This paper addresses the challenges of image transmission in satellite communication networks, where bandwidth constraints, high interference, and latency issues significantly limit conventional transmission methods. We propose a novel semantic communication framework that adapts to various computational capabilities of receiving terminals in Internet of Everything (IoE). Our approach leverages the Swin Transformer V2 architecture to extract and transmit task-relevant semantic features rather than raw image data, significantly reducing bandwidth requirements while maintaining high reconstruction quality. The proposed system dynamically adjusts its encoding and decoding processes based on receiver computational capacities, enabling efficient image transmission to heterogeneous terminals ranging from high-performance stations to resource-constrained devices. Extensive experiments on various datasets demonstrate that our framework outperforms conventional JPEG+LDPC schemes and state-of-the-art deep learning-based approaches in terms of both PSNR performance and semantic communication utility across various signal-to-noise ratios. The framework shows particular robustness in low-SNR and low-CBR environments, addressing the “efficiency-compatibility” dilemma in resource-constrained satellite communications.

For energy-sustainable Internet of Things (IoT) and wireless sensor networks (WSNs), this paper addresses the minimization of the sole external power consumption in a time-switching simultaneous wireless information and power transfer (TS-SWIPT) based multi-hop decode-and-forward (DF) relay network. In such networks, only the source is grid-powered, while relays operate solely on harvested energy, making global minimization of source power under a stringent end-to-end quality-of-service (QoS) constraint imperative for maximizing network lifetime in green IoT systems. The joint optimization of the source power and the per-hop TS ratios, however, is non-convex. To solve it efficiently and globally, we first transform the problem into an equivalent convex form via logarithmic variable transformations. We then develop a tailored logarithmic barrier interior-point method (IPM) that exploits the problem’s structure. By explicitly leveraging its inherent block-tridiagonal structure, the algorithm achieves per-iteration computational complexity scaling linearly with the number of relays. Simulation results demonstrate global optimality and significant gains of the proposed scheme: for a 5-relay network, it achieves a 5.8-dB source power reduction over the sub-optimal single-step successive convex approximation (SCA) benchmark, as well as a 14.8-dB reduction and a 34.2-percentage-point feasibility rate improvement over the uniform fixed-TS policy. These gains are achieved at a tractable computational cost of approximately 27.5 ms for a 5-relay network. Moreover, a warm-start strategy cuts per-period computation by over 80%, and the solution exhibits strong robustness when evaluated under a realistic logistic (sigmoidal) nonlinear energy harvesting (EH) model and imperfect channel state information (CSI), establishing an excellent performance-efficiency trade-off for practical IoT deployments.

Thermal infrared imaging is pivotal for all-weather 3D perception, yet analyzing thermal information remains a formidable challenge due to the complexity of heat conduction. Unlike visible light, heat conduction acts as a natural low-pass filter that suppresses high-frequency textural details, causing severe geometric ambiguities and "ghosting" artifacts in standard 3D reconstruction pipelines. To accurately model the inherently diffusive thermal field for high-fidelity reconstruction, we propose Frequency-Gated Graph Splatting (ThermalGate-GS), a framework that explicitly decouples the scene into diffusive thermal distributions (low-frequency) and sharp structural boundaries (high-frequency). Within this framework, we introduce a novel Frequency-Gated Anisotropic Diffusion mechanism. Specifically, the frequency-gating module utilizes extracted high-frequency structural cues to determine spatially-adaptive gating weights. Subsequently, these weights drive an anisotropic diffusion process that dynamically regulates thermal feature propagation, promoting smoothness on object surfaces while suppressing cross-boundary bleeding. Finally, these spectrally refined features are employed to regress 3D Gaussian attributes, substantially alleviating the ambiguity in thermal reconstruction. Extensive experiments demonstrate that ThermalGate-GS achieves state-of-the-art performance, with a notable 7.94 dB PSNR improvement on the ThermoScenes benchmark over prior physics-inspired baselines.

Orthogonal Time Frequency Space (OTFS) enables reliable communication in high-speed mobility scenarios, making it ideal for Low Earth Orbit (LEO) satellite communication. Furthermore sparse Code Multiple Access (SCMA) supports massive connection in uplink mobile communications. This paper proposes an OTFS-SCMA scheme for LEO satellite communications and the corresponding cross domain detection algorithm. At the transmitter, users are grouped, and a practical codebook is employed. At the receiver, cross-domain detection is utilized to obtain initial estimates, which are then refined using the Message Passing Algorithm (MPA) for optimized detection. Comparative analysis with other baseline schemes demonstrates the performance gain.

