Critical nodes in complex networks are of fundamental importance, as they play essential roles in maintaining the network’s functionality, performance, stability, robustness, and dynamic behavior. Identifying critical nodes in complex networks is a significant research topic with broad applications across diverse domains, including biology, sociology, transportation, information science, computer networks, and communication systems. Most existing algorithms for identifying critical nodes rely on predefined mathematical models or heuristic rules to determine target strategies. However, these manually designed, rule-based strategies are constrained by fixed-dimensional representations, which significantly limit their ability to extract meaningful features. To address this issue, this paper proposes a framework named GCRA: Graph Contrastive Reinforcement learning based on Adaptive augmentation. The framework introduces an adaptive graph contrastive learning algorithm that effectively captures both the structural topology and node attributes, thereby representing the overall state of nodes and the network. Subsequently, reinforcement learning is integrated to identify critical nodes by gradually disintegrating the network. The model, trained on randomly generated BA networks, can be directly applied to real-world networks. In experiments, the algorithm is evaluated on synthetic networks of varying scales and real-world networks from diverse domains, and compared against eight benchmark methods. The proposed GCRA achieves performance improvements of 4.91% (unweighted networks) and 14.26% (weighted networks) in critical node identification, demonstrating its effectiveness for complex network analysis.

Monocular 3D human pose estimation based on Transformer has recently attracted significant attention. Intuitively, general 3D human pose estimation simultaneously learns the camera coordinates in three dimensions. However, this approach highly couples two different tasks: (1) the scale estimation from 2D pixel to 2D camera coordinates in xy-axes; and (2) the depth estimation from 2D pixel coordinates to relative root depth in the z-axis. This results in the inability to adjust flexibly for different tasks, let alone achieve the best for each task. To this end, we propose a "Divide and Conquer" model framework, which decomposes the relevant learning tasks into the scale and depth estimation task. This way, we can train the two tasks separately to optimize both. The decoupled structure supports improving modules for a specific task to get a boost, such as adding masks to the scale learning task to enhance generalization and prevent overfitting, but without considering the negative impact on another. To verify the effectiveness of the proposed scheme, we conducted extensive experiments on two popular benchmark datasets, Human3.6M and MPI-INF-3DHP, achieving SOTA and reducing the number of parameters by 37.5%. The code is available at https://github.com/Mitanies/DCFormer.

Currently, transformer-based models have achieved state-of-the-art performance in 3D human pose estimation. However, they consistently need to work on balancing accuracy and training costs. Existing models often sacrifice the integrity of receptive fields to avoid the quadratic growth in complexity when computing the affinity matrix based on the number of joints and sequence length. In this paper, we propose an axis-based embedding module reorganizing the pose sequence’s joints into global feature vectors. Then, we employ frequency-domain representations with parallel Spatial-and Temporal-Frequency Attentions for feature extraction, aiming to compensate for the missing receptive field and improve global feature extraction efficiency without significantly increasing complexity. To validate the effectiveness of this approach, we conducted extensive experiments on two popular benchmark datasets, Human3.6M and MPI-INF-3DHP, achieving SOTA while reducing parameters by 60%. Code is available at https://github.com/Mitanies/3DGlobalFormer.

In this work, we decouple calibrated dual-view 3D human pose estimation (HPE) into the well-studied problems of 2D pose estimation, and 2D-to-3D pose lifting, focusing on the latter task. The key challenges stem from: 1) 2D pose is noisy and unreliable due to occlusion and motion blur, and 2) the trained model cannot generalize well to unseen camera configurations. To overcome these limitations, we propose three interconnected innovations: First, a Normalized Triangulation that transforms the 2D pose from pixel space to 3D normalized rays, which makes our approach robust to the camera parameters change. Second, a hybrid neural-geometry framework (i.e., including refinement and triangulation) that explicitly incorporates multi-view geometry into our models. Third, an analytical inverse kinematics (AnalyIK) solver that decomposes articulated motion with human topology, which simultaneously considers symmetry constraint and joint angle limit. Experiments show that the proposed framework achieves state-of-the-art performance on two widely used benchmarks (i.e., Huamn3.6M and HumanEva-I). Code is available at: https://github.com/Z-Z-J/Normalized-Triangulation.

