3D reconstruction faces challenges such as geometric warping and structural ambiguity, particularly in intricate topologies, heavy occlusions and complex backgrounds. These problems are partly attributed to excessive feature entanglement, which induces semantic confusion and spatial ambiguity. To address these limitations, we propose an occlusion sensitivity-guided semantic-disentangled Mamba-CNN network that enables human-controllable disentanglement of multi-attribute information within a bias-free semi-supervised framework. Specifically, we create various occlusion conditions and assign pseudo-labels to the augmented data within a semi-supervised framework, which enables the exploration of occlusion sensitivity of different semantic attributes for human-controllable semantic disentanglement. To reduce the bias between the augmented samples and their assigned pseudo-labels, we use linear PIoU and nonlinear MS-SSIM algorithms to calculate the confidence of pseudo-labels, which minimizes the error propagation caused by the bias. Then, we develop a disentangled multi-depth Mamba-CNN block that combines CNN’s local feature extraction capability with Mamba’s ability to capture long-range dependencies. This allows our model to effectively capture disentangled multi-attribute spatial features and semantic representations. However, critical cross-level semantic attribute connections could be lost in the disentanglement process. To tackle this, we propose a multi-attribute semantic query block to dynamically re-establish these connections and minimize cross-attribute information loss. Extensive quantitative and qualitative evaluations on object and face reconstruction demonstrate that our method outperforms existing state-of-the-art approaches. Codes and all data are publicly available at https://github.com/Ray-tju/sensitivity-guided-semantic-disentangled-Mamba-CNN.

Neural communication systems utilize human neurons to transmit data among biological entities and engineered nanodevices, forming the Internet of Nanothings (IoNT). As a novel research topic, the interference with neural communication systems is not well investigated and modeled. In this article, a simplified neural communication model with external and internal interference is proposed. In our model, external interference from neighboring neurons is characterized by a Poisson distribution, capturing the stochastic nature of interference spikes. Internal interference, attributed to relative refractoriness, which is a neural property reflecting the reduced probability of spike generation shortly after neuron activation, is modeled using a dynamic threshold. The simulation results show that although the only internal interference scenario causes minimal, it still introduces some extra symbol error when external and internal interference both exist. Enhancing the intensity of neural signal stimulation and lengthening the signal period are effective strategies for mitigating the effects of external and internal interference, respectively. The work contributes to a better understanding of neural communication systems and paves the way for the future potential applications of healthcare.

Systematic and scientific training is an essential aspect of improving athletes' performance and has garnered significant attention. Athletes are continuously seeking new technologies to aid in training and prevent injuries. Digital twin technology, which integrates the Internet of things (IoT), big data, and artificial intelligence (AI), shows potential for providing athletes with continuous monitoring and in-depth analysis to support their training. This study employs wireless body area network (WBAN) and digital twin (DT) technology to design a novel system for assisting athletes training. On the hardware level, the system utilizes a WBAN with multiple sensors to collect physical and physiological data from athletes. On the software level, the system comprises three modules: motion capture, behavior recognition, and motion correction modules. These modules enable the digital twin to reflect the athlete's movements, behaviors, and physiological attributes in real-time, helping athletes understand their physical condition, correct erroneous movements, and prevent potential health hazards. Furthermore, this study demonstrates how to construct a digital twin human, providing a method that can be transplanted to other scenarios.

Molecular communication (MC) is a promising way to achieve the Internet of nano things (IoNTs), and the theoretical system has been gradually improved in recent years. However, previous studies have more concentrated on system modeling, often simplifying the transceiver into an ideal point model due to the absence of a detailed transceiver design. In this paper, we choose DNA, the natural material to design the MC transceiver benefited from the promoting DNA technology. Specifically, we use DNA origami technology to design the transceiver and DNA strands as the information molecules (IMs). DNA strand displacement (DSD) reactions make up to the mechanism of the transmission and reception of IMs. Furthermore, amplifier is designed based on strand displacement amplification (SDA) mechanism, and the whole MC system works under the control of a timer mechanism which is achieved by the synergy of$\lambda$exonuclease ($\lambda$Exo) and fuel strands, one special DNA strands. For our designed nano transceiver, we modeled the MC system mathematically and analyzed the noise based on the properties of the transceiver. Based on the simulation results, we find the nano transceiver works well in nano-scale which promises the potential for further application in the future.