In edge computing networks, network delay jitter prove to be a critical factor for ensuring the responsiveness of real-time applications and enhancing overall user experience. It also serves as a key metric for optimizing task offloading in latency-sensitive network environments. However, the increasing complexity of access strategies, diverse resource demands, and evolving network topologies make accurate delay jitter prediction increasingly challenging. To address this, this paper proposes M-TransMLP, a delay jitter prediction model based on a hybrid Transformer-MLP (Multilayer Perceptron) architecture. The model intelligently aggregates critical link features within paths through weighted layers, effectively capturing the individual contributions of each link to the overall delay jitter. Additionally, it incorporates multi-layer path and link update blocks, enabling comprehensive modeling of the dynamic interactions between paths and links. By leveraging real-time updates of path and link states, M-TransMLP accurately captures transient network fluctuations, providing a robust framework that maps network features to delay jitter metrics. Experimental evaluations on the NSFNET and GEANT2 datasets demonstrate that M-TransMLP model outperforms state-of-the-art models, such as MixerNet and RouteNet, in both accuracy and stability for delay jitter prediction. The model not only adapts effectively to complex and dynamic network environments in real time but also sets a new benchmark for latency-sensitive network applications.

With the rapid growth in demand for intelligent services and the corresponding surge in massive data, semantic communication has emerged as a promising paradigm for intelligent service applications. However, existing approaches are limited by their capability to effectively extract semantic features. To address this challenge and improve the quality of transmitted image, this paper proposes an attention-based semantic communication system for image transmission. Specifically, by adaptively weighting spatial and channel features through the Convolutional Block Attention Module (CBAM), the proposed approach enhances the representation of crucial information. Furthermore, focusing on Artificial Intelligence of Things (AIoT) applications, where intelligent tasks require autonomous decision-making based on perceived information, the system is trained on two receiver tasks: reconstruction and classification, enabling it to adapt to diverse application. Experimental results demonstrate that our proposed method outperforms both deep learning based joint source and channel coding (JSCC) and the combination of joint photographic experts group(JPEG) and low-density parity-check (LDPC) in terms of image reconstruction quality. Furthermore, the proposed system achieves high classification accuracy and exhibits robustness across varying compression ratios.

Space-air-ground integrated networks (SAGIN) are a key technology in 6G, enabling seamless global connectivity through a unified communication framework, while integrated sensing and communication (ISAC) mitigates spectrum congestion by reusing spectrum and transceivers for dual communication and sensing functions. Despite its potential, existing ISAC research has largely focused on terrestrial networks, with limited exploration in SAGIN environments. This paper proposes a novel ISAC framework for SAGIN, incorporating orthogonal time frequency space (OTFS) modulation and non-orthogonal multiple access. OTFS enhances the system's robustness in doubly-dispersive channels and supports precise communication and sensing integration. These channels experience both time dispersion caused by multipath propagation and frequency dispersion caused by Doppler shifts. In this framework, high-altitude platform stations serve as relay nodes, amplifying communication signals and processing echo signals for accurate target parameter estimation. To further enhance system performance, we design a beamforming optimization strategy using successive convex approximation to improve communication energy efficiency. Simulation results demonstrate that the proposed scheme significantly enhances the overall performance of the SAGIN system.

Interference cancellation has always been a crucial task in wireless communications, especially in the presence of nonlinear distortions caused by power amplifier. However, when considering the ultra-wideband (UWB) orthogonal frequency division multiplexing (OFDM) systems, this task becomes more challenging as the channel estimation will be severely impacted by the nonlinearity, thus leading to significant performance degradation. Driven by solving this problem, this paper proposed a novel nonlinear signal processing method, referred to as log-sum-minimization sparse channel estimation based nonlinearity cancellation (LSMSCE-NC). In detail, an optimization model based on the log-sum norm minimization and nonlinearity cancellation is first established and then its iterative solution is also presented. The numerical results reveal that the proposed LSMSCE-NC method achieves significant bit error rate (BER) and normalized mean square error (NMSE) advantages compared to the state-of-the-art algorithm.

In the visionary ideals of “Internet of Everything” and “Digital Twins”, the future 6G will deeply integrate diverse heterogeneous networks such as satellite and aerial networks to support seamless connectivity and efficient interoperability, also known as space-air-ground integrated networks (SAGIN), in which the grant-free uplink random access based on Slotted ALOHA (S-ALOHA) can reduce access latency and complexity for massive Internet of Things (IoT) devices. However, with the increasing number of IoT users, the collision probability of S-ALOHA escalates and further degrades the system performance. In this paper, we focus on the massive IoT device uplink access in SAGIN aided by high altitude platform stations (HAPS), investigating power allocation for IoT devices to maximize system access capability and spectral efficiency (SE). Specifically, we first optimize 3D deployment of HAPS. Then the resilient massive access (RMA) based on flexible fusion of S-ALOHA and non-orthogonal multiple access methods is proposed. To maximize system SE with device power constraints, we model the sequential decision problem as a Markov decision process and solve it with the Advantage Actor-Critic (A2C) algorithm. Simulation results demonstrate the proposed RMA can significantly improve the IoT terminal successful access probability and the resource scheduling based on A2C also significantly increases the system SE with low complexity.