Network key nodes are very important in a network, after identifying them, network optimization can be carried out for these nodes. For example, increasing bandwidth or resource allocation, enhancing fault tolerance, protecting and strengthening key nodes. Or destroy key nodes to quickly break down the network, just like spreading rumors and preventing diseases. Different network optimization measures may vary in different network scenarios, but the core is to enhance or break key nodes, thereby affecting the overall network performance and effectiveness. However, most current network key node identification is based on fixed mathematical models or formulas, limiting the possibility of deep information mining in the network. To address this issue, this paper proposes a deep learning network architecture based on resource allocation to identify key nodes in the network. This allows for more in-depth feature extraction of network semantics and structures while combining the resource allocation situation of each node. In the process of gradually disintegrating the network through deep reinforcement learning, finding the strategy with the fastest decrease in cumulative connectivity rate is considered optimal. Finally, this paper compares the performance of six classical algorithms and the optimal algorithm on six real networks. The effectiveness improvement is 8.11% without sacrificing performance, validating the effectiveness of the method.

Existing multi-view 3D human pose estimation methods heavily rely on precise extrinsic calibration, which significantly restricts their practical deployment in uncontrolled environments. To address this limitation, we propose an Extrinsic Parameter-free Multi-view 3D Human Skeleton Estimation (EFMK) framework with three technical contributions. First, a Local-Global Pose Embedding scheme is proposed to simultaneously capture the fine-grained joint dependencies while establishing cross-view correspondences. Second, a Spatial-View Joint Transformer architecture is developed with three dedicated components: (1) Feature Transformation Modulation generates adaptive modulation vectors for distinct tokens to model heterogeneous relationship patterns; (2) Prior Knowledge Enhancement systematically integrates human kinematic constraints and multi-view geometric priors into attention computation through structural topology encoding; (3) Spatial-View Joint Attention implements decoupled spatial-view attention computation followed by joint distribution modeling to capture hierarchical spatial-view dependencies. Third, a Bone-wise Reprojection-based Multi-view Aggregation mechanism is introduced to consolidate multiple 3D outputs into a single, higher-quality 3D pose for practical applications. Extensive experiments on three benchmarks demonstrate that our method achieves state-of-the-art performance while maintaining a compact model size. Code and results are available at https://github.com/Z-Z-J/EFMK.

Identifying and safeguarding key nodes is crucial for maintaining the reliability and stability of business functions. Currently, most key node identification algorithms are based on manual or deductive models, which are complex and suboptimal. While deep reinforcement learning algorithms have demonstrated promising practical results, they often exhibit excessive randomness in network feature extraction. To address this challenge, this paper proposes a deep reinforcement learning framework based on multi attributes attention mechanism. This framework extends node attributes to encompass general business scenarios and employs graph convolutional networks combined with an attention mechanism to learn adaptive weights for different attributes of various nodes. Subsequently, reinforcement learning is utilized to determine the sequence of key nodes in the network. This algorithm is compared with six algorithms across six types of networks, consistently achieving optimal results, thereby validating the effectiveness of the proposed approach.

Unmanned aerial vehicles (UAVs) have emerged as a promising solution to support ground networks. In UAV-enabled wireless powered communication networks (WPCNs), the UAV provides data collection (DC) and energy transmission (ET) services for ground energy harvesting (EH) IoT devices. In view of the limited working efficiency of a single UAV, we propose a multi-UAV-enabled WPCN system. In order to further improve the efficiency of the system, we jointly optimize the data transmission performance of the network and the average harvested energy of IoT devices by designing the trajectories of the UAVs. The high mobility of UAVs causes the rapid change of channels, which leads to limitations of the centralized management scheme. Therefore, the problem is modeled as a multi-agent deep reinforcement learning (MADRL) problem. The scenario of multiple UAVs working collaboratively is formulated as a decentralized partially observable Markov decision process (Dec-POMDP). We develop a multi-agent deep deterministic policy gradient (MADDPG)-based approach to design UAVs' trajectories by adjusting the flying velocity and direction. Simulation results prove that the proposed algorithm far exceeds the benchmark schemes.

