold. By doing so, the proposed algorithm is insensitive to initial point selection and able to achieve a near global optimal precoder solution. Besides, it converges fast and offers high mutual information gain. These advantages are verified by numerical examples, which also show the large performance gain in mutual information also represents the large gain in the coded bit-error rate.

This paper investigates the linear precoder design that maximizes the average mutual information of multiple-input multiple-output channels with finite-alphabet inputs and statistical channel state information known at the transmitter. This linear precoder design is an important open problem and is extremely difficult to solve: First, average mutual information lacks closed-form expression and involves complicated computations; Second, the optimization problem over precoder is nonconcave. This study explores the solution to this problem and provides the following contributions: 1) A closed-form lower bound of average mutual information is derived. It achieves asymptotic optimality at low and high signal-to-noise ratio regions and, with a constant shift, offers an accurate approximation to the average mutual information; 2) The optimal structure of the precoder is revealed, and a unified two-step iterative algorithm is proposed to solve this problem. Numerical examples show the convergence and the efficacy of the proposed algorithm. Compared to its conventional counterparts, the proposed linear precoding method provides a significant performance gain.

In this paper, we focus on subcarrier and power allocation for layered multicast streams in OFDMA cellular networks, where the multicast stream is composed of a basic layer and an enhancement layer. Our goal is to maximize the system total throughput with a total power constraint and a minimum rate requirement. A low-complexity allocation algorithm is proposed, which combined the suitability-based subcarrier allocation (SSA) with the traditional water filling (TWF) for the base layer and an advanced water filling (AWF) for the enhancement layer. Besides, we present a throughput-based user selection (TUS) algorithm which selects a proper set of users to serve when the minimum rate requirement can not be satisfied. Simulation results show that our proposed algorithm can improve the system throughput and outage probability.

In this paper, we investigate the optimal precoding scheme for relay networks with finite-alphabet constraints. We show that the previous work utilizing various design criteria to maximize either the diversity or the transmission rate with the Gaussian inputs assumption may lead to significant loss for a practical system with finite constellation set constraint. A linear precoding scheme is proposed to maximize the transmission rate, i.e., the mutual information, for relay networks. We exploit the structure of the optimal precoding matrix, and develop a unified two-step iterative algorithm utilizing the theory of convex optimization and optimization on the complex Stiefel manifold. Numerical examples show that this novel iterative algorithm achieves significant gains compared to its conventional counterpart.

This paper focuses on the subchannel and power allocation for mixed multicast and unicast traffic in wireless OFDMA networks, where the multicast data is divided into basic layer and enhancement layer data. Our goal is to maximize the network total throughput with a total power constraint while guaranteeing the minimum rate requirement of both the unicast and muticast traffic. A heuristic allocation algorithm is proposed, which combines a cost-based subchannel allocation (CSA) with the traditional water filling (TWF) and an advanced water filling (AWF). The TWF is used for the subchannels allocated to satisfy the rate requirements of each unicast traffic and multicast traffic while the AWF is used for the remaining subchannels. Simulation results show that our proposed algorithm can improve the network throughput and outage probability compared with other algorithms.

This paper proposes a power optimization scheme with joint resource allocation (i.e. subcarrier and bit allocation) and mode selection in an OFDMA system with integrated D2D communications. Through the proper control of the base station (BS), users can communicate with each other either directly or via the BSs as in traditional cellular networks. Particularly, an optimization problem is formulated to minimize total downlink transmission power constrained by users' QoS demands; while a heuristic scheme exploiting joint subcarrier allocation, adaptive modulation and mode selection is contrived to solve the problem. Simulation results show that our proposed scheme may not only conserve total downlink transmission power effectively, but also save overall power consumption of BSs significantly, compared with existing algorithms used in traditional OFDMA systems.

Energy consumption of information and communication industry has generated increasing concerns recently, aiming at reducing power consumption or increasing energy efficiency. This paper proposes an energy efficient hybrid structure which introduces micro sites into traditional OFDMA cellular systems with only central macro base stations (BSs). The deployment of small and low power BSs alongside the central macro BSs is capable of advancing radio coverage, enhancing system capacity, and simultaneously increasing energy efficiency. Upon the proposed structure, two efficient solutions respectively based on Lagrangian dual decomposition (LDD) and serial carrier and power allocation (SCPA), are proposed to optimize energy efficiency. Simulation results show that our proposed hybrid structure can dramatically increase the energy efficiency and the downlink system capacity in OFDMA cellular systems.

