Integrated sensing and communication (ISAC) has been envisioned as a key enabler for next-generation wireless systems. Meanwhile, fluid antenna system (FAS) has emerged as a promising flexible antenna technology, offering enhanced multiple access capabilities and significant spatial diversity gains. This paper studies the problem of joint port selection and Cramér-Rao Bound (CRB) optimization in an ISAC-enabled FAS. Specifically, the objective is to minimize the CRB while ensuring compliance with communication quality of service (QoS) requirements, transmit power constraints, and port selection limitations. To address this problem, we propose an iterative optimization algorithm leveraging the majorization-minimization (MM) and block coordinate descent (BCD) methods. Numerical results validate the effectiveness of the proposed algorithm, demonstrating its ability to achieve near-optimal solutions with efficient search.

Perception-optimized Learned Image Compression (LIC) methods have recently made significant progress. They surpass both traditional image compression algorithms and non-perceptually optimized LIC methods in image sharpness, detail representation, and subjective perception, even at similar or lower bitrates. However, LIC methods optimized for perception often generate false details and textures in reconstructions, leading to underperformance in objective metrics like PSNR and MS-SSIM, which limits their applicability. In this paper, we introduce a comprehensive loss metric based on saliency detection that aids in achieving exceptional perceptual quality while minimizing distortions. By applying this metric in training, we develop the SaLIC model, i.e., Saliency-Enhanced Learned Image Compression. User study results indicate that, at similar or lower bitrates, SaLIC exhibits better human perceptual quality compared to HiFiC and VVC; quantitative results show that the PSNR of SaLIC significantly outperforms HiFiC (by 1-2dB), and the MS-SSIM of SaLIC even surpasses VVC, achieving a balance between perception and distortion.

In this paper, we propose a Cross-AttentionTransformer Based Method for Traffic Engineering, CATE, which employs the cross attention mechanism to analyze traffic patterns and dynamically apply different strategies based on traffic characteristics. CATE effectively balances normal network performance with burst traffic resilience, ensuring the delivery of high-quality solutions across various network topologies. Preliminary evaluations on real-world wide-area network (WAN) topology datasets demonstrate that CATE outperforms existing state-of-the-art machine learning-based TE approaches as well as traditional linear programming methods, achieving a significant reduction in average maximum link utilization and improved computational efficiency.

Immersive visual communication has many important applications and Gaussian Splatting (GS) is a recent breakthrough that uses learnable geometry and color representation to capture 3D world with a very efficient parallelizable rendering pipeline. However, the compression of GS data still lacks efficiency and is quite complex in computation which prevents its adoption and deployment as a streaming solution in the real world. In this work, we develop a lightweight scalable GS coding scheme that exploits the correlation between adjacent quality layers and come up with a lightweight novel prediction and residual coding scheme that creates layered representation and is friendly to the MPEG DASH-like receiver-driven scalable streaming solutions. Simulation results demonstrate the efficiency of the proposed compression solution, as well as low latency/complexity in the decoding and rendering process. To the best of our knowledge, this is the first high-efficiency and low-complexity scalable GS coding solution that can be deployed with the existing MPEG DASH framework.

We consider a rate-splitting multiple access (RSMA) assisted dual-functional integrated sensing and communications (ISAC) system, where the ISAC base station (BS) has the dual capability to simultaneously communicate with downlink users and to probe detection signals to a target. For this system, we focus on the problem of energy efficiency (EE) maximization and propose a new algorithmic framework that aims to optimize the beamforming matrices of RSMA such as to maximize the EE. Our framework is applicable to the optimization of both common and private streams’ beamforming matrices, and it accounts for a variety of constraints which includes power consumption constraint, communication rate constraints and a sensing quality constraint which is expressed with the aid of Cramér-Rao bound (CRB). Finally, the performance of our framework is compared to that of SDMA and NOMA based ISAC, and the superiority of RSMA-ISAC to SDMA-ISAC and NOMA-ISAC is revealed.

Virtual reality (VR) live streaming has established a remarkable transformation of music performances that facilitates a unique interaction between artists and their audiences within a virtual environment, offering an experience that significantly surpasses the conventional constraints of live music events. This article proposes a novel framework for enhancing VR music live streaming through the integration of semantic communication and rate splitting. The framework aims to improve user experience by efficiently transmitting music and speech components. It utilizes a semantic encoder to separately extract semantic information for music and speech, to capture the unique characteristics of music and speech. After having the extracted feature, we propose a rate-splitting-based algorithm in the transmission of music and speech to enhance user utility by designating music as a common message for all users and speech as a private message targeted to specific users based on their preferences. Simulation results demonstrate significant performance gain compared to the baseline methods.