The Internet of Bio-Nano Things (IoBNT) is a groundbreaking concept in communication and network engineering, aiming to establish connections between biological cells, tissues, nanodevices and the Internet. Within IoBNT, bio-inspired molecular communication (MC) is a promising communication paradigm for facilitating information exchange between biological cells and nanodevices. Blood vessels are essential channels for MC systems in IoBNT within the human body. Typically, cylindrical channels with Newtonian fluid flows are utilized to model vascular channels in MC research. However, blood behaves as a non-Newtonian fluid at low shear rates, resulting in distinct flow velocity profiles that affect the transport of IMs. To analyze the impact of non-Newtonian fluid flow velocity profiles on the transmission of IMs and the performance of MC systems within IoBNT, we develop an MC system focusing on a cylindrical channel with non-Newtonian fluid flow. We obtain the distribution of molecules by deriving the cross-sectional average concentration of IMs and formulate the expression for the probability of IMs observed by the receiver. Furthermore, we introduce a simple channel coding scheme to mitigate the influence of inter-symbol interference (ISI). Moreover, the impacts of the non-Newtonian fluids on MC systems are analyzed in terms of probabilities of detection and false alarm, probability of error, mutual information, and achievable rate. Finally, simulations are performed to validate the analytical results. The findings in this paper highlight the importance of considering the rheological behavior of the fluid in optimizing the system design.

Blood vessels are essential communication channels for in-vivo molecular communication (MC) systems. Typically, MC researchers model vascular channels as cylindrical tubes with Newtonian fluid flows. However, blood exhibits as a non-Newtonian fluid at a low shear rate. The characteristics of flow in a non-Newtonian fluid differ from those in a Newtonian fluid, resulting in the transport of information molecules (IMs) in the blood being different from that in Newtonian fluids. To analyze the influences of non-Newtonian fluid flows on the performance of MC systems, we develop an MC system in a tube with a non-Newtonian fluid flow in this paper. We obtain the distribution of molecules by deriving the asymptotic equation for the cross-sectional averaged concentration of IMs and formulate expressions for the first passage time probability (FPTP) and first passage time probability density function (FPTPDF). The derived expressions are validated through particle-based simulations. Moreover, the impacts of the non-Newtonian fluids on MC systems are examined in terms of the average bit error rate (BER) and the data transfer rate. Simulation results are presented to verify the theoretical results.

Optical coherence tomography (OCT) is widely used in high-resolution imaging of biological tissues, which can help diagnose coronary heart disease by segmenting the vessel lumen at the pixel-level. However, the lumen shape geometry is not well used in the state-of-the-art techniques for OCT image segmentation, especially the data-driven methods, leaving much room for performance improvement if some geometric features could be exploited to provide prior information. Thanks to the star shape geometry of vessel lumen, in this paper, a new Star Shape Prior based Regularizer (SSP-Regularizer) is proposed to improve segmentation performance. To validate its effectiveness, the proposed SSP-Regularizer is applied to improve the optimization scheme used in Mask-RCNN for vessel lumen segmentation. Experimental results show that superior performance is achieved with SSP-Regularizer, indicating its potentials in OCT imagery and optimization schemes.

Clustered cell-free networking that dynamically decomposes the whole network into a number of subnetworks operating in parallel could help avoid the severe cell-edge problem. In this paper, the clustered cell-free networking problem is investigated with access point (AP) selection considered for power saving. A novel user-centric ratio-fixed AP-selection based clustering (UCR-ApSel) algorithm is proposed. Simulation results show that the proposed UCR-ApSel algorithm improves not only the rate performance but also user fairness. The average per-user rate achieved with the proposed UCR-ApSel algorithm is further analyzed and a closed-form upper-bound is derived. Simulation results show that the gap between the derived upper-bound and the average per-user rate remains almost a constant as the system parameters vary, indicating that the closed-form upper-bound could provide direct guidance to practical system design and performance optimization.