In this paper, we propose a novel logic-rule learning approach for the Tower of Knowledge (ToK) architecture, based on Markov logic networks, for scene interpretation. This approach is in the spirit of the recently proposed Markov logic networks for machine learning. Its purpose is to learn the soft-constraint logic rules for labeling the components of a scene. In our approach, FOIL (First Order Inductive Learner) is applied to learn the logic rules for MLN and then gradient ascent search is utilized to compute weights attached to each rule for softening the rules. This approach also benefits from the architecture of ToK, in reasoning whether a component in a scene has the right characteristics in order to fulfil the functions a label implies, from the logic point of view. One significant advantage of the proposed approach, rather than the previous versions of ToK, is its automatic logic learning capability such that the manual insertion of logic rules is not necessary. Experiments of labeling the identified components in buildings, for building scene interpretation, illustrate the promise of this approach.

In this paper, we investigate the optimal power allocation scheme for relay networks with finite-alphabet constraints. It has been shown that the previous work utilizing various design criteria with the Gaussian inputs assumption may lead to significant loss for a practical system with finite constellation set constraint, especially when signal-to-noise ratio (SNR) is in medium-to-high regions, or when the channel coding rate is medium to high. An optimal power allocation scheme is proposed to maximize the mutual information for the relay networks under discrete-constellation input constraint. Numerical examples show that significant gain can be obtained compared to the conventional counterpart for nonfading channels and fading channels. At the same time, we show that the large performance gain on the mutual information will also represent the large gain on the bit-error rate (BER), i.e., the benefit of the power allocation scheme predicted by the mutual information can indeed be harvested and can provide considerable performance gain in a practical system.

A fault detection and diagnosis (FDD) method is proposed by combining transfer entropy (TE) and signed digraph (SDG). Given process historical data, transfer entropy is used to construct a SDG model to represent causal relationship between process variables. A fault severity evaluation method is then proposed based on the modified SDG model, where the nodes can take values of (0), (±1), (±3) and (±6). Then, an index named DoF is developed to measure fault severity. The application results can verify the effectiveness and feasibility of the proposed method.

Most conventional techniques for noise adaptation assume a clean initial speech model which is adapted to a specific noise condition using adaptation data accumulated from the condition. In this paper, a different problem is considered, i.e. adapting a noisy speech model to a specific noise condition. For example, the initial noisy model may be a multi-condition model which is used to provide more accurate transcripts for the adaptation data than could be provided by a clean model, thereby obtaining a more accurate adaptation. We develop the formulation for this new problem by combining and extending maximum likelihood linear regression (MLLR), constrained MLLR (CMLLR) and uncertainty decoding techniques. We also present an implementation which has been tested on the Aurora 4 database, assuming an initial multi-condition model trained using white noise corrupted data. Significant word error rate (WER) reductions are achieved in comparison with other approaches.

In this paper, a 4-transmit-antenna space time block coding (STBC) scheme with 1-bit differential feedback is proposed. This scheme gives full transmit diversity and spatial coding rate over time-selective fading channels by rotating the signal constellations for certain angles determined by the differential feedback information. By utilizing differential feedback, our method can provide much better channel orthogonality and track the variation of the channel more accurately and timely under time-selective fading circumstances, comparing to recent works presenting other quantized feedback schemes. Simulation results show that the 1-bit differential feedback scheme achieves near-optimum performance and outperforms existing algorithms without adding more overhead or complexity.

In this paper, we investigate the relaying scheme for multi-antenna cooperative networks without channel state information at the transmitters. It is shown that the classic opportunistic relaying scheme, which allows only one relay node to forward the message, may lead to significant loss when it is extended to the corresponding multi-antenna relay scenarios. A generalized opportunistic relaying approach, which selects more transmit antennas distributed on multiple relay nodes to transmit data simultaneously, is proposed to maximize the network throughput via balancing the source-relay and the relay-destination channels. Numerical examples show the large impact of multiple antennas and relays on the network throughput, and the significant gains obtained through the proposed opportunistic cooperation scheme.

Most existing works on resource allocation in cooperative OFDMA systems have focused on homogeneous users with same service and demand. In this paper, we focus on resource allocation in a service differentiated cooperative OFDMA system where each user has individual QoS requirement. We investigate joint power allocation, relay selection and subcarrier assignment to maximize overall system rates with considerations of QoS guarantees and service support. By introducing QoS price, this combinatorial problem with exponential complexity is converted into a convex one, and a two-level dual-primal decomposition based QoS-aware resource allocation (QARA) algorithm is proposed to tackle the problem. Simulation results reveal that our proposed algorithm significant outperforms previous works in terms of both services support and QoS satisfaction.