This paper proposes a fast four-parameter Affine Motion Compensation (AMC) algorithm. As shown in Fig. 1, the translation Motion Vector (MV) is derived by reusing the AMC sub-block MV derivation method firstly, which is used to conduct translation pre-transform. Secondly, a coordinate system whose coordinate origin is located on its top-left control point is established for the transformed block. Finally, the geometric relationship between two control point motion vectors (CPMVs) of the transformed block can be described as follows,\begin{equation*}\delta = \left| {\left({m{v_{0x}} - m{v_{1x}}}\right) \times H - \left({m{v_{0y}} + m{v_{1y}}}\right) \times W} \right| = 0\tag{1}\end{equation*}

This paper considers a joint design for communication beamforming matrices and radar waveform in a scenario where a multiple-input multiple-output (MIMO) radar and a MIMO communication system coexist. We propose a joint optimizing method to maximize the sum rate by designing the communication beamforming matrices and the radar waveform. We give consideration to power constraints for both radar and communication system. In order to guarantee the radar performance, we also consider the similarity constraint of the radar waveform. This paper formulates two convex optimization problems and proposes an alternating iterative algorithm. Finally, the simulation results verify that the proposed algorithm can effectively raise the sum rate.

In this paper, we investigate the design of energy-efficient beamforming for an ISAC system, where the transmitted waveform is optimized for joint multi-user communication and target estimation simultaneously. We aim to maximize the system energy efficiency (EE), taking into account the constraints of a maximum transmit power budget, a minimum required signal-to-interference-plus-noise ratio (SINR) for communication, and a maximum tolerable Cramér-Rao bound (CRB) for target estimation. We first consider communication-centric EE maximization. To handle the non-convex fractional objective function, we propose an iterative quadratic-transform-Dinkelbach method, where Schur complement and semi-definite relaxation (SDR) techniques are leveraged to solve the subproblem in each iteration. For the scenarios where sensing is critical, we propose a novel performance metric for characterizing the sensing-centric EE and optimize the metric adopted in the scenario of sensing a point-like target and an extended target. To handle the nonconvexity, we employ the successive convex approximation (SCA) technique to develop an efficient algorithm for approximating the nonconvex problem as a sequence of convex ones. Furthermore, we adopt a Pareto optimization mechanism to articulate the tradeoff between the communication-centric EE and sensing-centric EE. We formulate the search of the Pareto boundary as a constrained optimization problem and propose a computationally efficient algorithm to handle it. Numerical results validate the effectiveness of our proposed algorithms compared with the baseline schemes and the obtained approximate Pareto boundary shows that there is a non-trivial tradeoff between communication-centric EE and sensing-centric EE, where the number of communication users and EE requirements have serious effects on the achievable tradeoff.

In order to maximize the spectral efficiency (SE) in multicarrier-division duplex (MDD) enabled cell-free massive MIMO (CF-mMIMO), a heterogeneous graph neural network (HGNN), referred to as CF-HGNN, is specifically introduced to optimize the power allocation (PA). To efficiently manage the interference invoked, a meta-path based mechanism is applied in CF-HGNN to enable individual access point (AP) and mobile station (MS) nodes to aggregate information from the interfering and communication paths with different priorities during message passing. Moreover, the proposed CF-HGNN employs the adaptive node embedding layer and adaptive output layer to make it scalable to the various numbers of APs, MSs and subcarriers. For comparison, a quadratic transform and successive convex approximation (QT-SCA) algorithm is proposed to solve the PA problem in classic way. Numerical results show that CF-HGNN is capable of achieving 99% of the SE achievable by QT-SCA but using only 10−4 times of its operation time, and it can outperform the conventional learning-based and greedy unfair methods in terms of SE performance. Furthermore, CF-HGNN exhibits good scalability to the CF networks with various numbers of nodes and subcarriers, and also to the large-scale CF networks when assisted by user-centric clustering.

In this paper, we consider the energy-efficient waveform design for integrated sensing and communications systems, simultaneously performing multi-user communications and point-like/extended target sensing. We propose a performance metric to measure sensing-centric energy efficiency (EE) for the first time, namely sensing-centric EE. We formulate a problem to optimize sensing-centric EE with power budget, signal-to-interference-and-noise ratio (SINR) constraints for communication and a Cramér-Rao bound (CRB) constraint for sensing. For the point-like target case, we give the first-order approximations for the non-convex formulations and develop an effective iterative algorithm to handle the nonconvexity. For the extended target case, we show that the considered problem can be relaxed into semidefinite programming and the optimum can be reconstructed. Simulation results demonstrate significant performance gains on sensing-centric EE over the benchmarks.

In this paper, we propose a simplified cubic polynomial R-D model with corresponding rate control methods for Versatile Video Coding (VVC) intra frame coding. First, we explore the rate-distortion (R-D) characteristics of VVC intra coding. By comparing several potential R-D modeling approaches, a new intra coding R-D model has been proposed based on the simplified cubic polynomial function. Subsequently, we derive the corresponding$R-\lambda$model and introduce a complexity measurement to improve the performance of intra frame rate control. Furthermore, we propose a Coding Tree Unit (CTU)-level rate control method based on the newly proposed R-D model and further develop a pre-compression-based approach on this basis. Experimental results show that the proposed method can achieve 1.87% and 0.55% bit rate reduction for All-Intra (AI) and Random-Access (RA) configurations over the original rate control in VVC Test Model (VTM), while the computational complexity increment is negligible. Meanwhile, the enhanced bit rate accuracy from rate control has been observed in the proposed methods.