This paper addresses high-resolution vehicle positioning and tracking. In recent work, it was shown that a fleet of independent but neighboring vehicles can cooperate for the task of localization by capitalizing on the existence of common surrounding reflectors, using the concept of Team Channel-SLAM. This approach exploits an initial (e.g. GPS-based) vehicle position information and allows subsequent tracking of vehicles by exploiting the shared nature of virtual transmitters associated to the reflecting surfaces. In this paper, we show that the localization can be greatly enhanced by joint sensing and mapping of reflecting surfaces. To this end, we propose a combined approach coined Team Channel-SLAM Evolution (TCSE) which exploits the intertwined relation between (i) the position of virtual transmitters, (ii) the shape of reflecting surfaces, and (iii) the paths described by the radio propagation rays, in order to achieve high-resolution vehicle localization. Overall, TCSE yields a complete picture of the trajectories followed by dominant paths together with a mapping of reflecting surfaces. While joint localization and mapping is a well researched topic within robotics using inputs such as radar and vision, this paper is first to demonstrate such an approach within mobile networking framework based on radio data.

Abstract With the development of the internet of things, people pay more and more attention to wireless sensor networks. Designing the energy efficient routing is an essential objective for wireless sensor networks. Cluster routing is one of the most popular routing protocols to enhance the network lifetime. However, hotspot problem always exists in cluster-based routing protocol. The task of this study is designing a cluster routing protocol with mobile base station which aims at balancing the energy consumption and prolonging the network lifetime. In this article, we design a particle swarm optimization and artificial bee colony algorithm for clustering and mobile based software-defined wireless sensor networks. The software defined network architecture is used to reduce the energy overhead and computation overhead in sensor nodes. Particle swarm optimization-based cluster routing algorithm is used to calculate the cluster heads and the sojourn locations of base station. Artificial bee colony algorithm-based traversal path algorithm is used to design the move path of the base station. Comparing with relevant protocols, the proposed protocol reduces the energy consumption, enhances the network lifetime and reduces the control overhead.

Recently, wireless sensor networks play an important role in our life. Cluster routing has gained more attention in wireless sensor networks. However, hotpot problem always exists. One way to solve this problem is the unequal cluster routing. In most of the unequal routing protocols, nodes which closer to base station have the smaller cluster size than others. It will reduce the relay pressure of the node which near to the base station. In this paper, we propose the fuzzy-based multi-hop unequal cluster routing. In the cluster head election phase, relative inter cluster cost and relative intra cluster cost are proposed innovatively. Fuzzy system is used for unequal clustering which reduces the energy consumption for cluster members. In the cluster formation phase, a novel probability mechanism is proposed to let the cluster members decide which cluster to join. In the multi-hop routing phase, relative relay cluster cost is proposed innovatively for inter cluster routing. Two factors are considered for multi-hop routing which reduces the energy consumption for cluster heads. Self-adaptive rotation mechanism is proposed in the data transmission phase. It reduces the frequency for re-clustering self-adaptively which reduces the control overhead of the entire network. According to the simulation results, the proposed protocol balances and reduces the energy consumption as well as extends the network lifetime of the whole network.

The Content-Centric Network (CCN) is a very important structure in the future network, in which every node has caching ability. Caching strategy has a decisive influence on the performance of the CCN. In this paper, an Autonomous System Collaboration Caching Strategy (ASCCS) is proposed to achieve the explicit collaboration, reduce the cache redundancy, improve the cache utilization rate and increase the cache hit rate. Firstly, the network is divided into several Autonomous Systems (AS) before carrying out the centralized control. Then we select the control nodes according to the betweenness and the cache replacement rate of the nodes. Each AS is centralized controlled by the control node. At the same time, the selected control node computes the popularity statistics of different contents to determine the suitable cache policy for them. Under this strategy, nodes in the same AS cooperate with each other, which improves the transparency of the network cache, and thereby achieve the cache performance gain of the CCN. It makes the optimal performance of the cache become possible.