Adaptive network coded cooperation (ANCC) scheme has been shown to have better performance than the conventional repetition-based schemes and the space-time coded cooperation (STCC) frameworks for data transmissions from a large collection of terminals to a common destination in large wireless networks. However, the random selection strategy for ANCC protocol may generate many short cycles in the distributed low-density generator-matrix(LDGM) codes, which may cause error-floor and performance degradation. In this paper, an optimized relay selection strategy for ANCC is proposed. By exploiting information interaction between destination and terminals before data communication, and matching the instantaneous network topology, the proposed method generates ensembles of distributed LDGM codes free of short cycles. Simulation results demonstrate that the proposed relay selection protocol significantly outperforms the random relay selection strategy for various network sizes and different fixed numbers of selected terminals.

This paper considers an OFDMA relay network and proposes an auction algorithm for the subchannel allocation to balancing efficiency and fairness with service differentiation. This algorithm allows users to fairly compete for the using of the subchannel through a new bidding strategy. We use the second-price sealed auction mechanism under which the dominant strategy of the bidder is bidding the true valuation of the subchannel. The users' valuations to the subchannel are estimated by considering their minimum rate requirements, current channel conditions and the long term average data rates. The active index which can be chosen from the set provided by the system is introduced to differentiate the user's willingness to pay for different services. For each subchannel, the user bids highest will be allocated the subchannel. The auction procedure can naturally realize a competitive fairness from the perspective of the users. Simulation results show that the proposed algorithm can achieve different degrees of tradeoff between the system efficiency and fairness by using different active index set, satisfy the users' minimum rate requirements and also provide differentiated services for different users.

This paper considers a joint subcarrier and power allocation in an orthogonal frequency division multiple access (OFDMA) cellular system with base station (BS) cooperation. The problem is formulated as an average utility maximization subject to the per-antenna power constraint. Existing algorithms to solve similar problem in a single-sell system are shown not so efficient in the cooperative multi-cell system. Therefore, we specially design a method which contains a greedy search algorithm for subcarrier allocation and a Lagrange dual based algorithm for power allocation. Simulation results reveal that the proposed method achieves near-optimal performance and outperforms the counterpart derived originally for a single-cell system by 52% on average, yielding good tradeoff between system efficiency and fairness with proper utility functions.

Minimum distance lower bounds are derived using graphical enumeration and integer programming for girth-constrained repeat-accumulate (RA) code ensembles. Moreover, the worst subgraphs in Tanner graphs leading to the minimum distance lower bounds and the probabilities they occur are analyzed.

A low-complexity, near-optimal transmit antenna selection algorithm is proposed for multi-relay networks where all nodes are equipped with multiple antennas. We first establish a system model and a unified capacity maximization framework for a two-hop opportunistic relaying scheme where the source node (S) transmits signals to multiple relay nodes (R) in the first time slot, and the selected relay antennas and their corresponding relay nodes receive, decode and forward the messages to the destination (D) in the second time slot. Based on the system model, we develop a transmit antenna selection algorithm that maximizes the network capacity assuming that the channel state information is available at the receivers but not available at the transmitters, and total transmit power constraints are imposed on source/relay transmitters. The proposed algorithm first constructs a sorted list of relay antennas with decreasing S-R capacities, then iteratively maximizes the R-D capacity over a candidate antenna set using a low-complexity, near-optimal antenna selection scheme. The candidate set is reduced in the next iteration according to the selected antenna set of the current iteration. The overall network capacity is computed for the selected antenna sets of all iterations, and the set yielding the highest S-R-D capacity is the solution to the maximization problem. We show that this novel iterative algorithm achieves near-optimal solution and has a polynomial-time complexity. We also derive the lower and upper bounds of the achievable network capacity for both average capacity and outage capacity. Numerical examples show the significant performance gains obtained via the proposed scheme compared to its conventional counterparts.

Two major techniques are used to embed pilots into data, time-division multiplexed (TDM) and superimposed (SIP). In this paper, we compare these two techniques in an informative consideration under time varying fading channels. Besides the ordinary SIP without dealing with the interference, we also study two different kinds of performance improved iterative SIP: interference subtraction (IS) and interference exploitation (IE). Using Wiener filtering and accounting estimation error, the capacity lower bounds and BER expressions are derived. Numerical results show that great potential benefits are brought by SIP if the interference is utilized properly. With this potential exploited, SIP shows great superior to PSAM, especially in high speed environments.