In this paper, we present ShapeMatcher, a unified self-supervised learning framework for joint shape canonicalization, segmentation, retrieval and deformation. Given a partially-observed object in an arbitrary pose, we first canonicalize the object by extracting point-wise affine-invariant features, disentangling inherent structure of the object with its pose and size. These learned features are then leveraged to predict semantically consistent part segmentation and corresponding part centers. Next, our lightweight retrieval module aggregates the features within each part as its retrieval token and compare all the tokens with source shapes from a pre-established database to identify the most geometrically similar shape. Finally, we deform the retrieved shape in the deformation module to tightly fit the input object by harnessing part center guided neural cage deformation. The key insight of ShapeMaker is the simultaneous training of the four highly-associated processes: canonicalization, segmentation, retrieval, and deformation, leveraging cross-task consistency losses for mutual supervision. Extensive experiments on synthetic datasets PartNet, ComplementMe, and real-world dataset Scan2CAD demonstrate that ShapeMatcher surpasses competitors by a large margin. Code is released at https://github.com/Det1999/ShapeMaker.

Integrated sensing and communication (ISAC) and space-air-ground integrated networks (SAGIN) have been considered as key technologies of 6G. The challenge of achieving ISAC in uplink massive access scenarios within the SAGIN has become a major research topic. This paper introduces the Resilient Massive Access (RMA) protocol, which deeply integrates S-ALOHA and NOMA, effectively enhancing the system's access success probability. Additionally, a cascaded uplink detection algorithm based on LS and MUSIC (MUSIC-NOMA-TSA) is proposed, enabling signal decoding and target sensing even in the presence of collisions at the receiver. Simulation results demonstrate that, compared to traditional algorithms, the proposed algorithm not only achieves more accurate signal decoding and target sensing but also significantly improves the access success probability.

While video recommendation has been studied extensively in regular PC and smartphone settings, such a topic has been rarely discussed in the virtual reality (VR) context so far. On the other hand, as the popularity of VR videos continues to soar, its recommendation will play a crucial part in providing suggestions and guiding users through a deluge of available content. Given this unmet need, in this work, we present Bere, a video recommender system tailored for VR. Our approach leverages viewers' behavioral responses as they engage with VR videos to infer their preferences and thus make future recommendations. We integrate these new behavioral user-video interaction measures into the mainstream recommendation framework and renovate the graph learning-based paradigm to accommodate the new changes. The recommender system is further empowered with a novel domain adaptation approach named CMCCDA to address the data scarcity problem for model training. We also develop an energy-efficient adaptive encoding scheme to reduce the energy consumption on the VR device. We collect a behavioral dataset for video recommendation in VR and demonstrate through extensive evaluation that Bere significantly outperforms state-of-the-art schemes by up to 68.0% in precision and up to 28.8% in ranking quality.

Sparse code multiple access (SCMA) is a new nonorthogonal multiple access scheme, which effectively exploits the constellation shaping gain of multi-dimensional codebook. In this paper, we propose a new SCMA architecture of uplink satellite communication system. At the transmitter, we group the users and design a practical spherical codebook. At the receiver, a low-complexity multi-user detection algorithm, namely logarithm domain message passing algorithm (Log-MPA) is implemented. The results show that the reliability of the proposed codebook outperforms the existing SCMA codebook schemes in both AWGN and Rayleigh channels.

Aerial images often depict objects with arbitrary orientations, which pose challenges for conventional object detectors to detect and classify. To address this issue, rotation-equivariant Convolutional Neural Networks (CNNs) have been proposed to extract rotation-equivariant features. However, the orientation encoding in these networks is often unstable and noisy, deteriorating detection performance. In this paper, we first analyze the rotation-equivariant network. Then, we propose a Rotation-robust Prototype Generation (RPG) method, which consists of two parts, stabilization module and enhancement module. In stabilization module, we generate rotation-robust prototypes to increase the stability of cyclic shifts. In enhancement module, we use the obtained prototype to improve the response of the features to object semantics. The RPG method can be used as a plug-and-play module in both one-stage and two-stage detectors. With only 30 lines of code, we achieve an average 1% improvement on four challenging datasets, including DOTA-V1.5, DOTA-v1.0, DIOR-R, and HRSC2016.

Cell-free (CF) massive MIMO is considered one of the key technologies for 6G to achieve high spectral efficiency (SE) and ultralow latency. However, as the number of users increases, pilot contamination becomes more serious, and the optimal SE can not be achieved when the number of users exceeds the access points (APs). Therefore, we study the CF massive MIMO-NOMA system. Specifically, we design a user clustering algorithm based on the average Signal to Interference plus Noise Ratio (SINR), using orthogonal pilots between different clusters, and different users in the cluster using the same pilot, thereby reducing pilot contamination. Then we propose a flexible power allocation problem to maximize the system SE while taking into account user fairness. We model the problem as a Markov Decision Process (MDP) and then solve it using the asynchronous advantage actor-critic (A3C) algorithm in deep reinforcement learning. Simulation results show that the proposed A3C based power allocation scheme in CF massive MIMO-NOMA outperforms the baseline schemes in terms of fairness and SE.

