In this paper, we propose a novel mobility-aware resource provisioning scheme for edge-assisted extended reality (XR) services. The goal is to minimize resource consumption while satisfying user quality of experience (QoE) requirement, which is measured by the weighted sum of visual quality, quality variation, and round-trip interaction latency. Specifically, we present a mobility model to capture both user spatial movements and XR content interaction features. Since user viewing distance and interaction time are key model parameters that affect the spatiotemporal service demand for XR content rendering and delivery at the edge, we estimate user-specific model parameters and adopt a sample average approximation method to model the relationship between user QoE and the consumption of both communication and edge computing resources. We design a coordinate descent algorithm to make resource provisioning decisions, where a deep neural network provides a valuable initial point to accelerate convergence. Simulation results demonstrate that our proposed scheme is more efficient to utilize network resources in comparison with benchmark schemes while satisfying user QoE requirements.

In this paper, we propose a novelpolarforming antenna (PA)to achieve cost-effective wireless sensing and communication. Specifically, the PA can enable polarforming to adaptively control the antenna’s polarization electrically as well as tune its position/rotation mechanically, so as to effectively exploit polarization and spatial diversity to reconfigure wireless channels for improving sensing and communication performance. To analyze the performance gain of PA, we study a PA-enhanced integrated sensing and communication (ISAC) system that utilizes user location sensing to facilitate communication between a PA-equipped base station (BS) and PA-equipped users, by focusing on a new practical channel setup where the locations of users are nearly time-invariant but their orientations may change frequently (e.g., mobile phones rotated by spectators seated in a stadium while taking live photos). First, we model the PA channel in terms of transceiver antenna polarforming vectors and antenna positions/rotations. We then propose a two-timescale ISAC protocol, where in the slow timescale, user localization is first performed, followed by the optimization of the BS antennas’ positions and rotations based on the sensed user locations; subsequently, in the fast timescale, transceiver polarforming is adapted to cater to the instantaneous orientation of user devices in three-dimensional (3D) space, with the optimized BS antennas’ positions and rotations. We propose a new polarforming-based user localization method that uses a structured time-domain pattern of pilot-polarforming vectors to extract the common stable components in the PA channel across different polarizations based on the parallel factor (PARAFAC) tensor model. Moreover, we maximize the achievable average sum-rate of users by jointly optimizing the fast-timescale transceiver polarforming, including phase shifts and amplitude variations, along with the slow-timescale antenna rotations and positions at the BS. Simulation results validate the effectiveness of polarforming-based localization algorithm and demonstrate the performance advantages of polarforming, antenna placement, and their joint design in comparison with various benchmarks without polarforming or antenna position/rotation adaptation.

This paper proposes a novel flexible coupler antenna array that incorporates additional degrees of freedom (DoF) in radiation pattern reconfiguration to achieve strong mechanical beamforming gains and enhanced communication coverage with low hardware cost. Particularly, passive couplers move around a fixed active antenna so that the induced currents on the passive elements can be reshaped to achieve radiation pattern reconfiguration. A new form of mechanical beamforming can be obtained by moving only the passive couplers while keeping the active antenna stationary. In addition, the flexible coupler antenna can slide along a rail toward users, thereby enhancing communication coverage. To fully exploit the potential of the flexible coupler array, we formulate a two-timescale sum-rate maximization problem with statistical channel state information (CSI). The active antenna position is optimized based on scattering cluster-core statistics in the slow timescale, while mechanical beamforming is optimized based on multipath channel statistics in the fast timescale, subject to movement and energy constraints. To address the coupling between timescales and the high cost of extensive channel sampling, we develop a digital agent framework that leverages an electromagnetic (EM) map to generate statistical channel information for different user and antenna positions. Then, a deep neural network is trained to learn a slow-fast performance (SFP) surrogate, which is fine-tuned with a small number of real measurements and then applied for position optimization at the slow timescale using projected gradient ascent. Mechanical beamforming at the fast timescale is obtained by selecting per-antenna radiation patterns from a predefined dictionary via a convex relaxation. Simulation results demonstrate that the proposed flexible coupler array significantly improves system throughput, and the digital agent-assisted algorithm achieves satisfactory performance with greatly reduced online computational complexity.

Immersive communication, as a key usage scenario in 6 G, aims to provide interactive experiences by delivering real-time, high-fidelity sensory feedback (e.g., vision and touch). However, simultaneously achieving low latency, high data rate, and high reliability often poses a conflicting challenge from a transmission perspective. Unlike the optimization of a physical transmission environment (e.g., RIS-THz), in this work, we propose a cross-sensory transmission strategy that involves both encoding and networking, leveraging the potential correlations among various sensory modalities to support both data compression and enhancement. On the encoding side, we explore explainable surface semantics (e.g., texture, compliance) as intermediaries to associate visual and tactile sensory modalities for the design of a cross-sensory visual coding method. This method compresses the massive volume of visual data based on semantic correlations, significantly reducing bitrates to enable low-latency transmission. On the networking side, a cross-sensory masked pre-training approach is incorporated under a wide range of simulated packet loss. This approach facilitates fast and precise reconstruction of lost data using minimal observed data packets from both modalities, compensating for transmission reliability degradation under random and significant packet loss rates. Experimental results from a constructed VR education platform demonstrate that the proposed transmission strategy improves the data compression rate by more than 33% while maintaining a tolerance for packet loss rates of at least 50%.

Future 6G networks are envisioned to facilitate edge-assisted mobile augmented reality (MAR) via strengthening the collaboration between MAR devices and edge servers. In order to provide immersive user experiences, MAR devices must timely upload camera frames to an edge server for simultaneous localization and mapping (SLAM)-based device pose tracking. In this paper, to cope with user-specific and non-stationary uplink data traffic, we develop a digital twin (DT)-based approach for user-centric communication service provision for MAR. Specifically, to establish DTs for individual MAR devices, we first construct a data model customized for MAR that captures the intricate impact of the SLAM-based frame uploading mechanism on the user-specific data traffic pattern. We then define two DT operation functions that cooperatively enable adaptive switching between different data-driven models for capturing non-stationary data traffic. Leveraging the user-oriented data management introduced by DTs, we propose an algorithm for network resource management that ensures the timeliness of frame uploading and the robustness against inherent inaccuracies in data traffic modeling for individual MAR devices. Trace-driven simulation results demonstrate that the user-centric communication service provision achieves a 14.2% increase in meeting the camera frame uploading delay requirement in comparison with the slicing-based communication service provision widely used for 5G.

Six-dimensional movable antenna (6DMA) offers a potential solution to enhance wireless transmission performance by physically reconfiguring antenna positions and orientations. However, prevailing snapshot-based reactive methods are ill-suited for continuously tracking mobile user equipments (UEs) due to their neglect of antenna kinematic constraints and system latency. To address these limitations, in this paper, we propose a proactive approach by modeling UE tracking as a single, long-term 6DMA trajectory optimization problem to maximize sum spectral efficiency. Leveraging a channel knowledge map (CKM) for predictive data, our model holistically incorporates the system’s complex kinematics and physical constraints, including velocity limits and safety distances, to ensure a physically feasible trajectory. To solve this high-dimensional, non-convex problem, we develop a novel manifold optimization algorithm. This method maps the antenna’s rotational states onto the SO(3) Lie group and employs an adaptive penalty measure with tangent space backpropagation for an efficient solution. Simulation results demonstrate our approach significantly enhances sum spectral efficiency over benchmarks, while ensuring continuous and physically feasible antenna trajectories.

In this paper, we propose a novel digital twin (DT)assisted joint communication and control scheme, in which robots can achieve much better collaboration for search and rescue (SAR) tasks in disaster-affected areas. Particularly, the design of the scheme is decomposed into two sub-developments at different timescales. First, a communication-aware robust control policy is developed for each robot in a small timescale based on nonlinear model predictive control and control barrier function constraints, which mitigates the impact of device-todevice communication delay on task execution. Second, a controlaware radio spectrum resource allocation strategy is developed at the edge server in a large timescale to improve total control task effectiveness of robots. This is achieved by a DT-assisted deep reinforcement learning (DRL) algorithm, where DTs of robots are utilized to emulate potential robot movements for DRL state augmentation. Simulation results demonstrate that the proposed scheme outperforms benchmarks for SAR tasks. A simulation demo can be found at: https://youtu.be/9Salnh9EIVg.

Various distributed learning approaches emerge for enabling ubiquitous intelligence in Internet of Things (IoT) without sacrificing data privacy. To improve communication efficiency in frequent knowledge exchange over resource-constrained IoT, different techniques for client selection have been proposed. However, the intractable scalability issues remain to be addressed in massive IoT, since highly-coupled co-channel interference adds exponential complexity to combinatorial client selection. In this work, we develop a client selection framework highly-scalable to large-scale networks with thousands of devices, which exploits the inherent graph structure derived from knowledge exchange and co-channel interference. Specifically, we first model a client selection problem for jointly optimizing learning performance and system cost under volatile network conditions. The formulated problem is encoded into a node classification problem by a directed graph. Subsequently, a general yet simple solver is designed based on graph neural networks, which selects clients by classifying node status with recursive neighborhood aggregation of node representations. Finally, extensive experimental results demonstrate that the proposed approach can perform on par with state-of-the-art methods, while scaling to networks whose size is orders of magnitude larger than they can handle.

In this article, we investigate infrastructure-assisted environment sensing in satellite-terrestrial vehicular networks (STVN) for connected autonomous vehicles (CAVs), where satellites and roadside units (RSUs) cooperate to provide CAVs with fresh sensing data. To support satellite- and RSU-assisted environment sensing for CAVs, we formulate a long-term resource scheduling problem in STVN to satisfy sensing data freshness requirements with efficient resource usage. To deal with the challenges posed by the dynamic network environment as well as stringent data freshness requirements, we propose a cooperative satellite-terrestrial resource scheduling (CSTRS) scheme. CSTRS is a model-data co-driven approach that can jointly optimize the sensing interval and resource allocation in STVN. Specifically, benefiting from the multicast feature of the low Earth orbit satellite, coalition game, and particle swarm optimization-based algorithms are designed to partition CAVs into groups and optimize sensing intervals in large timescales. Then, a reinforcement learning-based algorithm is developed to make real-time computing and communication resource allocation decisions based on the CAV partition. Simulation results demonstrate that the proposed scheme outperforms benchmark methods in terms of resource usage and reliability performance.

Rate-splitting multiple access (RSMA) has recently gained attention in low earth orbit (LEO) satellite-terrestrial networks (LSTNs), due to its ability to provide high spectral efficiency in the context of constrained energy resources of LEO satellites. However, the impracticality of acquiring perfect real-time channel state information (CSI), due to high satellite mobility and long link delay, poses significant challenges to effective utilization of RSMA in LSTNs. To tackle this challenge, we propose a location-based robust RSMA scheme for downlink data transmission in LSTNs. First, we establish an optimization problem to minimize the power consumption of LEO satellites, while meeting user requirement on the real-time data rate violation probability. Subsequently, we transfer the probability constraints of rate violation probabilities into closed-form inequalities, by utilizing Markov inequality, Jensen’s inequality, and Cauchy-Schwarz inequality. The original problem is then transformed into a Markov decision process (MDP), and a Transformer encoder-based deep reinforcement learning (TDRL) algorithm is proposed to solve the complex problem based on the real-time locations of users and the LEO satellite. Additionally, a multi time-frame location-based training dataset generation method is proposed for the training of TDRL model, considering the mobility of LEO satellite. Simulation results demonstrate that the proposed scheme is effective in guaranteeing the rate violation probability requirement of each user, and RSMA significantly outperforms space division multiple access (SDMA) and non-orthogonal multiple access (NOMA), with TDRL achieving faster convergence than other baselines.

Ultradense low-Earth orbit (LEO) satellite-terrestrial network (ULSN) has evolved as a new paradigm to provide ubiquitous and high-capacity communications in next generation wireless networks. However, the direct LEO satellite broadband connectivity faces significant challenges in urban environments due to the masking effect, which limits the reliability and availability of communication links in ULSNs. To address this, reconfigurable intelligent surface (RIS) is emerging as a promising solution in ULSNs. In this article, we investigate RIS-aided downlink data transmission in urban environments of multiusers in ULSNs. We set up a mixed-integer programming (MIP) model for maximizing the sum rate of terrestrial users in ULSNs. To solve the complex MIP problem, we propose a two-phase joint optimization algorithm with a deep learning phase and an alternative optimization (AO) phase. In the deep learning phase, a deep neural network (DNN) algorithm is employed to obtain the optimal user association matrix based on the positions of terrestrial users and LEO satellites. Then in the AO phase, successive convex approximation is utilized to transform the nonconvex subproblems of beamforming and RIS phase design into convex formulations and iteratively solve them. Simulation results demonstrate that the proposed algorithm outperforms other baseline algorithms.

To fulfill the demands of emerging multi-modal services, the cross-modal semantic communication paradigm comes into being. It fully utilizes potential semantic correlations among modalities to address polysemy and ambiguity issues, enhancing transmission reliability. However, applying cross-modal semantic communication in resource-constrained mobile scenarios introduces new challenges, including radio spectrum bandwidth limitations and fluctuations for the transmitter, and computing resource constraints for the receiver, which leads to potential transmission failures. To bridge this gap, this paper proposes a cross-modal semantic transmission strategy for mobile scenarios (MobileCMST). We first construct the framework for MobileCMST. Within this framework, a semantic encoder is designed to achieve redundancy elimination for visual and haptic signals. Then, a semantic delivery approach is developed to cope with bandwidth fluctuations and multipath fading channels. Finally, an efficient semantic decoder based on a visual-haptic semantic-integrated diffusion model is proposed. It employs the Mamba backbone to reconstruct high-quality signals with lightweight computational complexity. Extensive experiments demonstrate the excellent performance of the proposed MobileCMST strategy in resource-constrained mobile scenarios.

Vehicular edge computing (VEC) helps improve the task computational performance of vehicles on roads but has difficulty in defending against eavesdropping and selfish attacks simultaneously. In this paper, we design a reputation-based smart contract with blockchain and propose a multi-agent reinforcement learning (RL) based secure offloading scheme for VEC against both eavesdropping and selfish attacks. This scheme has a three-level hierarchical structure for each vehicle and uses the reputations obtained from the blockchain as the basis to optimize the edge node selection, offloading ratio, and power allocation, which aims to reduce the task computational latency, the vehicle energy consumption and eavesdropping rate. By using a punishment function based on the constraints, this scheme avoids exploring dangerous policies that can cause task failure or severe data leakage. A multi-agent deep RL-based secure offloading scheme is proposed for vehicles with sufficient resources, which evaluates the long-term risk rather than the punishment function to further improve the secure offloading performance. The regret bound is analyzedand the cumulative reward upper bound is provided. Simulation results verify the effectiveness of our schemes as compared with the benchmark.

Delay-sensitive applications like telemedicine and VR/AR intensify competition for network resources and elevate congestion risks, particularly in mobile networks with highly dynamic link conditions. Traditional end-to-end congestion control methods suffer from prolonged response times, rendering them ineffective for Delay-sensitive applications. To this end, this paper proposes a novel In-Network Congestion Control (INCC) mechanism that accelerates congestion control by enabling network nodes to proactively identify bottlenecks and promptly notify end-hosts. Unlike traditional end-host-centric approaches, INCC facilitates collaborative congestion decision-making between end-hosts and in-network unit. INCC classifies congestion into two phases: “yellow” and “red” based on the local queue length bottleneck awareness and global congestion flow bottleneck statistics. For the “yellow” local congestion phrase, we design an in-network local control algorithm that performs proactive packet dropping and rate adjustment to mitigate emerging congestion. For the “red” global congestion phrase, we design an end-host and network cooperative global congestion control algorithm to make precise sending rate adaptation by proactive bottleneck awareness. We implement INCC via Linux kernel modifications and design three experiments to compare with Cubic, NewReno, and BBR. Experimental results demonstrate INCC has good performance on round-trip time and throughput, achieving 99.03% scheduling fairness in flow contention scenarios. Additionally, INCC has low execution overhead on CPU utilization and realize microsecond computational latency.

Tile-based streaming has been proposed to address the challenge of high transmission rate demand in 360° virtual reality (VR) video streaming. However, it suffers from network and viewing behavior dynamics (i.e., head movements), while encoded video tiles have various properties in terms of transmission priority, deadline, and reliability requirement. Hence, a supporting transmission protocol is imperative. In this paper, we propose a customized transmission protocol based on Quick UDP Internet Connections (QUIC) which operates over a VR video network slice in the core network. The QUIC protocol is tailored to accommodate the characteristics of tile-based VR video streaming where explicit mapping relations between requested video tiles and QUIC streams are established. Two customized in-network protocol functionalities including packet filtering and caching-based packet retransmission are proposed, to filter out outdated video data due to field-of-view (FoV) prediction errors under viewing behavior dynamics and to achieve efficient packet retransmissions with disparate transmission reliability requirements. A slice-level packet header is designed to support enhanced slice-based VR video transmission with the proposed protocol functionalities. Key transport parameters are determined via theoretical analysis. Simulation results are presented to demonstrate the effectiveness of our proposed transmission protocol in achieving short average video segment downloading time and high average video segment quality.

In this paper, we present a performance analytical model for end-to-end (E2E) service provisioning (i.e., processing or transmission) of layer-encoded video packets over a network slice in the core network. The disparate service reliability requirements of base layer (BL) and enhancement layer (EL) packets are considered in the proposed analytical model for the E2E packet delays, deadline violation probabilities, and throughputs of BL and EL packets. Specifically, a network function virtualization (NFV) node along the routing path of the video streaming slice is split into two consecutive logical nodes, one for packet processing and the other for transmission, based on which a segment-based analysis framework is proposed for E2E service performance modeling. A two-stage queuing model is established to obtain the approximate steady-state probability distribution of queue length at the first node in the first segment, upon which the BL/EL packet delay, deadline violation probability, and throughput at the segment are derived. In addition, the inter-departure time of successive packets departing from the first segment is analyzed based on an approximate M/D/1 system, and the packet departure process at the first segment is approximated as a Poisson process under the assumption of a large packet service rate of the first node. The independence between two consecutive segments is then achieved for analysis tractability, based on which the E2E performance measures are derived. Extensive simulation results demonstrate the accuracy of our proposed performance analytical model and its effectiveness such as in transport parameter determination.

Resource slicing in low Earth orbit satellite networks (LSN) is essential to support diversified services. In this paper, we investigate a resource slicing problem in LSN to reserve resources in satellites to achieve efficient resource provisioning. To address the challenges of non-stationary service demands, inaccurate prediction, and satellite mobility, we propose an adaptive digital twin (DT)-assisted resource slicing scheme for robust and adaptive resource management in LSN. Specifically, a slice DT, being able to capture the service demand prediction uncertainty through collected service demand data, is constructed to enhance the robustness of resource slicing decisions for dynamic service demands. In addition, the constructed DT can emulate resource slicing decisions for evaluating their performance, enabling adaptive slicing decision updates to efficiently reserve resources in LSN. Simulation results demonstrate that the proposed scheme outperforms benchmark methods, achieving low service demand violations with efficient resource consumption.

