Recent advances in diffusion models have achieved remarkable success in isolated computer vision tasks such as text-to-image generation, depth estimation, and optical flow. However, these models are often restricted by a ``single-task-single-model'' paradigm, severely limiting their generalizability and scalability in multi-task scenarios. Motivated by the cross-domain generalization ability of large language models, we propose a universal visual perception framework based on flow matching that can generate diverse visual representations across multiple tasks. Our approach formulates the process as a universal flow-matching problem from image patch tokens to task-specific representations rather than an independent generation or regression problem. By leveraging a strong self-supervised foundation model as the anchor and introducing a multi-scale, circular task embedding mechanism, our method learns a universal velocity field to bridge the gap between heterogeneous tasks, supporting efficient and flexible representation transfer. Extensive experiments on classification, detection, segmentation, depth estimation, and image-text retrieval demonstrate that our model achieves competitive performance in both zero-shot and fine-tuned settings, outperforming prior generalist and several specialist models. Ablation studies further validate the robustness, scalability, and generalization of our framework. Our work marks a significant step towards general-purpose visual perception, providing a solid foundation for future research in universal vision modeling.

Person re-identification (ReID), a crucial technology for intelligent surveillance in Internet of Things (IoT) systems, aims to search for the target person among the non-overlapping surveillance cameras. Video-based clothes-changing person re-identification (VCC-ReID) has become essential due to the rich information in videos and its broad applications. Because clothes are attached to the human body, the clothes and pedestrian features are highly coupled when extracting features, making VCC-ReID challenging. To solve this challenge, we propose a Multi-Task Disentanglement Framework guided by Pedestrian Attributes (MTDF-PAttr), whose core is the cross-domain attribute distillation decoupling mechanism. Pedestrian attribute recognition (PAR) is used as an auxiliary task in MTDF-PAttr to guide feature decoupling, thereby enhancing the main task, VCC-ReID’s performance. Since the existing VCC-ReID dataset lacks PAR annotations, we employ knowledge distillation to train the auxiliary task, where the teacher network is a pre-trained video-based PAR network. To make the PAR teacher network have better accuracy, stronger generalization, and can identify more attributes, we propose a Multi-Dataset Fusion Framework for Pedestrian Attribute Recognition (MDFF-PAttr), whose core is the multi-teacher collaborative self-distillation mechanism. MDFF-PAttr can simultaneously use multiple datasets for training and provide a powerful teacher model for MTDF-PAttr to distill its auxiliary task. Experimental results demonstrate that MTDF-PAttr can achieve state-of-the-art performance in the VCC-ReID task, providing an effective method for intelligent surveillance systems in the IoT. Additionally, MDFF-PAttr can effectively enhance the accuracy and generalization of the PAR network.

Fluid antenna systems (FAS) signify a pivotal advancement in 6G communication by enhancing spectral efficiency and robustness. However, obtaining accurate channel state information (CSI) in FAS poses challenges due to its complex physical structure. Traditional methods, such as pilot-based interpolation and compressive sensing, are not only computationally intensive but also lack adaptability. Current extrapolation techniques relying on rigid parametric models do not accommodate the dynamic environment of FAS, while data-driven deep learning approaches demand extensive training and are vulnerable to noise and hardware imperfections. To address these challenges, this paper introduces a novel self-supervised learning network (SSNet) designed for efficient and adaptive channel extrapolation in FAS. We formulate the problem of channel extrapolation in FAS as an image reconstruction task. Here, a limited number of unmasked pixels (representing the known CSI of the selected ports) are used to extrapolate the masked pixels (the CSI of unselected ports). SSNet capitalizes on the intrinsic structure of FAS channels, learning generalized representations from raw CSI data, thus reducing dependency on large labeled datasets. For enhanced feature extraction and noise resilience, we propose a mix-of-expert (MoE) module. In this setup, multiple feedforward neural networks (FFNs) operate in parallel. The outputs of the MoE module are combined using a weighted sum, determined by a gating function that computes the weights of each FFN using a softmax function. Extensive simulations validate the superiority of the proposed model. Results indicate that SSNet significantly outperforms benchmark models, such as AGMAE and long short-term memory (LSTM) networks by using a much smaller labeled dataset. A key observation is that the proposed model is more effectively trained using a small unmasked ratio of known CSI. Specifically, the proposed SSNet trained using CSI of 10 % total ports outperforms that trained using CSI of 25 % and 50 % total ports. This is because using a smaller number of known CSIs during training, the proposed model is forced to learn more effective channel correlation for channel extrapolation at the expense of higher training complexities. Ablation experiments reveal substantial performance gains from the MoE module’s integration. Furthermore, zero-shot learning experiments show a moderate performance degradation of about 3-5 dB, underscoring the model’s robust generalization ability. Finally, the inference speed experiments illustrate that the proposed model outperforms the benchmark models dramatically at the expense of a slightly longer execution time of 1.13 ms, 2.9 ms, and 3.12 ms on NVIDIA RTX 4090, 4060, and 3060 graphics processing units (GPU)s, respectively.

Compared to monocular 3D object detection, stereo-based 3D methods offer significantly higher accuracy but still suffer from high computational overhead and latency. The state-of-the-art stereo 3D detection method achieves twice the accuracy of monocular approaches, yet its inference speed is only half as fast. In this paper, we propose StereoDETR, an efficient stereo 3D object detection framework based on DETR. StereoDETR consists of two branches: a monocular DETR branch and a stereo branch. The DETR branch is built upon 2D DETR with additional channels for predicting object scale, orientation, and sampling points. The stereo branch leverages low-cost multi-scale disparity features to predict object-level depth maps. These two branches are coupled solely through a differentiable depth sampling strategy. To handle occlusion, we introduce a constrained supervision strategy for sampling points without requiring extra annotations. Compared with the existing published monocular and binocular 3D detection methods, StereoDETR breaks the trade-off between speed and accuracy. Through a concise framework, it achieves binocular-level accuracy while maintaining monocular-level inference speed. The code is available at https://github.com/shiyi-mu/StereoDETR-OPEN.

Recent advancements in high-definition (HD) map construction have demonstrated the effectiveness of dense representations, which heavily rely on computationally intensive bird’s-eye view (BEV) features. While sparse representations offer a more efficient alternative by avoiding dense BEV processing, existing methods often lag behind due to the lack of tailored designs. These limitations have hindered the competitiveness of sparse representations in online HD map construction. In this work, we systematically revisit and enhance sparse representation techniques, identifying key architectural and algorithmic improvements that bridge the gap with—and ultimately surpass—dense approaches. We introduce a dedicated network architecture optimized for sparse map feature extraction, a sparse-dense segmentation auxiliary task to better leverage geometric and semantic cues, and a denoising module guided by physical priors to refine predictions. Through these enhancements, our method achieves state-of-the-art performance on the nuScenes dataset, significantly advancing HD map construction and centerline detection. Specifically, SparseMeXt-Tiny reaches a mean average precision (mAP) of 55.5% at 32 frames per second (fps), while SparseMeXt-Base attains 65.2% mAP. Scaling the backbone and decoder further, SparseMeXt-Large achieves an mAP of 68.9% at over 20 fps, establishing a new benchmark for sparse representations in HD map construction. These results underscore the untapped potential of sparse methods, challenging the conventional reliance on dense representations and redefining efficiency-performance trade-offs in the field.

In modern wireless communication systems, the effective processing of Channel State Information (CSI) is crucial for enhancing communication quality and reliability. However, current methods often handle different tasks in isolation, thereby neglecting the synergies among various tasks and leading to extract CSI features inadequately for subsequent analysis. To address these limitations, this paper introduces a novel Multi-Task Channel Analysis framework named MTCA, aimed at improving the performance of wireless communication even sensing. MTCA is designed to handle four critical tasks, including channel prediction, antenna-domain channel extrapolation, channel identification, and scenario classification. Experiments conducted on a multi-scenario, multi-antenna dataset tailored for UAV-based communications demonstrate that the proposed MTCA exhibits superior comprehension of CSI, achieving enhanced performance across all evaluated tasks. Notably, MTCA reached 100% prediction accuracy in channel identification and scenario classification. Compared to the previous state-of-the-art methods, MTCA improved channel prediction performance by 20.1% and antenna-domain extrapolation performance by 54.5%.

Efficient power allocation and interference management are critical challenges in dynamic wireless communication systems. To address these challenges, graph neural networks (GNNs) have attracted significant attention, while knowledge graph further enhance this capability by representing structured interactions among entities. This article proposes the Power-focused Knowledge Graph Convolutional Network (PKGCN), a novel framework utilizing knowledge graph driven learning to model and optimize power allocation strategies. By integrating wireless-specific features such as channel conditions and interference metrics, PKGCN effectively captures the complex interactions and dependencies among network nodes. This model employs a message aggregation layer to extract local and global interactions and a power prediction layer to optimize resource allocation. Comprehensive evaluations reveal that PKGCN de-livers higher average user rates, lower interference levels, and greater robustness.

For the Internet of things (IoT) network, the integrated localization and communication (ILAC) is expected to provide high localization and communication performance simultaneously. However, the existing research to evaluate the performance of ILAC systems fails to reveal the fundamental performance of ILAC systems in practical IoT network topology analytically. In this paper, we develop a unified analytical ILAC framework using stochastic geometry. We then validate the theoretical results obtained from the proposed analytical framework with the simulation results via extensive Monte Carlo simulations. We further analyse the communication coverage and localization coverage probability with respect to the network density, time-frequency-power domain resource allocation, and communication throughout and localization threshold. Finally, based on the ILAC simulation results, we reveal design guidance for ILAC systems. Specifically, we observe the fundamental trade-off between localization and communication performance attributed to the time-frequency-power domain resource allocation. Network density positively affects the ILAC performance, while power control is much less effective due to the dense network topology. The major observations are that time-domain (TD) resource allocation is preferred in dense networks with low localization CRB thresholds, while frequency-domain (FD) resource allocation dominates in sparse networks with large localization CRB thresholds.

Orthogonal Time Frequency Space (OTFS) modulation is expected to address the performance degradation of orthogonal frequency division multiplexing (OFDM) modulated signals, particularly due to issues like Doppler shifts in mobile communication environments. In this paper, we propose a lightweight deep learning-based framework for end-to-end joint channel estimation and data detection (JCEDD) in an OTFS communication system. To fully exploit the characteristics of OTFS modulation, we introduce a data padding preprocessing method and a slicing data augmentation technique. Furthermore, the performance of the proposed deep learning-based framework could be enhanced dramatically with only a small overhead compared to the superimposed pilot scheme. Ablation experiments demonstrate that the proposed data padding preprocessing method and the slicing data augmentation technique significantly improve the performance of the deep learning-based framework. Simulation results show that the proposed framework outperforms existing algorithms in terms of JCEDD performance, while maintaining a relatively low level of computational complexity.

Extended reality (XR), blending virtual and real worlds, is a key application of future networks. While AI advancements enhance XR capabilities, they also impose significant computational and energy challenges on lightweight XR devices. In this article, we developed a distributed queue model for multitask deep neural network inference, addressing issues of resource competition and queue coupling. In response to the challenges posed by the high energy consumption and limited resources of XR devices, we designed a dual time-scale joint optimization strategy for model partitioning and resource allocation, formulated as a bi-level optimization problem. This strategy aims to minimize the total energy consumption of XR devices while ensuring queue stability and adhering to computational and communication resource constraints. To tackle this problem, we devised a Lyapunov-guided proximal policy optimization algorithm, named LyaPPO. Through numerical results, we show that our LyaPPO algorithm outperforms the baseline algorithms. Specifically, under different maximum local computational capacities, the proposed algorithm decreases 24.29%–56.62% energy compared to the suboptimal baselines.

Person re-identification (ReID) aims to search for the target person among the non-overlapping surveillance cameras. Video-based clothes-changing person re-identification (VCC-ReID) has become an essential branch of ReID due to the rich spatial and temporal information in the videos and the broad application of the scenarios. Appearance and gait are discriminative features in the video-based ReID, but appearance information is limited due to the clothes changing, which makes the VCC-ReID challenging. To solve this challenge, we propose a Framework with explicit Learning based on Appearance and Gait (FLAG), which can explicitly extract two corresponding types of information and be combined with most existing video-based ReID methods. The FLAG includes a multi-modal and multi-granularities Architecture (MGA), which is a large model, and a Cross-Modal Knowledge Distillation Scheme (CMKDS), which has a small model. They can be applied to devices with different computing resources. The MGA is designed to simultaneously take the visible light and silhouette modalities as input to explicitly learn the appearance and gait features, respectively. The silhouette modalities are composed of several levels of granularities to model global and local gait features and independently serve as input for MGA. The Embedding-Based parallel fusion module is proposed to fuse the appearance and multi-granularities gait feature efficiently. The CMKDS is present to distill the MGA to a small single-modal model that only uses the visible light modality as input. The Embedding-Based direct and indirect distillation strategies are designed in the CMKDS. Experimental results demonstrate that the FLAG combined with the existing video-based ReID methods can significantly improve their performance. In addition, when FLAG is combined with the AP3D method, the MGA can outperform state-of-the-art accuracy by 4.2%.