The third generation of Audio Video Standard(AVS3) has adopted affine prediction(including affine merge mode and affine inter mode), which can characterize non-translational motions such as rotation, zooming, and shearing. Though affine prediction leads to BD-Rate saving, it also results in substantial computational complexity. In this paper, we propose a fast algorithm to address this problem. Some useful features are introduced and used to train five decision tree classifiers to skip unnecessary calculations in affine prediction. Experimental results show that compared to HPM 14.0, the proposed algorithm reduces 19% affine merge time, 18% affine inter time, and 6% encoding time, with negligible coding performance loss under random access(RA) configurations.

In this paper, we propose a sensing-assisted neighbor discovery algorithm that utilizes the sensing capability of radar to improve the efficiency of neighbor discovery for vehicular ad hoc networks (VANETs). To store and manage the sensing information of radar, we design the sensing neighbor list (SNL) by analogy with the communication neighbor list (CNL). For vehicle mobility, we build a vehicle-to-vehicle (V2V) state evolution model and use extended Kalman filtering (EKF) to predict, track, and update the kinematic parameters of nodes, which are stored in the SNL. Specifically, the conversion relationship between CNL and SNL is implemented by the designed SNL based neighbor discovery (SBND) algorithm. Numerical simulation results show that the performance of the proposed algorithm is significant in terms of vehicle tracking and communication overhead reduction.

Semantic segmentation is regarded as an important technology for future communication and sensing networks due to its promising ability to extract features of transmit data. It integrates the functionality of computer vision and can realize high-fidelity transmission in the channel with lower bandwidth. Knowledge Base (KB) is a key component for the semantic segmentation framework. In this paper, we consider the scenario of intelligent transportation and propose a heterogeneous KB to extract the to-be-transmitted features and information at multiple levels, i.e., the raw level, symbol level, feature level and image level. The compressed features are obtained by a deep-learning-based framework. The proposed KB is shared with the transmitter and the receiver, by a service-oriented multi-stream transmission algorithm to meet various requirements of services. Experiments results demonstrate significant performance gain in terms of quality of service and encoding efficiency.

In this paper, we consider an energy efficient waveform design in integrated sensing and communication (ISAC) systems. The transmitted waveform simultaneously serves multiple communication users and estimates the parameters of a moving target. In order to improve its energy efficiency (EE) while guaranteeing target estimation performance, we maximize the EE of the emitted dual-use waveform, under a Cramér-Rao bound (CRB) constraint. However, the considered optimization problem is a fractional function that is highly non-convex. Thus, we firstly adopt fractional programming based on Dinkelbach’ method and then, solve the sub-problem by leveraging semi-definite relaxation (SDR). Numerical results demonstrate superior performance than the benchmark and show the trade-off between EE and CRB.

Person reidentification (re-ID), which is a significant and potential application in the Internet of Things (IoT), aims to retrieve pedestrians of interest given a labeled image in a camera network. Now, it is still existing many challenges that severely influence feature representation in practical scenarios. Many methods adopt the attention mechanism in convolutional neural network (CNN) to improve the ability of feature learning. Although they only apply 1-D attention block in the popular deep learning architecture, the learned features are not discriminative for the feature representation. In this work, we investigate a self-focusing network (SFNet) that considers both the channel-dimensional attention and spatial-dimensional attention to adaptively learn more discriminative features. Namely, we embed the new attention module into the common backbone network, which can focus on the salient region by inhibiting the redundant features. Specifically, we design eight variants of the channel-dimensional attention and spatial-dimensional attention throughout the entire network and explore the most powerful feature representation. The heatmaps of different layers are visualized to intuitively present the performance of SFNet. Furthermore, we compare SFNet with the prior work on three popular person re-ID benchmarks by abundant experiments.

The uncontrollability of the radio frequency (RF) environment is one of the main obstacles hindering the popularization of ambient backscatter communication devices, because the devices need strong signal to maintain the overhead of backscatter circuits and the reflection of the modulated signals. The intelligent reflector (IRS) can improve the radio frequency environment by adjusting the phase and amplitude of the incident signal, which provides the possibility for the widespread deployment of ambient backscatter communication devices. In this paper, we introduce a novel IRS-assisted ambient backscatter communications system (ABCS), in which the signal of ABCS rides on the signal of the primary system. The two systems share the same receiver, and the signals of the two systems can be demodulated separately based on continuous interference cancellation (SIC) technology. The purpose of this paper is to design the beamforming vector and IRS phase shift jointly to minimize the AP's transmit power while ensuring the quality of service of the ABCS and the primary communication system. Due to the non-convex nature of the problem, the time complexity of solving the problem through exhaustive search will be very high. Therefore, we propose an iterative-based beamforming vector and IRS phase shift joint design method to minimize the AP transmit power. This method can effectively reduce the transmission power of the access point, and the simulation results prove the effectiveness of the method.