Low earth orbit (LEO) satellite networks can seamlessly supplement terrestrial networks by providing a high capacity, wide coverage, and cost-effective solution. Positioned to play a significant role in the upcoming 5G/6G era thanks to reduced launch expenses, LEO satellite networks offer benefits such as multi-path transmission, aggregated link bandwidth, redundant paths, and enhanced mobility support. These advantages necessitate further exploration in integrated satellite-terrestrial networks. In this work, we leverage network conditions, underlying link status, and real-time service characteristics to achieve effective synergy, aiming to fulfill application requirements. We formulate the service-oriented multi-path scheduling (SOMPS) problem as a bounded multi-knapsack problem and employ dynamic programming methods for its solution. Simulation results demonstrate that our proposed scheme provides high transmission rate, low latency, and customized information delivery for services, in comparison with baseline schemes.

On behalf of the IEEE Vehicular Technology Society (VTS), it is my great pleasure to welcome you to the 99th IEEE Vehicular Technology Conference, VTC 2024-Spring, in vibrant and beautiful Singapore!

Dual connectivity (DC) is a feature that allows dual-wireless interface devices to concurrently utilize radio resources from two different wireless network technologies. The aggregate data rate achievable using DC is expected to enhance application performance and user experience if the radio resources of contributing wireless networks are efficiently allocated. Moreover, with the current 5G deployment stage, DC is envisioned as a promising solution to address the 5G coverage holes using the existing 4G long-term evolution (LTE) infrastructure. This article presents an approach to provide statistical delay guarantees for delay-sensitive applications running on devices with DC. We propose a stochastic delay-based DC analytical model using the effective bandwidth concept. The model is applied to two case studies, namely, LTE-WiFi connectivity (licensed with nonlicensed) and 5G-LTE connectivity (intergeneration). The proposed model is used as a tool for effective resource allocation by obtaining the optimal uplink traffic share for each network that minimizes the delay violation probability or data transmission cost. Furthermore, using the analytical model, an algorithm for node admission control (NAC) is developed for DC networks. Our simulation results demonstrate that the proposed model and the NAC algorithm can efficiently allocate resources with stochastic delay guarantees for DC networks.

The low Earth orbit (LEO) satellite-assisted communications are envisioned as a prospective solution in next-generation networks to provide reliable, flexible, cost-effective, and globally seamless services. In this paper, we investigate satellite-terrestrial vehicular network (STVN) supporting connected autonomous vehicle (CAV) applications anytime and anywhere. We first establish a model for the LEO satellite-CAV communication system with different satellite orbital parameters. Then the LEO satellite-CAV communication performance in terms of service availability, outage probability, and system throughput is analyzed when considering practical satellite constellations. Furthermore, the impact of different terrestrial infrastructure deployment strategies on the STVN performance is investigated. Extensive numerical results are provided to validate our theoretical analysis and demonstrate the improvement of CAV network performance thanks to LEO satellites in the STVN.

Recently, mobile crowdsensing (MCS) has emerged as a promising solution for traffic state estimation (TSE), which provides real-time and accurate traffic information for supporting diversified intelligent transportation systems (ITS) applications. However, the prohibitive overhead of collecting massive data in vehicular networks limits the available data amount, while the sparsification of MCS data incurs instability and degrades TSE accuracy. To this end, this paper proposes a novel sparse MCS framework to facilitate cost-effective TSE, which utilizes a small number of vehicular MCS participants distributed across all regions as data sources. By utilizing spatial and temporal correlations of traffic flow, an innovative spatiotemporal deep learning model, namely Transformer Graph Attentional Sample and Aggregate neural network (TGASA), is proposed to improve the TSE accuracy with sparse MCS data. Specifically, we design an incorporated graph neural network (GNN) to aggregate the spatial correlation by taking both node features and edge properties into account. And, the transformer neural network architecture is applied to capture the temporal correlation. Extensive simulation results based on real-world datasets demonstrate that the proposed framework can significantly address the instability incurred by the sparsification of MCS data and effectively achieve a more accurate TSE.

We consider a mobile edge computing (MEC) assisted Industrial Internet of Things (IIoT) network, where multiple assembly processing lines in a smart factory are equipped with sensing devices. They sense raw products, generate and offload computing tasks, and finally process the raw products based on the computing results. In this scenario, different positions of the processing machines lead to different priorities and diverse Quality-of-Service (QoS) requirements of tasks. Therefore, how to schedule tasks and allocate the network resources becomes a critical and challenging issue. In this study, we introduce a novel batch-based hybrid nonorthogonal multiple access (NOMA)/orthogonal multiple access (OMA) transmission scheme. The selection between NOMA and OMA schemes is optimized based on the QoS requirements of tasks. Then, we formulate a production efficiency maximization problem with the objective of maximizing the speed of the assembly lines subject to the deadline constraints of offloading and computing procedures. To this end, a two-layer decomposition method is used to decompose the formulated problem into two subproblems. Furthermore, we utilize a bisection searching method to approximate the optimal solution, and propose an efficient method to determine the feasibility of the top-layer subproblem. Simulation results demonstrate the significant performance improvement of our proposed method. In specific, the production efficiency is enhanced by 525% in comparison with pure NOMA scheme.

Edge-device collaboration has the potential to facilitate compute-intensive device pose tracking for resource-constrained mobile augmented reality (MAR) devices. In this article, we devise a 3-D map management scheme for edge-assisted MAR, wherein an edge server constructs and updates a 3-D map of the physical environment by using the camera frames uploaded from an MAR device, to support local device pose tracking. Our objective is to minimize the uncertainty of device pose tracking by periodically selecting a proper set of uploaded camera frames and updating the 3-D map. To cope with the dynamics of the uplink data rate and the user’s pose, we formulate a Bayes-adaptive Markov decision process problem and propose a digital twin (DT)-based approach to solve the problem. First, a DT is designed as a data model to capture the time-varying uplink data rate, thereby supporting 3-D map management. Second, utilizing extensive generated data provided by the DT, a model-based reinforcement learning algorithm is developed to manage the 3-D map while adapting to these dynamics. Numerical results demonstrate that the designed DT outperforms Markov models in accurately capturing the time-varying uplink data rate, and our devised DT-based 3-D map management scheme surpasses benchmark schemes in reducing device pose tracking uncertainty.

This is Part II of the two-part Special Issue (SI) on 5G/6G Precise Positioning on Cooperative Intelligent Transportation Systems (C-ITS) and Connected Automated Vehicles (CAV). The SI aims at bringing together contribution from both academia and industry to highlight the recent progress in various aspects of positioning systems. We have included 30 original contributions in this two-parts SI. We kindly refer readers to Part I of this SI for a comprehensive overview written by the Guest Editorial Team.

The metaverse is envisioned to create immersive and virtual worlds for people to experience interoperable 3D applications. However, the real-time, interactive, and multimedia characteristics of the metaverse applications require strict quality-of-service (QoS) on the underlying networking architecture, including high throughput, ultra-low delay, and human-centric service configurations. Network function virtualization (NFV)-enabled networking resource management can provide a promising solution to service-oriented QoS satisfaction for metaverse users. In this paper, we propose a blockchain-based collaborative and verifiable virtualized network function (VNF) management scheme for metaverse, named BVNF+. BVNF+ enables multiple network providers across different trust domains to abstract their services as VNFs and collaboratively manage end-to-end network slices for human-centric network services in metaverse. To address the design challenge of balancing the on-chain and off-chain overheads, we decouple the computations of VNF queries into modular components based on software and hardware verifiable computation (vc) approaches. Our modular strategy can achieve on/off-chain computation and communication efficiency while keeping low usage of the secure hardware. We conduct security analysis and extensive experiments based on a real-world blockchain testing network. The analysis and experimental results demonstrate that BVNF+ is both secure and efficient as compared with the existing works.

Multi-modal services, integrating various modalities such as audio, visual, and haptic, have emerged as leading multimedia applications in the 5G era and beyond. To fulfill the demands for low latency, high reliability, and large capacity, cross-modal transmission schemes have been proposed. Typically, these schemes emphasize on either audio-visual or haptic modality, and prioritize flawless transmission of one modality to assist the other modality streaming. However, these prerequisite and assumption do not hold for generic multi-modal services and communication environments, where determining the priority of modality and guaranteeing flawless transmission becomes challenging. To address this fundamental problem, in this paper, we introduce a strategy toward generic cross-modal transmission, enabling visual and haptic modalities to assist each other as needed. The strategy includes a visual-haptic mutual stream delivery mechanism at the sender and a visual-haptic mutual signal reconstruction approach at the receiver. The former aims to eliminate redundancy in visual and haptic streams through mutual assistance, while the latter adaptively handles impaired, missing, or delayed visual or haptic signals by leveraging modality-aware knowledge transfer and semantic-aware signal generation techniques. The proposed strategy demonstrates excellent performance through experiments conducted on a standard multi-modal dataset and a practical visual-haptic communication platform.

The ubiquitous data collection has raised potential risks of leaking physical and private attribute information associated with individuals in a collected dataset. A data collector who wants to collect data for provisioning its machine learning (ML)-based services requires establishing a privacy-preserving data collection protocol for data owners. In this work, we design, implement, and evaluate a novel privacy-preserving data collection protocol. Specifically, we validate the functionality of the data collection protocol on behalf of data owners. First, the ML-based services are not always predefined, it is challenging for a data collector to combat inference of private attributes and user identity from the collected data while maintaining the utility of data. To address the challenge, we reconstruct the data by designing a data transformation model based on the autoencoder and clustering. Second, it is necessary to ensure that the reconstructed data satisfy certain privacy-preserving properties as untrusted data collectors can provide the data transformation models. Therefore, we utilize detection models and design an efficient enclave-based mechanism to validate that the reconstructed data's private attribute estimation probability is bounded by the predefined thresholds. Extensive experiments demonstrate our protocol's effectiveness, such as significantly reducing the accuracy of private attribute detection

This paper focuses on trigger-based (TB) random access (RA) strategies for a multiple-packet reception channel with channel capability$M$($M$-MPR channel), where up to$M$packets can be received simultaneously, while more than$M$concurrent packet transmissions result in collisions and are considered lost. We model the contention for the TB MPR framework and derive the optimal RA strategies that maximize two metrics:i)the normalized saturation throughput, andii)the number of stations successfully occupying the MPR channel within each access round. We generalize the$p$-persistent carrier sense multiple access (CSMA) by enabling it to explore both the MPR dimension and the time dimension to adapt the access probabilities. We also propose suboptimal strategies to reduce the complexity, customized for the considered TB framework. Comprehensive performance evaluations and comparisons with respect to a wide range of system parameters and metrics are provided.

Iteration based collaborative learning (CL) paradigms, such as federated learning (FL) and split learning (SL), faces challenges in training neural models over the rapidly growing yet resource-constrained edge devices. Such devices have difficulty in accommodating a full-size large model for FL or affording an excessive waiting time for the mandatory synchronization step in SL. To deal with such challenge, we propose a novel CL framework which adopts an tree-aggregation structure with an adaptive partition and ensemble strategy to achieve optimal synchronization and fast convergence at scale. To find the optimal split point for heterogeneous clients, we also design a novel partitioning algorithm by minimizing the idleness during communication and achieving the optimal synchronization between clients. In addition, a parallelism paradigm is proposed to unleash the potential of optimum synchronization between the clients and server to boost the distributed training process without losing model accuracy for edge devices. Furthermore, we theoretically prove that our framework can achieve better convergence rate than state-of-the-art CL paradigms. We conduct extensive experiments and show that our framework is 4.6× in training speed as compared with the traditional methods, without compromising training accuracy.

To maintain high perception performance among connected and autonomous vehicles (CAVs), in this paper, we propose an accuracy-aware and resource-efficient raw-level cooperative sensing and computing scheme among CAVs and road-side infrastructure. The scheme enables fined-grained partial raw sensing data selection, transmission, fusion, and processing in per-object granularity, by exploiting the parallelism among object classification subtasks associated with each object. A supervised learning model is trained to capture the relationship between the object classification accuracy and the data quality of selected object sensing data, facilitating accuracy-aware sensing data selection. We formulate an optimization problem for joint sensing data selection, subtask placement and resource allocation among multiple object classification subtasks, to minimize the total resource cost while satisfying the delay and accuracy requirements. A genetic algorithm based iterative solution is proposed for the optimization problem. Simulation results demonstrate the accuracy awareness and resource efficiency achieved by the proposed cooperative sensing and computing scheme, in comparison with benchmark solutions.

Low Earth orbit (LEO) satellite networks have shown strategic superiority in maritime communications, assisting in establishing signal transmissions from shore to ship through space-based links. Traditional performance modeling based on multiple circular orbits is challenging to characterize large-scale LEO satellite constellations, thus requiring a tractable approach to accurately evaluate the network performance. In this paper, we propose a theoretical framework for an LEO satellite-aided shore-to-ship communication network (LEO-SSCN), where LEO satellites are distributed as a binomial point process (BPP) on a specific spherical surface. The framework aims to obtain the end-to-end transmission performance by considering signal transmissions through either a marine link or a space link subject to Rician or Shadowed Rician fading, respectively. Due to the indeterminate position of the serving satellite, accurately modeling the distance from the serving satellite to the destination ship becomes intractable. To address this issue, we propose a distance approximation approach. Then, by approximation and incorporating a threshold-based communication scheme, we leverage stochastic geometry to derive analytical expressions of end-to-end transmission success probability and average transmission rate capacity. Extensive numerical results verify the accuracy of the analysis and demonstrate the effect of key parameters on the performance of LEO-SSCN. Notably, with common parameter settings, after incorporating the space link, the transmission success probability increases by 886% with a 13 dB predefined signal-to-noise ratio (or signal-to-interference-plus-noise-ratio) threshold. This superior performance is attributed to the fact that the space link uses a wider bandwidth and greater power for signal transmission compared to the maritime link. It’s undeniable that the integration of the space link inevitably incurs additional expenses.

Cooperative perception (CP) is a key technology to facilitate consistent and accurate situational awareness for connected and autonomous vehicles (CAVs). To tackle the network resource inefficiency issue in traditional broadcast-based CP, unicast-based CP has been proposed to associate CAV pairs for cooperative perception via vehicle-to-vehicle transmission. In this paper, we investigate unicast-based CP among CAV pairs. With the consideration of dynamic perception workloads and channel conditions due to vehicle mobility and dynamic radio resource availability, we propose an adaptive cooperative perception scheme for CAV pairs in a mixed-traffic autonomous driving scenario with both CAVs and human-driven vehicles. We aim to determine when to switch between cooperative perception and stand-alone perception for each CAV pair, and allocate communication and computing resources to cooperative CAV pairs for maximizing the computing efficiency gain under perception task delay requirements. A model-assisted multi-agent reinforcement learning (MARL) solution is developed, which integrates MARL for an adaptive CAV cooperation decision and an optimization model for communication and computing resource allocation. Simulation results demonstrate the effectiveness of the proposed scheme in achieving high computing efficiency gain, as compared with benchmark schemes.

Cell-free massive multiple-input multiple-output (mMIMO) architecture is a promising solution for Industrial Internet of Things (IIoT) because it not only provides massive connectivity but also eliminates the traditional cell edges. Considering the heterogeneous traffic and requirements in the industry, in this paper, we propose a device priority-aware resource allocation policy under cell-free mMIMO IIoT networks. Specifically, we design a priority-aware frame structure that can be used to provide differentiated age of information (AoI) guarantees for devices of different priorities and locations. To characterize the proposed policy, we develop a general analysis framework to evaluate the signal-to-interference ratio meta distribution and the average AoI of a generic device. The framework captures multiple main features under wireless IIoT networks, including cell-free mMIMO architecture, frame structure, finite-sized geographic areas, densely deployed devices, device priority, retransmission, and interaction among different transmission links. The analytical framework is validated by simulations. Based on the analysis, we study a mean-variance optimization problem to improve the network average AoI, while guaranteeing the average AoI per device. Numerical results show that the proposed frame structure works effectively in enhancing the AoI performance of cell-free IIoT networks.

Mounting reconfigurable intelligent surface (RIS) on unmanned aerial vehicle (UAV), called UAV-RIS, combines the benefits of these two techniques, which can further improve the communication performance. However, high-quality air-ground channel links are more vulnerable to both the adversarial eavesdropping and the malicious jamming. Therefore, this paper proposes a reliable and secure communication approach assisted by the UAV-RIS to maximize the secrecy rate, while ensuring the quality of service (QoS) requirement of the legitimate user against both the eavesdroppers and the jammer. Specifically, with the imperfect channel state information and behaviors of mixed attacks, we try to maximize the achievable worst-case secrecy rate by jointly designing the transmit beamforming, artificial noise, UAV-RIS placement, and RIS’s passive beamforming. As the optimization problem is non-convex and the environment is highly dynamic, a post-decision state deep Q-network combined with Fourier feature mapping algorithm (called PDS-DQN-FFM) is further designed to effectively achieve the robust anti-attack transmission strategy. Simulation results demonstrate that our proposed learning based reliable and secure transmission approach significantly enhances both the secrecy rate and QoS satisfaction level as compared with existing approaches.

In this paper, we consider an unmanned aerial vehicle (UAV)-assisted post-disaster rescue scenario, where UAV-mounted aerial base stations (ABSs) compute tasks related to post-disaster rescue operations while also providing communication services to ground users (GUs). With the limited computation capacity of ABSs, we aim to minimize the task computation queuing delay and ensure the GU communication rate by jointly optimizing ABS-GU association, task offloading, and ABS trajectory. The problem is formulated as a mixed-integer nonlinear program, and a solution is proposed by integrating Lyapunov optimization and actor-critic based deep reinforcement learning. We utilize a model-based successive convex approximation technique in a critic module to acquire an accurate evaluation of actor module output. Simulation results demonstrate the effectiveness of the proposed approach in reducing the task computation queuing delay.

The advances in artificial intelligence (AI) and edge computing enable edge intelligence to support pervasive intelligent Internet of Things (IoT) applications in the future wireless networks. We focus on deep neural network (DNN)-based classification tasks, and investigate how to improve the confidence level and delay performance of DNN inference via device-edge collaboration. We first develop a stochastic cumulative DNN inference scheme that aggregates multiple random DNN inference results and generates a cumulative DNN inference result with improved confidence level. Then, based on a computation-efficient DNN model deployment strategy with shared computation between a locally deployed fast DNN model and a full DNN model partitioned between the device and edge, a closed-loop adaptive device-edge collaboration scheme is developed to support cumulative DNN inference for multiple devices. We adaptively determine how to offload DNN inference computation to the edge and how to allocate transmission and edge-computing resources among multiple devices, for Quality-of-Service (QoS) satisfaction in terms of both confidence level and inference delay with resource and energy efficiency. A reinforcement learning (RL) approach is used for adaptive offloading decision, which relies on a resource allocation solution for reward calculation. Simulation results demonstrate the effectiveness of the adaptive device-edge collaboration scheme for cumulative DNN inference, in terms of confidence level improvement, delay violation minimization, network resource efficiency, and device energy efficiency.