Multi-task perception system to simultaneously perceive various kinds of objects is essential for autonomous driving. Existing perception frameworks always rely on multi-labeled datasets, which encompass labels for all pertinent objects, thereby constraining their adaptability to leverage specialized, task-oriented datasets. This approach hinders the efficient utilization of abundant but focused data. Furthermore, stacking multiple expert networks to address these perception objectives inevitably introduces additional computational overhead. To address this limitation, we propose Uni-EPM (Unified Extensible Perception Model), with a novel training framework for multi-task perception using task prompt selection to decouple tasks, which enables perceiving traffic signs and traffic lights in addition to lane lines and traffic elements from existing task-specific datasets without re-labeling. To the best of our knowledge, Uni-EPM is the first model can do this in the field of autonomous driving. By introducing the parameter-sharing decoder among tasks, we alleviate the problems of stacking task heads, including significant parameter increase, etc. Uni-EPM achieves state-of-the-art results in multi-task algorithms without substantial increase in parameters, which also demonstrates comparable performance to existing standalone models. The efficiency of the design is validated through comprehensive ablation experiments and results.

The emergence of 6th generation (6G) mobile networks brings new challenges in supporting high-mobility communications, particularly in addressing the issue of channel aging. While existing channel prediction methods offer improved accuracy at the expense of increased computational complexity, limiting their practical application in mobile networks. To address these challenges, we present LinFormer, an innovative channel prediction framework based on a scalable, all-linear, encoder-only Transformer model. Our approach, inspired by natural language processing (NLP) models such as BERT, adapts an encoder-only architecture specifically for channel prediction tasks. We propose replacing the computationally intensive attention mechanism commonly used in Transformers with a time-aware multi-layer perceptron (TMLP), significantly reducing computational demands. The inherent time awareness of TMLP module makes it particularly suitable for channel prediction tasks. We enhance LinFormer’s training process by employing a weighted mean squared error loss (WMSELoss) function and data augmentation techniques, leveraging larger, readily available communication datasets. Our approach achieves a substantial reduction in computational complexity while maintaining high prediction accuracy, making it more suitable for deployment in cost-effective base stations (BS). Comprehensive experiments using both simulated and measured data demonstrate that LinFormer outperforms existing methods across various mobility scenarios, offering a promising solution for future wireless communication systems.

This paper introduces a novel framework for open-set speaker identification in household environments, playing a crucial role in facilitating seamless human-computer interactions. Addressing the limitations of current speaker models and classification approaches, our work integrates an pretrained WavLM frontend with a few-shot rapid tuning neural network (NN) backend for enrollment, employing task-optimized Speaker Reciprocal Points Learning (SRPL) to enhance discrimination across multiple target speakers. Furthermore, we propose an enhanced version of SRPL (SRPL+), which incorporates negative sample learning with both speech-synthesized and real negative samples to significantly improve open-set SID accuracy. Our approach is thoroughly evaluated across various multi-language textdependent speaker recognition datasets, demonstrating its effectiveness in achieving high usability for complex household multi-speaker recognition scenarios. The proposed system enhanced open-set performance by up to 27% over the directly use of efficient WavLM base+ model. For detailed information on open-sourced implementation in our project website 1.

Next-generation mobile networks are poised to leverage Integrated Sensing and Communication (ISAC) as a pivotal technology, offering unprecedented support for various sectors including industrial Internet of Things (IIoT), extended reality (XR), and smart home applications. A critical challenge in implementing ISAC lies in extracting sensing parameters from radio signals, a task that has proven difficult using conventional methods. This paper, for the first time, introduces a novel approach to this challenge by proposing the extrapolation of sensing channel information, specifically the power delay profile (PDP), from readily available communication channel state information (CSI). We present a transformer-based framework designed to accomplish this task efficiently. Our extensive simulations demonstrate the framework's effectiveness in CSI-to-PDP extrapolation, achieving high accuracy in predicting both the delay and signal strength of individual paths. This innovative method has the potential to significantly enhance sensing capabilities in future mobile networks, paving the way for more robust and versatile ISAC applications.

Federated learning (FL) is a viable technique to train a shared machine learning model without sharing data. Hierarchical FL (HFL) system has yet to be studied regrading its multiple levels of energy, computation, communication, and client scheduling, especially when it comes to clients relying on energy harvesting to power their operations. This paper presents a new two-phase deep deterministic policy gradient (DDPG) framework, referred to as “TP-DDPG”, to balance online the learning delay and model accuracy of an FL process in an energy harvesting-powered HFL system. The key idea is that we divide optimization decisions into two groups, and employ DDPG to learn one group in the first phase, while interpreting the other group as part of the environment to provide rewards for training the DDPG in the second phase. Specifically, the DDPG learns the selection of participating clients, and their CPU configurations and the transmission powers. A new straggler-aware client association and bandwidth allocation (SCABA) algorithm efficiently optimizes the other decisions and evaluates the reward for the DDPG. Experiments demonstrate that with substantially reduced number of learnable parameters, the TP-DDPG can quickly converge to effective polices that can shorten the training time of HFL by 39.4% compared to its benchmarks, when the required test accuracy of HFL is 0.9.

Extended reality (XR) is one of the most important applications of beyond 5G and 6G networks. Real-time XR video transmission presents challenges in terms of data rate and delay. In particular, the frame-by-frame transmission mode of XR video makes real-time XR video very sensitive to dynamic network environments. To improve the users’ quality of experience (QoE), we design a cross-layer transmission framework for real-time XR video. The proposed framework allows the simple information exchange between the base station (BS) and the XR server, which assists in adaptive bitrate and wireless resource scheduling. We utilize the cross-layer information to formulate the problem of maximizing user QoE by finding the optimal scheduling and bitrate adjustment strategies. To address the issue of mismatched time scales between two strategies, we decouple the original problem and solve them individually using a multi-agent-based approach. Specifically, we propose the multi-step Deep Q-network (MS-DQN) algorithm to obtain a frame-priority-based wireless resource scheduling strategy and then propose the Transformer-based Proximal Policy Optimization (TPPO) algorithm for video bitrate adaptation. The experimental results show that the TPPO+MS-DQN algorithm proposed in this study can improve the QoE by 3.6% to 37.8%. More specifically, the proposed MS-DQN algorithm enhances the transmission quality by 49.9%-80.2%.

In this paper, we present a new cascade architecture based on a differentiable sample module to satisfy the varied image resolution requirements of license plate detector and recognizer in end-to-end technologies. Based on this module, the network can detect license plates on downsampled low-resolution images and resample them from the original high-definition images to recognize the license plate numbers. Furthermore, since the optimization direction of the detector for the detection boxes and the input requirements of the recognizer are not consistent with each other, we introduce the Bias Detection Head, which decouples the two Bounding Boxes to circumvent this problem. In the meantime, a novel feature fusion module is presented, which simultaneously satisfies the fusion of multi-scale information and the interaction of two Bounding Box features. For the recognizer, we present a unified architecture based on a decoupled attention mechanism for recognizing single and double lines, varying lengths, and tilting on license plates.

License plate detection and recognition (LPDR) is of paramount importance in the Intelligent Transportation Systems. Most existing license plate (LP) detectors rely on anchors, rendering them vulnerable to multi-scale LPs, especially those of smaller scales, which limited the overall performance of LPDR. Another issue prevalent in LP datasets arises from the inherent ambiguity in manual labeling standards. Owing to this uncertainty, certain small-scale LPs that are distinctly detectable often suffer from annotation omissions. The presence of such noisy data has a detrimental impact on the training of LP detectors. In this paper, we propose ALPD, an anchor-free LP detector along with three key designs, namely the Multi-To-One scale-fusion block (MTO) for cross-scale feature integration, the Multi-Domain Feature Simulation (MDFS) for narrowing the disparities across multiple domains even the unseen ones, and the decoupled heads for better optimizing classification and regression tasks. Besides, ALPD incorporates a semi-supervised training framework using an abstention strategy known as arbitration, wherein a Teacher model and a Student model are trained collaboratively, enabling the supplementation of missing annotations for small license plates. Furthermore, it possesses immunity to model performance degradation when fed with massive quantities of unlabeled or even mislabeled data. The arbitration method along with a penalty factor can effectively guarantee the pseudo-label quality and balance complexity between the Teacher and Student tasks, thus preventing the Student from being constrained by ambiguous pseudo-labels. ALPD outperforms previous state-of-the-art methods on two widely recognized benchmarks and exhibits its robustness and generalizability on the All-round CCPD dataset.

Autonomous vehicle platooning has tremendous potential to relieve the burden of Vehicular Edge Computing (VEC) by sharing resources with nearby vehicles. Therefore, fleet formation and resource orchestration within vehicle platoons have recently ignited significant research interest. However, most fleet formation works focus on the intra-platoon configuration and information exchange, but few consider trajectory matching and joining willingness. Likewise, in multi-platoon scenarios, static resource orchestration for a single platoon no longer meets the demand from dynamic resource scheduling. To tackle these problems, we proposed the SFO scheme, an adaptive taskScheduling based on incentive fleetFormation and metrizable resourceOrchestration. First, we design a fleetFormation algorithm based onTrajectory matching andJoining willingness (FTJ) to ensure the stable underlying architecture. Second, we use theWeightedSum ofEnergyConsumption (WSEC) as the performance metric for resource orchestration and formulate the time-average WSEC minimization problem. Third, anAdaptive taskScheduling underPartitionableApplications and variableResources (ASPAR) is proposed for an asymptotic optimal solution in reaction to the changeable backlog of the timeout queue. Finally, our numerical results demonstrate that our approach is superior to other latest and classic works in energy consumption and execution latency.

Mobile edge computing (MEC) offers an opportunity for devices relying on wireless power transfer (WPT), to accomplish computationally demanding tasks. Such WPT-powered MEC systems have yet to be optimized for long-term efficiency, due to random and changing task demands and wireless channel states of the devices. This paper presents an augmented two-staged deep Q-network (DQN), referred to as “TS-DQN,” for online optimization of WPT-powered MEC systems, where the WPT, offloading schedule, channel allocation, and the CPU configurations of the edge server and devices are jointly optimized to minimize the long-term average energy requirement of the systems. The key idea is to design a DQN for learning the channel allocation and task admission, while the WPT, offloading time and CPU configurations are efficiently optimized to precisely evaluate the reward of the DQN and substantially reduce its action space. Another important aspect is that a new action generation method is developed to expand and diversify the actions of the DQN, further accelerating its convergence. As validated by simulations, the proposed TS-DQN is much more energy efficient and converges much faster, than its potential alternative directly using the state-of-the-art Deep Deterministic Policy Gradient algorithm to learn all decision variables.

License Plate Detection and Recognition (LPDR) is widely used in Intelligent Transportation Systems (ITS). Although there are typically multiple categories of license plates, the majority of existing research cannot be applied to multi-category plates due to that existing methods are not optimised for multi-category plate scenarios and the scarcity of large-scale multi-category plate datasets. In this paper, we propose a multi-category license plate recognition framework called GroupPlate, which consists of Group Module and Indirect Supervision Module, making full use of the implicit and explicit grouping information of license plate. In addition, the Category Decouple Module is intended to decouple the grouping information from the original features, allowing the decoder to concentrate on character features. Simultaneously, we propose a large-scale All-Category license Plate detection and recognition Dataset (ACPD) for vehicles on the Chinese mainland, which also includes annotations of plates’ categories. Considering the domain gap between synthetic data and real data, we propose a simple but effective strategy called Feature Shift to mitigate the performance degradation caused by this gap. Experiments demonstrate that GroupPlate achieves the comparable performance to the existing methods on single-category license plate dataset and outperforms our baseline on the multi-category license plates dataset. Ablation experiments demonstrate the effectiveness of the modules in GroupPlate. Extensive results demonstrate that the dataset we proposed can mitigate the problem of models trained on a single-category license plate dataset failing to recognize multi-category license plates, and that our model can generalizes well to unseen categories. The work will be available athttps://github.com/YilinGao-SHU/ACPD.