Person reidentification (re-ID), which aims at recognizing the pedestrians captured by multiple nonoverlapping cameras, has attracted more interest due to its significant and potential application in the Internet of Things like intelligent visual surveillance. However, person reID is still a challenging problem in the situations of various pose, similar appearances, partial occlusion, etc. To handle these obstacles, in this article, we investigate an innovative exciting-inhibition network (EINet) that is a two-branch network composed of the exciting branch and the inhibition branch. The channel-spatial attention block that recalibrates the relationship between channels and highlights features at different spatial positions is used in the exciting branch. A novel Soft Batch DropBlock that randomly selects a continuous region of the intermediate feature maps at the same location is applied in the inhibition branch to inhibit the trivial by an inhibitive mask and reinforce learning the remaining regions. We integrate the comprehensive features from both branches for evaluation and show the performance of EINet intuitively using the visualization method. Abundant experiments demonstrate the state-of-the-art performance by comparing with the previous methods on three popular person re-ID benchmarks. For example, our method obtains 95.64% Rank-1 and 88.75% mean average precision (mAP) on Market-1501, and 77.00% Rank-1 and 74.51% mAP on CUHK03-Detect in the single query mode, respectively.

Ambient backscatter communication (AmBC) is one of the candidate solutions for the 6G green internet of things (IoT) network. However, the uncontrollability of the radio frequency (RF) environment is one of the main obstacles hindering the popularization of AmBC. The intelligent reflective surface (IRS) can improve the radio frequency environment by adjusting the phase and amplitude of the incident signal, which provides the possibility for the widespread deployment of AmBC. Currently, there is no discussion about the joint optimization of AmBC and IRS. In this paper, we introduce a novel IRS and AmBC joint design method. The purpose of this method is to jointly design the beamforming vector, the IRS phase shift, and the reflection coefficient of AmBC to minimize the AP’s transmit power while ensuring the quality of service of the AmBC system and the primary communication system. Due to the nonconvexity of the problem, the time complexity of solving the problem through exhaustive search will be very high. Therefore, we propose a joint design method based on an iterative beamforming vector, IRS phase shift, and reflection coefficient to minimize the AP’s transmit power. This method can effectively reduce the transmission power of the access point (AP), and the simulation results prove the effectiveness of the method.

In recent years, directional antennas or phased array antennas are being widely used in communication systems due to the higher antenna gains. However, without external time synchronization and angle synchronization, the unsynchronized node usually takes a long time to synchronize with the existing nodes due to the narrow beams. Although the multibeam transmission or the digital phased array antenna can reduce this problem, it is clear that the cost of the digital phased array antenna is currently too high. Without external time synchronization and angle synchronization, a cooperative antenna selection method based on directional antennas is proposed in this paper. Our method only uses the narrow beams to transmit and to receive and reduces the time for self‐synchronization. In this paper, we give the expression of the expected average time for the self‐synchronization of multiple nodes, transform the problem into the problem of finding the minimum value of the infinite norm of the sequence, and then propose a cooperative antenna selection method which calculates the optimal transmission probability distribution of the node in different directions through parameter sharing and relative geometric position relationship between nodes. Finally, we verify the proposed method through simulation, and the number of beams is set between 6 and 10. In a typical scenario of five nodes, our method reduces the maximum average self‐synchronization time by 50% averagely, compared with the traditional method which sends the different antenna beams at equal probability.

AVS3 is the newest video coding standard developed by AVS (Audio Video coding Standard) group. AVS3 adopted QTBT(Quad-tree and Binary-tree) plus EQT(Extended quad-tree) block partition scheme, which makes the split process more flexible. The CU split structure is determined by a brute-force rate-distortion optimization (RDO) search. After the whole RDO search, the CU partition with minimum RD cost is selected. The flexible block partition and thorough RDO search bring promising coding gain while extremely complicate the encoder. To reduce the computational complexity of the CU split decision process in AVS3, this paper proposed a spatial information based fast split decision algorithm. In the proposed algorithm, the predicted value of split complexity was calculated firstly according to the information of spatial neighboring blocks. Then the predicted value was used to decide whether to split current CU or not. The experimental results show that the proposed algorithm resulted in average 31.03% encoding time saving with average 0.54% BD-BR loss for Random Access (RA) configuration. The proposed algorithm can greatly reduce the computational complexity of the CU split decision process with negligible performance loss.

Inverse tone mapping(iTM) is an operation to transform low-dynamic-range (LDR) content to high-dynamic-range (HDR) content, which is an effective technique to improve the visual experience. ITM has developed rapidly with deep learning algorithms in recent years. However, the great majority of deep-learning-based iTM methods are aimed at images and ignore the temporal correlations of consecutive frames in videos. In this paper, we propose a multi-scale video iTM network with deformable alignment, which increases time consistency in videos. We first align the input consecutive LDR frames at the feature level by deformable convolutions and then simultaneously use multi-frame information to generate the HDR frame. Additionally, we adopt a multi-scale iTM architecture with a pyramid pooling module, which enables our network to reconstruct details as well as global features. The proposed network achieves better performance compared to other iTM methods on quantitative metrics and gain a significant visual improvement.