As complementary way of mobile communication, Mobile Ad Hoc Network (MANET) has developed rapidly and been utilized for various scenarios, where the energy consumption and load balancing are considered as key issues. To address this problem, based on Ad hoc On-demand Multipath Distance Vector (AOMDV)routing protocol, we propose a multipath routing protocol based on energy aware and constrained queue length (AOMDV-EC). We define the congestion state into several levels according to the queue length in the MAC layer, and select paths considering residual energy and hop count. The simulation results show that the performance of the proposed routing protocol is significantly improved in terms of packet delivery ratio, average end-to-end delay, throughput, routing overhead and energy exhausted nodes.

This paper investigates a computation offloading and resource allocation policy for multiple vehicle user equipments (VUEs) in the Internet of Vehicles (IoV). Aiming at balancing the delay and energy consumption during the offloading procedure, a Support Vector Machine (SVM) is initially adopted to classify the offloading tasks into two categories according to different delay and energy consumption requirements. Consequently, VUEs can choose to offload the tasks to the mobile edge computing (MEC) server or other VUEs for completion. In particular, to further decrease the task offloading time in the MEC processing mode, the non-orthogonal multiple access (NOMA) scheme is adopted, which makes it possible for the MEC server to serve two VUEs simultaneously on the same sub-channel. To minimize the total cost, a Dueling Double Deep Q-Network (D3QN) based resource allocation algorithm is proposed, which can allocate the corresponding radio or computing resources under different task processing modes. Simulation results demonstrate that the proposed scheme can effectively reduce the total offloading cost within the maximum delay tolerance compared with existing methods.

Motivated by the development of single image super-resolution (SR) reconstruction in computer version, classic SR networks have been widely applied to the channel estimation of wireless communication system. To capture the spatial correlations in the reflection element-domain of reconfigurable intelligent surface (RIS), we propose a multi-scale supervised learning-based Laplacian pyramid wide residual network (LapWRes) to achieve the progressive reconstruction of cascaded channel in a coarse-to-fine fashion. The LapWRes can be divided vertically into feature extraction branch (FEB) and channel reconstruction branch (CRB), while it can also be viewed horizontally as multiple channel reconstruction modules (RMs) at different scales. In the FEB, the wide activation residual blocks are stacked to extract the high-frequency information of cascaded channel. In the CRB, the high-frequency and low-frequency information of cascaded channel is fused by utilizing the residual learning. Simulation results show that the LapWRes can achieve better estimation accuracy than other channel estimation schemes and faster convergence than existing SR network-based channel estimation models.

Nonorthogonal multiple access (NOMA) and symbiotic ambient backscatter communications (AmBCs) are both considered promising technologies for beyond 5G mobile communication technology by enabling low-powered and spectrum-efficient access in large-scale Internet of Things. In this article, we investigate the uplink symbiotic communication in AmBC enabled NOMA system. In contrast with existing works, we assume the carrier transmitter (CT) transmits information while providing energy to the backscatter devices (BDs), which improves the energy efficiency. However, the signal power transmitted by the CT to integrated receiver (IR) through the direct link is much larger than the signal reflected by the BD, which can seriously affect the detection of the reflected signal by BD. Therefore, we jointly design the signal waveforms of CT and BDs, and propose a multiuser blind detection algorithm based on interference cancellation at IR. The simulation results demonstrate that the proposed multiuser detection algorithm achieves improved performance even the active BD number is unknown or without direct link.

Traditional cellular system deploys base station at the center of each cell, which leads to inter-user/cell interference and limited spectral efficiency. In contrast, the cell-free massive MIMO can effectively mitigate these interference by deploying multiple access points distributed in the coverage that jointly serve all the users. In this paper, we investigate a cell-free massive MIMO system assisted by the non-orthogonal multiple-access (NOMA) and propose a sparse signal reconstruction algorithm based on extended approximate message-passing (EAMP). The simulation results show that the proposed algorithm outperforms the traditional baselines in terms of recovery rate, calculation time and system capacity.

Quality of Experience (QoE) assessment is a long-lasting but yet-to-be-resolved task. Existing approaches, especially for conversational voice services, are restricted to leveraging network-centric parameters. However, their performances are hardly satisfactory due to the failure to consider comprehensive QoE-related factors. Moreover, they develop a one-for-all model that is uniform for all individuals and thus incapable of handling user diversity in QoE perception. This paper proposes a personalized QoE assessment model, namely SpeechQoE. It exploits speaker's speech signals to infer individual's perceived quality in voice services. SpeechQoE fundamentally addresses the drawback of conventional models. Instead of enumerating and incorporating unlimited QoE-related factors, SpeechQoE takes as input speech signals that inherently bear rich information needed for QoE assessment of the speaker. SpeechQoE employs an efficient few-shot learning framework to adapt the model to a new user quickly. We additionally design a lightweight data synthetic scheme to minimize the overhead of data collection needed for model adaption. A modular integration with a conventional parametric model is further implemented to avoid issues caused by the clean-slate data-driven approach. Our experiments show that SpeechQoE achieves an accuracy of 91.4% in QoE assessment which outperforms the state-of-the-art solutions by a clear margin. As another contribution of this work, we build a dataset that would be the first source of annotated audio tracks for QoE assessment of conversational calls.