With the increasing on-board demand for intelligent connected vehicles (ICVs), the fifth-generation (5G) wireless systems are being massively utilized in vehicular networks. As an essential component, content retrieval in the ICV provides a basis for vehicle-to-vehicle or vehicle-to-infrastructure data interaction for many applications. However, content access is still subject to performance degradation due to congested communication channels, diverse requests patterns, and intermittent network connectivity. To mitigate these issues, in-network caching in 5G-enabled ICV has been leveraged to benefit content access by allowing edge nodes to store content for data generators. In this paper, we propose an in-network caching scheme to support various provisions of data sharing in the ICVs by exploring the advantages of information-centric networks (ICN). We first divide each on-board service into several content units. Then, we place these units at the ICV and small cell base stations (SBSs) to reduce the content retrieval delay, further model the proposed system as an integer nonlinear program (INLP) and attain the optimal QoE (Quality of Experience) by placing content units at appropriate cache entities. Finally, we verify the effectiveness and correctness of our proposed model through extensive simulations.

Pre-distortion is a key technique to compensate for the nonlinear distortions caused by the transmitter in wireless communication systems. Generally, pre-distortion can be classified into digital pre-distortion (DPD) and analog pre-distortion (APD), which focus on optimizing and assessing the nonlinearity in their own areas. In this paper, we propose a new DPD approach to optimize the performance metric of the analog RF-domain (i.e., inter-modulation distortion (IMD) or adjacent channel power ratio (ACPR)) and that of the digital IF-domain (i.e., mean square error (MSE)) simultaneously. To make the joint design feasible, we derive a new hybrid performance metric, where the analog preferred metric is defined in the form of digital signals to bridge the gap between digital and analog signal processing. On top of that, an effective DPD scheme is developed based on a new dual time-delayed neural network (TDNN) learning architecture. The coefficients of the TDNN for power amplifier (PA) modeling can be trained offline with a PA dataset, while those of the TDNN for pre-distortion are obtained adaptively by optimizing the proposed joint design metric. Experimental results show that the proposed scheme is able to significantly improve the IMD/ACPR performance without compromising the MSE, compared to conventional DPD schemes.

The high computational complexity and high energy consumption of artificial intelligence (AI) algorithms hinder their application in augmented reality (AR) systems. This paper considers the scene of completing video-based AI inference tasks in the mobile edge computing (MEC) system. We use multiply-and-accumulate operations (MACs) for problem analysis and optimize delay and energy consumption under accuracy constraints. To solve this problem, we first assume that offloading policy is known and decouple the problem into two subproblems. After solving these two subproblems, we propose an iterative-based scheduling algorithm to obtain the optimal offloading policy. We also experimentally discuss the relationship between delay, energy consumption, and inference accuracy.

This paper presents a new two-stage deep Q-network (DQN), referred to as "TS-DQN", for online optimization of wireless power transfer (WPT)-powered mobile edge computing (MEC) systems, where the WPT, offloading schedule, channel allocation, and the CPU configurations of the edge server and devices are jointly optimized to minimize the long-term average energy requirement of the systems. The key idea is to design a DQN to learn the channel allocation and task admission, while the WPT, offloading time and CPU configurations are efficiently optimized to precisely evaluate the reward of the DQN and substantially reduce its action space. A new action generation method is developed to expand and diversify the actions of the DQN, hence further accelerating its convergence. Simulation shows that the gain of the TS-DQN in energy saving is nearly 60% compared to its potential alternatives.

Network slicing is a critical driver for guaranteeing the diverse service level agreements (SLA) in 5G and future networks. Recently, deep reinforcement learning (DRL) has been widely utmzed for resource allocation in network slicing. However, existing related works do not consider the performance loss associated with the initial exploration phase of DRL. This paper proposes a new performance-guaranteed slicing strategy with a soft and hard hybrid slicing setting. Mainly, a common slice setting is applied to guarantee slices’ SLA when training the neural network. Moreover, the resource of the common slice tends to precisely redistribute to slices with the training of DRL until it converges. Furthermore, experiment results confirm the effectiveness of our proposed slicing framework: the slices’ SLA of the training phase can be guaranteed, and the proposed algorithm can achieve the near-optimal performance in terms of the SLA satisfaction ratio, isolation degree and spectrum efficiency after convergence.

The increasing demand for road freight has raised tremendous attention to vehicle platooning, which reduces air resistance and improves fuel economy. To achieve a small and safe spacing between vehicles while ensuring platoon stability, wireless communication assistance is indispensable. However, the imperfection of communication brings degradation of platooning performance (i.e., spacing error) and more possibilities for adversarial attacks. This article proposes a prediction-assisted platooning mechanism from the perspective of performance optimization, wherein each vehicle establishes its local platoon model based on the information received from the communication network, thereby reducing information latency. Then, to secure the system against malicious vehicles, we carry out analysis and design of a detection algorithm for a typical attack type, i.e., the data-injection attack. The detection is based on three indicators: 1) absolute spacing error; 2) spacing prediction error; and 3) acceleration prediction error. The advantages of the novel platooning mechanism and detection algorithm are ultimately demonstrated on a road-traffic simulation platform that considers the imperfection of realistic vehicle perceptions and Cellular-Vehicle-to-Everything (C-V2X) communication.

Edge infrastructures, including edge computing servers, are increasingly powered by renewable energy and smart grid combined. Two-way energy trading allows the surplus or shortfall of renewable energy to be traded between a server and the smart grid, but is non-trivial due to randomly varying computation demands and renewables. This paper proposes a new online policy, namely, distributed online resource allocation and load management (DORL), which enables such an edge server and its serving devices to minimize their energy cost and energy consumption, respectively, in a fully distributed manner. The key idea is that we employ the stochastic dual-subgradient method to interpret the battery of the server as a virtual queue. Based on the virtual queue and task queues, the CPU frequencies of the devices and the edge server, the offloading transmit rates of the devices (to the server) and the energy trading decisions of the server (with the smart grid) are decoupled over time and among devices, and optimized on an ongoing basis. Furthermore, we prove that the DORL yields a feasible and asymptotically optimal solution with a cost-backlog tradeoff of$[\eta, 1/\eta]$. Simulations show that the DORL reduces the system cost by nearly 50%, as compared to existing benchmarks.

The integration of non-orthogonal multiple access (NOMA) in vehicle-to-everything (V2X) communications has recently shown great potential to improve traffic efficiency, control, and reliability of beyond 5G transportation systems. In V2X communications, it is vital to inspect imperfect channel state information (CSI) because the high mobility of vehicles leads to more channel estimation uncertainties. This paper proposes an energy-efficient power allocation scheme for the road-side unit (RSU) assisted NOMA multicasting in beyond 5G cellular V2X networks. In particular, the energy efficiency maximization problem is investigated under the outage probability of vehicles under imperfect CSI, quality of services (QoS), and power limit constraints. Since the problem is non-convex and difficult to solve directly, we first convert outage probability constraint to non-probabilistic constraint through approximation and adopt a low complexity gradient assisted binary search (GABS) method to obtain the efficient power allocation at RSUs. Then, a successive convex approximation (SCA) technique is exploited to transform the power allocation problem of vehicles associated with each RSU into a tractable concave-convex fractional programming (CCFP) problem. The optimal solution to the CCFP problem is achieved through Dinkelbach and the dual decomposition method. The global optimal power allocation through the GABS-Exhaustive scheme act as a benchmark, which has considerable computational complexity. Simulation results unveil that the proposed suboptimal scheme (GABS-Dinkelbach) can achieve near-optimal performance with very low complexity.

With the rising demand for indoor localization, high precision technique-based fingerprints became increasingly important nowadays. The newest advanced localization system makes effort to improve localization accuracy in the time or frequency domain, for example, the UWB localization technique can achieve centimeter-level accuracy but have a high cost. Therefore, we present a spatial domain extension-based scheme with low cost and verify the effectiveness of antennas extension in localization accuracy. In this paper, we achieve sub-meter level localization accuracy using a single AP by extending three radio links of the modified laptops to more antennas. Moreover, the experimental results show that the localization performance is superior as the number of antennas increases with the help of spatial domain extension and angular domain assisted.

Precise indoor localization is one of the key requirements for fifth Generation (5G) and beyond, concerning various wireless communication systems, whose applications span different vertical sectors. Although many highly accurate methods based on signal fingerprints have been lately proposed for localization, their vast majority faces the problem of degrading performance when deployed in indoor systems, where the propagation environment changes rapidly. In order to address this issue, the crowdsourcing approach has been adopted, according to which the fingerprints are frequently updated in the respective database via user reporting. However, the late crowdsourcing techniques require precise indoor floor plans and fail to provide satisfactory accuracy. In this paper, we propose a low-complexity self-calibrating indoor crowdsourcing localization system that combines historical with frequently updated fingerprints for high precision user positioning. We present a multi-kernel transfer learning approach which exploits the inner relationship between the original and updated channel measurements. Our indoor laboratory experimental results with the proposed approach and using Nexus 5 smartphones at 2.4GHz with 20MHz bandwidth have shown the feasibility of about one meter level accuracy with a reasonable fingerprint update overhead.

In recent years, synthetic speech generated by advanced text-to-speech (TTS) and voice conversion (VC) systems has caused great harms to automatic speaker verification (ASV) systems, urging us to design a synthetic speech detection system to protect ASV systems.In this paper, we propose a new speech anti-spoofing model named ResWavegram-Resnet (RW-Resnet).The model contains two parts, Conv1D Resblocks and backbone Resnet34.The Conv1D Resblock is based on the Conv1D block with a residual connection.For the first part, we use the raw waveform as input and feed it to the stacked Conv1D Resblocks to get the ResWavegram.Compared with traditional methods, ResWavegram keeps all the information from the audio signal and has a stronger ability in extracting features.For the second part, the extracted features are fed to the backbone Resnet34 for the spoofed or bonafide decision.The ASVspoof2019 logical access (LA) corpus is used to evaluate our proposed RW-Resnet.Experimental results show that the RW-Resnet achieves better performance than other state-ofthe-art anti-spoofing models, which illustrates its effectiveness in detecting synthetic speech attacks.

Diverse application scenarios in 5G and beyond wireless communication systems have introduced various requirements in code lengths and rates of channel codes. For the decoding of polar codes, especially the belief-propagation (BP) decoding, flexible configuration of codeword length is still not involved in current decoders. In this paper, a semi-folded decoding structure is proposed which can be reconfigured to support multiple codeword lengths. Up to 16 codes can be decoded in parallel and the utilization of processing units is no less than 87.5% for various codeword lengths. The peak throughput of 19.29 Gbps can be achieved by the proposed decoder in SMIC 55 nm CMOS technology.

With the rapid development of indoor location-based services (LBSs), the demand for accurate localization keeps growing as well. To meet this demand, we propose an indoor localization algorithm based on graph convolutional network (GCN). We first model access points (APs) and the relationships between them as a graph, and utilize received signal strength indication (RSSI) to make up fingerprints. Then the graph and the fingerprint will be put into GCN for feature extraction, and get classification by multilayer perceptron (MLP). In the end, experiments are performed under a 2D scenario and 3D scenario with floor prediction. In the 2D scenario, the mean distance error of GCN-based method is 11m, which improves by 7m and 13m compare with DNN-based and CNN-based schemes respectively. In the 3D scenario, the accuracy of predicting buildings and floors are up to 99.73% and 93.43% respectively. Moreover, in the case of predicting floors and buildings correctly, the mean distance error is 13m, which outperforms DNN-based and CNN-based schemes, whose mean distance errors are 34m and 26m respectively.

Environmental sound classification (ESC) is a challenging problem due to the complexity of sounds. The classification performance is heavily dependent on the effectiveness of representative features extracted from the environmental sounds. However, ESC often suffers from the semantically irrelevant frames and silent frames. In order to deal with this, we employ a frame-level attention model to focus on the semantically relevant frames and salient frames. Specifically, we first propose a convolutional recurrent neural network to learn spectro-temporal features and temporal correlations. Then, we extend our convolutional RNN model with a frame-level attention mechanism to learn discriminative feature representations for ESC. We investigated the classification performance when using different attention scaling function and applying different layers. Experiments were conducted on ESC-50 and ESC-10 datasets. Experimental results demonstrated the effectiveness of the proposed method and our method achieved the state-of-the-art or competitive classification accuracy with lower computational complexity. We also visualized our attention results and observed that the proposed attention mechanism was able to lead the network tofocus on the semantically relevant parts of environmental sounds.