3D object detection in point cloud data is an important aspect of computer vision systems, especially for autonomous driving applications. Recent literature suggests two methods of point cloud encoders; grid-based methods tend to be fast but sacrifice accuracy, while point-based methods that are learned from raw data are more accurate, but slower. In this work, we present a novel and real-time two-stage 3D object detection framework, named PointPillars-RCNN (PP-RCNN). In the first stage, we use pillars network to encode the point cloud and generate high-qulaity 3D proposals. Benefiting from the pillars network, our framework realizes real-time detection. In the second stage, we use the Point-Pillars Feature Set Abstraction (PPSA) module to extract the point-based features from raw point cloud and pillars features, and then we use the RoI-grid feature abstraction for proposals refinement. All our detection pipelines are trained end-to-end. Extensive experiments on the KITTI benchmark shows that our approach has better performance than the one-stage PointPillars algorithm, and faster than current two-stage state-of-the-art algorithms.

This paper investigates efficient data delivery methods, through integrating non-orthogonal transmission with network coding techniques, in wireless networks with multiple sources, multiple relays, and multiple destinations. We propose properly scheduling terminals into transmitting clusters to optimize system error performance. Considering communication over general Nakagami-m fading channels, an error probability approximation analysis is first presented. To reduce computation complexity for solving the node scheduling design problem, we further propose transforming the problem into a data classification problem and applying data-driven machine learning tools to reach rapid decision-making. Taking two-user clustering as an example, simulation results show that our method can attain similar performance as the optimal solution, and significantly outperform conventional methods that orthogonalize or randomly schedule terminals. The proposed method and the analytical framework can be extended to larger networks with diverse node clusters and more complex signal propagation environments.

Intelligent connected vehicle (ICV) has been widely deemed as the key to reduce road accident rate and improve traffic efficiency. However, ensuring high communication reliability and low transmission delay in vehicular networks is challenging, especially in large-scale dynamic networks with diverse heterogeneous data exchange demands. In this paper, we investigate the potential of applying the deep reinforcement learning (DRL) technique to facilitate efficient transmission design in a class of complex multi-user vehicle-to-vehicle (V2V) networks, where conventional mathematical tools confront difficulties in solving the design optimization problems. The considered network contains several pairs of V2V links sharing the channel resource. Each link desires to communicate two types of delay-sensitive messages to support different safety-related applications with the maximum energy efficiency. We propose transforming the power/rate control problem into a Markov decision process and then solving it using the deep deterministic policy gradient (DDPG) algorithm. Simulation results show that in a two-user network our DRL-aided solution can achieve better performance than that with Lyapunov optimization. Extending the former to work in a larger network is straightforward, but it is not the case for the latter. The advantages of applying DRL to support wireless system design are thus demonstrated.

This letter investigates the performance of energy detector with multiple antennas in cognitive vehicular networks, where the sensing signals of the secondary user are temporally correlated. A novel analytical method, based on new results of confidence interval estimation for auto-correlated matrix in random matrix theory, is proposed to estimate the miss detection probability. The estimate accuracy is verified via simulations. Based on such results, the impacts of signal temporal correlation on sensing performance and decision threshold are analyzed.

Vehicular networking is potentially an effective solution to the problems in today's transportation system. However, realizing efficient communications among vehicles with satisfactory reliability and latency requirements is challenging, especially when diverse applications are taken into consideration. In this paper, we investigate a cross-layer energy- efficient transmission design for a class of vehicular communication networks, in which two pairs of vehicle-to-vehicle links non-orthogonally share the available spectrum. Each link desires to deliver two types of messages that can support different delay-limited applications. The periodically- generated heartbeat messages should be transmitted subject to a reliability requirement, and the randomly-appeared sensing messages should be delivered with finite latency. We propose a power control strategy to achieve high energy efficiency, while ensuring the expected quality-of-service requirements, based on both channel state information in the physical layer and queue state information in the media access control layer. Simulation results show that our proposed method notably outperforms conventional methods.