Unmanned aerial vehicle (UAV) assisted emergency communication is an important technique for future B5G/6G scenario. The UAV is usually considered as a mobile relay to forward information from the macro base station (MBS) to the users in emergency area. In this paper, the MBS power allocation, the UAV service zone selection, and the user scheduling are jointly investigated to enhance the sum spectrum efficiency. We formulate the MBS power allocation and UAV service zone selection problem as an Markov Decision Process (MDP) in the delay ignored system (DIS) and propose a deep reinforcement learning (DRL) algorithm based on Q-learning and Convolutional Neural Networks (CNN). Then the proposed DRL-based scheme is extended in time delay system (TDS) to estimate the current optimal action with the outdated channel information. We also formulate the user scheduling as a 0-1 optimization problem and solve it by dividing into sub-problems. Simulation results demonstrate that the proposed DRL-based resource scheduling scheme can effectively improve the spectrum efficiency compared with the existing schemes.

Mobile crowdsensing (MCS) is a promising diagram for data collecting based on smart mobile terminal. Nowadays, vehicles with embedded multiple sensors have been increasingly adopted as participants to complete various sensing tasks. Most existing researches are conducted in terms of MCS coverage, energy consumption or incentive mechanisms etc. In this paper, a new utility function F(Ω) for measuring the quality of MCS coverage is proposed, F(Ω) includes coverage percentage and coverage degree. We formulate the selection of taxis as an optimization of coverage quality. Therefore, an improved greedy algorithm to optimize the coverage quality (CQO) is proposed. We evaluate the proposed algorithm with trajectory dataset and study several factors influencing coverage quality. The results show that the proposed algorithm achieves a better coverage quality.

Interference alignment (IA) is thought to be an effective technique on reducing interference in wireless communications. To mitigate the interference and improve the system performance, this paper proposes a two-stage hybrid interference alignment (THIA) scheme for the two-tiered multi-user multiple-input-multiple-output (MIMO) heterogeneous network (HetNet). Specifically, we focus on the downlink transmission of two-tiered HetNet. Firstly, we align the cross-tier interference from macro base station (MBS) to the users in the same small cell to a specific subspace, through which the first-stage transmit beamforming matrix at MBS is designed. Then, an equivalent virtual distributed method is applied to mitigate the co-tier interference among macro users. Numerical results demonstrate that the proposed THDIA scheme outperforms the conventional schemes in degrees of freedom and system sum rate.

Sparse Code Multiple Access (SCMA) is a new air interface technology that is proposed for lower delay, higher capacity and spectrum utilization in 5G communications. The inter-symbol interference(ISI) in wireless communications is serious due to the effects of multipath propagation and background noise. In order to reduce inter-symbol interference, many wireless air interface and radio access technologies adopt cyclic prefixes to obtain channel estimation. However, the spectrum utilization of this method is too low. Considering the blind equalization algorithm is often used in the environment where spectrum resources are scarce, in this paper we adop the blind equalization algorithm in the SCMA technology. In SCMA, the equalization cannot placed on receiver, so we innovatively put the equalization algorithm in the signal transmitter according to the actual situation. In addition, we propose a channel estimation algorithm. Theory and simulation show that applying blind equalization to SCMA can reduce bit error rate and improve spectrum utilization.

GPUs are widely used in accelerating deep neural networks (DNNs) for their high bandwidth and parallelism. But tuning the performance of DNN computations is challenging, as it requires a thorough understanding of both underlying architectures and algorithm implementations. Traditional research, which focused on analyzing performance by CUDA C language or PTX instructions, has not combined hardware features tightly with source code. In this paper, we present a performance analysis framework at the assembly level. First, an instruction parser takes assembly source code, benchmark results, and hardware features as input to identify each instruction's efficiency and latency. Then, a DAG constructor builds a DAG that models instruction executions. Finally, a performance advisor incorporates block partitions, occupancy, and the generated DAG to predict running cycles of the source code and presents its potential bottlenecks. We demonstrate the effectiveness of our framework by optimizing DNNs' performance-critical kernels-GEMM and convolution. After taking steps to reduce bottlenecks, the experimental results show that our GEMM is 20% faster than cuBLAS, and our convolution outperforms cuDNN by 40%--60%. Because of the usage of assembly instructions, we can predict performance with an error as low as 2% in average.