Channel estimation is crucial to modern wireless systems and becomes more and more challenging with the growth of user throughput in sub-6 GHz multiple input multiple output configuration. Plenty of literature spends great efforts in improving the estimation accuracy, while the interpolation schemes are overlooked. To deal with this challenge, we exploit the super-resolution image recovery scheme to model the non-linear interpolation mechanisms. Moreover, in order to extend the estimation scheme into the non-stationary environment which is especially attractive in the coming 6G, we utilize the recurrent network structure to approximate the non-linear channel statistic correlation to model the non-stationary behavior which is difficult to accomplish in the theoretical way. To make it more practical, we offline generate numerical channel coefficients according to the statistical channel models to train the neural networks and directly apply them in different environments. As shown in this paper, the proposed unified super-resolution based channel estimation scheme can outperform the conventional approaches in both stationary and non-stationary scenarios, which we believe can significantly change the current channel estimation method in the near future.

In a wireless network that conveys status updates from sources (i.e., sensors) to destinations, one of the key issues studied by existing literature is how to design an optimal source sampling strategy on account of the communication constraints which are often modeled as queues. In this paper, an alternative perspective is presented—a novel status-aware communication scheme, namelyparallel communications, is proposed which allows sensors to be communication-agnostic. Specifically, the proposed scheme can determine, based on an online prediction functionality, whether a status packet is worth transmitting considering both the network condition and status prediction, such that sensors can generate status packets without communication constraints. We evaluate the proposed scheme on a Software-Defined-Radio (SDR) test platform, which is integrated with a collaborative autonomous driving simulator, i.e., Simulation-of-Urban-Mobility (SUMO), to produce realistic vehicle control models and road conditions. The results show that with online status predictions, the channel occupancy is significantly reduced, while guaranteeing low status recovery error. Then the framework is applied to two scenarios: a multi-density platooning scenario, and a flight formation control scenario. Simulation results show that the scheme achieves better performance on the network level, in terms of keeping the minimum safe distance in both vehicle platooning and flight control.

Real-time status update in future vehicular networks is vital to enable control-level cooperative autonomous driving. Cellular Vehicle-to-Everything (C-V2X), as one of the most promising vehicular wireless technologies, adopts a Semi-Persistent Scheduling (SPS) based Medium-Access-Control (MAC) layer protocol for its sidelink communications. Despite the recent and ongoing efforts to optimize SPS, very few work has considered the status update performance of SPS. In this paper, Age of Information (AoI) is first leveraged to evaluate the MAC layer performance of C-V2X sidelink. Critical issues of SPS, i.e., persistent packet collisions and Half-Duplex (HD) effects, are identified to hinder its AoI performance. Therefore, a piggyback-based collaboration method is proposed accordingly, whereby vehicles collaborate to inform each other of potential collisions and collectively afford HD errors, while entailing only a small signaling overhead. Closed-form AoI performance is derived for the proposed scheme, optimal configurations for key parameters are hence calculated, and the convergence property is proved for decentralized implementation. Simulation results show that compared with the standardized SPS and its state-of-the-art enhancement schemes, the proposed scheme shows significantly better performance, not only in terms of AoI, but also of conventional metrics such as transmission reliability.

This paper studies the status update performance in wireless sensor networks when status, describing the physical reality that is being sensed, is temporally and spatially correlated. The status is modeled as a time-varying Gauss-Markov Random Field (GMRF), whereby the estimation error of status update at the fusion center is analyzed. The transmission latency introduced by wireless networks is modeled as exponentially distributed random variables. We extend the existing queuing analysis results for Age of Information (AoI) with uncorrelated sources to GMRF in the considered scenario. Closed-form expressions of average remote estimation error are obtained for both one- and two-dimensional GMRFs assuming the exponential time-correlation function, both First-Come First-Served (FCFS) and Last-Come First-Served (LCFS) service disciplines, and a single wireless link. The analytical results are then extended to scenarios wherein multi-packet reception, i.e., multiple concurrent wireless links, is enabled; the difficulty of analyzing obsolete updates in this case is addressed leveraging a reasonable approximation validated by theoretical analysis in the regime where the number of sensors is far more than that of wireless links. Monte-Carlo simulation results are also presented which agree with our theoretical analysis. Based on the results, optimal time and spatial domain sampling rates (e.g., sensor density) can be obtained, providing helpful guidance to wireless sensor deployment.

The task of spatial-temporal action detection has attracted increasing researchers. Existing dominant methods solve this problem by relying on short-term information and dense serial-wise detection on each individual frames or clips. Despite their effectiveness, these methods showed inadequate use of long-term information and are prone to inefficiency. In this paper, we propose for the first time, an efficient framework that generates action tube proposals from video streams with a single forward pass in a sparse-to-dense manner. There are two key characteristics in this framework: (1) Both long-term and short-term sampled information are explicitly utilized in our spatio-temporal network, (2) A new dynamic feature sampling module (DTS) is designed to effectively approximate the tube output while keeping the system tractable. We evaluate the efficacy of our model on the UCF101-24, JHMDB-21 and UCFSports benchmark datasets, achieving promising results that are competitive to state-of-the-art methods. The proposed sparse-to-dense strategy rendered our framework about 7.6 times more efficient than the nearest competitor.

Motivated by the correlation between the depth and the geometric structure of a 360 indoor image, we propose a novel learning-based depth estimation framework that leverages the geometric structure of a scene to conduct depth estimation. Specifically, we represent the geometric structure of an indoor scene as a collection of corners, boundaries and planes. On the one hand, once a depth map is estimated, this geometric structure can be inferred from the estimated depth map; thus, the geometric structure functions as a regularizer for depth estimation. On the other hand, this estimation also benefits from the geometric structure of a scene estimated from an image where the structure functions as a prior. However, furniture in indoor scenes makes it challenging to infer geometric structure from depth or image data. An attention map is inferred to facilitate both depth estimation from features of the geometric structure and also geometric inferences from the estimated depth map. To validate the effectiveness of each component in our framework under controlled conditions, we render a synthetic dataset, Shanghaitech-Kujiale Indoor 360 dataset with 3550 360 indoor images. Extensive experiments on popular datasets validate the effectiveness of our solution. We also demonstrate that our method can also be applied to counterfactual depth.

The existing localization systems for indoor applications basically rely on wireless signal. With the massive deployment of low-cost cameras, the visual image based localization become attractive as well. However, in the existing literature, the hybrid visual and wireless approaches simply combine the above schemes in a straight forward manner, and fail to explore the interactions between them. In this paper, we propose a joint visual and wireless signal feature based approach for high-precision indoor localization system. In this joint scheme, WiFi signals are utilized to compute the coarse area with likelihood probability and visual images are used to fine-tune the localization result. Based on the numerical results, we show that the proposed scheme can achieve 0.62m localization accuracy with near real-time running time.

In this paper, we jointly design resource allocation and load management in a mobile-edge computing (MEC) system with wireless power transfer (WPT), to minimize the total energy consumption of the BS, while meeting computation latency requirements. For the first time, the cooling energy, which is non-negligible, is considered to minimize the energy consumption of the MEC system. By orchestrating the alternative optimization technique, Lagrange duality method and subgradient method, we decompose the original optimization problem and obtain the optimal solution in a semi-closed form. Extensive numerical tests corroborate the merits of the proposed algorithm over existing benchmarks in terms of energy saving.

Ultra-Reliable and Low-Latency Communications (URLLC) services in vehicular networks on millimeter-wave bands present a significant challenge, considering the necessity of constantly adjusting the beam directions. Conventional methods are mostly based on classical control theory, e.g., Kalman filter and its variations, which mainly deal with stationary scenarios. Therefore, severe application limitations exist, especially with complicated, dynamic Vehicle-to-Everything (V2X) channels. This paper gives a thorough study of this subject, by first modifying the classical approaches, e.g., Extended Kalman Filter (EKF) and Particle Filter (PF), for non-stationary scenarios, and then proposing a Reinforcement Learning (RL)-based approach that can achieve the URLLC requirements in a typical intersection scenario. Simulation results based on a commercial ray-tracing simulator show that enhanced EKF and PF methods achieve packet delay more than 10 ms, whereas the proposed deep RL-based method can reduce the latency to about 6 ms, by extracting context information from the training data.

Face Anti-spoofing (FAS) is a challenging problem due to the complex serving scenario and diverse face presentation attack patterns. Using single modal images which are usually captured with RGB cameras is not able to deal with the former because of serious overfitting problems. The existing multi-modal FAS datasets rarely pay attention to the cross domain problems, training FAS system on these data leads to inconsistencies and low generalization capabilities in deployment since imaging principles(structured light, TOF, etc.) and pre-processing methods vary between devices. We explore the subtle fine-grained differences betweeen multi-modal cameras and proposed a cross domain multi-modal FAS dataset GREAT-FASD and several evaluation protocols for academic community. Furthermore, we incorporate the multiplicative attention and center loss to enhance the representative power of CNN via seeking out complementary information as a powerful baseline. In addition, extensive experiments have been conducted on the proposed dataset to analyze the robustness to distinguish spoof faces and bona-fide faces. Experimental results show the effectiveness of proposed method and achieve the state-of-the-art competitive results. Finally, we visualize our future distribution in hidden space and observe that the proposed method is able to lead the network to generate a large margin for face anti-spoofing task.

Wireless communications for status update are becoming increasingly important, especially for machine-type control applications. Existing work has been mainly focused on Age of Information (AoI) optimizations. In this paper, a status-aware predictive wireless interface design, networking and implementation are presented which aim to minimize the status recovery error of a wireless networked system by leveraging online status model predictions. Two critical issues of predictive status update are addressed: practicality and usefulness. Link-level experiments on a Software-Defined-Radio (SDR) testbed are conducted and test results show that the proposed design can significantly reduce the number of wireless transmissions while maintaining a low status recovery error. A Status-aware Multi-Agent Reinforcement learning neTworking solution (SMART) is proposed to dynamically and autonomously control the transmit decisions of devices in an ad hoc network based on their individual statuses. System-level simulations of a multi dense platooning scenario are carried out on a road traffic simulator. Results show that the proposed schemes can greatly improve the platooning control performance in terms of the minimum safe distance between successive vehicles, in comparison with the AoI-optimized status-unaware and communication latency-optimized schemes-this demonstrates the usefulness of our proposed status update schemes in a real-world application.

Synchronization, as a critical factor of modern wireless communication systems, has attracted close research attention recent years. For the vehicle-to-anything (V2X) communication in 5G new radio, synchronization faces severe challenges due to the extremely low latency and high reliability requirements. In this paper, a terminal aided synchronization scheme is proposed for the vehicle platooning in V2X communication. The shared information, such as NSLIDfrom other cooperative vehicles, are utilized to recover the original transmitted sidelink synchronization signals. The synchronization ID detection probability is therefore improved by 49.6% compared to conventional schemes. Hardware implementation on FPGA Artix-7 AC701 board is performed of the proposed synchronization scheme and the hardware latency is reduced to 67.18 μs compared to 968,654.85 μs in conventional schemes.

With the development of deep learning, face recognition is attracting more and more attention in both industry and academia. Hardware implementation of face recognition systems on heterogeneous embedded devices, however has been rarely studies. In this paper, an embedded face recognition system is designed and implemented on FPGA SoC platforms. A hardware-software partition method is first introduced by analyzing the ratio between computation and memory access of critical tasks in the system. Several acceleration methods are then exploited to optimize the hardware implementation. The face recognition system is implemented on Xilinx FPGA MPSoC ZCU102 with 97.3% recognition accuracy and 203.7 ms latency. The neural network VIPLFace, as the most time consuming part of the system, has a 74 ms latency, 71× faster after hardware-software co-design.

Spatial Description Resolution, as a language-guided localization task, is proposed for target location in a panoramic street view, given corresponding language descriptions. Explicitly characterizing an object-level relationship while distilling spatial relationships are currently absent but crucial to this task. Mimicking humans, who sequentially traverse spatial relationship words and objects with a first-person view to locate their target, we propose a novel spatial relationship induced (SIRI) network. Specifically, visual features are firstly correlated at an implicit object-level in a projected latent space; then they are distilled by each spatial relationship word, resulting in each differently activated feature representing each spatial relationship. Further, we introduce global position priors to fix the absence of positional information, which may result in global positional reasoning ambiguities. Both the linguistic and visual features are concatenated to finalize the target localization. Experimental results on the Touchdown show that our method is around 24\% better than the state-of-the-art method in terms of accuracy, measured by an 80-pixel radius. Our method also generalizes well on our proposed extended dataset collected using the same settings as Touchdown. The code for this project is publicly available at https://github.com/wong-puiyiu/siri-sdr.