This paper considers the downlink ultra-dense cloud radio access network (C-RAN), which employs multiple radio remote head (RRH) cooperation to guarantee the minimum achievable transmission rate for each user equipment (UE). However, due to the limited orthogonal frequency resources, it is difficult to achieve this goal. To maximize the coverage probability of the system, we focus on the joint user-centric clustering and frequency allocation problem. To reduce the computational complexity, this problem is split into two sub-problems: user-centric clustering and frequency allocation. Firstly, we propose a novel binary user-centric clustering strategy, which includes serving clusters and silent clusters. This strategy determines the acceptable combination of serving clusters and silent clusters to guarantee the minimum transmission rate for each UE and simplify the complexity of the subsequent frequency allocation. Then based on the generated clusters, a new graph generation method is proposed. The advantage of this graph is that we can allocate frequency resources by simply judging the relationship between the serving clusters in the graph without complicated calculations. Numerical simulation results show that the joint binary user-centric clustering and location-based frequency allocation scheme is superior to the benchmark solutions in terms of the coverage probability.

With the complexity of the 5 G network environment and the diversified requirements for video transmission, multimedia content transmission in different channel environments is a direction worth studying in order to achieve good performance of digital media content transmission systems in an open network environment. Based on the code stream structure characteristics of screen content in HEVC, this paper has proposed a joint source channel coding (JSCC) scheme to study the transmission of compressed video in wireless channels. Combined with the clustering algorithm from the field of artificial intelligence in the environment of wireless channel classification problem, the parameters of the wireless channel and the influencing factors can be used to reduce the noise and then use the FCM (Fuzzy C-Means) clustering algorithm to classify the channel environment. According to the channel status, optimize the current remaining resources and implement channel coding through LDPC codes. By analyzing and minimizing the end-to-end distortion model, the adaptive bit rate allocation further guarantees the quality of the reconstructed video.

This work focuses on the secure beamforming problem in massive multiple input multiple output (MIMO) system. The optimization problem is modeled by the theory of reinforcement learning (RL). With asymptotic behavior of massive MIMO, detailed theoretical analysis of the proposed RL problem is presented. By policy gradient method we provide solution for the delay-aware large-scale RL problem. The proposed RL structure can dynamically optimize the system performance by observing the state of cache and acquiring feedback from the delay of packet without requiring channel estimation, which can avoid the imperfect channel state information (CSI) issue in massive MIMO system. We conduct numerical experiments by using asynchronous advantage actor critic (A3C) algorithm to solve the proposed RL problem with comparisons to the randomized policy in a time-variant wireless environment. It shows that by using the RL algorithm the delay of system can be reduced without using CSI.

Different from conventional video, screen content video often contains large movements and abrupt changes between adjacent frames. These distinct characteristics bring great challenges to the implementation of rate control in screen content coding (SCC). This paper proposes a novel rate control scheme for SCC considering the delay constraint. First, a pre-analyzer is designed to collect the information of the proceeding frames, which are about to be encoded. Then, after the information collection, more rational bit allocation strategy is adopted to keep the encoder from both buffer overflow and underflow. Furthermore, a delay constraint condition is derived for buffer and pre-analyzer to avoid additional delay. Experimental results demonstrate that the proposed scheme achieves more accurate rate control accuracy and 3.10 dB gain on average when compared with the existing rate control scheme in HM-16.8+SCM-7.0.

This work focuses on the analysis for multicast services in fifth-generation (5G) wireless communication system. We investigate the physical- layer wireless multicast technology in massive multi-input multi-output (MIMO) mutual coupling channel model, and proposed the hybrid unicast/multicast transmission system. The mutual coupling channel model is adopted to describe the channel characteristics under the linear antenna array scenario and the rectangular antenna array scenario. The proposed hybrid transmission scheme adopts multicast beamforming in the multicast groups as well as multi-user MIMO (MU-MIMO) linear precoding in the unicast group to increase system throughput. The null-space method based interference cancellation is further performed between each group to eliminate signal leakage generated from each group. Comparisons between two types of antenna array configurations, different channel models, linear precoding as well as multicast beamforming, and user grouping strategies for multicast services are presented and analyzed by simulation.

Image recognition, especially traffic sign recognition is an important task for autonomous driving and driver assistance systems. A new Convolutional Neural Network model with the ability of feature selection in frequency domain is presented in this paper, called Frequency Selective Filter Aided (FSFA) CNN model. The new model can integrate low-pass and high-pass filters into both forward and backward propagations in order to place special emphases on feature components in different frequency bands. The theoretical basis, as well as forward and backward propagations are also formulated. Experiments on CIFAR and GTSRB traffic sign recognition datasets show that the proposed model yields better performance for the task of image recognition compared with classic methods.