Wireless sensor network (WSNs) have been used to achieve seamless, energy efficient, reliable and low-cost remote monitoring and control in many applications. As the energy of sensor nodes are limited, a solid energy-efficient algorithm is necessary to improve the energy efficient in heterogeneous WSNs. In this paper, we propose an improved distributed energy efficient clustering algorithm (IDEEC) for heterogeneous WSNs. IDEEC considers the multi-level energy model. We simplify the probability threshold, improve the cluster head selection probability and optimize the estimation of the average energy of network. Simulation results confirm the performance supremacy of IDEEC compared to current clustering protocols in terms of stability period, number of messages, mean and variance of cluster heads (CHs), furthermore, IDEEC takes the minimum running time, which makes it easier to be applied in reality.

Millimeter wave (mmWave) communications are an important candidate technique in 5G networks for features, supporting ultra-dense small cells and mobile data offloading. However, ultra-dense nodes and increasing data traffic bring in vast interference. This paper investigates low complexity non-iterative interference alignment (IA) schemes for multiple-input multiple-output (MIMO) interference broadcast channels in mmWave communications. The authors focus on the three-cell mobile network model in which each base station supports no more than two users within its cell. There is already a closed-form IA solution for the case that one cell has two users, while the other two have one user in each, which can be denoted as {2,1,1}. This paper considers different settings and proposes corresponding IA schemes. First, two IA schemes based on multi-step for the asymmetric setting {2,2,1} are presented, five degree of freedom (DoF) could be achieved. Then, for the symmetric setting {2,2,2}, a novel IA solution with lower complexity and a joint method combining IA with non-iterative multi-user MIMO technique are proposed, and they can achieve six DoF. The simulation results indicate that our non-iterative schemes have similar sum-rate capacity performances with the iterative ones in existing work, and the complexity is effectively reduced.

This paper presents and characterizes a novel high gain circularly polarized (CP) antenna array for 5G applications. The array element is an antipodal curvedly tapered slot antenna (ACTSA) generating circularly polarized field. The CP ACTSA fed by substrate integrated waveguide (SIW) is convenient to integrate with substrates. By introducing two sheet metals on the two sides of the rectangular Rogers 6002 substrate, an impedance bandwidth of 18.2%, a wide 3-dB axial ratio (AR) bandwidth of 16.9%, and stable gain of 8 ± 0.6 dBic over the operating band are achieved. By employing the proposed CP ACTSA as radiating elements, a 4 × 4 high-gain wideband antenna array is proposed for 5G E-band and W-band millimeter-wave applications. Fabrications are carried out using wire cutting electrical discharge machine and print circuit board process, which have advantages of low cost. The whole feeding network is realized by SIW with low insertion loss at millimeter-wave band. Benefiting from the wide impedance and AR bandwidth of the new antenna element, good impedance matching and AR characteristics can be achieved over the whole working frequency band from 81 to 95 GHz by this antenna array. Gain up to 18.5 ± 1.3 dBic is also obtained.

In this paper we present the requirements, design and pre-deployment testing of a transportation bus as a Mobile Enterprise Sensor Bus (M-ESB) service in China that supports two main requirements: to monitor the urban physical environment, and to monitor road conditions. Although, several such projects have been proposed previously, integrating both environment and road condition monitoring and using a data exchange interface to feed a data cloud computing system, is a novel approach. We present the architecture for M-ESB and in addition propose a new management model for the bus company to act as a Virtual Mobile Service Operator. Pre-deployment testing was undertaken to validate our system.

Sensing coverage in wireless sensor network is one of the vital performance metrics which can arise in all design stages and usually considered together with some other performance metrics, such as connectivity or energy consumption. Whatever the metrics, the fundamental problem is to know at least how many sensors are needed to maintain both sensing coverage and network connectivity. In this paper, we proposed a percolation-based coverage & connectivity combined model (PCCC) to obtain the critical density at which the network abruptly becomes covered/connected. The PCCC is based on directional sensor network in which sensors are assigned a determined sense direction with the angular interval varies from 0 to 2π. Besides, we also discussed the coverage and connectivity together as a whole under the model proposed. It is worth mentioning that the theoretical analysis as well as simulation results of relationship between critical density and transmitting gives insights into the practical design of directional sensor network.

In this paper, an advanced anti-collision algorithm in RFID-sensor network based on inter-tag communication mechanism is proposed to improve the performance of the system. By employing the "Tag Communication Slot" (TCS) and designing the relevant hardware, the tags can communicate with each other, transmit useful data to fewer tags and reduce the number of tags participating in the identification process. In this way, we can efficiently reduce the collision rate with the increase of tag number and improve the tag starvation problem in Dynamic Framed-Slotted ALOHA algorithm (DFSA). The simulation results show that the TCS algorithm can greatly improve the system stability and identification efficiency especially in the case of large tag number.