Vehicular communications, considered as one of the key technologies for autonomous driving, are still faced with significant challenges, e.g., distributed resource allocation in the direct communication mode. In Cellular Vehicle-to-Everything (C-V2X), a Semi-Persistent Scheduling (SPS) based Medium-Access-Control (MAC) layer protocol is adopted by 3rd Generation Partnership Project (3GPP) for its sidelink communications. However, the performance of either the SPS scheme in standard or the state-of-the-art enhancement works is not satisfactory. In this paper, we propose a piggyback-based collaboration method and firstly introduce Age of Information (AoI) to evaluate the status update performance of C-V2X. In addition, the closed-form AoI performance is obtained. Through extensive simulations, the proposed scheme exhibits better performance than the current schemes, both in terms of AoI and transmission reliability.

Most existing algorithms for depth estimation from single monocular images need large quantities of metric ground-truth depths for supervised learning. We show that relative depth can be an informative cue for metric depth estimation and can be easily obtained from vast stereo videos. Acquiring metric depths from stereo videos are sometimes impracticable due to the absence of camera parameters. In this paper, we propose to improve the performance of metric depth estimation with relative depths collected from stereo movie videos using existing stereo matching algorithm. We introduce a new “relative depth in stereo” (RDIS) dataset densely labeled with relative depths. We first pretrain a ResNet model on our RDIS dataset. Then, we finetune the model on RGB-D datasets with metric ground-truth depths. During our finetuning, we formulate depth estimation as a classification task. This re-formulation scheme enables us to obtain the confidence of a depth prediction in the form of probability distribution. With this confidence, we propose an information gain loss to make use of the predictions that are close to ground-truth. We evaluate our approach on both indoor and outdoor benchmark RGB-D datasets and achieve the state-of-the-art performance.

Human detection is one of the long-standing computer vision tasks, and it has been a cornerstone for many real-world applications, such as photo album organization, video surveillance, and autonomous driving. Benefiting from deep learning technologies, such as convolutional neural networks and modern object detectors, have been achieving much improved accuracy in generic object detection tasks. In this paper, we aim to improve deep learning-based human detection. Our main idea is to exploit semantic context information for human detection by using deep-learnt semantic features provided by semantic segmentation masks. Segmentation masks play as an attention mechanism and enforce the detectors to focus on the image regions where potential object candidates are likely to appear. Meanwhile, the extra segmentation mask channel can also guide the convolutional kernels to automatically learn more discriminative features which make it easier to distinguish the background and foreground. We implement our methods with two popular detection frameworks, i.e., faster R-CNN and SSD and experimentally analyze the effectiveness of the proposed methods. Evaluation results on the widely used MS-COCO dataset and the very recent CrowdHuman dataset are provided. Our proposed methods outperform the baseline detectors and achieve better performance on highly occluded human detection.

Channel estimation is crucial for modern WiFi system and becomes more and more challenging with the growth of user throughput in multiple input multiple output configuration. Plenty of literature spends great efforts in improving the estimation accuracy, while the interpolation schemes are overlooked. To deal with this challenge, we exploit the super-resolution image recovery scheme to model the non-linear interpolation mechanisms without pre-assumed channel characteristics in this paper. To make it more practical, we offline generate numerical channel coefficients according to the statistical channel models to train the neural networks, and directly apply them in some practical WiFi prototype systems. As shown in this paper, the proposed super-resolution based channel estimation scheme can outperform the conventional approaches in both LOS and NLOS scenarios, which we believe can significantly change the current channel estimation method in the near future.

Indoor localization becomes a raising demand in our daily lives. Due to the massive deployment in the indoor environment nowadays, WiFi systems have been applied to high accurate localization recently. Although the traditional model based localization scheme can achieve sub-meter level accuracy by fusing multiple channel state information (CSI) observations, the corresponding computational overhead is significant. To address this issue, the model-free localization approach using deep learning framework has been proposed and the classification based technique is applied. In this paper, instead of using classification based mechanism, we propose to use a logistic regression based scheme under the deep learning framework, which is able to achieve sub-meter level accuracy (97.2cm medium distance error) in the standard laboratory environment and maintain reasonable online prediction overhead under the single WiFi AP settings. We hope the proposed logistic regression based scheme can shed some light on the model-free localization technique and pave the way for the practical deployment of deep learning based WiFi localization systems.

In this paper, we present a two-stage language identification (LID) system based on a shallow ResNet14 followed by a simple 2-layer recurrent neural network (RNN) architecture, which was used for Xunfei (iFlyTek) Chinese Dialect Recognition Challenge 1 and won the first place among 110 teams.The system trains an acoustic model (AM) firstly with connectionist temporal classification (CTC) to recognize the given phonetic sequence annotation and then train another RNN to classify dialect category by utilizing the intermediate features as inputs from the AM.Compared with a three-stage system we further explore, our results show that the two-stage system can achieve high accuracy for Chinese dialects recognition under both short utterance and long utterance conditions with less training time.

In this paper, a pre-RTL neural network simulator (SimuNN) is proposed which is initiated as the bridge between the algorithm design and hardware implementation of neural networks. SimuNN is compatible with TensorFlow interface, and supports inference in both floating-point numbers and configurable fixed-point numbers. It can provide inference results at layer-/module-/cycle-level to serve as a golden model for RTL designs. Besides, its embedded model for hardware performance estimation enables SimuNN to provide an accurate reference of processing speed and hardware cost at the ASIC-designed user end for algorithm designers.

Transmission control protocol (TCP) congestion control is one of the key techniques to improve network performance. TCP congestion control algorithm identification (TCP identification) can be used to significantly improve network efficiency. Existing TCP identification methods can only be applied to limited number of TCP congestion control algorithms and focus on wired networks. In this paper, we proposed a machine learning based passive TCP identification method for wired and wireless networks. After comparing among three typical machine learning models, we concluded that the 4-layers Long Short Term Memory (LSTM) model achieves the best identification accuracy. Our approach achieves better than 98% accuracy in wired and wireless networks and works for newly proposed TCP congestion control algorithms.

Although almost blank subframes (ABS) proposed in heterogeneous networks (HetNet) can enhance the performance of user equipments (UEs) in Pico-cell range expansion (CRE) area, it also significantly degrades the Macro-cell throughput. To address this issue, further-enhanced inter-cell interference coordination (FeICIC) scheme is considered in 3GPP Release 11, where low power ABS (LP-ABS) are adopted for the Macro-cell center region users to improve the Macro-cell throughput. However, LP-ABS power, Pico CRE bias and Pico base station (PBS) density will jointly affect on the system performance, which eventually deteriorates the network energy efficiency (EE) without careful configuration. In this paper, we first deduce the closed-form expression of network EE as a function of PBS density, Pico CRE bias and LP-ABS power based on stochastic geometry model. Then we provide Monte Carlo simulations to verify the accuracy of theoretical derivation of the network EE and analyze the impacts of these parameters on the network EE. The simulation results show that the reasonable PBS density, Pico CRE bias and LP-ABS power can improve the network EE obviously.

Heterogeneous network (HetNet) has been proposed as a promising solution for handling the wireless traffic explosion in future fifth-generation (5G) system. In this paper, a joint subchannel and power allocation problem is formulated for HetNets to maximize the energy efficiency (EE). By decomposing the original problem into a classification subproblem and a regression subproblem, a convolutional neural network (CNN) based approach is developed to obtain the decisions on subchannel and power allocation with a much lower complexity than conventional iterative methods. Numerical results further demonstrate that the proposed CNN can achieve similar performance as the Exhaustive method, while needs only 6.76% of its CPU runtime.

Wireless localization for mobile device has attracted more and more interests by increasing the demand for location based services. Fingerprint-based localization is promising, especially in non-Line-of-Sight (NLoS) or rich scattering environments, such as urban areas and indoor scenarios. In this paper, we propose a novel fingerprint-based localization technique based on deep learning framework under commercial long term evolution (LTE) systems. Specifically, we develop a software defined user equipment to collect the real time channel state information (CSI) knowledge from LTE base stations and extract the intrinsic features among CSI observations. On top of that, we propose a time domain fusion approach to assemble multiple positioning estimations. Experimental results demonstrated that the proposed localization technique can significantly improve the localization accuracy and robustness, e.g. achieves Mean Distance Error (MDE) of 0.47 meters for indoor and of 19.9 meters for outdoor scenarios, respectively.

In vehicular communications, intracell interference and the stringent latency requirement are challenging issues. In this paper, a joint spectrum reuse and power allocation problem is formulated for hybrid vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. Recognizing the high capacity and low-latency requirements for V2I and V2V links, respectively, we aim to maximize the weighted sum of the capacities and latency requirement. By decomposing the original problem into a classification subproblem and a regression subproblem, a convolutional neural network (CNN) based approach is developed to obtain real-time decisions on spectrum reuse and power allocation. Numerical results further demonstrate that the proposed CNN can achieve similar performance as the Exhaustive method, while needs only 3.62% of its CPU runtime.

Multiple-input multiple-output (MIMO) system is the key technology for long term evolution (LTE) and 5G. The information detection problem at the receiver side is in general difficult due to the imbalance of decoding complexity and decoding accuracy within conventional methods. Hence, a deep learning based efficient MIMO detection approach is proposed in this paper. In our work, we use a neural network to directly get a mapping function of received signals, channel matrix and transmitted bit streams. Then, we compare the end-to-end approach using deep learning with the conventional methods in possession of perfect channel knowledge and imperfect channel knowledge. Simulation results show that our method presents a better trade-off in the performance for accuracy versus decoding complexity. At the same time, better robustness can be achieved in condition of imperfect channel knowledge compared with conventional algorithms.

This paper proposes a new fully decentralized approach to online placement and optimization of virtual machines (VMs) for network functions virtualization (NFV). The approach is of practical value, as network services comprising a chain of virtual network functions (VNFs) are proposed to be queued on the basis of leading unexecuted VNFs at every server, rather than on the typical basis of services, reducing queues per server and facilitating queue management and signaling. It is also non-trivial because the VNFs of network services must be executed correctly in order at different VMs, coupling the optimal decisions of VMs on processing or offloading. Exploiting Lyapunov optimization techniques, we decouple the optimal decisions by deriving and minimizing the instantaneous upper bound of the NFV cost in a distributed fashion, and achieve the asymptotically minimum time-average cost. We also reduce the queue length by allowing individual VMs to (un)install VNFs based on local knowledge, achieving stable redeployment of VNFs, adapting to the network topology and the temporal and spatial variations of services. Simulations show that the proposed approach is able to reduce the time-average cost of NFV by 71% and reduce the queue length (or delay) by 74%, as compared with existing approaches.

With the raising demand for autonomous driving, vehicle-to-vehicle communications becomes a key technology enabler for the future intelligent transportation system. Based on our current knowledge field, there is limited network simulator that can support end-to-end performance evaluation for LTE-V based vehicle-to-vehicle platooning systems. To address this problem, we start with an integrated platform that combines traffic generator and network simulator together, and build the V2V transmission capability according to LTE-V specification. On top of that, we simulate the end-to-end throughput and delay profiles in different layers to compare different configurations of platooning systems. Through numerical experiments, we show that the LTE-V system is unable to support the highest degree of automation under shadowing effects in the vehicle platooning scenarios, which requires ultra-reliable low-latency communication enhancement in 5G networks. Meanwhile, the throughput and delay performance for vehicle platooning changes dramatically in PDCP layers, where we believe further improvements are necessary.

This paper proposes a new fully decentralized approach for online placement and optimization of virtual machines (VMs) for network functions virtualization (NFV). The approach is non-trivial as the virtual network functions (VNFs) constituting network services must be executed correctly in order at different VMs, coupling the optimal decisions of VMs on processing or forwarding. Leveraging Lyapunov optimization techniques, we decouple the optimal decisions by minimizing the instantaneous NFV cost in a distributed fashion, and achieve the asymptotically minimum time-average cost. We also reduce the queue length by allowing individual VMs to (un)install VNFs based on local knowledge, adapting to the network topology and the temporal and spatial variations of services. Simulations show that the proposed approach is able to reduce the time-average cost of NFV by 71% and reduce the queue length (or delay) by 74%, as compared to existing approaches.

The rapid increasing demand of wireless transmission has incurred mobile broadband to continuously evolve through multiple frequency bands, massive antennas and other multi-stream processing schemes. Together with the improved data transmission rate, the power consumption for multi-carrier transmission and processing is proportionally increasing, which contradicts with the energy efficiency requirements of 5G wireless systems. To meet this challenge, multi carrier power amplifier (MCPA) technology, e.g., to support multiple carriers through a single power amplifier, is widely deployed in practical. With massive carriers required for 5G communication and limited number of carriers supported per MCPA, a natural question to ask is how to map those carriers into multiple MCPAs and whether we shall dynamically adjust this mapping relation. In this paper, we have theoretically formulated the dynamic carrier and MCPA mapping problem to jointly optimize the traditional separated baseband and radio frequency processing. On top of that, we have also proposed a low complexity algorithm that can achieve most of the power saving with affordable computational time, if compared with the optimal exhaustive search based algorithm.