In time varying relay networks, imperfect channel estimation deteriorates the performance and must be accounted for system design. By doing this, this paper derives the achievable rate expressions first, and then studies the training optimization problem based on those rates for both Amplify-and-Forward (AF) and Decode-and-Forward (DF) relay networks. There are two kinds of training designs for relay networks. One is remaining the design just optimal for source-destination (SD) channel as if there is no relay node (we name it SD design). Another is optimizing over the whole relay network. The former way is simple but is shown to have a great degradation, while the latter one is optimal but too complex to get closed-form optimal solutions. In this paper, we simplify the latter way and propose new sub-optimal training designs in closed-forms. Simulations show that our proposed designs for both AF and DF can achieve considerable gains compared with SD design and have negligible performance degradation compared to the optimal design.

This paper considers joint schedule and resource allocation in downlink cooperative OFDMA systems with multiple sources, multiple relays and a single destination. Each user has individual QoS requirements and the relays operate in amplify-and-forward and half-duplex mode. We try to find optimal power allocation, relay selection and subcarrier assignment to maximize overall system rates. The problem is formulated as a Mixed Binary Integer Programming (MBIP). Although the original problem is comprehensive in nature, a novel two-level dual-primal decomposition based algorithm is proposed to tackle the problem. Optimal solutions are given in closed-form and the algorithm has a polynomial complexity in general. The efficiency of the algorithm is finally illustrated by numerical results.

A new scheme of the extended irregular repeat- accumulate (eIRA) coded tamed frequency modulation (TFM) for deep space communications is introduced in this paper. It is mainly based on the optimal concatenation of the eIRA code and the continuous phase encoder (CPE) decomposed from TFM. In order to obtain good performance of the eIRA coded TFM, the main principle of our scheme is to jointly optimize the concatenation by eliminating the short loops in the Tanner graph of the eIRA code and the coding part of TFM and to jointly decode between them. Then our scheme without interleaving and the contrast scheme with interleaving are simulated in an additive white Gaussian noise (AWGN) channel which is typical in deep space communications. Simulation results have shown that our scheme can obtain lower complexity and less decoding delay (due to the reduction of interleaving) at the cost of just 0.1-0.15 dB performance loss when compared to the scheme with interleaving given bit-error-ratio (BER) of 10 5. Therefore, our scheme can be used to implement the coded TFM system for deep space communications with good performance and low complexity.

A novel dynamic resource allocation algorithm is proposed for matching the OFDMA system in high speed mobile environment, which suffers from high inter-carrier interference and large dynamic range of mobile speed for versatile users. With the non-linear optimization, the objective function of the transmission power can be directly mapped with the variables, such as the mobile speed of users, channel state information, modulation and coding schemes etc. Compared with the hitherto resource allocation algorithms in IEEE 802.16e, the proposed scheme has even more comprehensive capability depicting the features for high speed mobile and better performance in BER and QoS guarantee, especially in case the speed is more than 300 km/h.

SD-OFDM has been proposed for information transmission with high spectrum efficiency. In this paper, an adaptive SD-OFDM (ASD-OFDM) is proposed for time- frequency selective fading channel. Compared with traditional OFDM, the ASD-OFDM improves the performance, i. e., spectrum efficiency and interference cancelation capability, in the high speed mobile scenario as well as short-delay multipath environment.

An improved design of receiving filter for non- ideally shaped modulated signals which provides better performance than existing schemes is proposed. By concerning both Inter-Symbol-Interference (ISI) and the degree of waveform mismatch between the transmitted signal and impulse response of the receiving filter, the exact expression of the Signal-to-Noise Ratio (SNR) loss that represents the performance degradation is derived, and the performance is compared with that of the ideally shaped signals' demodulation. The existing schemes such as root-raised-cosine (RRC) receiving filters don't perform well for non-ideally shaped situations, since large degrees of both ISI and waveform mismatch exist. To improve the performance and avoid the complexity of applying equalizer in receivers, the proposed scheme designs the impulse response of the receiving filter by optimizing the SNR loss to the minimum value, and it uses simulated annealing as the optimization algorithm. It is shown that the new method can achieve better performance than existing schemes, especially when the modulation order or the SNR is relatively high. Finally the conclusion is validated by simulation results.