Barrier coverage is a critical issue in Wireless Sensor Networks (WSNs) for various battlefield and homeland security applications. In this paper, we address the problem of how to efficiently heal coverage holes in hybrid Wireless Sensor Networks (WSNs) by relocating mobile nodes, and propose a hole recovery strategy called Minimal Patching Barrier Healing Strategy (MPBHS). In our strategy, a definition of Minimal Patching Barrier (MPB) is proposed, which can be healed by least number of nodes. Under this definition, our strategy can be divided into two steps: first is finding out the MPB among all possible barriers; second is dispatching the selected mobile nodes, which means moving mobile nodes to appropriate points in MPB to heal coverage holes, according to our Coverage Holes-Mobile Nodes Matching (CHNM). The experimental evaluation shows the efficiency of our proposed strategy with the respect of both sensor nodes' efficiency and low energy efficiency.

A new coarse synchronization scheme for 3rd Generation Partnership Project Long Term Evolution (3GPP-LTE) is proposed. Existing coarse synchronization schemes based on central symmetry utilize either primary synchronization signal (PSS) and secondary synchronization signal (SSS) to fulfill initial timing synchronization. Though these two schemes perform well with relative low complexity, they can be further enhanced. Therefore a new scheme is proposed in this paper, which combines PSS with SSS and utilizes hierarchical computing to implement initial timing synchronization. Simulation results show that the new scheme can acquire higher detection accuracy rate without increasing computational complexity.

This paper investigates joint relay selection (RS) and power allocation (PA) in cognitive radio (CR) systems, in which relay nodes operate in amplify-and-forward (AF) mode. In contrast to the conventional schemes, we take into account the performance of the secondary user (SU) which acts as the relay. A two-step optimization scheme is proposed to maximize system throughput with minimum rate guarantees for the source and the relay while the interference introduced to the primary user (PU) is kept below a specified threshold. The PA optimization in the first step is non-convex, and hence hard to solve in general. To tackle this, we introduce a power proportionality factor at the relay to decompose the non-convex optimization into two levels. Based on dual method and search method at inner level and outer level respectively, a closed-form solution for the optimal PA between the source and the relay is derived. In order to reduce complexity of repeating the PA procedures at all the relay candidates while maintaining reasonable performance, we also develop a suboptimal approach. Furthermore, simulation results and comparisons are presented to illustrate the performance of the proposed scheme.

In this paper, we consider the problem of hierarchical bandwidth sharing in cognitive radio (CR) environment. In the system model under consideration, a primary service provider (PSP) can sell its available spectrum bandwidth to a secondary service provider (SSP). In contrast to the conventional schemes, the SUs served by the SSP can also share the unsold bands of the PSP through the approach of opportunistic spectrum access (OSA), which will introduce interference to the PUs and make the PSP suffer a profit loss due to the sensing imperfections in practical networks. Based on microeconomic theory, this problem of bandwidth allocation is formulated as a market model, and an equilibrium where both the PSP and SSP are satisfied with the amount of allocated bandwidth and the price is derived by using the concept of demand and supply functions. Moreover, an iterative algorithm for distributed implementation of our scheme is also proposed. Furthermore, simulation results and comparisons are presented to illustrate the performance of the proposed scheme.

In a multi-secondary user (SU) and single primary user (PU) cognitive radio (CR) system, each terminal is equipped with multi-antenna. We propose an optimal beamforming design method aiming for the minimal power budget of SUs' network, where both the perfect and imperfect knowledge of PU's precoder are considered, e.g. optimal beamforming vector with perfect PU's precoder (OBV-perfect), optimal beamforming vector with imperfect PU's precoder (OBV-imperfect). In a spectrum sharing based CR network, SUs are allowed to coexist with the PU, provided that the interference power from the SUs to the PU is less than an acceptable value, such that the quality of service (QoS) of PU is guaranteed. Meanwhile, the QoS requirement of minimizing signal to interference and noise ratio (SINR) at the secondary receiver is also included as the constraint. Simulation results show that the system power of OBV-perfect is smaller than the imperfect scenario, since OBV-perfect knows of both the PU's channel state information (CSI) and precoder information, thus contributing to minimize the power under the minimal SINR constraints. However, the system rate of OBV-imperfect is larger than OBV-perfect scenario due to the reason that in our presumed resolution of OBV-imperfect, the employed signal to jamming and noise ratio (SJNR) is a much stricter constraint than SINR for the system. Therefore, both the schemes are optimal beamforming design, and the knowledge of PU's precoder is favorable for access capability in the cognitive MIMO system.

As a promising technology to effectively mitigate interference and improve the performance of a wireless communication network, interference alignment (IA) has been attracted extensive concern. In order to solve the problem that perfect IA needs a large number of antennas and facing the cell selection, this paper proposes a partial interference alignment scheme based on user grouping for a network with multi-cell multi-user multiple- input and multiple-output (MIMO) under downlink transmission scenario. In the proposed scheme, the interference at different directions does not need to be aligned to the same subspace. Furthermore, each BS eliminates inter-user interference within its cell and the dominant interference to users in its neighboring cells. Analysis and simulation results show that the proposed approach outperforms the extension of grouping method in terms of antenna number and sum capacity.