Due to the complicated fading environment of distributed multiple-input multiple-output (d-MIMO) systems, the closed-form expression of cell average spectral efficiency (CASE) is in general difficult to be obtained, not to mention the three-dimension model. Hence, by assuming appropriate number of antennas configured on the remote access unit (RAU) of d-MIMO system, we firstly derive out a simple approximate expression of spectral efficiency with fixed pathloss according to the central limit theorem. To be more realistic, the closed-form of CASE at different locations of the target cell, under three-dimensional model, i.e., with considering the height of the RAU, is then given out. The theoretical and simulation results reveal that the CASE of d-MIMO system is bounded by the density and the height of RAU, and the CASE is robust to the changes of the user's location except for the cell-edge. Further, about 50% penalty of CASE from the cell-center to the cell-edge can be found. Moreover, the analytical results fit the simulations well, which verifies our analysis.

In this paper, a low-complexity stochastic belief propagation (BP) detector for large-scale MIMO is first proposed. Its efficient hardware architecture, with parallel pipeline, is presented in detail. Thanks to the stochastic approach, all arithmetic operations of the detector are implemented with simple logic structures. Several approaches which can potentially improve the detection performance are exploited. Simulation results have demonstrated that the stochastic BP detector can achieve similar detection performance compared with deterministic one for 32 × 32 MIMO system with 4-quadrature amplitude modulation (4-QAM). With the increase of antenna number, the detection performance improves at the linear expense of complexity and latency. Therefore, the proposed stochastic BP detector is suitable for large-scale MIMO system applications with good balance of detection performance and implementation complexity.

In massive multiple-input multiple-output (MIMO) uplink, the minimum mean square error (MMSE) algorithm is near-optimal and linear, but still suffers from high-complexity of matrix inversion. Based on Gauss-Seidel (GS) method, an efficient architecture for massive MIMO soft-output detection is proposed in this paper. To further accelerate the convergence rate of the conventional GS method with acceptable overhead complexity, a truncated Neumann series of the first 2 terms, is employed for initialization. The architecture can meet various application requirements by flexibly adjusting the number of iterations. FPGA implementation for a 128 × 8 MIMO demonstrates its advantages in both hardware efficiency and flexibility.

As well known, the near-optimal K-best detection is popular in multiple-input and multiple-output (MIMO) systems. In this paper, we first propose the joint approaches of K-best detection and polar decoding. For joint detection and decoding (JDD) approach, both hard and soft decisions are considered. The simplified successive cancellation (SSC) decoding is exploited for hard decision, and the successive cancellation list (SCL) decoding is used as soft decision. The system setup for JDD is als o introduced, in which the modulation points across several channels are considered together. Simulation results have demonstrated the performance advantage of the JDD algorithms over the separated ones. For 1/2-rate polar codes, JDD schemes show 50% complexity reduction compared to the separated ones. Furthermore, by employing SSC hard decoding, the JDD algorithm is promising for high-throughput and low-complexity application s.

Because of the existence of channel noise, channel coding serves as an indispensable part of mobile communication system and the essential guarantee for the reliable, accurate, and effective transmission of information. As one of the most competitive channel code candidates for the 5th generation (5G) mobile communication, polar codes are the first codes which can provably achieve the symmetric capacity of binary-input discrete memoryless channels (B-DMCs). In this paper, the segmented CRC- aided successive cancellation list (SCA-SCL) polar decoding scheme is proposed for better tradeoff of performance and complexity. Numerical results on binary-input additive white Gaussian noise channel (BI-AWGNC) have shown that, at SNR of 0.5 dB, this approach successfully provides as high as 41.65% complexity reduction and similar decoding performance compared to state-of-the-art ones.

Cloud radio access network (C-RAN) was proposed recently to reduce network cost, enable cooperative communications, and increase system flexibility through centralized baseband processing. By pooling multiple virtual base stations (VBSs) and consolidating their stochastic computational tasks, the overall computational resource can be reduced, achieving the so-called statistical multiplexing gain. In this paper, we evaluate the statistical multiplexing gain of VBS pools using a multi-dimensional Markov model, which captures the session-level dynamics and the constraints imposed by both radio and computational resources. Based on this model, we derive a recursive formula for the blocking probability and also a closed-form approximation for it in large pools. These formulas are then used to derive the session-level statistical multiplexing gain of both real-time and delay-tolerant traffic. Numerical results show that VBS pools can achieve more than 75% of the maximum pooling gain with 50 VBSs, but further convergence to the upper bound (large-pool limit) is slow because of the quickly diminishing marginal pooling gain, which is inversely proportional to a factor between the one-half and three-fourth power of the pool size. We also find that the pooling gain is more evident under light traffic load and stringent quality of service requirement.

The baseband-up centralization architecture of radio access networks (C-RAN) has recently been proposed to support efficient cooperative communications and reduce deployment and operational costs. However, the massive fronthaul bandwidth required to aggregate baseband samples from remote radio heads (RRHs) to the central office incurs huge fronthauling cost, and existing baseband compression algorithms can hardly solve this issue. In this paper, we propose a graph-based framework to effectively reduce fronthauling cost through properly splitting and placing baseband processing functions in the network. Baseband transceiver structures are represented with directed graphs, in which nodes correspond to baseband functions, and edges to the information flows between functions. By mapping graph weighs to computational and fronthauling costs, we transform the problem of finding the optimum location to place some baseband functions into the problem of finding the optimum clustering scheme for graph nodes. We then solve this problem using a genetic algorithm with customized fitness function and mutation module. Simulation results show that proper splitting and placement schemes can significantly reduce fronthauling cost at the expense of increased computational cost. We also find that cooperative processing structures and stringent delay requirements will increase the possibility of centralized placement.

In this paper, we first reveal the similarity of polar encoder and fast Fourier transform (FFT) processor. Based on this, both feed-forward and feed-back pipelined implementations of polar encoder are proposed. It is pointed out that the feedback part of SC polar decoder is nothing but a simplified version of polar encoder and therefore can be pipelined implemented also. Moreover, a general approach which uniformly constructs most pipelined polar encoders via folding transformation is proposed. Implementation results have shown that both proposed pipelined polar encoder architectures achieve more than 98.3% complexity reduction and more than 9.86% speed-up compared to the conventional implementation.

Facing the explosion of mobile data traffic, cloud radio access network (C-RAN) is proposed recently to overcome the efficiency and flexibility problems with the traditional RAN architecture by centralizing baseband processing. However, there lacks a mathematical model to analyze the statistical multiplexing gain from the pooling of virtual base stations (VBSs) so that the expenditure on fronthaul networks can be justified. In this paper, we address this problem by capturing the session-level dynamics of VBS pools with a multi-dimensional Markov model. This model reflects the constraints imposed by both radio resources and computational resources. To evaluate the pooling gain, we derive a product-form solution for the stationary distribution and give a recursive method to calculate the blocking probabilities. For comparison, we also derive the limit of resource utilization ratio as the pool size approaches infinity. Numerical results show that VBS pools can obtain considerable pooling gain readily at medium size, but the convergence to large pool limit is slow because of the quickly diminishing marginal pooling gain. We also find that parameters such as traffic load and desired Quality of Service (QoS) have significant influence on the performance of VBS pools.

In this paper, we consider energy efficient coverage planning in cellular networks. To save energy, each base station (BS) can work in sleep mode when there is no user in its coverage or the users can be served by neighbor base stations. With increasing coverage overlap, there is a tradeoff between the number of BSs per unit area and the proportion of active base stations. Analytical and numerical methods are presented to evaluate coverage planning schemes with different inter-BS distance. Evaluation results show that compared with the minimum coverage overlap scheme, the optimal planning scheme can reduce the network energy consumption by more than 20%, and the performance improvement depends on the user density, network topology, and the power consumption of sleep mode.

To meet the global challenge of reducing greenhouse gas emissions and the explosive demand of wireless data traffic, green architecture design is becoming a critical issue for mobile network operators. Heterogeneous deployments of different cell types have been used to fulfill the challenges mentioned above. In this respect, a critical concern for operators is how to deploy small cells in a green manner such that the global network is spectrum-efficient as well as energy-efficient. In this paper, we characterize the spectrum efficiency (SE) and energy efficiency (EE) for heterogenous wireless networks, taking into account realistic network power consumption model and dynamic network configuration. We give first order closed form analysis to address this issue and show that SE and EE may not be contradictory to each other as in the traditional networks. Our study also provides useful insights for the modeling and deployment of future green wireless networks.

In this paper, we investigate an energy-efficient coordinated multiple point (CoMP) transmission strategy for downlink heterogeneous cellular networks. We combine CoMP joint processing (CoMP-JP) and coordinated beamforming (CoMP-CB), two special cases of CoMP, in a time division manner to improve both energy efficiency (EE) and spectral efficiency (SE). We formulate the problem as minimizing the total transmit power consumed by both the macro- and pico-base stations (BSs) under the constraints on the data rate requirements from the macro- and pico-users, and on the maximum transmit powers of the macro- and pico-BSs. Both the transmit time and the transmit powers allocated to the CoMP-JP and CoMP-CB transmissions are optimized. Simulation results show that the hybrid CoMP-JP and CoMP-CB strategy provides a larger capacity region than the CoMP-JP-only or CoMP-CB-only transmission. The time proportion of the CoMP-JP in the hybrid strategy decreases with the data rate requirement of the macro-user and increases with the maximum transmit power of the pico-BS and the average channel gain from the macro-BS to the macro-user. Increasing the transmit power of the pico-BS can improve the EE in the high SE region of the macro-user.

Bandwidth-power tradeoff, as one of the key relations in the green radio research framework, has attracted numerous research attentions in the recent years, due to the refreshed spectrum policies and the popularity of enabling techniques for bandwidth adjustment (such as software defined radio). Previous literatures show some preliminary results on the bandwidth-power tradeoff for heterogeneous networks, however, the practical issues such as site sleeping and inter-cell interference are still out of the research scope. In this paper, we formulate the network energy minimization problem and investigate the optimal bandwidth allocation strategy under the above two issues. Optimal bandwidth-power tradeoff is then derived to show the effects of key system parameters, including cell sizes and interference power levels, followed by some numerical examples to co-verify the analytical results.

In this paper, we investigate adaptive configuration of spatial and frequency resources to maximize energy efficiency (EE) and reveal the relationship between the EE and the spectral efficiency (SE) in downlink multiple-input-multiple-output (MIMO) orthogonal frequency division multiple access (OFDMA) systems. We formulate the problem as minimizing the total power consumed at the base station under constraints on the ergodic capacities from multiple users, the total number of subcarriers, and the number of radio frequency (RF) chains. A three-step searching algorithm is developed to solve this problem. We then analyze the impact of spatial-frequency resources, overall SE requirement and user fairness on the SE-EE relationship. Analytical and simulation results show that increasing frequency resource is more efficient than increasing spatial resource to improve the SE-EE relationship as a whole. The EE increases with the SE when the frequency resource is not constrained to the maximum value, otherwise a tradeoff between the SE and the EE exists. Sacrificing the fairness among users in terms of ergodic capacities can enhance the SE-EE relationship. In general, the adaptive configuration of spatial and frequency resources outperforms the adaptive configuration of only spatial or frequency resource.

Using relays, wireless relay systems can provide reliable transmissions when the distance between a source node (SN) and a destination node (DN) is sufficiently long and the transmission power is limited. In this paper, we study the application of the hybrid automatic repeat request (HARQ) protocol with incremental redundancy (HARQ-IR) to wireless relay systems for energy efficient reliable packet transmissions when a delay constraint is not stringent. The energy-delay tradeoff (EDT) is discussed for one-way and two-way relay systems. It is shown that direct transmissions without a relay node (RN) can be more energy efficient under a short delay constraint (or in the high power regime). On the other hand, in the low power regime (or under a long delay constraint), we can show that decoding at an RN plays a crucial role in improving energy efficiency. In particular, if decoding is performed at an RN in both one-way and two-way relay systems, relay-aided transmissions become more energy efficient than direct transmissions by a factor of greater than or equal to 8 (with a path loss exponent of 3 over Rayleigh fading channels) as the transmission power approaches 0.