Traditional congestion control protocols assume that each link provides a fixed capacity, while it is not always the case in wireless networks which have shared and variable medium. In this paper, we incorporate variable link capacity as a function of resource allocated, and random-access interferen

Signal-to-noise ratio (SNR) estimation is an important issue in many communication systems. This paper proposed a new non-data-aided (NDA) SNR estimator for BPSK and QPSK modulated signals. The estimation is based on pseudo-error probability (PEP), which can be estimated without additional cost for sampled systems. The performance of the proposed estimator is examined numerically in terms of its bias and normalized mean square error. The Cramer-Rao lower bound (CRLB) for PEP-based SNR estimation is also derived. Comparing with the existing SNR estimation schemes such as maximum likelihood (ML) estimators, the proposed algorithm can achieve similar performance with much less computational cost.

Current WLAN is still predominantly data centric, so it is not well equipped to meet the pressing voice capacity and quality of service (QoS) demands when transmitting VoIP traffic. One of the reasons is the large WLAN PHY/MAC overhead. This paper proposes and investigates a MAC queue aggregation scheme which aggregates multiple queuing MAC frames to reduce the overhead. The scheme will not incur any extra delay. Simulations in various scenarios show that the proposed scheme can improve the VoIP capacity by up to 65%.

This paper presents a novel unequal error protection (UEP) video transmission scheme based on dynamic resource allocation in a MIMO-OFDM system in order to improve the video transmission performance over wideband wireless channels. In this new scheme, a low density parity code (LDPC) is combined with a complexity reduced power reallocation algorithm to utilize the excess powers which margin from the powers necessary to meet the modulation level selected under a certain BER constraint. Furthermore, by taking full advantage of the residual excess powers, unequal error protection on important bits of video streams is provided by the LDPC code. Accordingly, the transmission efficiency and quality are both increased. Simulation results have shown that the proposed scheme may achieve great improvement in both systems with or without perfect channel estimation.

In this paper, a novel decoding algorithm using perturbation techniques for LDPC concatenated with CRC (LDPC-CRC) is proposed. The CRC is used to check the residual error of the BP algorithm, while a new parameter is introduced to LDPC decoding to describe the reliability of each bit in the iterative decoding algorithm. Upon an error is detected, re-decoding process with perturbation method will be aroused. Simulation results show that this method lowers the error floor for codeword length of several hundred bits effectively.

In this paper, we propose a novel joint source-channel coding (JSCC) scheme that combines error resilient arithmetic codes (ERAC) and low-density parity-check (LDPC) codes. In the proposed scheme, the error detection capability of ERAC is exploited by the bit-interleaving technique to improve the performance of LDPC decoder, and the overall decoding performance. Simulation results show that the proposed approach achieves significant performance gains with respect to the separated decoding techniques, with additional advantage of high scalability and flexibility.

IEEE fixed 802.16 network aims at providing broadband wireless access with quality of service (QoS) guarantee. In all its services, real-time video traffic plays an impeditive role because of its varying bit-rate. In this paper, by taking advantage of traffic characteristics, a new scheduling and call admission control (CAC) scheme for real-time video traffic in IEEE fixed 802.16 networks is proposed. Simulations using real life video traces show that the proposed scheme provides significant improvement in throughput while maintaining QoS performance at an acceptable level.

This paper presents a new carrier frequency offset (CFO) estimation algorithm with low-complexity for uplink OFDMA systems. Extended Kalman Filter (EKF) is employed in time domain, while the multiple access interference (MAI) cancellation strategy is combined in every recursion. In addition, an adaptive noise variance estimator is contrived for EKF, and a robust EKF method is derived for CFO estimation with data symbols. The proposed algorithm is suitable for online estimation, and can be applied to both preamble and data symbols. Simulation results show that it has a large estimation range, while approaching the Cramer-Rao Bound (CRB) closely with quite reduced complexity.

Multi-path fading and multiple access interference largely degrade the performance of carrier-frequency offset and timing offset estimations in OFDMA uplink systems. This paper proposes a robust algorithm with low complexity for joint estimation of carrier-frequency offset and timing offset. The phase differences in the received sub-carrier signals between two consecutive training symbols are employed and a robust sequential least squares algorithm is used to improve the performance of the estimation. Simulation results show that the proposed low complexity algorithm outperforms the linear least squares (LLS) estimator and approaches the performance of the iteratively reweighted least squares (IRLS) estimator

The combination of Unified Modeling Language (UML) and SystemC has led to an object-oriented high-level design automation methodology. In this paper, a novel bi-directional UML-SystemC translation tool UMLSC is proposed. Specifically, a set of principles for modeling SystemC design in UML and an algorithm for UML-SystemC bi-directional translation are addressed. The principles and the algorithm are integrated into UMLSC, which provides a smooth link between visual specification, implementation and verification. An implementation example is given to verify the effectiveness of the proposed principles and the algorithm.