In this paper, we consider the problem of joint relay selection and power allocation in a cognitive radio (CR) network, where a secondary user (SU) source communicates with a secondary access point (AP) assisted by a set of SU relays. Each SU in this set not only acts as a potential relay but also has its own data transmission requirement. Keeping the interference to the primary user (PU) below a specified limit, we propose an optimal scheme with quality of service (QoS) support. On the premise of meeting the minimum transmission rate requirements of all SUs, the goal of the proposed scheme is to maximize the weighted sum rate of the network under transmission power and energy constraints. A closed-form solution for the optimal power allocation between the source and the relay can be obtained by the Karush-Kuhn-Tucker (KKT) conditions. Furthermore, simulation results and comparisons are presented to illustrate the performance of the proposed scheme.

Joint User and Antenna Selection (JUAS) with global optimal solution for multi-cell Multi-User Multiple Input Multiple Output (MU-MIMO) systems needs the centralized processing and full Channel State Information (CSI) sharing that both require high capacity backhaul links. In this paper, decentralized JUAS algorithm is considered for multi-cell MU-MIMO systems with Block Diagonalization (BD) precoding. First, by introducing the eigenvalue-based lower bound on the capacity, the global optimal problem of JUAS is reformulated to a new problem that can achieve local optimum. Second, a decentralized fairness-based algorithm is proposed to select the antenna subset with no CSI sharing. Simulation results demonstrate that the proposed algorithm outperforms existing algorithms, such as capacity-based antenna selection and interference-aware user selection, with the same computation complexity. Moreover, the performance losses compared to centralized JUAS with full CSI sharing are marginal.

Uplink Inter-Cell Interference (ICI) and interference fluctuation degrades the uplink throughput of LTE/LTE-Advanced systems seriously. To deal with these problems, a coordinated scheduling method with resource sorting is proposed. In the proposed method, the uplink interference fluctuation is resisted by exploiting resource sorting and limiting the ICI generated by a given user to a predefined range. Meanwhile, the resources are allocated by differentiating the users' ability to bear the interference. Simulation results show that the proposed method outperforms traditional ICIC schemes, such as Fractional Frequency Reuse, fractional Transmit Power Control (TPC), and overload indicator based TPC.

Femtocell technology features prominently in future communication system owing to its characteristic advantages. However, the coexistence of femtocells and traditional cellular system causes severe contradictions between spectrum utilization and interference. This paper studies the cross tier resource management and intra tier resource management in hybrid macro/femto network, and a novel Cross-Cognition scheme for cross tier resource management is proposed in order to achieve better spectrum utilization and lower interference. Moreover, a resource allocation scheme based on graph Coloring is also proposed for intra tier resource management to decrease the intra tier interference. Simulations show that the proposed schemes significantly increase the throughput of femtocell system, and outperform traditional cognitive resource allocation schemes.

Energy saving becomes one of the most important design considerations in wireless cooperative networks which are composed of nodes typically powered by batteries that can supply only a finite amount of energy. In this paper, we propose a dynamic relaying scheme based on relay selection and physical-layer power control with the objective of minimizing the energy consumption for data transmission. We first develop a mathematical model for the cooperative relaying and analyze the total battery energy consumption to forward a symbol. Based on the analytic results and Markov channel model, we formulate the optimization problem that minimizes total average energy consumption as a Markov decision process, with which we can decide an optimal relay and the transmission power distributely. In the proposed scheme, the relay selection process and cooperation mode starts only when the direct transmission between source and destination node failed, which is energy efficient from a network sense. Numerical simulation show that the proposed scheme achieves significant energy savings.

Relay aided wireless transmission realizes extended coverage and increases capacity for wireless communication networks, especially for the TDD/FDD LTE-A system. The relay node processes the received signal from source under certain rules and forwards the signal to destination. Amplify-and-Forward (AF) and Decode-and-Forward (DF) are two of the conventional relay schemes commonly used and cited. Estimate-and-Forward is a well-know but much less studied approach. The basic idea is to forward an estimate version of original signal without hard decision. A soft symbol estimating approach by solving linear equation set structured with received symbol probabilities. This algorithm makes a least squares fit of the estimated symbol probabilities to a single shot noisy symbol a posteri probability (APP) model. In the simulation, we take frame error rate (FER) to demonstrate the proposed scheme outperforms existing relay schemes.

In a mobile relay system, the idle users can be formed as a cluster to support nearby active users. In order to reduce the complexity of the processing in the relay nodes and destination nodes, a novel transmit preprocessing method is proposed for the cluster based multi-user relay system. Block diagonalization (BD) algorithm is adopted twice in the proposed method to remove inter-cluster and intra-cluster interference simultaneously, and the relay nodes are just in simple amplify-and-forward (AF) mode. Compared to the conventional signal processing methods, the proposed method can achieve better tradeoff between sum capacity performance and processing complexity in the relay nodes.