To bring 5G systems and networks to life in large-scale commercial applications, academia community has started the research beyond 5G (B5G), in which network slicing (NS) is proposed as a new paradigm for building service-tailored B5G networks. In each network slice, to precisely control the service quality and cost, deploying the service-required virtual network functions (VNFs) by utilizing the linkage between the characteristics of this slicing task and the characteristics of different servers in the B5G network is essential. Therefore, aiming at gaining the ability of learning and adapting new tasks quickly and cost effectively, we view the NFV deployment problem as a meta relational learning process that explores the meta mapping relation between service-tailored slicing tasks and the B5G physical network and propose a service-tailored VNF deployment framework, abbreviated as StailNet. Instead of training a one-strategy-fits-all deployment model, we focus on “learning” how to train a deployment model and propose to learn the features of servers and slicing tasks from the perspective of knowledge graph-based representation learning, then locate the initial meta mapping relation by extracting meta information in the task-agnostic meta space and exploring the service-tailored meta mapping relation in the task space for each task, so that we can quickly obtain the solution by a few gradients on the initial meta mapping relation. To highlight the performances of StailNet, we do comprehensive simulations. Simulation results demonstrate that our StailNet outperforms the selected representative algorithms in the literature.

The advancement of connected intelligent transportation systems (C-ITS) and connected automated vehicles (CAV) has brought about a growing need for precise positioning solutions. Positioning technologies play a crucial role in many use cases such as emergency call systems, disaster rescue operations, automated robotics, and more. To ensure the availability, reliability, and quality of location systems both indoors and outdoors, the evolution of cellular technology, particularly in the form of 5G/6G networks, promises to provide a new pathway towards achieving high precision positioning.

Split learning (SL) is a collaborative learning framework, which can train an artificial intelligence (AI) model between a device and an edge server by splitting the AI model into a device-side model and a server-side model at a cut layer. The existing SL approach conducts the training process sequentially across devices, which incurs significant training latency especially when the number of devices is large. In this paper, we design a novel SL scheme to reduce the training latency, namedCluster-basedParallelSL(CPSL) which conducts model training in a “first-parallel-then-sequential” manner. Specifically, the CPSL is to partition devices into several clusters, parallelly train device-side models in each cluster and aggregate them, and then sequentially train the whole AI model across clusters, thereby parallelizing the training process and reducing training latency. Furthermore, we propose a resource management algorithm to minimize the training latency of CPSL considering device heterogeneity and network dynamics in wireless networks. This is achieved by stochastically optimizing the cut layer selection, device clustering, and radio spectrum allocation. The proposed two-timescale algorithm can jointly make the cut layer selection decision in a large timescale and device clustering and radio spectrum allocation decisions in a small timescale. Extensive simulation results on non-independent and identically distributed data demonstrate that the proposed solution can greatly reduce the training latency as compared with the existing SL benchmarks, while adapting to network dynamics.

In China, some e-commerce platform (EP) companies such as Alibaba and JD are now allowed to partner with network operators (NOs) to act as virtual network operators (VNOs) to provide mobile data services for mobile users (MUs). However, it is a question worth researching on how to generate more profits for all network players, with EP companies being VNOs, through appropriate integration of the VNO business and the companies' own e-commerce business. To address this issue, in this work we propose a novel incentive mechanism for advertising via mobile data reward, and model it as a three-stage static Stackelberg game. We obtain the closed-form optimal solution of the Nash equilibrium by backward induction. Besides, for the scenario lack of knowledge on the interaction between the NO and VNO in a dynamic game, we propose a deep Q-network (DQN) based algorithm to derive the optimal strategies of the NO and VNO. Simulation results show impact of system parameters on the utilities of game players and social welfare. We also study the impact of system parameters on different algorithms and discover that the proposed DQN-based algorithm can learn a good strategy as compared with the Stackelberg equilibrium solution.

Traditional one-bit compressed stochastic gradient descent can not be directly employed in multi-hop all-reduce, a widely adopted distributed training paradigm in network-intensive high-performance computing systems such as public clouds. According to our theoretical findings, due to the cascading compression, the training process has considerable deterioration on the convergence performance. To overcome this limitation, we implement a sign-bit compression-based learning synchronization framework, Marsit. It prevents cascading compression via an elaborate bit-wise operation for unbiased sign aggregation and its specific global compensation mechanism for mitigating compression deviation. The proposed framework retains the same theoretical convergence rate as non-compression mechanisms. Experimental results demonstrate that Marsit reduces up to 35% training time while preserving the same accuracy as training without compression.

This paper presents a novel approach for computing resource management of edge servers in vehicular networks based on digital twins and artificial intelligence (AI). Specifically, we construct two-tier digital twins tailored for vehicular networks to capture networking-related features of vehicles and edge servers. By exploiting such features, we propose a two-stage computing resource allocation scheme. First, the central controller periodically generates reference policies for real-time computing resource allocation according to the network dynamics and service demands captured by digital twins of edge servers. Second, computing resources of the edge servers are allocated in real time to individual vehicles via low-complexity matching-based allocation that complies with the reference policies. By leveraging digital twins, the proposed scheme can adapt to dynamic service demands and vehicle mobility in a scalable manner. Simulation results demonstrate that the proposed digital twin-driven scheme enables the vehicular network to support more computing tasks than benchmark schemes.

In this paper, we propose a Secure and Flexible Data Sharing (SFDS) scheme for distributed storage, where data owners can outsource their data to a distributed storage network and share the data with authorized users. To preserve confidentiality, all data are encrypted by data owners’ secret keys before being outsourced, and fine-grained access policies are enforced on the encrypted data (ciphertexts) to achieve flexible data sharing. Furthermore, based on the ciphertext puncturable encryption and the hierarchical identity-based encryption, we design an efficient key and ciphertext update mechanism, which enables data owners to update their secret keys and the corresponding ciphertexts periodically to deal with side-channel attacks and system vulnerabilities. Update tokens are constructed to directly derive new keys and ciphertexts. Through detailed security analysis, it is demonstrated that SFDS can achieve all three essential security properties, i.e., forward security, post-compromise security, and collusion attack resistance.

Many Internet of Things (IoT) devices generate relatively small-sized data and have limited energy supply. These two factors limit their ability to connect directly to cloud servers through a wireless backbone network without imposing a burden on this network in providing efficient data transfer. In this article, we consider an IoT network architecture where a number of different IoT devices send their data wirelessly to an IoT gateway (or a fog node) via a WiFi network. We focus on characterizing incoming traffic patterns to the gateway for three typical IoT applications with real-time and nonreal-time data transfer requirements, such as video surveillance, smart city, and e-healthcare. Our study is based on generating real IoT traffic traces in a lab environment from various sensors and devices for the aforementioned applications and employing these traces to emulate a network of IoT nodes connected to a gateway via WiFi. In the conducted experiments, different homogenous and nonhomogeneous traffic patterns of the selected applications are examined for synchronized and unsynchronized data sources. Based on our empirical data, the experimental results reveal that the packet interarrival time distribution at the gateway is close to generalized Pareto distribution for homogeneous eHealth and smart city traffic, whereas the Weibull distribution is the nearest to model the empirical packet interarrival time for the rest of the examined traffic patterns. Moreover, we show that employing the experimental findings to analyze the delay performance of connecting the gateway to the cloud, given certain backbone network resources, leads to accurate results.

In this article, we propose a multilayer Ka/Q-band satellite–terrestrial integrated network for the Internet of Remote Things (IoRT) to achieve a high transmission rate with communication robustness in dynamic network environments. Under this architecture, we investigate how to jointly manage the offloading path selection and resource allocation to offload computation-intensive and delay-sensitive tasks in the IoRT. Considering continuous low earth orbit (LEO) satellite movements and Markovian rainfall changes, the computation offloading problem is described as a Markov decision process (MDP) formulation with the objective of maximizing the number of offloaded tasks with satisfied delay requirements and minimizing the power consumption of the LEO satellites. A deep reinforcement learning (DRL) approach is leveraged to make optimal decisions by taking account of dynamic queues of IoRT devices, channel conditions that vary with rainfall intensities and satellite positions, and computing capabilities of ground stations. Extensive simulations are conducted to validate the effectiveness and superiority of our proposed scheme.

Secure cross-domain authorization and authentication (AA) enable application service providers (ASPs) to allow users for resource access from different trusted domains. In this article, we propose a unified blockchain-assisted secure cross-domain AA framework for smart city, which can guarantee transparent cross-domain resource access while preserving user privacy. In the framework, ASPs can flexibly delegate their authentication capabilities to the blockchain, and users authorized by different ASPs can be authenticated by the blockchain where the authentication events are publicly audited and traced. Since the blockchain is publicly accessible, users’ sensitive identity attributes may be exposed during the authentication process. To address privacy leakage caused by the authentication events, several privacy-preserving techniques, including threshold-based homomorphic encryption, zero-knowledge proof, and random permutation, are exploited to hide users’ sensitive information on the blockchain. Moreover, to improve user revocation efficiency, we integrate a cryptographic accumulator and secure hash functions into the framework where ASPs are allowed to revoke their users through a global revocation contract. Our security analysis shows that the proposed framework can achieve all desirable security and privacy properties, and a proof-of-concept prototype has been developed to demonstrate the correctness and efficiency of the proposed framework.

Federated learning (FL) over resource-constrained wireless networks has recently attracted much attention. However, most existing studies consider one FL task in single-cell wireless networks and ignore the impact of downlink/uplink inter-cell interference on the learning performance. In this paper, we investigate FL over a multi-cell wireless network, where each cell performs a different FL task and over-the-air computation (AirComp) is adopted to enable fast uplink gradient aggregation. We conduct convergence analysis of AirComp-assisted FL systems, taking into account the inter-cell interference in both the downlink and uplink model/gradient transmissions, which reveals that the distorted model/gradient exchanges induce a gap to hinder the convergence of FL. We characterize the Pareto boundary of the error-induced gap region to quantify the learning performance trade-off among different FL tasks, based on which we formulate an optimization problem to minimize the sum of error-induced gaps in all cells. To tackle the coupling between the downlink and uplink transmissions as well as the coupling among multiple cells, we propose a cooperative multi-cell FL optimization framework to achieve efficient interference management for downlink and uplink transmission design. Results demonstrate that our proposed algorithm achieves much better average learning performance over multiple cells than non-cooperative baseline schemes.

Future networks incorporate artificial intelligence to enable smart resource management and adaptive service provisioning. With a heterogeneous architecture and a large number of users in future networks, transparent and decentralized data sharing is required to promote data circulation and break data silos, for which blockchain is a potential solution to allow intelligent access permission control. However, it remains a challenging task to achieve flexible authorization management for blockchain-based data sharing and efficient key update for multi-users in case of key exposure. In this paper, we propose an intelligent blockchain-based data-sharing scheme with key update for future networks. First, we design a new encryption scheme, where keywords of data are extracted using machine learning algorithms that are published on the blockchain. Then, keywords of data and time validity are used to encrypt different types of data for flexible data authorization. Second, using hierarchical identity-based encryption, we construct an efficient key update mechanism, where update tokens are generated by invoking a smart contract deployed on the blockchain to facilitate key and ciphertext updates. We formally prove that the proposed scheme can guarantee three essential security properties: forward security, post-compromise security, and collusion attack resistance. On-chain and off-chain experiment results are provided to demonstrate that the proposed scheme can achieve computational and communication efficiency for key and ciphertext updates.

Federated meta-learning (FML) has emerged as a promising paradigm to cope with the data limitation and heterogeneity challenges in today’s edge learning arena. However, its performance is often limited by slow convergence and corresponding low communication efficiency. In addition, since the available radio spectrum and IoT devices’ energy capacity are usually insufficient, it is crucial to control the resource allocation and energy consumption when deploying FML in practical wireless networks. To overcome the challenges, in this paper, we rigorously analyze the contribution of each device to the global loss reduction in each round and develop an FML algorithm (called NUFM) with a non-uniform device selection scheme to accelerate the convergence. After that, we formulate a resource allocation problem integrating NUFM in multi-access wireless systems to jointly improve the convergence rate and minimize the wall-clock time along with energy cost. By deconstructing the original problem step by step, we devise a joint device selection and resource allocation strategy to solve the problem with theoretical guarantees. Further, we show that the computational complexity of NUFM can be reduced from$O(d^{2})$to$O(d)$(with the model dimension$d$) via combining two first-order approximation techniques. Extensive simulation results demonstrate the effectiveness and superiority of the proposed methods in comparison with existing baselines.

In this article, a two-level soft-slicing scheme is proposed for 5G-and-beyond radio access networks to support ultrareliable and low-latency communications (URLLC) and enhanced mobile broadband (eMBB) services with delay/reliability and throughput requirements, respectively. At the network level, we first determine the number of radio resources required for eMBB services and analyze the delay violation probability for URLLC services. Then, an integer nonlinear program is formulated for the network-level resource preallocation. Since the formulated problem is NP-complete, a low-complexity heuristic algorithm is proposed to obtain near-optimal solutions. Given the preallocated resources at each gNodeB (gNB), a gNB-level resource scheduling scheme is designed to enable real-time resource sharing among URLLC services considering the reliability and delay requirements. Simulation results show that the proposed soft-slicing scheme meets stringent quality-of-service requirements for both URLLC and eMBB services and achieves high resource utilization efficiency when compared with conventional hard resource slicing schemes.

With the increasing popularity of plug-in electric vehicles (PEV), charging infrastructure becomes widely available and offers multiple services to PEV users. Each charging service has a distinct quality of service (QoS) level that matches user expectations. The charging service demand is interdependent, i.e., the demand for one service is often affected by the prices of others. Dynamic pricing of charging services is a coordination mechanism for QoS satisfaction of service classes. In this article, we propose a differentiated pricing mechanism for a multiservice PEV charging infrastructure (EVCI). The proposed framework motivates PEV users to avoid over-utilization of particular service classes. Currently, most of dynamic pricing schemes require full knowledge of the customer-side information; however, such information is stochastic, non-stationary, and expensive to collect at scale. Our proposed pricing mechanism utilizes model-free deep reinforcement learning (RL) to learn and improve automatically without an explicit model of the environment. We formulate our framework to adopt the twin delayed deep deterministic policy gradient (TD3) algorithm. The simulation results demonstrate that the proposed RL-based differentiated pricing scheme can adaptively adjust service pricing for a multiservice EVCI to maximize charging facility utilization while ensuring service quality satisfaction.

Recently, 360-degree mobile virtual reality video (MVRV) has become increasingly popular because it can provide users with an immersive experience. However, MVRV is usually recorded in a high resolution and is sensitive to latency, which indicates that broadband, ultra-reliable, and low-latency communication is necessary to guarantee the users’ quality of experience. In this paper, we propose a mobile edge computing (MEC)-based 360-degree MVRV streaming scheme with field-of-view (FoV) prediction, which jointly considers video coding, proactive caching, computation offloading, and data transmission. To meet the requirement of stringent end-to-end (E2E) latency, the user’s viewpoint prediction is utilized to cache video data proactively, and computing tasks are partially offloaded to the MEC server. In addition, we propose an analytical model based on diffusion process to study the packet transmission process of 360-degree MVRV in multihop wired/wireless networks and analyze the performance of the MEC-enabled scheme. The simulation results verify the accuracy of the analysis and the effectiveness of the proposed MVRV streaming scheme in reducing the E2E delay. Furthermore, the analytical framework sheds some light on the impacts of system parameters, e.g., FoV prediction accuracy and transmission rate, on the balance between computation delay and communication delay.

Network function virtualization is a key enabling technology in future wireless networks for flexible and efficient sharing of network resources. Due to the increasing heterogeneity of network resource providers, a blockchain-based distributed architecture is a promising solution to enable reliable and transparent virtualized network function (VNF) management. However, since on-chain storage and computation are costive, it becomes a challenging task to achieve efficient VNF management with blockchain. In this paper, we first introduce a consortium blockchain for collaborative VNF management among network resource providers. Then, we propose an authenticated VNF dictionary that can be stored as a succinct authenticator on blockchain to support rich VNF query functionalities and efficient verifications of query results. Moreover, we design a dictionary pruning strategy to securely generate a compact authenticator for a given query, which reduces unnecessary memory accesses of the original dictionary when VNF queries are represented as arithmetic circuits. Finally, we conduct extensive experiments with a consortium blockchain network. The experimental results demonstrate that our pruning strategy is efficient for both on-chain and off-chain VNF management.

In this paper, we investigate a computing task offloading problem in a cloud-based autonomous vehicular network (C-AVN), from the perspective of long-term network wide computation load balancing. To capture the task computation load dynamics over time, we describe the problem as an Markov decision process (MDP) with constraints. Specifically, the objective is to minimize the expectation of a long-term total cost for imbalanced base station (BS) computation load and task offloading decision switching, with per-slot computation capacity and offloading latency constraints. To deal with the unknown state transition probability and large state-action spaces, a multi-agent deep Q-learning (MA-DQL) module is designed, in which all the agents cooperatively learn a joint optimal task offloading policy by training individual deep Q-network (DQN) parameters based on local observations. To stabilize the learning performance, a fingerprint-based method is adopted to describe the observation of each agent by including an abstraction of every other agent’s updated state and policy. Simulation results show the effectiveness of the proposed task offloading framework in achieving long-term computation load balancing with controlled offloading switching times and per-slot QoS guarantee.

In China, some e-commerce platform (EP) companies such as Alibaba and JD have been now allowed to partner with network operators (NOs) to act as virtual network operators (VNOs) to provide mobile data services for mobile users (MUs). However, it is a question worth researching on how to generate more profits for all network players after EP companies being VNOs through appropriate integration of the VNO business and the companies’ own e-commerce business. To address this issue, in this work we propose a novel incentive mechanism for advertising via mobile data reward, and model it as a three-stage Stackelberg game. In Stage I, the NO decides the price of mobile data for the VNO; in Stage II, the VNO decides its data plan fee for MUs and the ad price for e-commerce merchants (EMs); in Stage III, the MUs make their own decisions on the data plan subscription and the number of ads to be watched, while the EMs decide the number of ad slots they buy from the EP. We obtain the closed-form optimal solution of the Nash equilibrium by backward induction. Simulation results show the impact of the system parameters on the utilities of game players and social welfare, and reveal that the solution can indeed lead to a quadri-win outcome in some cases. At the same time, we summarize some insights that have economic guidance.

With a rapid increase in the number of vehicles over recent years, urban parking systems have encountered more and more challenges. In this article, a dual-lens millimeter wave (MMW) radar antenna is designed for a smart parking system in the context of the Internet of Things (IoT). A flat dielectric punch lens is used to increase the gain of the transmitting antenna in order to compensate for the penetration loss in MMW. In addition, a dielectric rod lens is used to correct beam direction and maintain a wide beamwidth in order to overcome received energy loss due to scattering of the car chassis. The combined dual-lens antenna can improve the accuracy and stability of MMW radar operating at 24 GHz. The measured gain is 15.8 dBi for the transmitting antenna and 7.9 dBi for the receiving antenna, and the 3-dB beamwidth is approximately 65°. The system measurement results show that the proposed antenna has stable measurement effect and is suitable for the MMW radar smart parking system.