Recently, research on mobile molecular communication (MC) has become a trend and hotspot. The timevarying channel impulse response (IR) resulting from the mobile feature makes signal detection schemes for static MC no longer applicable. In this paper, a low complexity and non-coherent detection scheme based on the energy difference between two adjacent symbols is proposed for blind signal detection in mobile scenario providing high data rates. Specifically, we consider the non-coherent signal detection scheme with respect to both the moving transmitter and the moving receiver for the first time. In contrast to existing methods for low data rates and without considering inter-symbol interference (ISI), the proposed method utilizes the ISI to achieve quality detection results at high data rates without knowing the channel state information (CSI). The influences of the mobility parameters and data rates on the bit error rate (BER) are evaluated by simulations. The simulation results demonstrate that the BER performance of the proposed scheme is much lower at the same signal intensity compared with a non-coherent scheme, and the computational complexity can be significantly reduced as well. Consequently, the proposed scheme can reduce the effect of the mobility of the nanomachines in signal detection and has the potential to implement molecular communications, especially in mobile scenarios.

This paper investigates the communication problem in a class of multi-user dual-hop networks in which multiple source terminals desire to distribute their independent messages to multiple destinations through the assistance of multiple relay terminals. We consider an efficient transmission strategy that combines network coding and non-orthogonal transmission techniques to balance the achievability of spatial diversity and channel utilization. Specifically, in addition to applying a class of finite-field network codes in the relays, we divide the sources and the relays into clusters such that terminals within each cluster can access the same channel resource. The achievable error performance under Nakagami-m fading environment is derived and is shown to significantly outperform the conventional transmission methods.

Broadcasting of periodic messages is critical for intelligent transportation systems (ITS). In this paper, we analyze the performance of the delivery of periodic messages via stochastic geometry and the technique of Padá approximation (PA) under Weibull-lognormal composite fading channels in highway scenarios. The locations of the vehicles are assumed to follow a one-dimensional homogeneous Poisson Point Process (PPP), and the slotted ALOHA MAC scheme is employed. The closed-form expressions of coverage probability, mean number of covered vehicles, and mean packet reception rate (PRR) are presented. Monte-Carlo simulations validate the accuracy of our analytical results.

Molecular communication (MC) is a promising paradigm for conveying information at a micro- to nano-scale. Particles such as molecules are emitted and sensed by nanomachines. MC system for fixed nano-machines have been extensively researched while literature about the scenario where nanomachines are mobile is very limited. In this paper, a diffusion-based mobile MC system is considered. Both the transmitter and the receiver move randomly in a free diffusion manner. The closed-form expressions for the mean and the variance of the received signal by considering the randomness of both the mobility of nanomachines and particle counting noise are derived using double integrals. Simulation results validate the derived expressions for the mean and variance of the received signal.

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record

We investigate the achievable sum degrees of freedom (DoF) in a class of single-antenna multi-user multi-hop relay networks. The networks consist of multiple information sources and destinations, without direct signal propagation link between them, so that multiple layers of relays are deployed to assist in information delivery. We consider the situation that relays are unable to shield their receptions from the harmful self-interference and from the interference generated by other relays. Hence, ideal full-duplex relaying is not applicable. Utilizing half-duplex decode-and-forward relays, a cluster successive relaying (CSR) transmission scheme is adopted to conduct message transmission. The CSR scheme divides each layer of relays into two successively activated relay clusters to compensate the extra channel consumption demanded by the half-duplex operation. We propose two interference alignment strategies to deal with the interference issues. By properly clustering the relays in each layer, we find the asymptotically achievable sum DoF, subject to time-varying and frequency-selective fading, respectively. These results can lead to new lower bounds for the available DoF in the considered class of multi-user multi-hop networks.