Content-centric Networking (CCN) is considered as a promising architecture to achieve reliable content distribution at large scale. One of the key research items of CCN is cache strategy, and most of the existing approaches consider little of the dynamicity of user interests. In this paper, we present a new cache policy, named as the betweenness and content popularity prediction (BEACON). Betweenness measures the importance of nodes in the whole network, and content popularity represents the user preference for service contents. By taking into account both network topology characteristics and flow distribution, the load of network and server is optimized. Moreover, we use the gray model to predict the content popularity, tracking the trend of user interest. The simulation results demonstrate that the BEACON scheme can effectively improve the cache hit rate, shorten access distance and reduce the delay of transmission.

One important feature of Content-centric Networking (CCN) is to utilize built-in network caches to improve the transmission efficiency of content dissemination. In CCN, all nodes have the function of caching contents. Consequently, caches are ubiquitous and diverse. How to make full use of these caches to reduce the network load and the transmission delay has become a hot research topic in recent years. In order to speed up content distribution and improve network resource utilization, a cooperative caching strategy called Tracker-based Cache Utilization Strategy (TBCUS) is proposed in this paper. In TBCUS, an idea of partial centralized management of the caches is introduced into traditional CCN. Moreover, in order to alleviate the pressure that the incurred overhead imposes on network bandwidth, TBCUS divides the whole caching system into several sub-systems. Each of the sub-systems has a tracker server as the controller and some peers. The contents cached in a sub-system are transparent to each other through the coordination of the tracker server. Once a content request reaches to a sub-system, all the caches in the sub-system can be selected to respond to this request. While the traditional caching strategy in CCN is that only the caches on the delivery path can be used to respond to the content request. TBCUS improves the cache utilization and has higher cache hit ratio and lower transmission delay and network load compared with the traditional caching strategy.

Mobile Ad hoc Network (MANET) is a collection of self-organizing wireless nodes without any fixed infrastructure and centralized administration. This article presents an investigation with an objective to compare the performance of two typical routing protocols, AODV and DSR, in real multi-hop environment. Apart from testing the end-to-end packet loss, delay and routing path parameters, we also assess the performance of AODV and DSR in terms of some applications based on Internet of Things (IOT), such as Radio Frequency Identification (RFID) service, voice service and temperature monitoring service. Two scenes are considered in this work: indoor pre-selected multi-hop scene with Media Access Control (MAC) filtering and outdoor self- organizing multi-hop scene with changeable and complicated wireless links. From the test results, it can be concluded that DSR has a better performance indoors while AODV can better adapt to outdoor complex electromagnetic environment. Additionally, the existing problems found in practical test are analyzed and the possible directions of future improvements in Ad hoc network test-bed are discussed.

Content-centric Networking (CCN) is a new Internet architecture, relies on in-network caching to achieve an efficient and reliable content distribution. However, the existing forwarding strategy cannot fully sense the location of cache, because it is rely on the ports of routing table. In this paper, we propose a Bidirectional Cache Locating Algorithm based on Ant Colony Optimization (BCLA) using bidirectional ants to diffuse and exploit multiple content copies. One direction of ant called Announcing Ant is responsible for advertising the information of cached content to neighbors in a limited area, the other side, including Probing Ant and Updating Ant, are responsible for seeking cached content for the routers outside the area. Our goal is to find the optimal cache, and making efficient utilization of available cache resources within a certain area. Extensive experiments have been carried to evaluate BCLA against other forwarding strategies. The simulation results prove that BCLA outperforms other strategies in the content delivery latency, cache hit ratio, as well as the path stretch.