In the second part of this article, we develop a centralized packet transmission scheduling scheme to pair with the protocol designed in Part I and complete our medium access control (MAC) design for machine-type communications in the industrial Internet of Things. For the networking scenario, fine-grained scheduling that attends to each device becomes necessary, given stringent Quality-of-Service (QoS) requirements and diversified service types, but prohibitively complex for a large number of devices. To address this challenge, we propose a scheduling solution in two steps. First, we develop algorithms for device assignment based on the analytical results from Part I, when parameters of the proposed protocol are given. Then, we train a deep neural network for assisting in the determination of the protocol parameters. The two-step approach ensures the accuracy and granularity necessary for satisfying the QoS requirements and avoids excessive complexity from handling a large number of devices. Integrating the distributed coordination in the protocol design from Part I and the centralized scheduling from this part, the proposed MAC protocol achieves high performance, demonstrated through extensive simulations. For example, the results show that the proposed MAC can support 1000 devices under an aggregated traffic load of 3000 packets per second with a single channel and achieve <; 0.5 ms average delay and <; 1% average collision probability among 50 high priority devices.

In this two-part paper, we propose a novel medium access control (MAC) protocol for machine-type communications in the Industrial Internet of Things. The considered use case features a limited geographical area and a massive number of devices with sporadic data traffic and different priority types. We target supporting the devices while satisfying their Quality-of-Service (QoS) requirements with a single access point and a single channel, which necessitates a customized design that can significantly improve the MAC performance. In Part I of this paper, we present the MAC protocol that comprises a new slot structure, corresponding channel access procedure, and mechanisms for supporting high device density and providing differentiated QoS. A key idea behind this protocol is sensing-based distributed coordination for significantly improving channel utilization. To characterize the proposed protocol, we analyze its delay performance based on the packet arrival rates of devices. The analytical results provide insights and lay the groundwork for the fine-grained scheduling with QoS guarantee as presented in Part II.

Through network function virtualization (NFV), virtual network functions (VNFs) can be mapped onto substrate networks as service function chains (SFCs) to provide customized services with guaranteed Quality of Service (QoS). In this article, we solve a multi-SFC embedding problem by a game-theoretical approach considering the heterogeneity of NFV nodes, the effect of processing-resource sharing among various VNFs, and the capacity constraints of NFV nodes. Specifically, each SFC is treated as a player whose objective is to minimize the overall latency experienced by the supported service flow, while satisfying the capacity constraints of all NFV nodes. Due to processing-resource sharing, additional delay is incurred and incorporated into the overall latency for each SFC. The capacity constraints of NFV nodes are considered by adding a penalty term into the cost function of each player, and are guaranteed by a prioritized admission control mechanism. We prove that the formulated resource-constrained multi-SFC embedding game (RC-MSEG) is an exact potential game admitting at least one pure Nash equilibrium (NE) and has the finite improvement property (FIP). Two iterative algorithms are developed, namely, the best response (BR) algorithm with fast convergence and the spatial adaptive play (SAP) algorithm with great potential to obtain the best NE. Simulations are conducted to demonstrate the effectiveness of the proposed game-theoretical approach.

To efficiently allocate heterogeneous resources for customized services, in this article, we propose a network virtualization (NV)-based network architecture in cybertwin-enabled 6G core networks. In particular, we investigate how to optimize the virtual network (VN) topology (which consists of several virtual nodes and a set of intermediate virtual links) and determine the resultant VN embedding in a joint way over a cybertwin-enabled substrate network. To this end, we formulate an optimization problem whose objective is to minimize the embedding cost, while ensuring that the end-to-end (E2E) packet delay requirements are satisfied. The queueing network theory is utilized to evaluate each service’s E2E packet delay, which is a function of the resources assigned to the virtual nodes and virtual links for the embedded VN. We reveal that the problem under consideration is formally a mixed-integer nonlinear program (MINLP) and propose an improved brute-force search algorithm to find its optimal solutions. To enhance the algorithm’s scalability and reduce the computational complexity, we further propose an adaptively weighted heuristic algorithm to obtain near-optimal solutions to the problem for large-scale networks. Simulations are conducted to show that the proposed algorithms can effectively improve network performance compared to other benchmark algorithms.

Next-generation core networks are expected to achieve service-oriented traffic management for diversified Quality-of-Service (QoS) provisioning based on software-defined networking (SDN) and network function virtualization (NFV). In this article, a learning-based transmission protocol customized for Video-on-Demand (VoD) streaming services is proposed for a Cybertwin-enabled next-generation core network, which provides caching-based congestion control and throughput enhancement functionalities at the edge of the core network based on traffic prediction. The per-slot traffic load of a VoD streaming service at an ingress edge node is predicted based on the autoregressive integrated moving average (ARIMA) model. To balance the tradeoff between network congestion and throughput enhancement, a multiarmed bandit (MAB) problem is formulated to maximize the expected overall network performance in a long run, by capturing the relationship between transmission control actions and QoS provisioning. A comprehensive transmission protocol operation framework is also presented with in-network congestion control and throughput enhancement modules. Simulation results are presented to validate the efficacy of the proposed protocol in terms of packet delay, goodput ratio, throughput, and resource utilization.

When running in Parameter Server (PS), the Distributed Stochastic Gradient Descent (D-SGD) incurs significant communication delays and huge communication overhead due to the model synchronization. Moreover, considering the heterogeneity of computational capability among workers, traditional synchronization modes incur under-utilization of computational resources because fast workers have to wait for slow ones finishing the computation. Although our previous work OSP can effectively solve these problems by overlapping the computation and communication procedures and allowing adaptive multiple local updates in distributed training, it causes the staleness problem brought by the overlap, yielding a performance degradation. In this paper, we propose a new method named LOSP by introducing local compensation to our previous synchronization mechanism, which mitigates adverse effects caused by the overlapping synchronization. We theoretically prove that LOSP (1) preserves the same convergence rate as the sequential SGD for non-convex problems, and (2) exhibits good scalability due to the linear speedup property with respect to both the number of workers and the average number of local updates. Evaluations show that LOSP significantly improves performance over the state-of-the-art ones in terms of both convergence accuracy and communication cost.

In this paper, we investigate a radio access network (RAN) slicing problem for Internet of vehicles (IoV) services with different quality of service (QoS) requirements, in which multiple logically-isolated slices are constructed on a common roadside network infrastructure. A dynamic RAN slicing framework is presented to dynamically allocate radio spectrum and computing resource, and distribute computation workloads for the slices. To obtain an optimal RAN slicing policy for accommodating the spatial-temporal dynamics of vehicle traffic density, we first formulate a constrained RAN slicing problem with the objective to minimize long-term system cost. This problem cannot be directly solved by traditional reinforcement learning (RL) algorithms due to complicatedcoupled constraintsamong decisions. Therefore, we decouple the problem into a resource allocation subproblem and a workload distribution subproblem, and propose atwo-layer constrainedRL algorithm, namedResourceAllocation andWorkload diStribution (RAWS) to solve them. Specifically, anouter layerfirst makes the resource allocation decision via an RL algorithm, and then aninner layermakes the workload distribution decision via an optimization subroutine. Extensive trace-driven simulations show that the RAWS effectively reduces the system cost while satisfying QoS requirements with a high probability, as compared with benchmarks.

Millimeter-wave (mmWave) frequency bands, which offer abundant underutilized spectral resources, have been explored and exploited in the past several years to meet the requirements of emerging wireless services highlighted by high data rates, ultrareliability, and ultralow delivery latency. Yet, the unique characteristics of mmWave, e.g., continuous wide bandwidth, large path, and penetration losses, along with hardware constraints, call for innovative technologies for mmWave communication. Recently, an extensive amount of work on mmWave communication has been carried out by researchers and practitioners from both academia and industry, and various technologies have been developed for mmWave communication systems to fulfill the full potential of mmWave frequency bands. In this article, we present a comprehensive survey of the standardization of mmWave communication, the latest progress and outcomes of the research on mmWave communication technologies, and the emerging applications of mmWave communication. In particular, we provide a timely and in-depth summary of the state-of-the-art technology specifications of mmWave communication with an emphasis on the physical (PHY) layer. Then, we elaborate on a number of well-established or promising antenna architectures in mmWave communication systems and investigate the enabling PHY layer transmission technologies. Finally, we show some existing and emerging applications of mmWave communication and discuss the potential open research issues.

In controller area networks (CANs), electronic control units (ECUs) such as telematics ECUs and on-board diagnostic ports must protect the message exchange from spoofing attacks. In this paper, we propose a CAN bus authentication framework that exploits physical layer features of the messages, including message arrival intervals and signal voltages, and applies reinforcement learning to choose the authentication mode and parameter. By applying the Dyna architecture and using a double estimator, this scheme improves the utility in terms of authentication accuracy without changing the CAN bus protocol or the ECU components and requiring knowledge of the spoofing model. We also propose a deep learning version to further improve the authentication efficiency for the CAN bus. The learning scheme applies a hierarchical structure to reduce the exploration time, and uses two deep neural networks to compress the high-dimensional state space and to fully exploit the physical authentication experiences. We provide the computational complexity and the performance analysis. Experimental results verify the theoretical analysis and show that our proposed schemes significantly improve the authentication accuracy as compared with benchmark schemes.

Driven by an explosive increase in the number of users and data usage, energy consumption becomes a significant concern for information and communication technology industry. In softwarization based networks, energy-efficient Network Function (NF) resource allocation is imperative yet challenging for service provisioning. In this paper, we investigate dynamic NF resource allocation (NFRA) problem for service function chains (SFCs) with aim to minimize the long-term energy consumption, while guaranteeing the end-to-end delay requirements for the packets traversing the SFCs. We formulate the problem as an infinite horizon Markov Decision Process (MDP) problem and obtain the global optimal solution based on the value iteration algorithm which has high computational complexity. The global optimal solution serves as a performance upper bound due to its high computational complexity. To cater for efficient on-line NFRA decisions, we further design a suboptimal distributed value iteration based dynamic NF resource allocation (DDRA) algorithm. The numerical results based on real-world data traces demonstrate the proposed DDRA algorithm achieves a close-to-optimal performance and a significant performance improvement compared with two known NF resource allocation algorithms.

Accurate cellular traffic load prediction is a pre-requisite for efficient and automatic network planning and management. Considering diverse users' activities at different locations and times, it is technically challenging to characterize the network resource demands at different time scales via traditional prediction methods. In this paper, we propose to combine the long short-term memory (LSTM) and Gaussian process regression (GPR) to achieve accurate single-cell level cellular traffic prediction, using the open Milan cellular traffic dataset provided by Telecom Italia. Firstly, the dominant periodic components of the cellular data are extracted, and then the small components are fed to the LSTM network. To further improve the prediction accuracy, GPR is used to recover the residual components. Extensive experiments are conducted based on the dataset, and it is shown that the proposed LSTM-GPR scheme outperforms the benchmark schemes, especially for a relatively long time and burst traffic prediction.

In this paper, a robust Pareto suboptimal strategy for an uncertain susceptible-infected-recovered (SIR) model with state delay is investigated, based on the static output feedback (SOF). After linearizing the original nonlinear SIR model, a sufficient condition for the existence of a proposed strategy set is derived in terms of high-order cross-coupled matrix equations (HCMEs). Using the guaranteed cost control technique, both robust stability and existence of the cost bound are attained. To avoid high complexity of directly solving the HCMEs, a recursive algorithm based on the linear matrix inequality (LMI) is presented. Finally, a practical SIR time-delay model is used to demonstrate the effectiveness and reliability of the proposed strategy.

Internet of Things (IoT) is becoming increasingly prevalent in transportation systems. The traffic system depends on safer, faster, and more intelligent vehicles. Vehicular networks [vehicle-to-vehicle (V2V) and vehicle-to- Infrastructure (V2I)] and automated driving technique are two of the cornerstone technologies enabling the construction of the future-generation highly functional and intelligent transportation system. The IoT-based transportation system can provide enormous connections of devices and sensors for the networked automated vehicles. The capacity of connected autonomous vehicles (CAVs) is expected to be dramatically enhanced by employing IoT techniques.

In this article, a rugged linear array is proposed for covering both the LTE and 5G bands with an intermediate gain. The antenna is composed of a driven element, a set of directors, and a set of reflectors, where the excited element is a wideband high-efficiency electromagnetic structure (WHEMS) and the parasitic elements consist of metal rods. To achieve a rugged design, similar to the classic Yagi antenna, all of the elements should be conductively connected, so that it can be welded. The weldable mechanism is started on the driven radiating element. In addition, a balun is introduced in the antenna to reduce the influence of unbalanced common-mode currents. A wind resistance analysis is also presented, where the drag force of the proposed antenna is approximately a quarter of that for an antenna with a metal plate. The antenna exhibits a gain of 10.8-13.3 dBi for a 78% fractional bandwidth (1.7-3.7 GHz), which is a sevenfold increase from that of the Yagi antenna, without sacrificing the gain or rugged design. The proposed antenna has the advantages of a simple feeding arrangement, low cost, lightweight, low-wind resistance, and rugged structure; and is suitable for all-weather large-scale Internet-of-Things (IoT) deployment at a rural site or in a harsh networking environment.

High-precision positioning is critical for many Internet-of-Things (IoT) applications; however, most existing approaches are too expensive to be used in commercial products. A highly accurate differential global positioning system (DGPS) has not been widely used because of the difficulty in solving integer ambiguities in the single-frequency carrier phase. Multipath interference and receiver noise are the main reasons for limiting the DGPS accuracy and efficiency in solving integer ambiguities. In this article, we propose a combination of anti-multipath antennas and high-performance GPS receivers to effectively mitigate impairments due to multipath propagation and receiver noise. Furthermore, by exploiting more data available from high-performance GPS receivers, we can improve the efficiency of solving carrier-phase integer ambiguities. For applications in a smart snow blower, we installed two GPS receivers with a constant separation between them. The distance between the GPS receivers was used to verify the DGPS results. Furthermore, using the proposed DGPS technology, the smart snow blower can obtain a high-precision orientation estimation, with a standard deviation of 0.299 cm in positioning accuracy and 0.409° in orientation accuracy.

Providing energy-efficient and delay-aware channel access in cellular networks is essential to many anticipated massive Internet of Things (IoT) applications. However, as the number of devices increases, the contention over the limited network radio resources increases, leading to network congestion. The congestion increases the channel access delay and energy consumption of IoT devices, and reduces the number of supported devices. Node clustering and data aggregation are potential approaches to support the massive number of devices while meeting the various service quality requirements of diverse applications. As the number of devices increases, optimizing the node clustering and data aggregation process becomes critical as many tradeoffs arise among different network performance metrics. In this article, we present a novel nonorthogonal multiple access-enabled two-stage transmission architecture to enable massive cellular IoT communications. Concepts from queuing theory and stochastic geometry are jointly exploited to derive tractable models for different network performance parameters, such as coverage probability, two-hop access delay, and the number of served devices per transmission frame. The established models characterize relations among various network parameters, and hence facilitate the design of two-stage transmission architecture. The numerical results demonstrate that the proposed solution improves the overall access delay and energy efficiency as compared to traditional-orthogonal-multiple-access-based clustered networks. They also highlight that, for the scenario considered, there is an optimal number of aggregators at which the tradeoffs among the different network performance measures are optimized.

Node clustering and data aggregation help extend the coverage of cellular networks and increase the number of supported devices, while meeting the various service quality requirements and reducing energy consumption, making them suitable for enabling the future massive cellular Internet-of-Things (IoT) applications. Consequently, we propose to overlay the cellular network with a layer of data aggregators (DAs) to act as relays. DAs use cellular backhauling, and thus, the network provides both single- and two-hop routes; however, DAs share radio resources with single-hop devices, creating a dependency between the two routes. Thus, the proper design of the DA-enabled network becomes critical for cost effectiveness and efficient radio resource utilization. In this article, we formulate a joint access point association, resources utilization, and energy-efficient communication optimization problem that takes into account various networking factors, such as the number of devices, the number of DAs, the number of available resource units, interference, the transmission power limitation of the devices, DA transmission performance, and channel conditions. The objective is to show the usefulness of data aggregation and shed light on the importance of network design when the number of devices is massive. We propose a coalition game theory-based algorithm PAUSE to transform the optimization problem into a simpler form that can be successfully solved in polynomial time. Different network scenarios are simulated to showcase the effectiveness of PAUSE and to draw observations on cost-effective network design with DAs.

Drive-thru Internet has been considered as an effective Internet access method for Internet of Vehicles (IoV). Through the opportunistic vehicle-to-roadside WiFi connection, it can provide high throughput performance with low communication cost for IoV applications, such as intelligent transportation system, automotive infotainment, etc. However, its usability is highly affected by a fundamental issue called rate adaptation (RA), which is to adjust the modulation and coding rate to adapt to the dynamic wireless channel between the vehicle and the roadside access point (AP). Conventional WiFi RA schemes are designed for indoor or quasistatic scenarios and do not account for the channel variations in drive-thru Internet. In this article, we study the limitation of applying existing RA schemes in drive-thru Internet and propose a reinforcement learning (RL)-based RA scheme to capture the potential channel variation patterns and efficiently select the rate for every vehicle's egress frame. Simulation results demonstrate that the proposed RA scheme outperforms the existing schemes in network throughput and that the efficiency of the learning model can be generalized under various conditions. The proposed RA method can provide useful inspirations for designing robust and scalable link adaptation protocols in IoV.

The paradigm of network function virtualization (NFV) with the support of software defined networking (SDN) emerges as a promising approach for customizing network services in fifth generation (5G) networks. In this paper, a multicast service orchestration framework is presented, where joint traffic routing and virtual network function (NF) placement are studied for accommodating multicast services over an NFV-enabled physical substrate network. First, we investigate a joint routing and NF placement problem for a single multicast request accommodated over a physical substrate network, with both single-path and multipath traffic routing. The joint problem is formulated as a mixed integer linear programming (MILP) problem to minimize the function and link provisioning costs, under the physical network resource constraints, flow conservation constraints, and NF placement rules; Second, we develop an MILP formulation that jointly handles the static embedding of multiple service requests over the physical substrate network, where we determine the optimal combination of multiple services for embedding and their joint routing and placement configurations, such that the aggregate throughput of the physical substrate is maximized, while the function and link provisioning costs are minimized. Since the presented problem formulations are NP-hard, low complexity heuristic algorithms are proposed to find an efficient solution for both single-path and multipath routing scenarios. Simulation results are presented to demonstrate the effectiveness and accuracy of the proposed heuristic algorithms.

Network function virtualization (NFV) fosters innovation in the networking field and reduces the complexity involved in managing modern-day conventional networks. Via NFV, the provisioning of a network service becomes more agile, whereby virtual network functions can be instantiated on commodity servers and data centers on demand. Network functions can be either mandatory or best-effort. The former type is strictly necessary for the correctness of a network service, whereas the latter is preferrable yet not necessary. In this paper, we study the online provisioning of NFV-enabled network services. We consider both unicast and multicast NFV-enabled services with multiple mandatory and best-effort NF instances. We propose a primal-dual based online approximation algorithm that allocates both processing and transmission resources to maximize a profit function, subject to resource constraints on physical links and NFV nodes. The online algorithm resembles a joint admission mechanism and an online composition, routing and NF placement framework. The online algorithm is derived from an offline formulation through a primal-dual based analysis. Such analysis offers direct insights and a fundamental understanding on the nature of the profit-maximization problem for NFV-enabled services with multiple resource types.