Future heterogeneous networks (HetNets) will have to face a great challenge of overwhelming demand of spectrum resource, due to the exponential increase in mobile internet traffic driven by a new generation of wireless devices. In this paper, we propose a spectrum sensing and scheduling scheme for circular array, in order to make better use of the spectrum resource and improve the performance of multi-user MIMO (MU-MIMO) in HetNets. The proposed scheme can effectively detect the users and frequency use based on angles, and schedule the users with optimized codebook. Simulation results show that our proposed scheme can achieve considerable gain in terms of throughput and users' data rate, with significantly reduced system complexity and increased efficiency.

Heterogeneous network (HetNet) is a promising technology to improve the capacity of future generations of cellular network, in which a mobile station can be served by multiple base stations (BSs) with different scales of coverage range, including short range low power nodes (LPNs). In HetNet, a major challenge is how to provide guaranteed quality-of-service (QoS) for all users. To address this issue, we investigate a practical scenario in which the massive multiple-input multiple-output (MIMO) technology is adopted by the cooperation of one macro-cell BS and several LPNs. Furthermore, we provide a lightweight channel state information (CSI) acquisition scheme for the implementation. Numerical simulation results demonstrate that the signal-to-interference-and noise ratio (SINR) of intended users in LPNs covered with small cells can be significantly increased by this proposed massive MIMO precoding scheme, whereas oppressing the impact on neighboring victim users.

This paper develops a novel scheme of game theory based spectrum sharing to mitigate the inter-cell interference in LTE-A heterogeneous networks (HetNets). In particular, we first assume that the macro-cell protects itself by pricing the bandwidth allocated to small-cells. We then employ the Stackelberg game to jointly consider the utility maximization of macro-cell and small-cells. During game formulation, our scheme takes the advantage of LTE-A standards, such as almost blank sub-frames (ABS) for enhanced inter-cell interference coordination (eICIC) and cell range expansion (CRE) for small cell. Simulation results justify that our approach can significantly improve the throughput of multi-tier cellular network as well as leverage the spectral efficiency of the macro-users in the vicinity of small-cells.

The next generation cellular networks target significant capacity improvements and require dense frequency reuse of the scarce radio spectrum, resulting in a danger of severe interference. Fractional frequency reuse (FFR) techniques have been proposed for macro-cell networks to mitigate interference. However, these static schemes are not adaptive to meet the traffic demand changes from mobile users, and thus inefficient with respect to power consumption in many scenarios. This paper starts with the basic soft fractional frequency reuse (SFFR) framework and proposes a traffic demand orientated green power control scheme that can make power allocation by considering different traffic demands. Afterwards, this green concept is extended to heterogeneous network (HetNet) for the power control strategies in low-power nodes (LPNs). Numerical results show that our proposed schemes can achieve considerable improvement in terms of throughput and power consumption, as well as extra rewards of high flexibility and low complexity.

Screen content usually has much different motion characteristics compared with conventional videos, which makes exiting rate control schemes unsuitable. This paper analyses the motion characteristics of screen content and proposes an efficient rate control method for screen content coding, by improving the bit allocation and model parameter adaptation strategies. The proposed rate control algorithm is able to both control bitrate accurately and improve the quality of the entire video sequence. The experimental results demonstrate that the proposed algorithm achieves smaller bitrate errors and better coding performance. Compared with the existing rate control scheme in High Efficiency Video Coding (HEVC) reference software, the proposed algorithm improves the coding efficiency by 5.6% on average while obtaining smaller bitrate errors.

Emerging screen content coding brings great challenges to rate control due to significantly different characteristics of screen content from conventional video, e.g. large motion, frequent scene changes. This paper proposes an efficient rate control scheme for screen content coding by considering the characteristics of screen content. We first classify pictures of a video sequence into different groups by comparing the current picture with its neighbours. Then for each group, we apply different strategies to bit allocation and parameter updating process based on the characteristics of each group. The proposed algorithm can control the bitrate accurately without introducing any additional encoding delay. Experiment results show that the proposed scheme can significantly improve PSNR with a more accurate bitrate compared with the existing rate control scheme.

Distributed resource management becomes a popular concept in next generation cellular network. It however also results in less information available for each eNB to make appropriate decisions. Signal to Interference plus Noise Ratio (SINR), as one of the important inputs for eNBs when considering allocating appropriate resource blocks (RBs) to User Equipment (UE), is designed as the function of CQI (Channel Quality Indicator) reported from UE. However, too much signaling overhead and estimation error is induced with CQI mechanism. This paper presents an improved downlink SINR estimation method together with a corresponding resource allocation scheme for Low Power Nodes (LPNs) in Heterogeneous Networks (HetNets). Cognitive Radio (CR) is introduced to construct a distributed adaptive channel evaluation model. The work makes use of the Frequency Domain Packet Scheduling (FDPS) implementation in resource block level without CQI report from UEs. Simulation results show that the proposed scheme can achieve considerable system performance improvement in OFDMA based HetNets.