Hybrid automatic repeat request (HARQ) protocols can be employed for reliable transmissions of delay tolerant traffics over fading channels. Relay-aided transmissions can offer various advantages over direct transmissions in wireless communications including transmission power saving. In this paper, we consider the energy efficiency of a relaying protocol that is based on the HARQ protocol with incremental redundancy (HARQ-IR) when distributed cooperative beamforming (DCB) is employed in a wireless relay system. It is assumed that multiple relays between a source node (SN) and a destination node (DN) are available for relaying and some of them are selected for DCB. In the proposed relaying protocol, the signal transmission consists of two phases and each phase can have an independent HARQ-IR protocol. It is shown in this paper that although the performance can be improved when the number of selected relays for DCB increases as the diversity gain increases, it is not true in terms of the energy efficiency. For Rayleigh fading channels, closed-form expressions are derived for the average numbers of transmissions for each phase. From them, under a certain energy efficiency criterion, we can find the optimal number of multiple selected relays for DCB to forward signals from relays to DN.

The tremendous popularity of smart phones and electronic tablets has spurred the explosive growth of high-rate multimedia services and promptly boomed energy consumption in wireless networks. Therefore, energy-efficient design in wireless networks is very important and is attracting more and more attention, just like the conventional spectral-efficient design. In this paper, we study energy-efficient design in downlink orthogonal frequency division multiple access (OFDMA) networks with effective capacity-based delay provisioning for delay-sensitive traffic. By integrating information theory with the concept of effective capacity, we formulate an energy efficiency (EE) optimization problem with statistical delay provisioning, which is a complicated nonconvex combinatorial fractional programming problem. To solve the problem, we first relax it with an upper bound on the original one and then prove and exploit the quasiconcave property of the EE-versus-transmit power curve, which facilitates the optimal algorithm development. Then, we demonstrate that the resultant solution is quite close to the true optimal value when the number of subcarriers is larger than that of the users. We also analyze the tradeoff between EE and delay, the relationship between spectral-efficient and energy-efficient designs, and the impact of system parameters, including circuit power and delay exponents, on the overall performance. Numerical results show that the proposed energy-efficient design scheme greatly improves EE while maintaining the delay requirement.

To meet the global challenge of reducing greenhouse gas emissions and the explosion demand of wireless data traffic, green architecture design is becoming a critical issue for mobile network operators. Heterogeneous deployments of different cell types have been used to fulfill the challenges mentioned above. In this respect, a critical concern for operators is how to deploy small cells in a green manner such that the global network is cost-effective as well as energy-efficient. In this paper, we characterize the energy efficiency (EE) and deployment efficiency (DE) for heterogenous wireless networks, taking into account realistic network power consumption model and dynamic network configuration. We give first order closed form analysis to address this issue and show that a proper density of small cells is required to obtain the maximum achievable EE/DE. Our study also provides useful insights for the modeling and deployment of future green wireless networks.

The ubiquitous applications of high-data-rate realtime wireless services have promptly boomed energy consumption in wireless networks. Therefore, energy-efficient design in wireless networks is very important and is attracting more and more research attention. In this paper, we study the quality-of-service (QoS) driven energy-efficient design in the downlink orthogonal frequency division multiple access (OFDMA) network. By integrating information theory with the concept of effective capacity, we formulate an energy efficiency (EE) optimization problem with statistical QoS provisioning. To solve the problem, we first modify it with a tight upper bound on the original EE and solve the modified problem. Then, we demonstrate that the resultant solution is quite close to the true optimal value when the number of subcarriers is large than that of the users. We also find out the tradeoff relation between EE and delay. Numerical results show that the proposed energy-efficient design scheme greatly improves EE whiling maintaining QoS requirements.

In this paper, we investigate adaptive configuration of spatial and frequency resources to maximize energy efficiency (EE) and reveal the relationship between the EE and the spectral efficiency (SE) in downlink multiple-input-multiple-output (MIMO) orthogonal frequency division multiple access (OFDMA) systems. We formulate the problem as minimizing the total power consumed at the base station under constraints on the average data rates from multiple users, the total number of subcarriers, and the number of radio frequency (RF) chains. We develop a two-step searching algorithm to solve this problem, which first finds the near-optimal numbers of subcarriers for multiple users based on Karush-Kuhn-Tucker (KKT) conditions and then optimize the number of active RF chains. Simulation results demonstrate that increasing frequency resource improves both the SE and the EE, and is more efficient than increasing spatial resource. Consequently, there exists tradeoff between the SE and the EE only when the frequency resource is limited. In general, the adaptive configuration of spatial and frequency resources outperforms the adaptive configuration of only spatial resource and that of only frequency resource.

Widespread application of multimedia wireless services and requirement of ubiquitous access have triggered rapidly booming energy consumption at both transmitter and the receiver sides. Hence, energy-efficient design in wireless networks is very important and is becoming an inevitable trend. In this paper, we take decoding power into consideration when studying joint transmitter and receiver design for achieving high energy efficiency (EE). Based on a new function between transmit power and data rate that is derived by minimizing a lower bound on the overall transmit and receiver power, we investigate when and how should the decoding power be considered for optimizing EE. We find that the decoding power cannot be ignored for short-range wireless communications with a large bandwidth where the transmit power is usually low and give an analytical expression for quantifying the impact of decoding power on EE.

In this paper, we focus on the power-delay tradeoff for downlink transmission systems. In particular, we target to design the adaptive modulation scheme that achieves the optimal power-delay tradeoff under practical concerns, such as the practical power consumption model of a base station and the dynamics of user traffic. The optimal modulation level selection problem is formulated in a Markov decision problem and a cross-layer approach is adopted to take both queue and channel dynamics into consideration. Simulation results show that the proposed policy achieves about 20% power saving for the same delay performance, compared with fixed modulation level schemes. In addition, the impact of the practical power consumption model under dynamic traffic does change the optimal modulation level selection philosophy.

A general energy-efficient multi-user management framework for incremental redundancy (IR) HARQ is proposed. We first develop a basic tradeoff between energy efficiency, power, bandwidth and delay of IR-HARQ, which reveals that frequency domain, rather than power domain, offers a larger space for exploiting energy efficiency with delay constraints. Based on the basic tradeoff, the proposed energy-efficient multi-user management framework, consisting of user scheduling and power/bandwidth allocation, optimizes energy efficiency (EE) in both power and frequency domain. It is shown that while achieving EE, power-domain optimization is more effective and provide higher throughput for large-bandwidth scenarios, and frequency-domain optimization produces shorter delay in small bandwidth scenarios.

One of the main tasks for future wireless systems is to reduce the environmental impacts of the wireless transmission under different network architectures, which motivates the research on the green radio. Traditional research focuses on study of the tradeoff relation in the homogeneous network architecture and the extension to the heterogeneous network is still open in the literature. In this paper, we shall investigate this open issue and derive the closed-form expression for the optimal bandwidth allocation scheme as well as the bandwidth-power tradeoff relation. The analytical result is then extended to the practical settings with the network layout of 19 hexagonal macro-cells. Both the analytical and numerical results show that the proposed bandwidth allocation scheme achieves the best bandwidth-power tradeoff in the heterogeneous network architecture, which is of great importance for the heterogeneous network deployment and the frequency planning. Moreover, we also show that the radius of the network and the power budget play an important role in the bandwidth-power tradeoff relation, which should be carefully considered in the heterogeneous network planning and optimization.

The explosively increasing demand of high-data-rate multimedia wireless services and ubiquitous access has triggered rapidly booming energy consumption at the wireless network operator side. Therefore, energy-efficient design is becoming a mainstream for future wireless networks. In this paper, we study energy-efficient resource allocation in downlink OFDMA networks with partial channel state information at the transmitter (CSIT). To reduce the channel state information (CSI) feedback overhead while maintaining relatively high achievable energy efficiency (EE), we propose a novel CSI feedback scheme, which leads to higher EE with lower feedback overhead compared with the conventional selective feedback (SF) and bit-map based feedback (BF) schemes. Simulation results show that the energy-efficient design greatly improves EE compared with that of the conventional spectral-efficient design and our CSI feedback scheme outperforms the conventional CSI feedback schemes in EE and spectral efficiency (SE) with almost the same feedback overhead.

The widespread application of multimedia wireless services and requirements of ubiquitous access have triggered rapidly booming energy consumption at both the base station side and the user equipment (UE) side. Hence, energy-efficient design in wireless networks is very important and is becoming an inevitable trend. In this paper, we study the energy-efficient resource allocation in both downlink and uplink cellular networks with orthogonal frequency division multiple access (OFDMA). For the downlink transmission, the generalized energy efficiency (EE) is maximized while for the uplink case the minimum individual EE is maximized, both under certain prescribed per-UE quality-of-service (QoS) requirements. For both transmission scenarios, we first provide the optimal solution and then develop a suboptimal but low-complexity approach by exploring the inherent structure and property of the energy-efficient design. For the downlink case, by modifying the original problem, we also find a computationally efficient and numerically tractable upper bound on the EE, which indicates the performance limit and is demonstrated to be quite tight if the number of subcarriers is larger than that of UEs and motivates us to find a near-optimal approach relying on the quasiconcave relation between the modified EE and transmit power. Simulation results show that the energy-efficient design greatly improves EE compared with the conventional spectral-efficient design and the low-complexity suboptimal approaches can achieve a promising tradeoff between performance and complexity.

In this paper, power allocation between pilots and data symbols is investigated to maximize energy efficiency (EE) for downlink orthogonal frequency division multiple access (OFDMA) networks. We first derive an EE function considering channel estimation error, which depends on large-scale channel gains of multiple users, allocated power to pilots and data symbols, and circuit power consumption. Then an optimization problem is formulated to maximize the EE under overall transmit power constraint. Exploiting the quasiconcavity property of the EE function, we propose an alternating optimization method in the low transmit power region and reformulate a joint quasiconcave problem in the high transmit power region. Analysis and simulation results show that the power ratio for pilots decreases with the circuit power. When the circuit power is small, the optimal overall transmit power increases with the circuit power. Otherwise, the optimal transmit power does not depend on it. Transmitting more data symbols to the users with higher channel gains improves the EE but at a cost of sacrificing the fairness among multiple users. Simulation results also demonstrate that compared with spectral efficiency (SE)-oriented design, the EE-oriented design can improve the EE performance significantly with a relatively small SE loss.

This paper addresses the problem of frequency domain packet scheduling (FDPS) incorporating spatial division multiplexing (SDM) multiple input multiple output (MIMO) techniques on the 3GPP Long-Term Evolution (LTE) downlink. We impose the LTE MIMO constraint of selecting only one MIMO mode (spatial multiplexing or transmit diversity) per user per transmission time interval (TTI). First, we address the optimal MIMO mode selection (multiplexing or diversity) per user in each TTI in order to maximize the proportional fair (PF) criterion adapted to the additional frequency and spatial domains. We prove that both single-user (SU-) and multi-user (MU-) MIMO FDPS problems under the LTE requirement are NP-hard. We therefore develop two types of approximation algorithms (ones with full channel feedback and the others with partial channel feedback), all of which guarantee provable performance bounds for both SU- and MU-MIMO cases. Based on 3GPP LTE system model simulations, our approximation algorithms that take into account both spatial and frequency diversity gains outperform the exact algorithms that do not exploit the potential spatial diversity gain. Moreover, the approximation algorithms with partial channel feedback achieve comparable performance (with only 1-6 percent performance degradation) to the ones with full channel feedback, while significantly reducing the channel feedback overhead by nearly 50 percent.

The widespread application of multimedia wireless services and requirement of ubiquitous access have triggered rapidly booming energy consumption at both the base station side. Hence, energy-efficient design in wireless networks is very important and is becoming an inevitable trend. In this paper, we study energy-efficient resource allocation in downlink cellular OFDMA networks. For the downlink transmission, the weighted energy efficiency (EE) is maximized under certain prescribed per-user quality- of-service (QoS) requirements. We first obtain the optimal solution then propose a suboptimal approach by exploring the inherent structure and property of the energy-efficient design to reduce complexity. Simulation results show that the energy-efficient design greatly improves EE compared with that of the conventional spectral-efficient design and our low- complexity suboptimal approaches can achieve promising tradeoff between performance and complexity.

In this paper, power allocation between pilots and data symbols is investigated aiming at maximizing energy efficiency(EE) for downlink orthogonal frequency division multiple access (OFDMA) networks. We first derive an EE function when the channel estimation error is considered, which depends on the large-scale channel gains of multiple users, the allocated power to pilots and data symbols, and the circuit power consumption. Then an optimization problem is formulated to maximize the EE under overall transmit power constraint. The relationship between the power for pilots and data symbols is analyzed based on Karush-Kuhn-Tucker (KKT) conditions and the impacts of channel gains on both power allocation and the EE are studied. Exploiting the quasiconcavity property of the EE function, a bisection searching algorithm is developed to find the optimal power allocation. Simulation results demonstrate the performance gain of the proposed optimal power allocation scheme in terms of the EE and the required overall transmit power.