A mesh-enabled WPAN (mesh-WPAN) built on the standard IEEE 802.15.3 MAC allocates channel time node by node along a route. This allocation causes unnecessary packet delays, which is not desirable for multimedia-dominating scenarios. Also, the variability of wireless link may result in relaying nodes' starvation or buffer overflow. In this work an enhanced MAC protocol for high-rate mesh-WPAN named group relay and token solicitation (GRATS) is proposed. Group relay enables dynamic channel time sharing among the nodes along a route. Token solicitation allows for temporarily adjusting the number of packets in a group. Both mathematical analysis and simulation results show that the GRATS not only solves the relaying nodes' starvation and buffer overflow problems, but also reduces average packet delay noticeably. Simulation results also show that the GRATS outperforms the IEEE 802.15.3 MAC significantly for delay bounded VBR video traffic in terms of job failure rate

The OVSF-CDMA system must allocate user codes to support a new call, when it supports variable user data rates. Accordingly, the user code would be easily blocked without an efficient code reassignment algorithm. This paper presents a right first dynamic code assignment (RFDCA) algorithm, which aims at always keeping the OVSF codes tree in an optimal right first (RF) state on the basis of the number-topology count (Nt). This algorithm realizes a quick assignment of the OVSF codes to the new callers so that the reassignment of OVSF code could be minimized when new calls arrive, further improving the spectral efficiency of the systems. In this paper, we have analyzed the algorithm by adopting Markov process and have practiced the simulation experiment. The theoretical analysis and the simulation experiment acquire the same results, proving the effectiveness of the algorithm. More importantly, the result of the simulation experiment indicates the number of reassigned codes (NRC) is far fewer than that of other algorithms when new calls arrive

Robustness and discriminability are two key issues in face recognition. In this paper, we propose a new algorithm which extracts micro-structural Gabor feature to achieve good robustness and discriminability simultaneously. We first design a family of directional block partitions to compute the block-level directional projections of the classical Gabor feature. Then we use two statistical kernels, i.e, the mean kernel and the variance kernel, to extract the micro-structural statistics. Analysis of both robustness and discriminability is conducted to show that the new feature is not only more robust to misalignment, but also more discriminative than the classical down-sampling Gabor feature, which is further demonstrated by three groups of experiments on the BANCA dataset.

Orthogonal frequency-division multiplexing (OFDM) is a promising technology in broadband wireless communications, with its ability to transform a frequency-selective fading channel into multiple flat-fading channels. However, the time-varying characteristics of wireless channels induce the loss of orthogonality among OFDM subcarriers, which was generally considered harmful to system performance. In this paper, we propose a bit-interleaved time-frequency coded modulation (BITFCM) scheme for OFDM to achieve both the time and frequency diversity inherent in broadband time-varying channels. We will show that the time-varying characteristics of the channel are beneficial to system performance. Using the BITFCM scheme, and for relatively low maximum normalized Doppler frequency, a reduced-complexity maximum-likelihood decoding approach is proposed to achieve good performance with low complexity. For high maximum normalized Doppler frequency, the intercarrier interference (ICI) can be large, and an error floor will be induced. To solve this problem, we propose two ICI-mitigation schemes by taking advantage of the second-order channel statistics and the complete channel information, respectively. It will be shown that both schemes can reduce the ICI significantly.

Acquisition and tracking are two crucial stages necessary to the carrier frequency synchronization in orthogonal frequency division multiplexing (OFDM) systems. In this letter, by employing the rotation property of OFDM data subcarriers, a simple time-frequency decision-feedback loop without the use of pilot subcarriers is proposed for the fine carrier frequency offset (CFO) tracking. Specifically, with proper loop parameters, a residual CFO less than 10% of the subcarrier spacing may be well tracked for quarternary phase-shift keying (QPSK) modulation in the presence of noise, while for systems using QPSK, 16-QAM, and 64-QAM modulation schemes, the bit-error rate (BER) performance very close to that of an offset-free system may be achieved in both additive white Gaussian noise (AWGN) and frequency selective fading channels. Moreover, a hardware implementation in a practical OFDM system is fulfilled which verifies the effectiveness of the proposed scheme.

A carrier frequency offset (CFO) tracking scheme without the use of any pilot subcarriers for the uplink orthogonal frequency division multiple access (OFDMA) system is proposed in this paper. We first analyze the interference due to CFO in uplink OFDMA systems, then, an offset tracking scheme is presented. The proposed scheme employs multiple time-frequency decision-feedback carrier recovery loops for individual users to restore the original symbols. Moreover, a modularized and flexible structure design is achieved for a low-complexity implementation of the loop. Simulation results show that the proposed scheme is resistant to multipath fading, while the BER (bit-error-rate) performance is very close to that of an ideal reception system without CFO.