This paper investigates the heterogeneity of delay-tolerate networks (DTNs) by considering that among regular nodes some infostations are equipped with large-capacity buffer and connected by high-speed links to form an infostation system. We analytically model epidemic routing behavior of such DTNs using extended Susceptible-Infectious-Recovered model. Specifically, closed expressions for the number of infectious nodes with varying time, Cumulative Distribution Function (CDF) of packet delivery delay and CDF of infostations infected time are derived. Based on extensive numerical results, the impact of transmission range and inforstation number on the network performance is revealed.

The opportunistic routing is popularly employed in the wireless multi-hop network. To obtain higher delivery rate and longer lifetime of network, we propose an adaptive energy-based updating opportunistic routing (AEUOR). We firstly introduce a novel priority metric of the routing selection in order to guarantee delivery rate. Then, an adaptive update algorithm is realized by efficiently detecting the dump energy of the node. In addition, we discusse the candidates including the primary candidate and the secondary candidate in the paper, the latter of which plays a key role in extending the lifetime of the network. Moreover, we test AEUOR and compare it with ExOR in NS2.34, and observe significant performance improvement.

Content-centric networking represents a promising architectural direction to develop effective and efficient solutions for ad hoc networks, in which communications are driven by contents instead of host addresses. In this paper, we propose Best Route, Error Broadcast (BREB), a content-centric forwarding protocol for MANETs. BREB lays emphasis on the optimal use of available routes to address high overhead of signaling and maintain reliability of the communication process. A simple retransmission count based algorithm is introduced for the dynamic routing adjustment. Performance comparison against LFBL demonstrates the superiority of BREB.

Long Term Evolution (LTE) is commonly deemed to be a promising system for the future High-Speed Railway (HSR). The object of this paper is modeling of the broadband multipleinput multiple-output (MIMO) train-ground channel in LTE system based on the measurements on HSR viaducts. A couple of measurement campaigns are conducted along the Harbin-Dalian passenger dedicated line. The statistical distributions of some critical large and small scale parameters are firstly presented including delay spread, angle spread at transceivers and K factor. Then the channel model is proposed following a geometry-based stochastic modeling approach and the channel transfer matrix is derived to depict the time-varying environment.In particular, the spatial cross-correlation function (CCF) between subchannels,power delay profile (PDP) and the Doppler spectrum are simulated and analyzed to validate the practicality of our model. All these results significantly promote the evaluation and development of the HSR communication system based on LTE.

Content-Centric Networking (CCN) focuses on content acquisition rather than end-to-end communication by decoupling content resources from special hosts. CCN does not need maintain stable paths between nodes and is independent of IP addresses, which make CCN convenient and efficient for mobile ad hoc network (MANET). In this paper, we develop an analytical model of content acquisition for Content-Centric MANET (CCM) in two-dimensional case, which considers in-network caching and nodes mobility. Closed-form expressions for round trip time and throughput are derived as functions of miss probabilities. From the expressions, we can obtain the relationship among the round trip time, cache size and content popularity. Simulations show that our model can be used as a guide to design CCM and to evaluate its performance.

With the rapid development of high speed railway (HSR), propagation characteristics of channels in HSR scenarios are therefore in urgent demand. We conducted numerous single input single output (SISO) measurements at 2.6 GHz with a bandwidth of 20 MHz along the Harbin-Dalian passenger dedicated railway line. Here, first analytical results in hilly terrains are provided. A double-slope path loss model fits measured data well and shadow fading is extracted to be log-normal distributed. Statistical results of small-scale fading are presented and compared in near regions and far regions relative to the transmitter, including the mean excess delay, root-mean-square (RMS) delay spread and the number of paths. Meanwhile, the delay Doppler spectrum is given out and verified. Finally, tapped-delay-line (TDL) channel model is established in detail based on the measured data. It is supposed that these results and models have a promotion for the further evaluation, simulation and design of the wireless communication system in HSR.