Conventional design of wireless networks mainly focuses on system capacity and spectral efficiency (SE). As green radio (GR) becomes an inevitable trend, energy-efficient design in wireless networks is becoming more and more important. In this paper, the fundamental tradeoff relation between energy efficiency (EE) and SE in downlink orthogonal frequency division multiple access (OFDMA) networks is addressed. We obtain a tight upper bound and lower bound on the optimal EE-SE tradeoff relation for general scenarios based on Lagrange dual decomposition, which accurately reflects the optimal EE-SE tradeoff relation. We then focus on a special case that priority and fairness are considered and derive an alternative upper bound, which is even proved to be achievable for flat fading channels. We also develop a low-complexity but near-optimal resource allocation algorithm for practical application of EE-SE tradeoff. Numerical results demonstrate that the optimal EE-SE tradeoff relation is a bell shape curve and can be well approached with our resource allocation algorithm.

Energy efficiency has become an important metric for the future system design. In traditional design methods, the link transmission strategies were investigated with only transmit power considered. We shall show in this paper that from the whole system's aspect, the radiated energy used for data transmission is only a portion of the overall power consumption, whose ratio depends on many practical issues such as the transmission distance, the modulation level, the non-ideal power amplifier efficiency, as well as the circuit and processing power. Through closed-form formula derivation, analysis remarks, and numerical examples, we shall show the impact of different system parameters and configurations on the whole system's power consumption and energy efficiency, which in turn, sheds a light on the design philosophy for maximizing the energy efficiency of a base station as a whole.

In this paper, both configuration of active radio frequency (RF) chains and resource allocation are investigated for improving energy efficiency of downlink multiple-input-multiple-output (MIMO) orthogonal frequency division multiple access (OFDMA) systems. We first formulate an optimization problem to minimize the total power consumed at the base station with the maximum transmit power constraint and ergodic capacity constraints from multiple users. Then a two-step suboptimal algorithm is proposed. Specifically, the continuous variable optimization problem is first solved, and then a discretization algorithm is presented to obtain the number of active RF chains and the number of subcarriers allocated to each user. Simulation results demonstrate that the proposed algorithm can provide significant power-saving gain over the all-on RF chain scheme and the adaptive subcarrier allocation helps to save more power.

Conventional design of wireless networks mainly focuses on system capacity and spectral efficiency (SE). As green radio (GR) becomes an inevitable trend, energy-efficient design is becoming more and more important. In this paper, the fundamental tradeoff between energy efficiency (EE) and SE in downlink orthogonal frequency division multiple access (OFDMA) networks is addressed. We first set up a general EE-SE tradeoff framework, where the overall EE, SE and per-user quality-of-service (QoS) are all considered, and prove that under this framework, EE is strictly quasiconcave in SE. We then discuss some basic properties, such as the impact of channel power gain and circuit power on the EE-SE relation. We also find a tight upper bound and a tight lower bound on the EE-SE curve for general scenarios, which reflect the actual EE-SE relation. We then focus on a special case that priority and fairness are considered and suggest an alternative upper bound, which is proved to be achievable for flat fading channels. We also develop a low-complexity but near-optimal resource allocation algorithm for practical application of the EE-SE tradeoff. Numerical results confirm the theoretical findings and demonstrate the effectiveness of the proposed resource allocation scheme for achieving a flexible and desirable tradeoff between EE and SE.

The pressure of the energy bill from operators and the request of the environmental protection from the governments and publics results in more urgent requirement on the energy consumption and CO2emission. Traditionally, adaptive modulation has been proven to be an efficient solution to improve the system performance over the radio fading channels. To obtain the design insights for energy efficient adaptive modulation scheme, we shall introduce a circuit-level power modeling to analyze the effects and characterize the bandwidth-power-energy efficiency tradeoff relations for wireless communication systems. We show that different bandwidth, power and modulation schemes shall be used under different channel conditions to maximize the energy efficiency and the adaptive modulation scheme can greatly help to improve the tradeoff curves.

This paper addresses the problem of frequency domain packet scheduling (FDPS) incorporating spatial division multiplexing (SDM) multiple input multiple output (MIMO) techniques on the 3GPP long term evolution (LTE) downlink. We impose the LTE MIMO constraint of selecting only one MIMO mode (spatial multiplexing or transmit diversity) per user per transmission time interval (TTI). First, we address the optimal MIMO mode selection (multiplexing or diversity) per user in each TTI in order to maximize the proportional fair (PF) criterion extended to frequency and spatial domains. We prove that the SU-MIMO (single-user MIMO) FDPS problem under the LTE requirement is NP-hard and therefore, we develop two approximation algorithms (one with full channel feedback and the other with partial channel feedback) with provable performance bounds. Based on 3GPP LTE system model simulations, the approximation algorithm with partial channel feedback is shown to have comparable performance to the one with full channel feedback, while significantly reducing the channel feedback overhead by nearly 50%.

With the power consumption issue of mobile handset taken into account, single-carrier FDMA (SC-FDMA) has been selected for 3GPP long-term evolution (LTE) uplink multiple access scheme. Like in OFDMA downlink, it enables multiple users to be served simultaneously in uplink as well. However, its single carrier property requires that all the subcarriers allocated to a single user must be contiguous in frequency within each time slot. This contiguous allocation constraint limits the scheduling flexibility, and frequency-domain packet scheduling algorithms in such system need to incorporate this constraint while trying to maximize their own scheduling objectives. In this paper we explore this fundamental problem of LTE SC-FDMA uplink scheduling by adopting the conventional time-domain proportional fair algorithm to maximize its objective (i.e. proportional fair criteria) in the frequency-domain setting. We show the NP-hardness of the frequency-domain scheduling problem under this contiguous allocation constraint and present a set of practical algorithms fine tuned to this problem. We demonstrate that competitive performance can be achieved in terms of system throughput as well as fairness perspective, which is evaluated using 3GPP LTE system model simulations.

After passing the letter ballot earlier this year, 802.11e is close to final approval. It is widely expected that this QoS enhancement for 802.11 WLAN will enable a huge market for AV transmission in WLAN-based home networks, as well as applications like VoIP in hot-spots. In this paper, along with an overview of the emerging 802.11e technology, a series of important questions are addressed, including why WLAN QoS and 11e are desired, why there are two QoS mechanisms in 11e, what the critical new features in 11e are and why, and when the upcoming new standard is expected to obtain final approval.

In this paper, we report and reveal an unfairness problem among TCP connections in an IEEE 802.11-based wireless multi-hop network. This problem is not the same as those unfairness problems reported before. In each of the cases we identified, the one TCP connection is completely shut down, even if it starts much earlier than the competing TCP traffic. By illustrating the TCP layer and MAC layer traces, we show that this kind of unfairness problem is rooted in the IEEE 802.11 MAC layer. The hidden node problem and the exposed node problem along with the exponential back-off scheme in the MAC layer are the major causes for that problem.

Abstract Wireless packet ad hoc networks are characterized by multi‐hop wireless connectivity and limited bandwidth competed among neighboring nodes. In this paper, we investigate and evaluate the performance of several prevalent TCP algorithms in this kind of network over the wireless LAN standard IEEE 802.11 MAC layer. After extensively comparing the existing TCP versions (including Tahoe, Reno, New Reno, Sack and Vegas) in simulations, we show that, in most cases, the Vegas version works best. We reveal the reason why other TCP versions perform worse than Vegas and show a method to avoid this by tuning a TCP parameter— maximum window size. Furthermore, we investigate the performance of these TCP algorithms when they run with the delayed acknowledgment (DA) option defined in IETF RFC 1122, which allows the TCP receiver to transmit an ACK for every two incoming packets. We show that the TCP connection can gain 15 to 32 per cent good‐put improvement by using the DA option. For all the TCP versions investigated in this work, the simulation results show that with the maximum window size set at approximately 4, TCP connections perform best and then all these TCP variants differ little in performance. Copyright © 2001 John Wiley & Sons, Ltd.

In this paper, we report and reveal a so-called "incompatibility" problem among TCP connections in an IEEE 802.11-based wireless multi-hop network. This problem is not the same as those unfairness problems reported before. In each of the cases we identified, one TCP connection is completely shut down at a time, and, after a while, it will kill the other TCP connection-just like a turn-over. There is no unfairness in the long-term average throughput. By illustrating the TCP layer and MAC layer traces, we show that this kind of incompatibility problem is rooted in the IEEE 802.11 MAC layer. The hidden node problem and the exposed node problem, along with the exponential back-off scheme in the MAC layer, are the major causes for that problem.

We report the so-called "TCP instability problem" in 802.11-based wireless mobile multi-hop networks. Our results show that the throughput performance of multi-hop TCP traffic in such a network may suffer from severe oscillation. By illustrating the multi-layer traces, we show that this problem is rooted in the MAC layer. Furthermore, a resolution is proposed to eliminate this problem.

In multi-hop wireless networks, the TCP data packet stream has to compete for limited bandwidth with its ACK stream. In this paper, we investigate and evaluate the performance of several prevalent TCP algorithms using the delayed acknowledgment (DA) option defined in IETF RFC 1122, which allows the TCP receiver to transmit an ACK for every two incoming packets. After extensively comparing the existing TCP versions (including Tahoe, Reno, New Reno, Sack and Vegas) by simulation, we show that with the DA option, the TCP connection can gain 15-32% output improvement. We strongly recommend using the DA option in TCP applications in wireless multi-hop networks.

Generally speaking, the parameter-based call admission control (CAC) approaches are conservative and lead to low network utilization since they use a worst-case model to ensure complete commitment conformance. Even if additional information of the new connection is available, they cannot take advantage of it. In this paper, we introduce some enhancements to the existing parameter based CAC algorithms for those applications in which one or two simple additional traffic characteristics are available, i.e. mean rate of the new connection and its rate variance. As well as showing how to do this, we give the reasons why we do this. We show that with these simple additional parameters, a much higher network utilization can be achieved. The numerical results demonstrate that the number of calls admitted to the network with our schemes is very close to (even equals) the best value.

We investigate and evaluate the performance of these two TCP variants in a wireless mobile ad hoc network. The results in this paper are based on simulations using the NS2 network simulator from Lawrence Berkeley National Laboratory (LBNL), with extensions from the MONARCH project at Carnegie Mellon. We present our experimental results in two parts. The first part of the experiments does not include node movement. The effect of mobility and the link breakage are considered in the second part experiments. We consider one type of topology in the first part experiments: a string topology with 8 nodes as shown. The distance between any two-neighbor nodes is equal to 200 m, which lets a node can only connect to its neighbor node. In other words, only those nodes between which a line exists can directly communicate. In this performance study, we set up a single TCP connection between a chosen pair of sender and receiver nodes and measured the successively received packets over the lifetime of the connection. The TCP good-put result was measured for each connection, averaged over ten runs. We compare these two TCP variants and discuss the effect of TCP maximum window size window.

We propose an analytically tractable approach to model compressed video traffic called C-DAR(1). The C-DAR(1) model combines an approach utilizing a discrete-time Markov chain with a continuous-time Markov chain. We show that this approach accurately models the distribution and exponential autocorrelation characteristics of video conferencing traffic. Also, we show that by comparing our analytical results against a simulation using actual video conferencing data, our model provides realistic results. In addition to presenting this new approach, we address the effects of long-range dependencies (LRD) in the video traffic. Based on our analytical and simulation results, we are able to conclude that the LRD have minimal impact on videoconference traffic modeling.

Several works have concluded that the number of cells per frame of variable bit-rate (VBR) video teleconference follows a gamma distribution. But few video models meet this characteristics. We propose a novel video model, called first-order autoregressive (AR) gamma sequence, in which random variables are marginally distributed as gamma variables. We give the definition of a GAR model and a simple realization method of GAR(1). Parameter estimation methods are studied, too. Simulation results show that our model can meet the characteristics of VBR video better than existing models.

Up to now, there is no analysis model suitable to describe the Gamma distribution and exponential autocorrelation characteristics presented in the literature, which is very important for video traffic. In this paper, we propose such a video model called the C-DAR(1) model. We believe that it is the first model meeting the distribution and correlation characteristics of video conference traffic. A scheme is given to link the continuous-time Markov chain model with a discrete-time one. A doubtful conclusion in the work of Skelly et al. (1993) is discussed.