Internet-of-Vehicles (IoV) connects vehicles, sensors, pedestrians, mobile devices, and the Internet with advanced communication and networking technologies, which can enhance road safety, improve road traffic management, and support immerse user experience. However, the increasing number of vehicles and other IoV devices, high vehicle mobility, and diverse service requirements render the operation and management of IoV intractable. Software-defined networking (SDN) and network function virtualization (NFV) technologies offer potential solutions to achieve flexible and automated network management, global network optimization, and efficient network resource orchestration with cost-effectiveness and are envisioned as a key enabler to future IoV. In this article, we provide an overview of SDN/NFV-enabled IoV, in which SDN/NFV technologies are leveraged to enhance the performance of IoV and enable diverse IoV scenarios and applications. In particular, the IoV and SDN/NFV technologies are first introduced. Then, the state-of-the-art research works are surveyed comprehensively, which is categorized into topics according to the role that the SDN/NFV technologies play in IoV, i.e., enhancing the performance of data communication, computing, and caching, respectively. Some open research issues are discussed for future directions.

MapReduce plays a critical role as a leading framework for big data analytics. In this paper, we consider a geo-distributed cloud architecture that provides MapReduce services based on the big data collected from end users all over the world. Existing work handles MapReduce jobs by a traditional computation-centric approach that all input data distributed in multiple clouds are aggregated to a virtual cluster that resides in a single cloud. Its poor efficiency and high cost for big data support motivate us to propose a novel data-centric architecture with three key techniques, namely, cross-cloud virtual cluster, data-centric job placement, and network coding based traffic routing. Our design leads to an optimization framework with the objective of minimizing both computation and transmission cost for running a set of MapReduce jobs in geo-distributed clouds. We further design a parallel algorithm by decomposing the original large-scale problem into several distributively solvable subproblems that are coordinated by a high-level master problem. Finally, we conduct real-world experiments and extensive simulations to show that our proposal significantly outperforms the existing works.

Software defined networking (SDN) and network function virtualization (NFV) are key enabling technologies in fifth generation (5G) communication networks for embedding service-level customized network slices in a network infrastructure, based on statistical resource demands to satisfy long-term quality of service (QoS) requirements. However, traffic loads in different slices are subject to changes over time, resulting in challenges for consistent QoS provisioning. In this paper, a dynamic flow migration problem for embedded services is studied, to meet end-to-end (E2E) delay requirements with time-varying traffic. A multi-objective mixed integer optimization problem is formulated, addressing the trade-off between load balancing and reconfiguration overhead. The problem is transformed to a tractable mixed integer quadratically constrained programming (MIQCP) problem. It is proved that there is no optimality gap between the two problems; hence, we can obtain the optimum of the original problem by solving the MIQCP problem with some post-processing. To reduce time complexity, a heuristic algorithm based on redistribution of hop delay bounds is proposed to find an efficient solution. Numerical results are presented to demonstrate the aforementioned trade-off, the benefit from flow migration in terms of E2E delay guarantee, as well as the effectiveness and efficiency of the heuristic solution.

Caching popular videos at mobile edge servers (MESs) has been confirmed as a promising method to improve mobile users (MUs) perceived quality of experience (QoE) and to alleviate the server load. However, with the multiple bitrate encoding techniques prevalently employed in modern streaming services, caching deployment is challenging for the following three facts: (1) cooperative caching should be explored for MUs located at overlapped coverage areas of MESs; (2) there exists tradeoff consideration for caching either high bitrate videos or high diversity videos; and (3) the relationship between MU perceived QoE and MU received bitrate, known as QoE function, varies in different services. Aiming to maximize the MU perceived QoE, we formulate the multiple bitrate video caching problem, and prove this problem is NP-hard for any given positive and strictly increasing QoE function. We then propose a polynomial complexity algorithm based on a general QoE function, which can achieve an approximate ratio arbitrarily close to 1/2. Specifically, for a linear QoE function, we explore useful property of optimal solutions, based on which more efficient algorithms are proposed. We demonstrate the effectiveness of our solutions via both theoretical analysis and extensive simulations.

To facilitate the deployment of private industrial Internet-of-Things (IoT), applying long-term-evolution (LTE) over unlicensed spectrum (LTE-U) is a promising technology, which can deal with the licensed spectrum scarcity problem and the stringent quality-of-service (QoS) requirement via centralized control. In this paper, we investigate the computing offloading problem for LTE-U-enabled IoT, where computing tasks on an IoT device are either executed locally or offloaded to the edge server on an LTE-U base station. Considering a constrained edge computing cost (e.g., operation power consumption) for offloaded tasks, the task scheduling problem is formulated as a constrained Markov decision process (CMDP) to maximize the long-term average reward, which integrates both task completion profit and task completion delay. In order to address the uncertainty of task arrivals and channel availability, a constrained deep Q-learning-based task scheduling algorithm with provable convergence is proposed, where an adaptive reward function can appropriately bound the average edge computing cost. Extensive simulation results show that the proposed scheme considerably enhances the system performance.

In order to tackle the spectrum scarcity problem and enhance the spectrum efficiency, deploying LTE in unlicensed band (LTE-U) is an emerging technology for supporting massive connections in future networks. By taking into account of the coexistence between the LTE-U cellular user equipments (CUEs) and the legacy Wi-Fi stations (STAs) in the unlicensed band, a partial non-orthogonal multiple access (NOMA)-based scheme is proposed in this paper. By dividing all UEs into two groups and making the Wi-Fi STA as the UE with the weakest channel gain in its group, we can exploit the multiplexing gain of NOMA by introducing no extra modification to Wi-Fi STAs. Accordingly, a fairness-oriented resource allocation framework is formulated as a max-min problem to jointly optimize the inter-group time occupancy ratio and the intra-group power allocation when the guaranteed bit rate (GBR) requirements for each UE are considered. A modified two-dimensional bisection algorithm is proposed to search the optimal time occupancy ratio and the max-min rate in this coexisting network. Numerical results validate the effectiveness of the partial NOMA scheme and outperform the traditional orthogonal multiple access method, in terms of both efficiency and robustness.

In this paper, a comprehensive software-defined networking (SDN) based transmission protocol (SDTP) is presented for fifth generation (5G) communication networks, where an SDN controller gathers network state information from the physical network to improve data transmission efficiency between end hosts, with in-path packet retransmission. In the SDTP, we first develop a new two-way handshake mechanism for connection establishment between a pair of end host. With the aid of SDN control module, signaling exchanges for establishing E2E connections are migrated to the control plane to improve resource utilization in the data plane. A new SDTP packet header format is designed to support efficient data transmission with in-path packet caching and packet retransmission. Based on the new data packet format, a novel in-path receiver-based packet loss detection and caching-based packet retransmission scheme is proposed to achieve in-path fast recovery of lost packets. Extensive simulation results are presented to validate the effectiveness of the proposed protocol in terms of low connection establishment delay and low end-to-end packet transmission delay.

Service-oriented virtual network deployment is based on statistical resource demands of different services, while data traffic from each service fluctuates over time. In this paper, a delay-aware flow migration problem for embedded services is studied to meet end-to-end (E2E) delay requirement with time-varying traffic. A non-convex multi-objective mixed integer optimization problem is formulated, addressing the trade-off between maximum load balancing and minimum reconfiguration overhead due to flow migrations, under processing and transmission resource constraints and QoS requirement constraints. Since the original problem is non-solvable in optimization solvers due to unsupported types of quadratic constraints, it is transformed to a tractable mixed integer quadratically constrained programming (MIQCP) problem. The optimality gap between the two problems is proved to be zero, so we can obtain the optimum of the original problem through solving the MIQCP problem with some post-processing. Numerical results are presented to demonstrate the aforementioned trade-off, as well as the benefit from flow migration in terms of E2E delay performance guarantee.

Massive machine-type communication (mMTC) is a new focus of services in fifth generation communication networks. The associated stringent delay requirement of end-to-end (E2E) service deliveries poses technical challenges. In this paper, we propose a joint random access and data transmission protocol for mMTC to guarantee E2E service quality of different traffic types. First, we develop a priority-queueing-based access class barring (ACB) model and a novel effective capacity is derived. Then, we model the priority-queueing-based ACB policy as a noncooperative game, where utility is defined as the difference between effective capacity and access penalty price. We prove the existence and uniqueness of Nash equilibrium (NE) of the noncooperative game, which is also a submodular utility maximization problem and can be solved by a greedy updating algorithm with convergence to the unique NE. To further improve the efficiency, we present a price-update algorithm, which converges to a local optimum. Simulations demonstrate the performance of the derived effective capacity and the effectiveness of the proposed algorithms.

Providing energy efficient and delay-aware access is essential to many anticipated cellular Internet of Things (IoT) applications. In cellular networks, before devices transmit their data, they use a contention-based association protocol, known as random access channel (RACH), which introduces extensive access delays and energy wastage as the number of contending devices increases. Modeling the performance of the RACH protocol is a challenging task due to the complexity of uplink transmission that exhibits a wide range of interference components; nonetheless, it is an essential process that will help determine the applicability of cellular IoT communication paradigm. This paper presents a novel mathematical framework based on stochastic geometry to analyze the RACH protocol and identify its limitations in the context of cellular IoT applications with a massive number of devices. To do so, we study the traditional cellular association process and derive a mathematical model for its association success probability. The derived model accounts for device density, spatial characteristics of the network, power control employed, and mutual interference among the devices. Our analysis and results highlight the shortcomings of the RACH protocol and give insights into the potentials brought on by employing power control techniques. The developed framework can be applied to evaluate the performance of other contention-based access schemes by incorporating their unique operational principles.

In this paper, an analytical end-to-end (E2E) packet delay modeling is established for multiple traffic flows traversing an embedded virtual network function (VNF) chain in fifth generation communication networks. The dominant-resource generalized processing sharing is employed to allocate both computing and transmission resources among flows at each network function virtualization (NFV) node to achieve dominant-resource fair allocation and high resource utilization. A tandem queueing model is developed to characterize packets of multiple flows passing through an NFV node and its outgoing transmission link. For analysis tractability, we decouple packet processing (and transmission) of different flows in the modeling and determine average packet processing and transmission rates of each flow as approximated service rates. An M/D/1 queueing model is developed to calculate packet delay for each flow at the first NFV node. Based on the analysis of packet interarrival time at the subsequent NFV node, we adopt an M/D/1 queueing model as an approximation to evaluate the average packet delay for each flow at each subsequent NFV node. The queueing model is proved to achieve more accurate delay evaluation than that using a G/D/1 queueing model. Packet transmission delay on each embedded virtual link between consecutive NFV nodes is also derived for E2E delay calculation. Extensive simulation results demonstrate the accuracy of our proposed E2E packet delay modeling, upon which delay-aware VNF chain embedding can be achieved.

Network function virtualization (NFV) decouples the traditional network functions from specific or proprietary hardware, such that virtualized network functions (VNFs) can run in software form. By exploring NFV, a consecutive set of VNFs can constitute a service function chain (SFC) to provide the network service. From the perspective of network service providers, how to maximize the network utility is always one of the major concerns. To this end, there are two main issues need to be considered at runtime: 1) how to handle the unpredictable network traffic burst? and 2) how to fairly allocate resources among various flows to satisfy different traffic demands? In this paper, we investigate a fairness-aware flow scheduling problem for network utility maximization, with joint consideration of resource allocation and rate control. Based on a discrete-time queuing model, we propose a low-complexity online-distributed algorithm using the Lyapunov optimization framework, which can achieve arbitrary optimal utility with different fairness levels by tuning the fairness bias parameter. We theoretically analyze the optimality of the algorithm and evaluate its efficiency by both simulation and testbed-based experiments.

Recently, edge caching and multicasting arise as two promising technologies to support high-data-rate and low-latency delivery in wireless communication networks. In this paper, we design three transmission schemes aiming to minimize the delivery latency for cache-enabled multigroup multicasting networks. In particular, full caching bulk transmission scheme is first designed as a performance benchmark for the ideal situation where the caching capability of each enhanced remote radio head (eRRH) is sufficient large to cache all files. For the practical situation where the caching capability of each eRRH is limited, we further design two transmission schemes, namely partial caching bulk transmission (PCBT) and partial caching pipelined transmission (PCPT) schemes. In the PCBT scheme, eRRHs first fetch the uncached requested files from the baseband unit (BBU) and then all requested files are simultaneously transmitted to the users. In the PCPT scheme, eRRHs first transmit the cached requested files while fetching the uncached requested files from the BBU. Then, the remaining cached requested files and fetched uncached requested files are simultaneously transmitted to the users. The design goal of the three transmission schemes is to minimize the delivery latency, subject to some practical constraints. Efficient algorithms are developed for the low-latency cloud-edge coordinated transmission strategies. Numerical results are provided to evaluate the performance of the proposed transmission schemes and show that the PCPT scheme outperforms the PCBT scheme in terms of the delivery latency criterion.

In cache-enabled heterogeneous cloud radio access networks (HC-RANs), mobile station (MS) association for multimedia content delivery should consider both the content caching location and the wireless channel quality. This paper studies economically optimal MS association to tradeoff the cache-hit ratio and the ratio of MSs with satisfied quality of service (QoS). When the associated enhanced remote radio unit (eRRU) stores the requesting content, the content can be fetched directly from the local cache. Otherwise, fronthaul has to be used to fetch the content. The use of fronthaul resource and cache is treated as costs, and payments of QoS-satisfied MSs are treated as incomes. Thus, the economic MS association is formulated as an optimization problem to maximize the system utility, i.e., total profit of the network operator, which is defined as the difference between incomes and costs. A belief propagation-based method is employed to solve the problem on a developed factor graph. Simulation results show that the proposed economically optimal MS association achieves much higher profit than the existing schemes and works well in the network with various loads. Moreover, the profit of the proposed scheme can be improved with inter-cell interference coordination. For the case with extremely skewed content popularity, the proposed scheme can avoid MS overloading at eRRUs storing most popular multimedia contents. Furthermore, it can support more MSs with satisfied QoS, which leads to a higher profit.

Non-orthogonal multiple access (NOMA) is one of the potential multiuser supporting techniques in the fifth generation (5G) cellular systems due to its higher spectrum efficiency (SE) and cell-edge throughput. Vector-perturbation (VP) is widely known as one of the nonlinear precoding schemes that achieves near-capacity performance in practical wireless multi-input-multi-output (MIMO) communication systems. In this paper, we propose a hybrid transmission strategy based on VP and NOMA (VP-NOMA) by designing a beamforming matrix with the power allocation strategy to minimize total transmit power for certain quality of service (QoS) requirements. Rather than searching for the optimal beamforming matrix, we propose a more intuitive sub-optimal algorithm, called iteration beamforming for VP-NOMA systems (IBVP-NOMA), to find beamforming vectors. Further, different user clustering strategies are considered and compared to enhance the performance of the VP-NOMA systems. The simulation results demonstrate that the proposed method requires lower transmit power than the NOMA system without VP.

We consider a heterogeneous ultra dense network (HUDN) with both the hexagon and Poisson point process (PPP) layouts, which is more relevant for practical scenarios. A user-centric and adaptive interference-aware non-coherent coordinated multi-point transmission (IA-CoMP) scheme is used as a system setup to reduce both the cross-tier and the co-tier inter-cell interference (ICI) for the HUDN with range expansion (RE) in small cells. Due to the involvement of hexagonal macrocells, it is intractable to analyze the coverage performance of HUDNs with IA-CoMP. To this end, we present a mobile station GrouPing (MSGP)-based coverage analysis method, which partitions all MSs into four groups according to their main interference, and the whole coverage is obtained as the sum of the coverage of each MS group. We demonstrate that the proposed MSGP-based coverage analysis method can provide a tight upper bound when compared with Monte Carlo simulations. As the small cell density increases, the system coverage of HUDNs with hexagonal macrocells reduces exponentially, while the system coverage of HUDNs with PPP-based macrocells remains unchanged. Moreover, the system coverage increases with the larger one of the main ICI judging coefficient and the RE bias.

Beam alignment (BA) is to ensure the transmitter and receiver beams are accurately aligned to establish a reliable communication link in millimeter-wave (mmwave) systems. Existing BA methods search the entire beam space to identify the optimal transmit-receive beam pair, which incurs significant BA latency on the order of seconds in the worst case. In this paper, we develop a learning algorithm to reduce BA latency, namely Hierarchical Beam Alignment (HBA) algorithm. We first formulate the BA problem as a stochastic multi-armed bandit problem with the objective to maximize the cumulative received signal strength within a certain period. The proposed algorithm takes advantage of the correlation structure among beams such that the information from nearby beams is extracted to identify the optimal beam, instead of searching the entire beam space. Furthermore, the prior knowledge on the channel fluctuation is incorporated in the proposed algorithm to further accelerate the BA process. Theoretical analysis indicates that the proposed algorithm is asymptotically optimal. Extensive simulation results demonstrate that the proposed algorithm can identify the optimal beam with a high probability and reduce the BA latency from hundreds of milliseconds to a few milliseconds in the multipath channel, as compared to the existing BA method in IEEE 802.11ad.

The software defined networking (SDN) enabled network function virtualization (NFV) architecture emerges as a cost-effective solution for service customization in fifth generation (5G) networks. In this paper, a joint traffic routing and virtual network function (VNF) placement problem is studied for a multicast service request accommodated over a physical substrate network, where the multipath traffic routing is considered between embedded VNFs. The joint problem is formulated as a mixed integer linear programming (MILP) problem to minimize the provisioning cost of both VNFs and links, under the physical network resource constraints, flow conservation constraints, and VNF placement rules. Since the problem is NP-hard, low complexity heuristic algorithms, with the consideration of both the single-path and multipath routing cases, are proposed to determine an efficient solution. Simulation results are presented to demonstrate the effectiveness and accuracy of the proposed heuristic algorithms especially for a large-size network.

To facilitate the private deployment of industrial Internet-of-Things (IoT), applying LTE in unlicensed spectrum (LTE-U) is a promising approach, which both tackles the problem of lacking licensed spectrum and leverages an LTE protocol to meet stringent quality-of- service (QoS) requirements via centralized control. In this paper, we investigate the computing offloading problem in an LTE-U-enabled network, where the task on an IoT device is carried out either locally or is offloaded to the LTE-U base station (BS). The offloading policy is formulated as an optimization problem to maximize the long term discounted reward, considering both task completion profit and the task completion delay. Due to the stochastic task arrival process at each device and the Wi-Fi's contention-based random access, we reformulate the computing offloading problem into a Q-learning problem and solve it by a deep learning network-based approximation method. Simulation results show that the proposed scheme considerably enhances the system performance.

Network function virtualization (NFV) is one of the enabling technologies for fifth generation (5G) networks. How to allocate physical resources to customized network services both fairly and efficiently remains a challenging research issue in NFV. This paper proposes a two-stage approach to jointly optimize the chaining and embedding of virtual network functions (VNFs), to obtain feasible composition and embedding results with low complexity, while the average embedding cost is minimized and the total revenue is increased. In the first stage, the VNF chaining order is optimized based on the location and functionality of substrate nodes, and the ratio of outgoing data rate over incoming data rate for each required VNF. In the second stage, we allocate the physical resources based on the preliminary VNF ordering under the resource capacity constraints. A node splitting mechanism is also employed to improve the resource allocation fairness and increase the service acceptance ratio for the substrate network. Simulation results are presented to validate the feasibility and effectiveness of the proposed approach.