By utilizing the multi-channel technology and small world properties, the average path length of a wireless network can be reduced drastically, leading to a significant improvement of network performance. It has been proven that, by upgrading a fraction of nodes into the high-level nodes with multi interfaces and higher transmission powers, small world effect can be introduced owing to the shortcuts created by the high-level nodes. However, it is difficult to control the small world properties in wireless networks by adjusting the rewiring probability. This work, in particular, investigates the relationship between the network parameters and the equivalent rewiring probability by building an analytical model. Two parameters, communication range multiplier γ and upgrading ratio ρ, are defined to describe this relationship concisely. Simulation results indicate that when γ and ρ are within the optimal ranges, compared with single interface, performance of the double-channel wireless network can be improved by 90%, without introducing any additional mechanisms.

This paper presents a relay routing method for Radio and TV services, this method through obtaining node’s timedelay and power information, obtains the value of system interrupt decisions, and as a decision threshold to select relay node. While in consideration of link priorities and fairness, we design a relay routing protocol that can dynamically change the route when network is changed. Simulation results show that this protocol can expand coverage, reduce communication blind spots, increase system throughput and enhance the quality of service.

Recently, many multi-channel protocols and architectures have been proposed to exploit the non-overlapping channels, especially in MANETs and Mesh networks. With the development of modulation and encoding technology, the diversity of data transmitting rate could lead an increment of discrepancy between channels. The contribution of this paper is threefold. First, when allocating channels with quality discrepancy, whether the competition on the high-quality channel should be avoided depends on the degree of the discrepancy. A channel competition model is built to quantify the discrepancy. Second, by modeling the channel allocation problem as a game, we found that when the relatively poor-quality channels are good enough to share the other channels burden, a mixed Nash Equilibrium is needed to improve the fairness of allocation mechanism. Third, a mixed strategy-based channel allocation mechanism (MSCA) is proposed to improve the overall performance by introducing randomcity into channel allocation. Simulation results on NS2 show that, by allowing the users to adaptively changing their strategy, MSCA can improve the throughput by 10%-15% and reduce the end-to-end delay by 20%-30%.

For multiuser multiple-input multiple-output (MU-MIMO) systems with limited feedback in the downlink scenario, an end-to-end robust Tomlinson-Harashima Precoding (THP) transceiver design that incorporates receiver combining and transmit precoder is proposed based on sum mean squared error (SMSE) minimization. The channel state information (CSI) available at the transmitter for precoding is the quantized channel direction information (CDI) relayed back from each receiver using feedback bits in such system. The proposed transceiver design method is robust to the channel uncertainties arising from the quantization error and the lack of channel magnitude information (CMI). As an aside, the authors analyse the SMSE performance of the proposed algorithm. Our simulation results show that the new THP scheme outperforms the conventional precoding in limited feedback systems with respect to Bit Error Ratio (BER).

Distributed cooperation among the forwarding candidates is the main challenge of opportunistic routing design for wireless multi-hop networks. Timer-based coordination is an efficient way to solve this problem. However this method leads to unnecessary duplicate transmission problem which will degrade the system performance. In the scenario where nodes are nonadjacent, the duplicate transmission problem gets even worse. To avoid the useless retransmission, this paper proposes a pure network layer scheme called Dual Priority Cooperative Opportunistic Routing Scheme (DPCOR). DPCOR exploits the global network information to complete the network layer confirmation. The nodes with lower global priority receive a duplicate packet will not respond again. Furthermore, it combines the local link quality variation and adjacent information to classify the forwarding candidates into three levels with local priority. As the packets are broadcasted at different time according to the local priority, the scheme can get more time diversity, which can avoid collision and improve performance effectively. We test DPCOR by using the simulation in various network scenarios. The results show that DPCOR achieve significant performance improvement with less transmission cost compared to the traditional routing and typical opportunistic routing.