In broadband wireless communications, extensive research has been devoted to orthogonal frequency division multiplexing (OFDM) due to its ability in mitigating the multipath effects. In OFDM, however, carrier frequency offsets (CFO) induce the loss of orthogonality among the sub-carriers and this results in significant performance degradation. In this paper, we propose a reduced complexity algorithm for CFO estimation with the aid of null sub-carriers. Using only one training OFDM symbol with N sub-carriers, the estimation range of the proposed scheme can be N/2 times larger than conventional CFO estimation schemes As a result, the precision of the oscillators can be significantly-reduced, thus, enabling low cost implementation. This paper will also investigate null sub-carrier allocations for the proposed scheme. It will be shown that the allocations should be based upon some specific binary sequences such as the extended m-sequences proposed in this paper.

In wireless communications, multiple receive antennas can be used with orthogonal frequency division multiplexing (OFDM) to significantly improve system capacity and performance. However, the complexity of such combination can be very large because multiple discrete Fourier transform (DFT) blocks, each for one receive antenna, are required to fully take advantage of the space diversity. In this paper, based on orthogonal designs, we present a general receiver architecture for an OFDM system employing multiple receive antennas. Using the proposed architecture, the number of DFT blocks, the number of receive antennas and system performance can be effectively traded off. As a result, with a little performance degradation, one can reduce system complexity and consequently reduce the power consumption in the receivers. The proposed receivers can also be featured as a performance enhancing scheme for an existing OFDM system where performance improvement is achieved through the use of software radio and without major modifications to the existing system hardware. For example, with only one DFT block, the performance can be improved by 5.1 dB using one more antenna. Finally, it is shown that the proposed scheme exhibits superior performance to that of selection diversity especially when the channels are rich in frequency diversity.

A general companding transform method is proposed to effectively reduce peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) signals. By compressing large signals while enhancing small signals along with taking into account their statistical characteristics, this method can achieve significant reduction in PAPR with low implementation complexity. Specifically, we present the design criteria of the transform, which enable effective tradeoff between reduction in PAPR and bit-error rate performance of the OFDM system. It is shown by simulations that the proposed method may significantly improve the performance of OFDM systems in radio channels by carefully choosing the companding form and parameters.

In variable spreading length access code division multiple access (VSL-CDMA) systems, the performance of the group successive interference cancellation (GSIC) detector degrades significantly in fading channels, and the decorrelator is too complex to be used in practical systems. In this paper, we propose an improved hybrid interference cancellation scheme with adaptive group allocation (AHIC) for dual-rate VSL-CDMA systems. The proposed AHIC detector adaptively groups users based on their received power level. In this way, the high-rate users, especially the weak ones, would not be penalized by MAI from strong low-rate users. Simulation results have shown that the proposed AHIC scheme can achieve the best bit error rate (BER) performance with relatively low computational complexity.

A new frequency offset synchronization algorithm is proposed for OFDM systems over time varying multipath fading channels. This algorithm employs pilot symbols embedded in data symbols to estimate the frequency offset. In particular, a fine frequency offset estimate is achieved by properly combining frequency-offset estimates from pilots over different frequency locations. Moreover, the effects of time varying channels and the number of sub-carriers are analyzed and the rule for the selection of the number of OFDM symbols used for the synchronization algorithm is given. Simulations are conducted to demonstrate the effectiveness of the proposed scheme.

Orthogonal frequency division multiplexing (OFDM) with multiple antennas makes it possible for efficient transmission of high-speed multimedia over wireless channels. This paper proposes an approach for power-efficient video transmission of MPEG-4 fine granularity scalable (FGS) bit-stream over OFDM systems with multiple antennas at both transmitter and receiver. Our proposed approach minimizes the total distortion while satisfying the constraint of transmission power, which is achieved by optimal power allocation and transmission rate control of power efficient assignment of scalable source to spatial subchannels. The performance of our proposed scheme is compared with one using fixed transmission rate or power allocation. Simulation results show significant performance gain of our approach.