LTE/WiFi Aggregation (LWA) provides a promising approach to relieve data traffic congestion in licensed bands by leveraging unlicensed bands. Critical challenges arise from provisioning quality-of-service (QoS) through heterogenous interfaces of licensed and unlicensed bands. In this paper, we minimize the required licensed spectrum without degrading the QoS in the presence of multiple users. Specifically, the aggregated effective capacity of LWA is firstly derived by developing a new semi-Markov model. Multi-band resource allocation with the QoS guarantee between the licensed and unlicensed bands is formulated to minimize the licensed bandwidth, convexified by exploiting Block Coordinate Descent (BCD) and difference of two convex functions (DC) programming, and solved efficiently with a new iterative algorithm. Simulation results demonstrate significant performance gain of the proposed approach over heuristic alternatives.

Edge computing for mobile devices in vehicular ad hoc networks (VANETs) has to address rogue edge attacks, in which a rogue edge node claims to be the serving edge in the vehicle to steal user secrets and help launch other attacks such as man-in-the-middle attacks. Rogue edge detection in VANETs is more challenging than the spoofing detection in indoor wireless networks due to the high mobility of onboard units (OBUs) and the large-scale network infrastructure with roadside units (RSUs). In this paper, we propose a physical (PHY)- layer rogue edge detection scheme for VANETs according to the shared ambient radio signals observed during the same moving trace of the mobile device and the serving edge in the same vehicle. In this scheme, the edge node under test has to send the physical properties of the ambient radio signals, including the received signal strength indicator (RSSI) of the ambient signals with the corresponding source media access control (MAC) address during a given time slot. The mobile device can choose to compare the received ambient signal properties and its own record or apply the RSSI of the received signals to detect rogue edge attacks, and determines test threshold in the detection. We adopt a reinforcement learning technique to enable the mobile device to achieve the optimal detection policy in the dynamic VANET without being aware of the VANET model and the attack model. Simulation results show that the Q-learning based detection scheme can significantly reduce the detection error rate and increase the utility compared with existing schemes.

With the rapid expansion of Internet-of-Things (IoT), tremendous traffic produced by a vast number of IoT devices is being injected into networks and straining their capacity. To address the challenge, device-to-device (D2D) communications offer a promising technique that relieves network overloading by localizing traffic between devices. In this paper, we investigate how to exploit D2D communications to support data dissemination and offload traffic. In particular, we focus on an important problem that aims to effectively pair request devices with cache devices in close proximity. Due to the interference among D2D links, we prove that this device pairing problem is NP-hard and thus requires an approximation algorithm to solve it efficiently. Here, we propose a three-step approach, in which the first step uses Lagrangian relaxation to obtain an upper bound solution, the second step derives a feasible solution from the initial pairing and further augments it, and the last step uses a swapping algorithm to refine the pairing to guarantee its stability. The proposed approach is proved to converge to a two-sided exchange stable matching. Extensive simulation results show that our three-step approach performs closely to the optimal solution and achieves significant performance gain over the existing schemes.

Space information network (SIN) is a promising networking architecture to significantly broaden the observation area and realize continuous information acquisition for earth observation. Over the dynamic and complex SIN environment, it is a key issue to coordinate multi-dimensional heterogeneous network resources (e.g., observation resource and transmission resource) in the presence of multi-resource variations and severe conflicts, such that diverse earth observation service requirements can be satisfied. To this end, this paper studies the multi-resource coordinate scheduling problem in SINs. Specifically, we first characterize the relationship among multi-resource using an event-driven time-expanded graph (EDTEG). Based on the EDTEG, observation resource and transmission resource are jointly considered, and an integer linear programming optimization problem is formulated to maximize the sum priorities of the successfully scheduled tasks. An iterative optimization technique is employed to decompose the problem into separate observation scheduling and transmission scheduling sub-problems, which can be efficiently solved by extended transmission time sharing graph and directed acyclic graph methods, respectively. Simulation results demonstrate the effectiveness of the proposed algorithm and performance impacts of different network parameters.

In this paper, an uplink cross-layer resource allocation problem based on imperfect channel state information (CSI) is modeled as min-max fractional stochastic programming for heterogeneous wireless access. The resource allocation is subject to constraints in delay, service outage probability, system radio bandwidth, and total power consumption. The joint bandwidth and power allocations are based on CSI at the physical layer and queue state information (QSI) at the link layer. In order to determine the transmission rate of each mobile terminal according to the queue buffer occupancy, a probability upper bound of exceeding the maximum packet delay in terms of a required transmission rate is presented based on M/D/1 model. Then, the bandwidth and power allocation problem is transformed into bi-convex programming, and an optimal distributed bandwidth and power allocation algorithm is proposed. To reduce computational complexity, a suboptimal distributed bandwidth and power allocation algorithm is presented. Simulation results demonstrate that the proposed algorithms improve the energy efficiency greatly.

Next generation (5G) wireless networks are expected to accommodate proliferation of connected devices and multimedia services. To support multimedia services in an agile, cost-effective, and flexible way, network virtualization is a potential solution. This paper investigates service- oriented network virtualization for 5G wireless networks, to efficiently allocate heterogeneous resources to accommodate multimedia services. Specifically, we study joint resource allocation for virtual network requests (VNRs) and online embedding the resultant VNRs in core networks (CNs). With the deployment of multiple traffic aggregation points (TAPs) in radio access networks (RANs), the end-to- end traffic from heterogeneous access technologies can be aggregated and then grouped based on their destinations. Queueing models are developed in determining the minimal capacity required at each core network element. Virtual network embedding (VNE) in the core network is further proposed to achieve efficient physical resource sharing in CNs. Simulation results validate the VNE process in core networks based on the optimized capacities.

Vehicular ad-hoc networks (VANETs) are vulnerable to jamming attacks, and frequency hopping-based anti- jamming techniques are not always applicable in VANETs due to the high mobility of the onboard units (OBUs) especially under a large scale network topology. In this paper, we use unmanned aerial vehicles (UAVs) to deal with VANET jamming, especially smart jamming that changes the jamming policy based on the ongoing communication status of the VANET. More specifically, the UAV relays the data of OBUs to another roadside unit (RSU) with a better transmission condition if the serving RSU is located in a heavily jammed area. The interactions between the UAV and the jammer are formulated as an anti-jamming UAV relay game, in which the UAV decides whether or not to relay the data of the OBU to another RSU that is far away from the jammer, and the latter chooses the jamming power. The Nash equilibria (NE) of the game are derived to reveal how the best UAV relay strategy depends on the transmission cost and the radio channel model. A hotbooting policy hill climbing (PHC)-based UAV relay strategy is proposed to address jamming in the dynamic UAV-aided VANET game without the knowledge of network model and jamming model. Simulation results show that the proposed relay strategy can efficiently reduce the bit error rate (BER) of OBU data and thus increase the utility of VANET in comparison with a Q-learning based scheme.

In Earth observation satellite networks (EOSNs), imbalance between the observation and transmission opportunities can cause poor network performance, e.g., a reduced number of successfully scheduled targets. To tackle this issue, in this paper, we investigate the multi-dimensional resource scheduling problem in EOSNs to ensure that each EOS can observe an appropriate subset of targets with matched downloading capacity to the destination. Specifically, an optimization problem is formulated and proved to be NP-hard. Then, an iterative optimization technique is employed to decompose the problem into separate observation scheduling and transmission scheduling subproblems, which are further efficiently solved by acyclic directed graph and particle optimization methods, respectively. Extensive simulations have been conducted to demonstrate the efficiency of the proposed scheduling algorithm.

Platooning has been identified as a promising vehicular traffic management strategy to improve road capacity, energy efficiency, and on-road safety in intelligent transportation systems (ITS). Inter-vehicle communications within a platoon and among multiple platoons can assist platoon control by maintaining a constant inter-vehicle distance, which in turn enhances road safety. An efficient method of sharing inter-vehicle information successfully and timely is critical to many platooning applications. In this paper, a resource allocation (RA) approach is proposed to support inter-vehicle communications underlaying cellular network for a multiplatooning (a chain of platoons) scenario. By applying the evolved multimedia broadcast multicast services (eMBMS) in the Evolved Node B (eNB), the transmission delay for intra-platoon and inter-platoon communications can be reduced. Then, using the proposed subchannel allocation and power control schemes, the number of required subchannels and the transmission powers of each vehicle and the eNB can be minimized. Numerical results show that the proposed approach outperforms the candidate RA scheme in terms of transmission delay, especially in a multiplatooning scenario with a large number of vehicles.

In this paper, we propose a distributed and adaptive hybrid medium access control (DAH-MAC) scheme for a single-hop Internet of Things (IoT)-enabled mobile ad hoc network supporting voice and data services. A hybrid superframe structure is designed to accommodate packet transmissions from a varying number of mobile nodes generating either delay-sensitive voice traffic or best-effort data traffic. Within each superframe, voice nodes with packets to transmit access the channel in a contention-free period (CFP) using distributed time division multiple access, while data nodes contend for channel access in a contention period (CP) using truncated carrier sense multiple access with collision avoidance. In the CFP, by adaptively allocating time slots according to instantaneous voice traffic load, the MAC exploits voice traffic multiplexing to increase the voice capacity. In the CP, a throughput optimization framework is proposed for the DAH-MAC, which maximizes the aggregate data throughput by adjusting the optimal contention window size according to voice and data traffic load variations. Numerical results show that the proposed MAC scheme outperforms existing quality-of-service-aware MAC schemes for voice and data traffic in the presence of heterogeneous traffic load dynamics.

In this paper, a distributed token-based adaptive medium access control (TA-MAC) scheme is proposed for a two-hop Internet of Things (IoT)-enabled mobile ad hoc network. In the TA-MAC, nodes are partitioned into different one-hop node groups, and a time division multiple access (TDMA)-based superframe structure is proposed to allocate different TDMA time durations to different node groups to overcome the hidden terminal problem. A probabilistic token passing scheme is devised to distributedly allocate time slots to nodes in each group for packet transmissions, forming different token rings. The distributed time slot allocation is adaptive to variations of the number of nodes in each token ring due to node movement. To optimize the medium access control (MAC) design, performance analytical models are presented in closed-form functions of both MAC parameters and network traffic load. Then, an average end-to-end delay minimization framework is established to derive the optimal MAC parameters under a certain network load condition. Analytical and simulation results demonstrate that, by adapting the MAC parameters to the varying network condition, the TA-MAC achieves consistently minimal average end-to-end delay, bounded delay for local transmissions, and high aggregate throughput. Further, the performance comparison with other MAC schemes shows the scalability of the proposed MAC in an IoT-based two-hop environment with an increasing number of nodes.

Human-machine systems required a deep understanding of human behaviors. Most existing research on action recognition has focused on discriminating between different actions, however, the quality of executing an action has received little attention thus far. In this paper, we study the quality assessment of driving behaviors and present WiQ, a system to assess the quality of actions based on radio signals. This system includes three key components, a deep neural network based learning engine to extract the quality information from the changes of signal strength, a gradient-based method to detect the signal boundary for an individual action, and an activity-based fusion policy to improve the recognition performance in a noisy environment. By using the quality information, WiQ can differentiate a triple body status with an accuracy of 97%, whereas for identification among 15 drivers, the average accuracy is 88%. Our results show that, via dedicated analysis of radio signals, a fine-grained action characterization can be achieved, which can facilitate a large variety of applications, such as smart driving assistants.

Auction is a promising approach for dynamic spectrum access in cognitive radio networks. Existing auction mechanisms are mainly strategy-proof to stimulate bidders to reveal their valuations of spectrum truthfully. However, they can suffer significantly from a new cheating pattern, named false-name bids, where a bidder can manipulate the auction by submitting bids under multiple fictitious names. We show such false-name bid cheating is easy to make but difficult to detect in dynamic spectrum auctions. To address this issue, we propose ALETHEIA, a novel flexible, false-name-proof auction framework for large-scale dynamic spectrum access. ALETHEIA not only guarantees strategy-proofness but also resists false-name bids. Moreover, ALETHEIA enables spectrum reuse across a large number of bidders, to improve spectrum utilization. Following that, we extend ALETHEIA to its general version that supports more practical and flexible auction, where bidders accept the spectrum allocation under their partial satisfactions. Theoretical analysis and simulation results show that ALETHEIA achieves both high spectrum redistribution efficiency and auction efficiency.

The increasing number of road accidents has led to the evolution of vehicular ad hoc networks (VANETs), which allow vehicles and roadside infrastructure to continuously broadcast safety messages, including necessary information to avoid undesired events on the road. To support reliable broadcast of safety messages, distributed time division multiple access (D-TDMA) protocols are proposed for medium access control in VANETs. Existing D-TDMA protocols react to a transmission failure without distinguishing whether the failure comes from a transmission collision or from a poor radio channel condition, resulting in degraded performance. In this article, we present the importance of transmission failure differentiation due to a poor channel or due to a transmission collision for D-TDMA protocols in vehicular networks. We study the effects of such a transmission failure differentiation on the performance of a node when reserving a time slot to access the transmission channel. Furthermore, we propose a method for transmission failure differentiation, employing the concept of deep-learning techniques, for a node to decide whether to release or continue using its acquired time slot. The proposed method is based on the application of a Markov chain model to estimate the channel state when a transmission failure occurs. The Markov model parameters are dynamically updated by each node (i.e., vehicle or roadside unit) based on information included in the safety messages that are periodically received from neighboring nodes. In addition, from the D-TDMA protocol headers of received messages, a node approximately determines the error in estimating the channel state based on the proposed Markov model and then uses this channel estimation error to further improve subsequent channel state estimations. Through mathematical analysis, we show that transmission failure differentiation, or transmission collision detection, helps a node to efficiently reserve a time slot even with a large number of nodes contending for time slots. Furthermore, through extensive simulations in a highway scenario, we demonstrate that the proposed solution significantly improves the performance of D-TDMA protocols by reducing unnecessary contention on the available time slots, thus increasing the number of nodes having unique time slots for successful broadcast of safety messages.

In this paper, we study how to determine concurrent transmissions and the transmission power level of each link to maximize the spectrum efficiency and minimize energy consumption in a wireless ad hoc network. The optimal joint transmission packet scheduling and power control strategy is determined when the node density goes to infinity and the network area is unbounded. Based on the asymptotic analysis, we determine the fundamental capacity limits of a wireless network, subject to an energy consumption constraint. We propose a scheduling and transmission power control mechanism to approach the optimal solution to maximize spectrum and energy efficiencies in a practical network. The distributed implementation of the proposed scheduling and transmission power control scheme is presented based on our medium access control (MAC) framework proposed by Rahimi Malekshan. Simulation results demonstrate that the proposed scheme achieves 40% higher throughput than the existing schemes. In addition, the energy consumption using the proposed scheme is about 20% of the energy consumed using existing power saving MAC protocols.

In this paper, we investigate the PHY-layer authentication that exploits radio channel information to detect spoofing attacks in multiple- input multiple-output (MIMO) systems. We formulate the interactions between a receiver and a spoofing node in the spoofing detection as a zero-sum game. In this game, the receiver chooses the test threshold of the hypothesis test in the PHY-layer authentication to maximize its utility based on the Bayesian risk in the spoofing detection, while the adversary chooses its attack frequency, i.e., how often a spoofing packet is sent over multiple antennas. The unique Nash equilibrium of the static MIMO authentication game is derived and the condition for its existence is discussed. We investigate the impact of the number of antennas on the performance of the dynamic authentication game. We propose a PHY-layer spoofing detection based on Q-learning for MIMO systems to achieve the optimal test threshold in the spoofing detection via trials, and implement it over universal software radio peripherals. The performance of the spoofing detection algorithm is evaluated via experiments in indoor environments.

After big disasters, a damaged area can be out of contact because of severe damage of existing network infrastructures. Meanwhile, high demands for network connections to the disaster area will arise to collect damage information and disseminate rescue instructions. In this paper, we propose to construct a network for disaster management using mobile stations equipped with sensors and network interfaces. They are controlled by a disaster management center via wide area network technology, and conduct various disaster management tasks, such as damage sensing, information collection and message dissemination. To address the challenges of unpredictable tasks and limited number of mobile stations with working capability constraints, we propose an online algorithm that schedules mobile stations for disaster management tasks with weights in each time slot, without any knowledge of future task arrivals. Our objective is to maximize the total weight of finished tasks under constraints of maximum working capability of mobile stations. We prove that the performance of proposed online algorithm is no worse than e-1/e of optimal solutions. Extensive simulations are conducted to evaluate our proposed algorithm.

Facing the spectrum supply-demand gap, heterogeneous network (HetNet) is a promising approach to achieve drastic gains in network coverage and capacity compared with macro-only networks, thus making it especially attractive to network operators. On the other hand, software-defined networking brings a number of advantages along with many challenges. One particular concern is on the resilience for in-band fashioned control plane. Existing approaches mainly rely on a local rerouting policy when performing the routing protection for the target sessions in software-defined networks. However, such a policy would potentially bring congestions in the neighbouring links of the failed one. To this end, we study a weighted cost-minimization problem, where the traffic load balancing and control-channel setup cost are jointly considered. Because this problem is NP-hard, we first propose a near-optimal Markov approximation-based approach for in-band-fashioned software-defined HetNets. We then extend our solution to an online case that handles a single-link failure. We also conduct theoretical analysis on the performance fluctuation due to the single-link failure. We finally carry out experiments by experimental simulation. The extensive numerical results show that the proposed algorithm has fast convergence and high efficiency in resource utilization.

Efficient radio spectrum utilization and low energy consumption in mobile devices are essential in developing next generation wireless networks. This paper presents a new medium access control (MAC) mechanism to enhance spectrum efficiency and reduce energy consumption in a wireless ad hoc network. A set of coordinator nodes, distributed in the network area, periodically schedule contention-free time slots for all data transmissions/receptions in the network, based on transmission requests from source nodes. Adjacent coordinators exchange scheduling information to effectively increase spatial spectrum reuse and avoid transmission collisions. Moreover, the proposed MAC scheme allows a node to put its radio interface into a sleep mode when it is not transmitting/receiving a packet, in order to reduce energy consumption. Simulation results demonstrate that the proposed scheme achieves substantially higher throughput and has significantly lower energy consumption in comparison with existing schemes.

In this paper, we investigate the performance of cooperative communication in decentralized wireless networks under unsaturated traffic conditions with randomly positioned single-hop source-destination pairs and relays, where interference is the main performance-limiting factor. The traffic unsaturation and concurrent cooperative transmissions introduce a correlation between the interferer density and the packet retransmission probability, and a correlation of interference power in both space and time domains, which complicate the interference characterization. Based on queueing theory and stochastic geometry, the stationary interferer density is derived by solving a fixed-point equation, which is proved to have a unique solution. According to the relay selection scheme, we characterize the correlation of interference power in two consecutive time-slots by identifying the densities of source and relay retransmissions. Based on the interferer density and interference correlation, we derive the outage probability and average packet delay of the cooperative scheme, while taking into account the dynamic traffic arrivals, interference correlation, relay selection scheme, and spatial node distribution. The performance analysis is validated by extensive simulations. The analytical results provide useful insights on cooperative communication in large-scale networks.