The increasing interest that has been drawn to LTE system used in High-Speed Railway therefore requires measurement and analysis of broadband mobile channel for train-ground communications. By the approval of the Ministry of Railways of China, extensive and practical measurements are performed on Harbin- Dalian passenger dedicated line with the maximum running speed of 370 km/h. Based on the hierarchical two-hop network structure, this paper first presents comprehensive analyzed results of channel characteristics in viaduct scenario, including both the large-scale and small-scale fading parameters. The proposed path loss model benefits LTE system link budget and feasible transceiver-range determination. In particular, channel impulse response, power delay profile, the delay spread, the number of paths and delay-Doppler spectrum are extracted, evaluated and reported. All these informative results promote the evaluation and verification of broadband communication system on High-Speed Railway.

The increasing interest that has been drawn to the LTE system used in High-Speed Railway therefore requires measurements and analysis of wideband mobile channel for train-ground communications. Based on the hierarchical two-hop network structure, extensive and practical measurements are performed on Harbin-Dalian passenger dedicated line with a speed up to 370 km/h by the approval of the Ministry of Railways of China. This paper first presents comprehensive analyzed results of the statistical channel properties, including both the large-scale and small-scale fading parameters. The proposed path loss model is applicable to high-speed railway plain scenarios and benefits the link budget of the LTE system. In particular, channel impulse response, power delay profile, the delay spread, the number of paths and delay-Doppler spectrum are extracted, evaluated and reported. All these results promote the evaluation and verification of wireless wideband communications on the high-peed railway.

Channel estimation plays a vital role in coherent detection and demodulation in orthogonal frequency division multiplexing (OFDM) systems. However, the channel timevariation leads to tremendous unknown parameters to estimate and detrimental inter-carrier interference (ICI) for pilot-aided channel estimation, degrading the system performance significantly. This paper proposes a joint estimation of the highly mobile channel with dual-ICI cancellation for OFDM systems in the absence of prior channel information. First, the linear time-varying (LTV) model is employed to get an initial estimation and leaves the rest channel component with much less ICI to the next estimation in generalized complex exponential basis expansion model (GCEBEM). In the second stage, the output of estimation in GCEBEM is fed back to LTV model to mitigate the ICI compact on the pilots for another iteration. Besides the dual-ICI cancellation, the spectral leakage of GCEBEM is also alleviated. Numerical results verify the priority of the proposed joint estimation over the existing estimations.

Time-varying channel gains are approximated by basis expansion model (BEM) to make the tremendous unknowns estimation feasible in orthogonal frequency division multiplexing (OFDM) systems. However, sparse dispersive channel requires overloaded subcarriers for pilot-aided estimation in the framework of BEM. This paper proposes a channel estimation scheme over time-varying and sparse dispersive channel with circular grouped pilot pattern, which occupies much less subcarriers. In this scheme, the sparse tap delays are detected with the interpolated channel frequency response and the modified BEM based estimation deals with only the detected taps, eliminating the noise interference from the null taps. The proposed estimator outperforms the existing methods applied to sparse dispersive channel with higher spectral efficiency. Our claims are verified by simulation results, which are obtained in COST207 typical urban channel model.

In this paper, an interference-aware linear precoding scheme, called generalized singular value decomposition based interference suppression precoding (GSVD-ISP), is proposed. GSVD-ISP scheme employs a whitening filter for interference suppression at the receiver and a novel precoder considering other cells' interference for each user at the transmitter. Through slightly loosening the constraint on the signal-to-leakage-plus-noise (SLNR) maximization for multi-user multiple-input multiple-output (MU-MIMO) systems with multiple data streams per user and the use of GSVD algorithms, the proposed scheme decreases redundant computational complexity and reduces the gap between any couple of the stream effective channel gains compared with the original interference-aware enhancement to SLNR precoding scheme-multiuser generalized eigenmode transmission (MGET). Simulation results demonstrate that the proposed GSVD-ISP technique achieves considerable gains in terms of bit error rate (BER) over MGET technique with multi-stream and the traditional interference-aware block diagonalization (BD) scheme while maintaining roughly the same achievable sum-rate.