To overcome the challenge of large data demanding in future 5G cellular networks, heterogeneous networks (HetNets) take advantage of low power nodes (LPNs) to enhance capacity and coverage. This paper aims at 5G HetNets and presents a novel scheme of adaptive self-organization network (SON) by integrating cognitive radio (CR) with inter-cell interference coordination (ICIC). Particularly, we combine the spectrum sensing function from CR and the radio resource layering function from ICIC. Our work addresses the issues of smart low-power node (SLPN) development, which associates appropriate sectorization with radio resource allocation during the self-organization process. We further develop a Hungarian algorithm based self-organization strategy to improve the SLPN adaptive optimization. Simulation results show that our proposed scheme can achieve considerable gain in terms of throughput and coverage, with extra rewards of high flexibility and low complexity in HetNet SON.

Abstract Multiple‐input multiple‐output orthogonal frequency division multiplexing has become an attractive air‐interface solution for the next generation wireless networks because of its high spectrum efficiency. This paper addresses the security concern and proposes to achieve covert orthogonal frequency division multiplexing transmission using cyclic delay diversity featured multiple‐input multiple‐output technology. Particularly, our physical layer security scheme takes the advantage of cyclic delay diversity formed periodical frequency selective channel and utilizes uneven comb pilots to confuse unauthorized receivers and benefit authorized receivers. We conduct simulation study to evaluate the impact of different cyclic delay, antenna number, and interpolation algorithms on our scheme. Numerical results show that our scheme can provide authorized users with significant advantage over eavesdroppers without complicated upper‐layer encryption and decryption. Moreover, the scheme has flexible choice of parameters and thus can be easily deployed in a variety of wireless networks with different requirements. Copyright © 2014 John Wiley & Sons, Ltd.

An efficient model-based bit allocation algorithm, in this paper, is proposed for quality scalability coding of H.264/scalable video coding (SVC). The conventional Rate-Distortion models are not available for quality scalability coding of H.264/SVC. To overcome this issue, the relationship between the percentage of header bits and quantization parameter is investigated to obtain an accurate single layer rate model. Moreover, MGS/CGS inter-layer MAD affine relationship is employed to extend the Rate-Distortion models from the base layer (BL) to the enhancement layer (EL). Finally the Lagrange solution of the bit allocation problem is worked out. Experimental results show that the proposed bit allocation algorithm outperforms Joint Scalable Video Model (JSVM) software algorithm.

This paper presents a novel scheme of adaptive self-organization network (SON) by integrating cognitive radio (CR) with inter-cell interference coordination (ICIC) for LTE-A heterogeneous networks (HetNets). Particularly, we take advantage of the spectrum sensing function from CR and the radio resource layering function from ICIC. Our work addresses the issues of smart low-power node (SLPN) development, which associates appropriate sectorization with radio resource allocation during the self-organization process. We further develop a Hungary algorithm based self-organization strategy to improve the SLPN adaptive optimization. Simulation results show that our proposed scheme can achieve considerable gain in terms of throughput and coverage, with extra rewards of high flexibility and low complexity in HetNet SON.

Gabor filters are one of the most successful methods for face recognition. However they dramatically increase the data volume for face representation. To extract compact and distinctive information, we propose the Variable Length Dominant Gabor Local Binary Pattern (VLD-GLBP) for face recognition. It significantly reduces the face representation data volume whereas the performance is comparable to that of the complex state-of-the-art techniques. Specifically, local binary pattern (LBP) features are first computed from the Gabor images. Then, the most frequently occurred patterns are extracted to form VLD-GLBP. Finally the distance between VLD-GLBPs is computed to realize the face image classification. The experiment results on FERET database verify the efficiency of the proposed VLD-GLBP method.

Orthogonal Frequency Division Multiplexing (OFDM) has become an increasingly common technique in communication systems and raised tremendous security concerns recently. This paper proposes to achieve covert OFDM transmission with cyclic delay diversity (CDD) featured multiple input multiple output (MIMO) technology. Particularly, our physical layer security scheme takes the advantage of CDD formed frequency selective channel and addresses uneven comb pilots to confuse unauthorized receivers and benefit authorized receivers. Numerical results justify that, with our design, the CDD paradigm can efficiently safeguard OFDM system, and the performance can be further optimized by fine tuning channel estimation for authorized users.

In this paper, an improved method for reconstruction of doubly selective wireless channels in piloted-aided OFDM systems based an existing estimation method is proposed. In this re-expansion channel estimation process, the first few Fourier coefficients of each channel tap are estimated from the pilot information and the received signal firstly. Then the channel taps are estimated in the framework of Basis Expansion Model (BEM) from their respective Fourier coefficients. In the process of recovering BEM coefficients, instead of using the inverse method which is a Least Square (LS) problem, this paper proposes an improved method of recovering BEM coefficients from the estimated Fourier coefficients based on the Minimum Mean Square Error (MMSE) criterion. The proposed method is validated by simulating a system conforming to the IEEE 802.16e standard. Numerical results illustrate the performance gains achieved by the improved method.