In this paper, we investigate the PHY-layer authentication in wireless networks, which exploits PHY-layer channel information such as the received signal strength indicators to detect spoofing attacks. The interactions between a legitimate receiver node and a spoofer are formulated as a PHY- authentication game. More specifically, the receiver chooses the test threshold in the hypothesis test of the spoofing detection to maximize its expected utility based on Bayesian risk to detect the spoofer. On the other hand, the spoofing node decides its attack strength, i.e., the frequency to send a spoofing packet that claims to use another node's MAC address, based on its individual utility in the zero-sum game. As it is challenging for most radio nodes to obtain the exact channel models in advance in a dynamic radio environment, we propose a spoofing detection scheme based on reinforcement learning techniques, which achieves the optimal test threshold in the spoofing detection via Q-learning and implement it over universal software radio peripherals (USRP). Experimental results are presented to validate its efficiency in spoofing detection.

Enabling vehicular communications is expected to revolutionize the transport infrastructure and support many traffic management applications such as platooning. Sharing vehicle information such as speed and acceleration wirelessly among platoons plays an effective role in platoon control by maintaining a constant inter-vehicle and inter-platoon distances. However, the performance of (inter and intra-) platoon communications in terms of throughput, transmission delays and packet transmission collisions can undermine the effectiveness of information sharing on platoon control. In this paper, we present probabilistic performance analysis of IEEE 802.11p Distributed Coordination Function (DCF) for inter-platoon communications in a multiplatooning scenario (i.e, a chain of platoons). The expressions for the transmission attempt probability, packet collision probability, network throughput and packet delay are derived accordingly. Numerical results show that the performance of inter-platoon communications depends on the vehicle's role in one platoon and its platoon position within the multiplatoon and that the end-to- end delay of platoons can be reduced by adjusting the contention window size.

Time-frequency resource conversion (TFRC) is a recently proposed network resource allocation strategy. By exploiting user behavior, it withdraws spectrum resources strategically from connection(s) not focused on by the user, to relieve network congestion effectively. Aiming at supporting the exponentially increasing traffic volume, especially on-demand data services, in this work we propose TFRC-based scheduling techniques. Considering an LTE-type cellular network, we formulate the problem of service scheduling as a joint request, channel, and slot allocation problem, which is a mixed integer nonlinear programming (MINLP) problem. A deflation and sequential fixing based algorithm with only polynomial-time complexity is proposed to solve the MINLP problem. Simulation results not only demonstrate the efficiency of the proposed algorithm in terms of quality-of-service (QoS) provisioning and network resource utilization, but also show the effectiveness of the proposed TFRC-based scheduling techniques when integrating with the existing scheduling strategies such as first in first served (FIFS) and earliest deadline first (EDF).

Auction is a promising approach for dynamic spectrum access in Cognitive Radio Networks. Existing auction mechanisms are mainly proposed to be strategy-proof to stimulate bidders to reveal their valuations of spectrum truthfully. However, they would suffer significantly from a new cheating pattern named false-name bids, where a bidder can manipulate the auction by submitting bids under multiple fictitious names. We show such false-name bid cheating is easy to make but hard to be detected in dynamic spectrum auctions. To resolve this issue, we propose ALETHEIA, a novel flexible, false-name-proof auction framework for large-scale dynamic spectrum access. ALETHEIA has the following important features: (1) it not only guarantees strategy-proofness but also resists false-name bids, (2) it enables spectrum reuse across a large number of bidders, (3) it provides the bidders the flexibility of diverse demand formats, and (4) it incurs low computational overhead. Simulation results show that ALETHEIA achieves both high spectrum redistribution efficiency and auction efficiency.

In this paper, energy efficient uplink communications are investigated for battery-constrained mobile terminals (MTs) with service quality requirements and multi-homing capabilities. A heterogeneous wireless medium is considered, where MTs communicate with base stations (BSs) and access points (APs) of different networks with overlapped coverage. Different from the existing works, we develop a quality of service (QoS)-based optimization framework for joint uplink bandwidth and power allocation to maximize energy efficiency for a set of MTs with multi-homing capabilities. The proposed framework is implemented in a decentralized architecture, through coordination among BSs/APs of different networks and MTs, which is a desirable feature when different networks are operated by different service providers. A suboptimal framework is presented with a reduced computational complexity as compared with the optimal framework. Simulation results demonstrate the improved performance of both the optimal and suboptimal frameworks over a state-of-the-art benchmark.

In this paper, we investigate the network throughput achieved by both spatial diversity and spatial frequency reuse in a wireless ad hoc network with randomly positioned single-hop source-destination pairs and relays. Compared with conventional direct transmissions, cooperative communication can enhance single-link transmission reliability but reduce network-wide spatial frequency reuse due to relay transmissions. To study the tradeoff between these two competing effects, we construct a geographically constrained region for relay selection based on channel state information. The network throughput, defined as the product of the success probability of each link and the expected number of concurrent transmissions, is derived as a function of the total number of links, relay density, size of relay selection region, and distance between the source and destination. The performance analysis is carried out for both selection combining and maximum ratio combining at the destination. Such analytical results can evaluate the effectiveness of cooperative communication and provide useful insights on the design of large-scale networks. Finally, extensive simulations are conducted to validate the performance analysis.

This paper investigates energy efficient uplink communications for battery-constrained mobile terminals (MTs). We consider a heterogeneous wireless medium where MTs communicate with base stations (BSs) and access points (APs) of different networks with overlapped coverage. Unlike the existing research, we develop a joint bandwidth and power allocation framework that maximizes energy efficiency for a set of MTs, in different service areas, with best effort service and multi-homing capabilities. The problem formulation captures the heterogeneity of the medium, in terms of different service areas, channel conditions, available resources at BSs/APs of different networks, and different available maximum power at the MTs. In addition, the framework is implemented in a decentralized manner which is desirable in a case that different networks are operated by different service providers. Simulation results are presented to demonstrate the performance of the proposed framework.

In this paper, we study distributed generation (DG) integration in smart grid, with a focus on the voltage regulation in smart distribution systems. To ensure the operation of a smart distribution system at an acceptable voltage level, voltage regulators are deployed at some strategic locations for voltage control. The two-way communication functionality of the smart distribution system is leveraged such that the voltage regulators are coordinated by a distribution substation. Based on the measurement reports from remote terminal units (RTUs) deployed at DG unit and load connection points, stochastic information management is performed by the distribution substation to address the randomness in renewable power generation and load demand. In this paper, we formulate a voltage regulation problem in the smart distribution system based on power flow analysis, while taking into account communication delays. We show that the problem can be represented as a partially observed Markov decision process (POMDP). Since voltage regulation is performed at a relatively low frequency to avoid excessive wear and tear on the voltage regulators, a large amount of measurements should be reported by the RTUs and processed by the distribution substation for optimal voltage regulation. In order to reduce the communication and computational overhead, we further investigate the voltage regulation problem and mathematically prove that a relatively small amount of information is sufficient for the distribution substation to make an optimal decision. The theoretical results are evaluated based on a case study of IEEE 13-bus test system with real DG power generation and demand data.

A vehicular ad hoc network (VANET) is an emerging technology which has a great potential in realizing a variety of new applications. This paper presents a new packet routing scheme which allows a vehicle to discover the existence of a gateway to the Internet and to send/receive packets to/from the gateway via multihop communications. The proposed routing scheme is based on a multichannel medium access control protocol, known as VeMAC [1], [2], using time division multiple access. The performance of this cross-layer design is evaluated for a multichannel VANET in terms of the end-to-end packet delay and the percentage of occupied time slots per frame in a highway scenario. Both packet queueing and service delays are considered in the end-to-end delay calculation by modeling each relay vehicle as a queueing system, in which the packets are served in batches of no more than a specified maximum batch-size.

Node clustering is a potential approach to improve the scalability of networking protocols in vehicular ad hoc networks (VANETs). High relative vehicle mobility and frequent topology changes inflict new challenges on maintaining stable clusters. As a result, cluster stability is a crucial measure of the efficiency of clustering algorithms for VANETs. This paper presents stochastic analysis of the vehicle mobility impact on single-hop cluster stability in terms of the overlapping state between neighboring clusters. A stochastic mobility model is adopted to capture the time variations of inter-vehicle distances (distance headways). Firstly, we propose a discrete-time lumped Markov chain to model the time variation of the distance between two neighboring cluster heads. Secondly, first passage time analysis is used to derive probability distributions of the time to the first change in cluster-overlapping state and the inter-cluster overlapping time as measures of cluster stability. Finally, numerical results are presented to evaluate the proposed model, which demonstrate a close agreement between analytical and simulation results.

Cooperative communication (CC) can offer high channel capacity and reliability in an efficient and low-cost way by forming a virtual antenna array among single-antenna nodes that cooperatively share their antennas. It has been well recognized that the selection of relay nodes plays a critical role in the performance of multiple source-destination pairs. Unfortunately, all prior work has made an unrealistic assumption that spectrum resources are unlimited and each source-destination pair can communicate over a dedicated channel with no mutual interference. In this paper, we study the problem of maximizing the minimum transmission rate among multiple source-destination pairs using CC in a cognitive radio network (CRN). We jointly consider the relay assignment and channel allocation under a finite set of available channels, where the interference must be considered. In order to improve the spectrum efficiency, we exploit the network coding opportunities existing in CC that can further increase the capacity. Such max-min rate problems for cognitive and cooperative communications are proved to be NP-hard and the corresponding MINLP (Mixed-Integer Nonlinear Programming) formulations are developed. Moreover, we apply the reformulation and linearization techniques to the original optimization problems with nonlinear and nonconvex objective functions such that our proposed algorithms can produce high competitive solutions in a timely manner. Extensive simulations are conducted to show that the proposed algorithms can achieve high spectrum efficiency in terms of providing a much improved max-min transmission rate under various network settings.

A vehicular ad hoc network (VANET) is a promising addition to our future intelligent transportation systems, which supports various safety and infotainment applications. The high node mobility and frequent topology changes in VANETs impose new challenges in maintaining a long-lasting connection between network nodes. As a result, the lifetime of communication links is a crucial issue in VANET development and operation. This paper presents a probabilistic analysis of the communication link in VANETs for three vehicle density ranges. First, we present the stationary distribution of the communication link length using mesoscopic mobility models. Second, we propose a stochastic microscopic mobility model that captures time variations of intervehicle distances (distance headways). A discrete-time finite-state Markov chain with state-dependent transition probabilities is proposed to model the distance headway. Third, the proposed stochastic microscopic model and first passage time analysis are used to derive the probability distribution of the communication link lifetime. Numerical results are presented to evaluate the proposed model, which demonstrate a close agreement between analytical and simulation results.

The distributed nature of the CSMA/CA-based wireless protocols, for example, the IEEE 802.11 distributed coordinated function (DCF), allows malicious nodes to deliberately manipulate their backoff parameters and, thus, unfairly gain a large share of the network throughput. In this paper, we first design a real-time backoff misbehavior detector, termed as the fair share detector (FS detector), which exploits the nonparametric cumulative sum (CUSUM) test to quickly find a selfish malicious node without any a priori knowledge of the statistics of the selfish misbehavior. While most of the existing schemes for selfish misbehavior detection depend on heuristic parameter configuration and experimental performance evaluation, we develop a Markov chain-based analytical model to systematically study the performance of the FS detector in real-time backoff misbehavior detection. Based on the analytical model, we can quantitatively compute the system configuration parameters for guaranteed performance in terms of average false positive rate, average detection delay, and missed detection ratio under a detection delay constraint. We present thorough simulation results to confirm the accuracy of our theoretical analysis as well as demonstrate the performance of the developed FS detector.

We study cross-layer design in random-access-based fixed wireless multihop networks under a physical interference model. Due to the complexity of the problem, we consider a simple slotted ALOHA medium access control (MAC) protocol for link-layer operation. We formulate a joint routing, access probability, and rate allocation optimization problem to determine the optimal max-min throughput of the flows and the optimal configuration of the routing, access probability, and transmission rate parameters in a slotted ALOHA system. We then also adapt this problem to include an XOR-like network coding without opportunistic listening. Both problems are complex nonlinear and nonconvex. We provide extensive numerical results for both problems for medium-size mesh networks using an iterated optimal search technique. Via numerical and simulation results, we show that: 1) joint design provides a significant throughput gain over a default configuration in slotted-ALOHA-based wireless networks; and 2) the throughput gain obtained by the simple network coding is significant, especially at low transmission power. We also propose simple heuristics to configure slotted-ALOHA-based wireless mesh networks. These heuristics are extensively evaluated via simulation and found to be very efficient.

Most existing research studies in cooperative communication are based on a half-duplex assumption. Motivated by recent successes in hardware implementation of wireless full-duplex transmission, we propose a full-duplex cooperative communication (FDCC) approach to maximize the minimum transmission rate among a set of users to a common destination with the help of a dedicated relay. Under the consideration of hardware cost, only the relay node requires full-duplex wireless equipment in our design. We derive the achievable transmission rate for the proposed FDCC scheme under both amplify-and-forward (AF) and decode-and-forward (DF) modes. Further, as the transmission scheduling of users plays a critical role in determining the achievable transmission rate in FDCC, we formulate the max-min rate scheduling problem as a nonconvex mixed integer nonlinear programming (MINLP) problem. By applying linearization and convex approximation techniques, we propose an optimal algorithm based on a branch-and-bound framework to solve the problem efficiently. Extensive simulation results show that FDCC can significantly improve the transmission rate as compared with direct transmission and half-duplex cooperative communication (HDCC).

In this paper, an energy management sub-system is proposed for mobile terminals (MTs) to support a sustainable multi-homing video transmission, over the call duration, in a heterogeneous wireless access medium. Through statistical video quality guarantee, the MT can determine a target video quality lower bound that can be supported for a target call duration. The target video quality lower bound captures the MT available energy at the beginning of the call, the time varying bandwidth availability and channel conditions at different radio interfaces, the target call duration, and the video packet characteristics in terms of distortion impact, delay deadlines, and video packet encoding statistics. The MT then adapts its energy consumption to support at least the target video quality lower bound during the call. Simulation results demonstrate the superior performance of the proposed framework over two benchmarks and some performance trade-offs.

Energy efficiency is an important performance measure of wireless network protocols, particularly for battery-powered mobile devices such as smartphones. This paper presents a new energy efficient medium access control (MAC) scheme for fully connected wireless ad hoc networks. The proposed scheme reduces energy consumption by putting radio interfaces in the sleep state periodically and by reducing transmission collisions, which results in high throughput and low packet transmission delay. The proposed MAC scheme can also address energy saving in realtime traffics, which require very low packet transmission delay. An analytical model is established to evaluate the performance of the proposed MAC scheme. Analytical and simulation results demonstrate that the proposed scheme has significantly lower power consumption, achieves substantially higher throughput, and has lower packet transmission delay in comparison with existing power saving MAC protocols.

In this paper, to address network congestion stemmed from traffic generated by advanced user equipment, we propose a novel network resource allocation strategy, i.e., time-frequency resource conversion (TFRC), via exploiting user behavior, a specific kind of context information. The key idea is to use radio resources mainly on the traffic/connection to which a user pays attention. The TFRC withdraws spectrum resources strategically from connection(s) not focused on by the user, providing reuseable spectrum called “virtual spectrum hole”. Considering an LTE-type cellular network, a double-threshold guard channel policy is proposed to facilitate the implementation of TFRC. An analytical model is established to study benefits of exploiting TFRC in terms of call-level performance, including new call blocking, handoff call dropping, and recovering call dropping probabilities. Numerical results demonstrate the effectiveness of the proposed approach, in increasing the cell capacity (maximum user number per cell) while limiting potential service quality degradation introduced by the newly proposed technique.

The mobile tactical network is a practical implementation of the mobile ad hoc network. Formed across tactical radios operating in the military very high frequency and low ultrahigh frequency bands, the mobile tactical network has distinctive characteristics when compared with generic mobile ad hoc networks, in particular with respect to its network topological behaviors and connectivity attributes. These characteristics must be understood and considered when selecting suitable network protocols. To this end, in this paper, a network science-based systematic modeling approach is applied to analyze typical deployment scenarios and identify fundamental tactical network properties. The novel framework employs realistic scenario models as well as radio physical layer performance parameters and channel models to effectively capture the dynamic network behavior that needs to be considered for protocol design. The results provide critical insights and guidance to the development of tactical network solutions.

Although extensive research has been carried out on the issue of how to optimally select relays in wireless relay networks, relay selection for mobile users is still a challenging problem because of the requirement that the dynamic selection should adapt to user mobility. Moreover, because the selected relays consume their energy on relaying data for the users, it is required that the users have to pay for this relay service. The price of selecting relays will affect the users' decisions. Assuming that different relays can belong to different service providers, we consider the situation that the service providers can strategically set the prices of their relays to maximize their profits. In this paper, we jointly study the dynamic relay selection for mobile users and profit maximization for service providers. Also, we design a Stackelberg-game hierarchical framework to obtain the solution. At the lower level, we investigate the relay selection problem for the mobile users under given prices of selecting the relays. It is formulated as a Markov decision process problem with the objective to minimize the mobile user's long-term average cost (which consists of the payment to the relay service and the cost due to packet loss), and solved by applying the linear programming technique. At the upper level, we study the game of setting relay prices for the service providers, with the knowledge that the mobile users will make relay selections based on their given prices. Nash equilibrium is obtained as the solution. Our results can help to provide a guidance for service providers to compete for providing relay services.

A vehicular ad hoc network (VANET) is a promising addition to our future intelligent transportation systems, which supports various safety and infotainment applications. The high node mobility and frequent topology changes in VANETs impose new challenges on maintaining a long-lasting connection between vehicular nodes. As a result, the lifetime of communication links is a crucial measure in VANET development and operation. This paper presents a probabilistic analysis of the communication link lifetime in VANETs. Firstly, we propose a stochastic microscopic mobility model that captures time variations of inter-vehicle distances (distance headways), using a discrete-time finite-state Markov chain with state dependent transition probabilities. Secondly, the proposed stochastic model and first passage time analysis are used to derive the probability distribution of the communication link lifetime. Finally, numerical results are presented to evaluate the proposed model, which demonstrate a close agreement between analytical and simulation results.

Cooperative medium access control (MAC) protocols have been proposed for improving communication reliability and throughput in wireless networks. In a recent study, a cooperative MAC scheme called Cooperative ADHOC MAC (CAH-MAC) has been proposed to increase the network throughput by reducing the wastage of time slots under a static network scenario. Particularly, neighbor nodes cooperate to increase the transmission reliability by utilizing unreserved time slots for retransmission of failed packets. In this paper, we focus on a mobile networking scenario and study the effects of time slot reservation on the performance of CAH-MAC under highly dynamic vehicular environments. We find out that the introduction of time slot reservation results in cooperation collisions, degrading the system performance. To tackle this challenge, we present an enhanced CAH-MAC (eCAH-MAC) that is able to avoid cooperation collisions and thus efficiently utilize a time slot. In eCAH-MAC, the cooperative relay transmission phase is delayed, so that cooperation collisions can be avoided and time slots can be efficiently reserved. Through extensive simulations, we demonstrate that eCAH-MAC uses time slot more efficiently than CAH-MAC in direct and/or cooperative transmissions and in reserving time slots in the presence of relative mobility among nearby nodes.