In this paper, we propose a piecewise-scales transform (PST) for the reduction of peak-to-average power ratio (PAPR) of OFDM signals. By transforming the signal with different scales according to the statistical property of OFDM signals, this method can compress large signals while enhancing small signals, achieving significant reduction in PAPR with low complexity. Specifically, with the complementary cumulative distribution function (CCDF) of PAPR of OFDM signals, this paper proves that the proposed method can compromise well the reduction in PAPR with BER performance in practical OFDM systems by choosing the transform parameters properly. It is confirmed by computer simulations that the proposed PST can outperform the well-known partial transmit sequence and the clipping-filtering methods substantially.

In this paper we present a channel-optimized video transmission (COVT) scheme over WCDMA system. Specifically, the video encoder is composed of multiple descriptions coding (MDC) and layered coding (LC). This hybrid video encoder features that one description of MDC. is compatible with the base layer coder of the LC. The COVT system is designed to provide high quality video in case of no channel fading, and acceptable quality while in deep and long fading. Simulation results confirm that the COVT scheme outperforms traditional source-channel independent transmission system remarkably for the time-varying mobile channels.

Abstract Joint source–channel coding schemes have been proven to be very effective for reliable multimedia communications. In this paper, we develop a joint source–channel coding scheme that combines the hidden Markov source (HMS) estimation and the low‐density parity‐check (LDPC) codes with an iterative estimation/decoding scheme. With this innovative combination, multimedia source redundancy could be accurately extracted by the hidden Markov estimation without any a priori information about the source. Moreover, the interleaver that is usually used to separate the source coding and channel coding can be avoided by exploiting the randomizing property of the LDPC codes. Furthermore, the channel decoding procedure may be implemented in parallel, resulting in good performance with a fairly low decoding complexity and delay. Simulation results have shown that the proposed scheme can achieve much better performance than the standard coding scheme over the binary input additive white Gaussian noise (BIAWGN) channels. Copyright © 2002 John Wiley & Sons, Ltd.

Video communication over wireless link is a challenging task due to the time-varying characteristics of a wireless channel and limited battery resource in the handheld devices. This paper proposes a power-optimized approach for video communication, which simultaneously controls the transmission power, source rate and error protection level to minimize the total power consumption for all users. The performance of the cellular CDMA system using our proposed scheme is compared with the one using a fixed power-control scheme. The simulation results show that our proposed joint power control and source-channel coding scheme achieves significant power saving compared to the fixed scheme.

An improved pre-sorting and reordering algorithm is proposed for enhancing the quality of transmitting real-time MPEG-4 AAC (advanced audio coding) audio over EGPRS (Enhanced General Packet Radio Service). Compared with the standard Huffman codeword reordering (HCR) algorithm, lower implementation complexity and much better audio signal reconstruction quality may be achieved with the proposed scheme.

Based on the properties of cyclic codes, a new decoding algorithm for burst-error-correction is proposed in this paper. This algorithm can effectively correct burst errors with length that approaches to n-k for (n,k) Reed-Solomon (RS) codes. Moreover, due to the use of error locations correlation within a burst, divisions are exempted from the decoding process, achieving a fast decoding algorithm with much less computational complexity compared with existing algorithms. It is shown that the proposed algorithm can be widely used in wireless communications, wireless digital broadcast systems, and so on.

In this paper, we introduce a simple geometric approach that is based on signal-space concepts to efficiently evaluate the performance of M-ary phase-shift keying (M-PSK) and M-ary quadrature amplitude modulation (M-QAM) schemes over an additive white Gaussian noise channel. In particular, new bit error rate approximations are derived and shown to be in excellent agreement with Monte Carlo simulation results.

We develop a unique set of techniques to support reliable and efficient video transmission over bandwidth-limited mobile networks. The video coder is based on the H.263 coding standard, while its transmission system design is based on a joint study of combined source and channel coding, diversity reception, and pre/postprocessing techniques. In particular, a finite-state Markov model for representing a correlated fading channel with diversity is developed. Based on this model, a recursive algorithm is further devised for an efficient estimation of block-code performance. By doing so, an important means for finding proper diversity and channel coding schemes for correlated video data in correlated fading channels is obtained. In addition, robust decoding together with a combined spatial-temporal concealment is employed to mitigate the effects of decoding mismatch caused by residual errors. Simulation results confirm that the proposed system can significantly reduce the bursty error effects on the H.263 coded video data while maintaining high transmission efficiency.

The three-step search (TSS) algorithm for block-matching motion estimation, due to its simplicity, significant computational reduction, and good performance, has been widely used in real-time video applications. A new search algorithm is proposed for further reduction of computational complexity for motion estimation. It is shown that the proposed algorithm is simple and efficient and requires about one half of the computation for the TSS while keeping the same regularity and good performance.