Video multicast over wireless networks suffers from both heterogeneous packet loss resulting from different channel conditions and user capacity heterogeneity in screen resolution and mobile device battery life. To solve resource scheduling problem in video multicasting in heterogeneous network, an Asymmetric Nash Bargaining Game model in layered hybrid FEC/ARQ for scalable video multicast is proposed in this paper. The scheme is applied in multicast server for each time slot, and the server will play the bargaining game for all users according to their real-time channel conditions and device capacities. By solving the bargaining problem, the server achieves to provide fair and efficient multicast utility for each user. Moreover, a formula of bargaining power is proposed in the asymmetric game model to adjust resource allocation according to system bias and user priority.

A new detection method based on sphere decoding (SD) is proposed in this paper to approach near-maximum likelihood (ML) performance for multi-input multi- output (MIMO) detection. The feature of the proposed method is that the complexity which means the electric power consumption in detection processing is stable for a wide range of signal-to-noise ratios (SNRs) and a number of antennas. We hold the complexity by a tree pruning mechanism which obtains detection radius through the close-form expression for the SD expected complexity R.Gowaikar and B.Hassibi, 2007. And the Increasing Radii Algorithm (IRA) mechanism is used in the method to reduce the SER at the stable expected complexity. The simulation results show that the method gets a low stable expected complexity without sacrificing much in terms of performance.

In this paper, we propose a pilot-aided channel estimation scheme for Orthogonal Frequency-Division Multiplexing (OFDM) systems where channels are assumed to be both time-varying and sparse. Basis Expansion Models (BEM) are often used to model and reconstruct time-varying channel taps. In this paper, the framework of BEM is applied to OFDM systems with time-varying sparse channels. A new method to detect the positions of significant taps is proposed based on the use of Constant Amplitude Zero Auto Correlation (CAZAC) sequence. Based on the results of detection, BEM based estimation is implemented to estimate the detected taps. The numerical simulations illustrate that the proposed two-step estimation scheme for significant channel taps outperforms direct estimation methods for all the channel taps and also this method can reduce the required pilots and thus reduce the computational load and improve the spectral efficiency.

Sphere detection (SD) can significantly reduce the complexity of multiple-input multiple-output (MIMO) maximum-likelihood (ML) detection, but the complexity of SD is still too high to be applied to the practical system. In order to reduce the complexity of SD, two novel reordering methods based minimum mean square error (MMSE) detection are presented in this paper. Rule-R is based on the reliability of the MMSE detector's decision variable which depends on the magnitude and the SINR of the decision variable. The other method, Rule-M, is a simplified version of Rule-R and only depends on the magnitude of the decision variable. We show that both of them can significantly reduce the complexity with few additional computations. For original SD, both the rules cause no performance degradation. While if some other suboptimal SD were used, the proposed algorithms can both provide computational efficiency and performance improvement.

Orthogonal Frequency Division Multiplexing (OFDM) is a promising technique for high speed data transmission over multipath fading channels. In an MIMO-OFDM system, cyclic delay diversity (CDD) serves as a simple and elegant solution to exploit transmit diversity. This paper investigates the uneven comb pilots based channel estimation for CDD-OFDM system with periodical frequency selective channel. Our study reveals that uneven comb pilots outperform their even counterpart in complicated channel conditions, such as adaptive CDD, where cyclic delay parameters may change from time to time. Furthermore, we identify the interpolation as a key problem in uneven-pilot based OFDM system and study several scattered data interpolation algorithms. Simulation results show that radial basis function (RBF) interpolation has the best tradeoff in terms of accuracy and computational complexity.

Tree pruning can significantly reduce the complexity of sphere decoding (SD). How to determine the pruning rule is an open problem of tree pruning. In this paper, we propose a pruning strategy for SD based on path metric. Because only the nearest lattice point is concerned, if the ratio of the metric to the minimum metric is larger than a threshold, the path whose metric is large enough can be pruned. We analyze the influence of the choice of the thresholds on the performance and the complexity. Through analysis and the simulations, we can show that the complexity reduction is significant while maintaining the negligible performance degradation when proper thresholds are chosen. Besides, tradeoff between complexity and performance can be easily achieved by adjusting the thresholds.

IEEE 802.16 is a promising technology in broadband wireless access area. In multiservice IEEE 802.16 network, connection admission control (CAC) plays a critical role for resource management and QoS guarantee. In this paper, a statistical CAC mechanism is proposed for IEEE 802.16 network. In order to avoid the QoS degradation, the proposed CAC mechanism considers the traffic variability and overflow. Furthermore, a model of traffic and air interface capacity is provided to make the CAC mechanism easy to be implemented. Then, a performance analysis model based on Markov chains is proposed. Numerical and simulation results show that the proposed CAC mechanism can prevent the traffic overflow and achieve a good packet level QoS.