We investigate the information transmission in a typical communication scenario in the intelligent transportation systems (ITS), that is, infrastructures providing ITS services that intend to broadcast independent messages to the vehicles within their coverage areas. Due to the dynamic nature of wireless signal propagation links and interuser interference, it is hard to guarantee satisfactory performance by direct infrastructure to vehicle (I2V) transmissions. To solve this problem, we propose allowing certain vehicles to serve as relays to assist in the information distribution process and applying a class of finite-field network codes to efficiently use the available spectrum resources. Considering that the infrastructure information sources and all vehicles are randomly distributed following Poisson point processes, we model a general urban ITS communication network and based on the stochastic geometry we derive the probability that a vehicle can successfully recover all the desired messages from its serving infrastructure. The analytical and numerical results clearly demonstrate that our proposed network coding based relay-assisted I2V transmission can significantly improve the communication performance of the conventional direct I2V transmission strategy.

Magnetic induction (MI) is a promising technique for communications in the very harsh propagation conditions like underground or underwater, etc. In this paper we investigate the connectivity issue of MI ad hoc networks deployed in a three-dimensional space. The transmitting distance of MI covers a quasi- ellipsoid space other than a standard sphere space in a traditional wireless propagation case. We derive a closed-form expression for the effective coverage space of an MI node to evaluate the expectation of node degree. Moreover, the probability of node isolation is derived, which provides an upper bound of network connectivity probability. With these results, pairs of the critical transmitting magnetic momentum and node density can be obtained to keep the network connected with a probability close to one. Our analytic method provide a guidelines for future studies on MI networking.

This paper studies how to efficiently reduce the mutual interference between primary and secondary networks. Different to conventional cognitive system, in which primary and secondary networks have no information exchange, the media access control mechanism we propose allow an operating PU to share the length of its remaining occupation time to SUs. With the help of such information exchange, the primary network can be better protected and the secondary network has the potential to reduce the energy consumed by spectrum sensing. To realize such a dynamic information sharing, we propose a novel spectrum-sculpting-aided scheme in this paper. Specifically, the PU deliberately inserts one zero-subcarrier into its data carriers, and uses the zero-subcarrier's position to claim its status. Through two-step sensing, each SU can attain the PU's remaining operation time, and then adjust its sensing strategy to avoid interference to the PU. Analysis of interference probability, together with simulation results, clearly exhibit the advantages of the proposed scheme in terms of the mutual interference and the amount of SU's sensing times.

This paper applies device-to-device technique to vehicular-to-vehicular communications (D2D-V) in an underlay fashion. Different from conventional D2D systems focusing on smart phones, whose positions are usually assumed to be statical, the D2D-V system suffers more mobility and uncertainties. These dynamic features indeed complicate the design of D2D-V system, but also, give more potential to accomplish some geographic based algorithms. Armed with the geographic knowledge of highway and the dynamic GPS information in each vehicle, we propose a geographic based reuse cellular user selection scheme and two different distributed power control schemes to serve various applications in vehicular networks. We model the problem firstly, then formulate two metrics: the sum rate and the minimum-achievable rate to evaluate the performance of the proposed schemes. Simulations are proposed to validate our schemes and analysis. Influences of different system settings are also discussed here.

This work aims to provide a review of the routing protocols in the Internet of Vehicles (IoV) from routing algorithms to their evaluation approaches. We provide five different taxonomies of routing protocols. First, we classify them based on their transmission strategy into three categories: unicast, geocast, and broadcast ones. Second, we classify them into four categories based on information required to perform routing: topology-, position-, map-, and path-based ones. Third, we identify them in delay-sensitive and delay-tolerant ones. Fourth, we discuss them according to their applicability in different dimensions, i.e., 1-D, 2-D, and 3-D. Finally, we discuss their target networks, i.e., homogeneous and heterogeneous ones. As the evaluation is also a vital part in IoV routing protocol studies, we examine the evaluation approaches, i.e., simulation and real-world experiments. IoV includes not only the traditional vehicular ad hoc networks, which usually involve a small-scale and homogeneous network, but also a much larger scale and heterogeneous one. The composition of classical routing protocols and latest heterogeneous network approaches is a promising topic in the future. This work should motivate IoV researchers, practitioners, and new comers to develop IoV routing protocols and technologies.