Multi-radio multi-channel allocation has been studied in different networks, like Wireless Mesh Networks, Cellular Networks. In this paper, we study the multi-radio multi-channel allocation in competitive wireless Ad hoc networks from the game-theoretic point of view. Nash Equilibrium (NE) and Pareto-optimal are used to study the selfish devices and global optimality. The decreasing utility function is analyzed and a Pareto-optimal channel allocation algorithm based on NE is proposed to achieve the system optimization. A charging scheme is also considered to represent the cost of transmission. Finally, we show that, our proposal can achieve higher utility than NE with both charging and no-charging schemes, especially when there are more players for the no-charging scheme.

This paper proposes a novel recursive approach for orthogonal frequency division multiplexing (OFDM) channel estimation over highly mobile channels without any priori channel statistics. The key idea is to decompose each objective channel into the known component and the rest for estimation. The known component is the output of the prior iteration except that the first one is attained in linear time-varying (LTV) model. In each iteration, the rest channel component, obtained by subtracting the known channel component from the original channel, is estimated in generalized complex exponential basis expansion model (GCEBEM). Since the rest component varies much milder than the original channel, both modeling error and inter-carrier interference (ICI) are significantly reduced in GCEBEM-based estimation. Monte Carlo simulations demonstrate the superiority of the proposed approach with a few iterations over the conventional methods under highly mobile conditions.

In multi-hop ad hoc networks, sources may pump more traffic into the networks than that can be supported, resulting in high end-to-end delay and packet-loss rate. Controlling the offered load at the sources can eliminate this problem. To conduct traffic control, the throughput of the network is necessary. In addition, the authors propose a concept of bandwidth mapping to analyse how much traffic in application can be injected to make the network supporting the maximum throughput. A multi-dimensional Markov model is built to analyse the performance of IEEE 802.11 DCF in unsaturated condition, in which M/G/1/K queuing model is used to analyse the service condition of the packet queue inside the node. The effects of carrier sensing property, hidden-node problem and signal capture property are considered together to analyse the contention experienced by the nodes in the network. Furthermore, the bandwidth mapping situation of voice and video traffic is analysed in string topology of a multi-hop ad hoc network. This analysis can provide a theoretical guide for the traffic admission control.

All traditional on-demand routing protocols for wireless networks perform well if routing is provided with few optimised fixed routes before transmissions, and each hop uses a fixed neighbour for forwarding a packet. This strategy, however, cannot be adopted effectively in practical dynamic environments with frequent transmission failures. By taking advantage of the broadcast nature of wireless media the authors propose a novel opportunistic routing scheme to overcome the drawbacks of wireless transmission called ‘network layer broadcast with confirmed opportunity routing’ (NBC-OPP). NBC-OPP features a forwarding list built between source and destination upon the route request and route reply messages. Using the simulation of various network scenarios the authors have demonstrated the superior performance of this new method over traditional deterministic routing schemes for packet delivery, end-to-end delay and route control message load, especially for large node density, bad channel state and low traffic rate conditions.

An important feature of the difference between wireless sensor networks and traditional wireless networks is that energy of sensor node is limited. Therefore, how to design energy efficient sensor network system has been the focus of the study in related fields. Now there are many self-organization protocols in wireless sensor networks, but most only take the initial energy consumption into account and neglect of the maintenance phase of the corresponding topology consumption. Therefore, we take the two stages into consideration on omnet simulation and real testbed. we research and realize different types of self-organization protocols and compare their performances to find the principle between energy consumption and self-organization algorithm to guide the design and choice.

The IEEE 802.11e standard is specified to support quality-of-service in wireless local area networks, and different contention parameters are designated to each type of service. This developed model is presented to analyse the scheduling and the contention between packets with different priorities, where the new features of the enhanced distributed channel access such as virtual collision, backoff, minimum contention window and different arbitration inter-frame spaces are taken into account. Based on the model, the delay performance of differentiated service traffic is analysed and a recursive method is proposed, which is capable of calculating the mean access delay. Simulations show that the model and the analysis provide an insight into the protocol and the effects of different parameters on the performance.