Visible light communication (VLC) has recently emerged to become a promising wireless communication technology. Vehicle lights and traffic lights have started to utilize LEDs and due to their shorter response time, they can be easily modified to become VLC transmitters. In addition, cameras embedded in smartphones can be used as VLC receivers. As a result, Vehicular VLC (V2LC) between vehicle lighting and smartphone cameras has the potential to enable a great number of applications with low cost. In this paper, a prototype V2LC system that utilizes undersampled frequency shift ON-OFF keying (UFSOOK) modulation is proposed. The system utilizes rolling shutter cameras as the receiver and takes advantages of its characteristics to improve the receiving performance. An off- the-shelf vehicle LED taillight is used as the transmitter. Information is transmitted in the continuous state (ON-OFF) changes of LEDs which are invisible to human eyes. The performance evaluation results demonstrate that the communication prototype is robust and can resist common optical interferences and noises within the image.

Many researchers have recently proposed using LTE to support traffic safety and transportation efficiency applications in vehicular ad hoc networks (VANETs). The deviceto- device (D2D) communication technique underlay LTE network has been considered as an effective way to potentially conduct vehicle-to- vehicle (V2V) communications. Since the vehicular communication environment has different characteristics compared with conventional cellular systems, efficient resource allocation strategies for D2D communications in VANETs are demanded. In this paper, we propose two such algorithms aiming at minimizing the consumption of cellular radio resources. We divide the vehicles on a road into multiple clusters, each of which utilizes a single cellular user's resource. One of the proposed algorithms is constructed based on the dynamic programming technique to search the optimal clustering solution. The other seeks to solve the resource allocation problem with low computing complexity. The performances of the algorithms are exhibited through simulations under practical traffic environments.

Device-to-device (D2D) communications as underlays of cellular networks facilitate diverse local services and reduce base station traffic. However, D2D communication may cause interference with the primary cellular network. To avoid this problem, the network should flexibly allocate its resources and select a proper mode for users. Here, we formulate a joint mode selection and resource allocation problem to maximize the system throughput with a minimum required rate guarantee. A mode selection and resource allocation scheme based on particle swarm optimization (PSO-MSRA) is proposed in which solutions are mapped onto particles and a fitness function embodies the constraints in a penalty function. Simulation results show its superiority over other schemes in terms of throughput and minimum required rate guarantee.

In this paper, we use complex network theory to analyze the evolving topology of wireless sensor networks (WSNs). Based on the BA model, we propose a novel complex network model, i.e., Neighborhood Log-on and Log-off model with energy awareness (NLL-E). NLL-E assumes locally preferential attachment, which exists in many complex networks. We further consider node addition and invalidation in topology evolvement, and add compensation mechanism for network robustness. The statistical properties and dynamics of NLL-E are analytically studied. Numerical simulations indicate that, comparing with BA, NLL-E has decreased average path length and increased network connectivity.

The problem of resource block allocation for downlink Long Term Evolution (LTE) systems is addressed. To maximize system throughput and fulfill quality of service (QoS) requirements among users and modulation and coding scheme (MCS) selection constraint brought by LTE, a Particle Swarm Optimization (PSO) based resource block allocation algorithm is designed. That is, we propose a coding scheme to map solutions onto particles, and find an operation to discretize the position and velocity of particles and then establish a fitness function after handling constraints through penalty function. Simulations show that the proposed algorithm outperforms in the achievement of high system throughput with QoS guarantee when resource is enough and plays a worthwhile tradeoff between QoS and throughput in overloaded systems.

This paper proposes a novel approach of cross-layer resource allocation based on Quality of Experience (QoE) for wireless video transmission. We model and formulate the cross-layer resource allocation problem in accordance with the relationship between transmission bit rate and QoE. Our objective is to ameliorate the system QoE level while guaranteeing the fairness for users. The video quality fluctuation that affects the system QoE is considered as well. The proposed algorithm designs an adaptive resource allocation approach depending on limited network resources. We use a downlink LTE system as a simulation example of a high speed downlink access system. The simulation results demonstrate that the proposed method considerably and agreeably improves system QoE more than other algorithms.

In Multi-user MIMO system, traditional user scheduling methods are usually aiming at maximizing the sum-rate capacity of the target system. Such methods select the users which have the best channel quality to service but ignore users' satisfaction. In this paper, we proposed a user satisfaction based judgment criteria, and designed a novel scheduling method in order to ensure users' satisfaction. We apply zero-forcing beam forming (ZFBF) precoding into downlink transmission, and assume perfect knowledge of CSI at the base station. Simulation results show that the algorithm achieved a higher system average user satisfaction degree while suffered a little performance loss compared with the classical SUS-ZFBF strategy.

Wireless sensor networks have been applied in many service application areas, which consist of numerous autonomous sensor nodes with limited energy. In order to optimize the wireless sensor networks performance and meanwhile expand the cellular network's service applications, mobile cellular networks and wireless sensor networks are evolving from heterogeneous to converge. However, the sensor nodes need to be informed the mobile sink's location for data collection purpose. Unfortunately, frequent location updates from multiple sinks can lead to both excessive drain of sensors' limited battery supply and increased collisions in wireless transmission. In this paper, we describe the movement direction based path selection strategy which adopts the grid structure and provides an efficient mobile sink's location updates approach. When the sinks have mobility, they only inform their current locations to the specific nodes on the previous path instead of the source nodes. We evaluate the proposed method through both analysis and extensive simulations. The results show that our proposed theory not only provides an energy-efficient location update approach, but also generates a minimal delay path for data dissemination.

MIMO, OFDMA and cooperative relaying are the key technologies in future wireless communication systems. However, under the usage of these technologies, resource allocation becomes a more crucial and challenging task. In multi-user MIMO-OFDMA relay-enhanced cellular networks, we formulate the optimal instantaneous resource allocation problem including user group selection, path selection, power allocation, and subchannel scheduling to maximize system capacity. We first propose a low-complex resource allocation algorithm named `CP-CP' under constant uniform power allocation and then use a water-filling method named `CP-AP' to allocate power among transmitting antennas. Moreover, we solve the original optimization problem efficiently by using the Jensen's inequality and propose a modified iterative water-filling algorithm named `AP-CP'. Based on `AP-CP', the `AP-AP' algorithm is proposed to allocate power adaptively not only among subchannels but also among multiple transmitting. Finally, we compare the performance of the four schemes. Our results show that allocating power among subchannels is more effective than among transmitting antennas if the average signal-to-noise radio of users is low, and vice versa. Furthermore, the `AP-AP' algorithm achieves the highest throughout especially for users near the cell edge.

Distributed antenna systems (DAS) reduce the access distance for users in areas away from the cell center, thereby, enhancing the transmission capability for those users. Recent studies have shown that antenna selection transmission (AST) is preferable to maximum ratio transmission (MRT) in a multicell DAS environment, owing to its interference reduction. However, it is obvious that users still suffer from strong interference at the cell edge when applying universal frequency reuse in DAS. We propose an efficient cooperative beamforming transmission (CBT) algorithm, whose basic idea is taking the distributed transmitting antennas within a cell and receiving antennas including desired users and adjacent cells' interfered users, as a distributed multiple-input multiple-output (D-MIMO) channel, by which CBT mitigates the interference through joint precoding. We analyze the proposed algorithm mathematically and compare it with other existing algorithms. A Monte-Carlo simulation is also carried out to verify the derivation. Both the analysis and simulation results show that the CBT algorithm outperforms both AST and MRT especially near the cell edge.

In this contribution, we derive the spread function and power spectrum of the so-called "relay channel'' along which electromagnetic waves propagate from a source station to a destination station via multiple amplify-and-forward (AF) relay stations. These results show analytically that the propagation over such a relay channel can be influenced by the propagation in individual "hops'' and the responses of the relay stations. Furthermore, we find that the uncorrelated-scattering assumption is inapplicable for the AF-relay channels in the case where the relay stations receive and retransmit signals using mulitple antennas. We propose to simulate the relay channels by three steps: i) selecting channel models for individual "hops''; ii) defining the correlations of model parameters among the hops; and iii) constructing realizations of relay channels by taking into account the responses of relay stations, which are specified by considering the response of antennas and the relay mode according to system configurations. We also show in this contribution that the spreads of a multi-relay channel in delay, Doppler frequency and directions are lower-bounded by the weighted summation of the spreads of individual source-relay-destination channels.

Vehicular wireless communication should be able to provide vehicles with reliable and efficient data transmissions for various applications, especially safety applications. We present a dedicated multi- channel MAC protocol that has an adaptive broadcasting mechanism, specifically designed to provide collision-free and delay-bounded transmissions for safety applications under various traffic conditions. Besides defining the implementation of this protocol, we conducted simulation evaluations showing that it has promising performance on critical statistics.

The deployment of relay stations in OFDMA cellular networks is a promising solution to provide ubiquitous high-data-rate coverage. However, it makes the resource allocation a more crucial and challenging task. In OFDMA relay-enhanced cellular networks, we formulate the optimal instantaneous resource allocation problem including path selection, power allocation and subchannel scheduling to achieve the proportional fairness in the long term. We first propose a low-complex resource allocation algorithm named 'VF w PF' under the constant uniform power allocation, and then use a void filling method to make full use of the wasted resources caused by the unbalanced data rates of the two hops in a relaying path. We further use a dual decomposition approach to solve the original optimization problem efficiently in its Lagrangian dual domain, and propose a modified iterative water-filling algorithm named 'PA w PF'. Simulation results show that our resource allocation algorithms improve the throughput for cell-edge users, and achieve a tradeoff between system throughput maximization and fairness among users. Moreover, compared with the constant power allocation, the optimal power allocation can not gain much in system throughput but can significantly improve the throughput for cell-edge users and also the fairness.

The cooperative selection diversity (CSD) scheme which dynamically selects the best transmission scheme between decode-and-forward relaying and direct transmission outperforms the other complex cooperative diversity schemes in terms of throughput and implementation complexity. In this paper, we formulate the optimal resource allocation problem with a fairness constraint in the downlink of OFDMA relay-enhanced system when CSD based transmissions are considered. The problem is linearized into a binary integer programming problem by using a constant power allocation. To solve the linear programming problem, we proposed a heuristic joint path selection and subchannel allocation algorithm. Moreover, a void filling algorithm is designed to make full use of the resources. Simulation results demonstrate that our path selection rule based on the end-to- end achievable data rate and the void filling algorithm achieve more system throughput especially when the proportion of the two zones used in the two-slot relaying pattern is far away from the optimal value. The heuristic resource allocation algorithm provides a suboptimal solution to gain a tradeoff between system throughput maximization and fairness.

OFDMA-based relay-enhanced cellular systems have been proposed as a promising solution for the next-generation wireless communications. The deployment of relay stations (RS) in cellular networks makes resource scheduling a more crucial and challenging task. In this paper, a novel centralized scheduling scheme called centralized scheduling with void filling (CS-VF) is proposed for two-hop relay-enhanced networks. In CS-VF, the remaining slots in the RS-subframe are filled with packets destined to users who directly communicate with the base station (BS). Moreover, based on our CS-VF scheduling scheme, four representative single-hop scheduling algorithms: round-robin, max C/I, max-min fairness, and proportional fairness, are extended to multihop scenarios. Simulation results indicate that when compared with the existing centralized scheduling scheme, which does not consider void filling, our proposed CS-VF scheme is more adaptable to different traffic distributions caused by dynamic network topology and user mobility. And it enhances not only the system throughput but also the fairness among users.