Power consumption is an important consideration in deploying wireless networks, especially for battery-powered mobile devices such as smartphones. Radio interface is the main source of energy consumption in mobile devices. Medium access control (MAC) directly controls the radio interface operation; therefore, an efficient MAC protocol can significantly decrease the energy consumption. This paper presents a new energy efficient MAC scheme for fully connected wireless networks. Time is divided into beacon intervals and a temporary coordinator node manages transmission in each beacon interval. The proposed scheme reduces energy consumption by putting radio interfaces in the sleep state periodically and reducing transmission collision. Simulation results show that the proposed scheme has lower power consumption, higher throughput and lower delay than existing power saving MAC protocols.

Sharing radio resources of multiple wireless networks with overlapped coverage areas has a potential of improving the transmission throughput. However, the improvement cannot be achieved in congestion scenarios using independent congestion control procedures among the end-to-end paths. Although various network characteristics make the congestion control complex, this variety can be useful for congestion avoidance if the networks cooperate with each other. In this way, the traffic can be shifted from a congested network to non-congested ones, and the overall transmission throughput does not degrade in a congestion scenario. In this paper, first, a cooperative congestion control algorithm is proposed in which the state of an end-to-end path is provided at the destination terminal by measuring the queuing delay and estimating the congestion level. Second, the decision on when to start/stop cooperation is determined based on the network characteristics, instantaneous traffic condition, and requested quality of service (QoS). Simulation results demonstrate the throughput improvement of the proposed scheme over non-cooperative congestion control.

The harmonic coexistence of secondary users (SUs) and primary users (PUs) in cognitive radio networks requires SUs to identify the idle spectrum bands. One common approach to achieve spectrum awareness is through spectrum sensing, which usually assumes known distributions of the received signals. However, due to the nature of wireless channels, such an assumption is often too strong to be realistic, and leads to unreliable detection performance in practical networks. In this paper, we study the sensing performance under distribution uncertainty, i.e., the actual distribution functions of the received signals are subject to ambiguity and not fully known. Firstly, we define a series of uncertainty models based on signals' moment statistics in different spectrum conditions. Then we present mathematical formulations to study the detection performance corresponding to these uncertainty models. Moreover, in order to make use of the distribution information embedded in historical data, we extract a reference distribution from past channel observations, and define a new uncertainty model in terms of it. With this uncertainty model, we propose two iterative procedures to study the false alarm probability and detection probability, respectively. Numerical results show that the detection performance with a reference distribution is less conservative compared with that of the uncertainty models merely based on signal statistics.

In this paper, we study the differences of applying cooperation to fully-connected and multi-hop wireless networks, and find out that both the enlarged interference area and link density play a pivotal role in making the beneficial cooperation decision in a multi-hop network. Through characterizing effects of the enlarged interference area and link density on the overall network performance, a beneficial cooperation opportunity can be identified. By employing a randomized scheduling scheme and deriving the interference-free probability of any two links, the expected numbers of concurrent direct and cooperative transmissions can be obtained, where the ratio of these two numbers is defined as the beneficial cooperation ratio. Such a ratio translates the reduced spatial reuse to a requirement of the cooperation gain and provides a guideline for enabling beneficial cooperation on a single-link basis. Finally, the analytical and simulation results demonstrate that the beneficial cooperation criterion for a multi-hop network derived in this paper is more accurate than that in [1].

In cognitive radio networks, channel information is desired by unlicensed secondary users (SUs) to perform effective power control so as to avoid undue interference to licensed primary users (PUs). However, in general, there is no regular information exchange between PUs and SUs, which implies that SUs are unable to obtain up-to-date channel information at the PU side. Besides, the small-scale fading, in addition to shadowing, brings great uncertainty in SUs' channel estimation. In this paper, we consider limited information exchange between SUs and PUs, and study the impact of channel uncertainty on SUs' throughput performance with power control. We model the uncertain channel gain to be a random variable following a state-dependent probability distribution function, and design a power control method that is robust against the channel uncertainty. We formulate the robust power control problem as a chance constrained robust optimization and solve it by an iterative algorithm. Numerical results show that the proposed power control can provide better protection for PUs than existing methods that overlook the uncertainty in channel measurement.

Due to the rapid advancement in the wireless communication technology and automotive industries, the paradigm of vehicular ad-hoc networks (VANETs) emerges as a promising approach to provide road safety, vehicle traffic management, and infotainment applications. Cooperative communication, on the other hand, can enhance the reliability of communication links in VANETs, thus mitigating wireless channel impairments due to the user mobility. In this paper, we present a cooperative scheme for medium access control (MAC) in VANETs, referred to as Cooperative ADHOC MAC (CAH-MAC). In CAH-MAC, neighboring nodes cooperate by utilizing unreserved time slots, for retransmission of a packet which failed to reach the target receiver due to a poor channel condition. Through mathematical analysis and simulation, we show that our scheme increases the probability of successful packet transmission and hence the network throughput in various networking scenarios.

In cognitive radio networks, it is often impossible to have regular information exchange between PUs and SUs. This implies that SUs are unable to obtain up-to-date channel information at the PU side, and will face technical challenges in accurately controlling their interference to PUs through power control. In this paper, we assume that SUs can estimate the channel information in the reciprocal channel, and study the channel uncertainty due to estimation errors and its impact on SUs' performance and PUs' protection. Specifically, we model the uncertain channel gain to be a random variable following a state-dependent distribution function, and propose a power control mechanism that is robust against the channel uncertainty. We study the robust power control in two cases. In the first case, all SU transmitters (e.g., secondary base stations) transmit with the same power, while in the second case each SU transmitter may choose distinct transmit power based on its own preference. In either case, we formulate the power control problem as a chance constrained robust optimization problem and design an iterative algorithm, respectively. Numerical results show that our robust power control mechanism can provide better protection for PUs than existing methods that overlook the uncertainty in channel measurement, and the second-case power control generally provides better Quality of Service (QoS) for SUs than that in the first case.

Quality of service (QoS) support over cognitive radio networks (CRNs) is challenging due to the random spectrum availability. Elastic data traffic is a popular service whose service response time is an important QoS parameter. We analyze the mean response time of elastic data traffic service operating over a single channel time-slotted centralized CRN under three main service disciplines, namely, shortest processing time without preemption (SPTNP), shortest processing time with preemption, and shortest remaining processing time, in comparison with the processor sharing (PS) service discipline. It is shown that the SPTNP is a better choice over the PS service discipline when the traffic load is high, and that the preemption reduces the mean response time when the data file size (service time requirement) follows a heavy tailed distribution. The response time analysis can be used for call admission control to ensure service satisfaction.

Successive interference cancellation (SIC) is an effective way of multipacket reception to combat interference in wireless networks. We focus on link scheduling in wireless networks with SIC, and propose a layered protocol model and a layered physical model to characterize the impact of SIC. In both the interference models, we show that several existing scheduling schemes achieve the same order of approximation ratios, independent of whether or not SIC is available. Moreover, the capacity order in a network with SIC is the same as that without SIC. We then examine the impact of SIC from first principles. In both chain and cell topologies, SIC does improve the throughput with a gain between 20 and 100 percent. However, unless SIC is properly characterized, any scheduling scheme cannot effectively utilize the new transmission opportunities. The results indicate the challenge of designing an SIC-aware scheduling scheme, and suggest that the approximation ratio is insufficient to measure the scheduling performance when SIC is available.

The need of a medium access control (MAC) protocol for an efficient broadcast service is of great importance to support the high-priority safety applications in vehicular ad hoc networks (VANETs). This paper introduces VeMAC, a novel multichannel TDMA MAC protocol proposed specifically for a VANET scenario. The VeMAC supports efficient one-hop and multihop broadcast services on the control channel by using implicit acknowledgments and eliminating the hidden terminal problem. The protocol reduces transmission collisions due to node mobility on the control channel by assigning disjoint sets of time slots to vehicles moving in opposite directions and to road side units. Analysis and simulation results in highway and city scenarios are presented to evaluate the performance of VeMAC and compare it with ADHOC MAC, an existing TDMA MAC protocol for VANETs. It is shown that, due to its ability to decrease the rate of transmission collisions, the VeMAC protocol can provide significantly higher throughput on the control channel than ADHOC MAC.

This paper studies how to select a path with the minimum expected end-to-end delay (EED) in a multiradio multichannel (MR-MC) wireless mesh network. While the existing studies mainly focus on the packet transmission delay due to medium access control (MAC), our new EED metric further takes into account the queuing delay at the MAC layer. In particular, in the MR-MC context, we develop a generic iterative approach to compute the multiradio achievable bandwidth (MRAB) for a path, taking the impact of inter-/intraflow interference and space/channel diversity into consideration. The MRAB is then combined with the EED to form the metric weighted end-to-end delay (WEED). As a byproduct of MRAB, a channel diversity coefficient is defined to quantitatively represent the channel diversity for a given path. Moreover, we design and implement a distributed WEED-based routing protocol for MR-MC wireless networks by extending the well-known AODV protocol. Extensive simulation results are presented to demonstrate the performance of EED/WEED-based routing, with comparison to some existing well-known routing metrics.

This paper addresses the relation between message delivery delay and reliability for the communication between a vehicle and a road side unit (RSU). We focus on sparse vehicular sensor networks (VSNs), where timely message delivery and reliable transmission are of significant importance. We present a mathematical framework for the message delivery delay distribution for a two-lane road, where vehicles in one direction act as message carriers for the ones in the other direction and have the freedom to leave the road from randomly distributed road junctions with a certain probability. Packet generator vehicles store the original packets till meeting an RSU while sending multiple copies of each packet to packet carrier vehicles. Our analysis offers an analytical tool for an intelligent transportation system (ITS) service provider to determine the minimum RSU density required to cover a road for meeting a probabilistic requirement of the message delay. Extensive computer simulation results show the accuracy of our analysis and clearly indicate the relation of packet delay and the number of packet replicas.

In this paper, radio resource allocation in a heterogeneous wireless access medium is investigated. Mobile terminals (MTs) are equipped with multiple radio interfaces and are assumed to have multi-homing capabilities. A novel algorithm, namely prediction based resource allocation algorithm, is proposed for the resource allocation. Unlike the existing solutions in literature, the proposed algorithm does not require a central resource manager to perform the radio resource allocation. The MT plays an active role in the resource allocation operation by requesting a bandwidth share from each available network based on the available resources at the network, such that the total allocated bandwidth from different networks satisfies the MT service requirement. The proposed algorithm is suitable for implementation in a dynamic environment with call arrivals and departures, and relies on network cooperation to perform the decentralized radio resource allocation in an efficient manner. Simulation results are presented to investigate the performance tradeoffs of the proposed algorithm.

This paper studies video transmission using a multi-homing service in a heterogeneous wireless access medium. We propose an energy and content aware video transmission framework that incorporates the energy limitation of mobile terminals (MTs) and the quality-of-service (QoS) requirements of video streaming applications, and employs the available opportunities in a heterogeneous wireless access medium. In the proposed framework, the MT determines the transmission power for the utilized radio interfaces, selectively drops some packets under the battery energy limitation, and assigns the most valuable packets to different radio interfaces in order to minimize the video quality distortion. First, the problem is formulated as MINLP which is known to be NP-hard. Then we employ a piecewise linearization approach and solve the problem using a cutting plane method which reduces the associated complexity from MINLP to a series of MIPs. Finally, for practical implementation in MTs, we approximate the video transmission framework using a two-stage optimization problem. Numerical results demonstrate that the proposed framework exhibits very close performance to the exact problem solution. In addition, the proposed framework, unlike the existing solutions in literature, offers a choice for desirable trade-off between the achieved video quality and the MT operational period per battery charging.

The paradigm of vehicular ad-hoc networks (VANETs) emerges as a promising approach to provide road safety, vehicle traffic management, and infotainment applications. Thus, it is important to develop a VANET medium access control (MAC) protocol that provides an efficient and reliable delivery of packets for diverse applications. Cooperative communication, on the other hand, can enhance the reliability of communication links in VANETs, thus mitigating wireless channel impairments due to a poor channel condition. Recently, a cooperative scheme for MAC in VANETs based on time-division multiple access, referred to as Cooperative ADHOC MAC (CAH-MAC), has been proposed [1]. CAH-MAC is an efficient protocol capable of increasing the network throughput by reducing the wastage of time slots. In CAH-MAC, neighboring nodes cooperate by utilizing the unreserved time slots, for retransmission of a packet which failed to reach its target receiver due to a poor channel condition. In this paper, we study the reliability of CAH-MAC in terms of packet transmission delay (PTD) and packet dropping rate (PDR). Through mathematical analysis and computer simulation, we show that CAH-MAC provides reliable communication by decreasing the PTD and PDR as compared with existing approaches.

Cognitive radio networks (CRNs) have emerged as a promising solution to spectrum under-utilization and congestion. Supporting quality of service (QoS)-aware services over CRNs is always challenging due to the random spectrum availability. In this work, we consider a single channel CRN with centralized control, operating over a time-slotted primary network, accessing the channel at the spectrum holes without interfering with primary users. The central controller transmits elastic data traffic to the secondary users (SUs). The service response time is an important QoS parameter for elastic data traffic, defined as the duration from the instant that an SU requests a data file from the central controller until it completes the reception. We analyze the response time under the shortest processing time (SPT) first service discipline, and compare it with the processor sharing (PS) service discipline. We show that the SPT service discipline outperforms the PS service discipline in medium and heavy traffic load conditions. The response time analysis can be used for call admission control to ensure service satisfaction.

For the future smart grid, decentralized inverter control is essential in distributed generation (DG) microgrids where a powerful central controller is unavailable for cost and reliability concerns. However, decentralized inverter control suffers from a limited system stability mainly because of the lack of communications among different inverters. In this paper, we investigate the stability enhancement of the droop based decentralized inverter control in microgrids. Specifically, we propose a power sharing based control strategy which incorporates the information provided by a wireless network to improve system stability. The wireless network is used to acquire the total real and reactive power generation of all DG units in a decentralized manner. Based on the desired power sharing of each DG unit and the acquired information of total generation, additional control terms are added to the traditional droop controller. We evaluate the performance of our proposed control strategy based on small-signal stability analysis. Extensive numerical results are presented to demonstrate the system stability.

Delay constraint and sensor energy consumption requirements are two core issues for e-healthcare applications in wireless body area networks. In this paper, we investigate the data transmission scheduling problem to utilize the sleep and opportunistic transmission for energy efficiency, while guaran- teeing the worst-case delay for medical data transmission. To achieve sensor energy saving by exploiting propagation channel quality with a deterministic delay requirement poses challenges in developing a scheduling policy. We address this problem using a Lyapunov optimization formulation and propose a two-step scheduling algorithm. We prove that the algorithm can provide worst-case delay guarantee under certain conditions. Theoretical analysis and simulation results are presented to demonstrate the tradeoff between the transmission delay and energy consumption.

Cognitive radio networks (CRNs) have emerged as a promising solution to spectrum under-utilization and congestion. Supporting quality of service (QoS) aware services over CRNs is always challenging due to the randomness of the spectrum availability. In this paper, we consider a set of fully-connected cognitive radios (secondary users) operating over a time-slotted primary network, accessing the channel at the spectrum holes without interfering with primary users. As the capacity analysis is one of the basic steps to guarantee QoS, we analyze the on-off voice capacity of single-channel single-hop fully-connected CRNs under distributed channel access control. The voice capacity is represented in terms of the number of simultaneous independent voice calls that the secondary network can support, providing stochastic delay guarantee. Our analytical results have a close match with the simulation results. With proper medium access control, capacity analysis can help to develop a call admission control policy for QoS provisioning in non-fully-connected CRNs.

ZigBee, a unique communication standard designed for low-rate personal area networks, has extremely low complexity, cost, and power consumption for wireless connectivity in inexpensive, portable, and mobile devices. Among the well-known ZigBee topologies, the cluster tree is especially suitable for wireless sensor networks because of its support for power saving operations and light-weight routing. However, the restricted routing paths caused by the cluster tree would form transmission bottlenecks such that it is difficult to guarantee quality of service (QoS) for critical packet deliveries in mission-critical networks. To address the problem, we propose a QoS-oriented framework with an analytical model to provide stochastic QoS guarantees for urgent information deliveries in ZigBee cluster-tree networks. Through analysis and simulations, we demonstrate the efficacy of the proposed framework.

Cooperative communication (CC) can offer high channel capacity and reliability in an efficient and low-cost way by forming a virtual antenna array among single-antenna nodes that cooperatively share their antennas. It has been well recognized that the selection of relay nodes plays a critical role in the performance of multiple source-destination pairs. Unfortunately, all prior work has made an unrealistic assumption that each source-destination pair communicates over a dedicated channel with no mutual interference. In this paper, we study the problem of capacity maximization using cooperative communication in a cognitive radio network by jointly considering the relay assignment and channel allocation under a finite set of available channels, where the interference must be considered. It is proved to be NP-hard and a heuristic algorithm is proposed. Moreover, we exploit the network coding opportunities existing in CC that can further increase the capacity. Extensive simulations are conducted to show that the proposed algorithms can achieve high total capacity under various network settings.

Successive interference cancellation (SIC) is an effective way of multipacket reception to combat interference. As conventional CSMA (Carrier Sense Multiple Access) is designed for single packet reception, it is unclear whether or not CSMA performs well to exploit the SIC capability. In this paper, we analyze the performance of a simple CSMA protocol in a network with SIC. For a given link, we derive the residing areas of an interfering node when simultaneous transmission is allowed and when the interference is harmful, respectively. We show that, though SIC provides many new transmission opportunities, CSMA cannot effectively exploit them. There is a fundamental tradeoff in a CSMA protocol between exploiting the transmission opportunities from SIC and capturing the harmful interference. In many cases, when CSMA achieves its best performance, almost all new transmission opportunities are not exploited. It is therefore very necessary to design a new distributed access protocol in wireless networks with SIC.

A quadrature signaling based two-phase cooperation framework for cooperative cognitive radio networking is proposed. By leveraging the degrees of freedom provided by orthogonal modulation, secondary users are able to relay the traffic of primary users and transmit their own in the same time slot without interference. To evaluate the cooperation performance of the proposed framework, a weighted sum throughput maximization problem is formulated, and closed-form solutions of the optimal power setting/allocation are obtained in the amplify-and-forward and decode-and-forward relaying modes. Simulation results validate the efficiency of the proposed framework.

As an important component of smart grid, the vehicle-to-grid (V2G) system is recently introduced to enable bidirectional energy delivery between the power grid and plug-in electric vehicles. Communication technology is incorporated to facilitate the energy delivery by providing electricity pricing and energy demand information. However, different from the stationary energy storage systems, the energy store-carry-and-deliver mechanism for a V2G system poses new challenges for performance optimization, such as bi-directional energy flow and non-stationary energy demand. How to utilize the statistical information provided by the communication system to achieve efficient energy delivery is critical for a V2G system and is still an open issue. In this paper, we address a specific problem in this new research area, i.e., daily energy cost minimization of vehicle owners under time-of-use (TOU) electricity pricing. We investigate a plug-in hybrid electric vehicle (PHEV) with a realistic battery model, which is general for both battery electric cars and plug-in hybrids. A dynamic programming formulation is established by considering the bidirectional energy flow, non-stationary energy demand, battery characteristics, and TOU electricity price. We prove the optimality of a state-dependent double-threshold (or (S, S')) policy based on the stochastic inventory theory. A modified backward iteration algorithm is devised for practical applications, where an exponentially weighted moving average (EWMA) algorithm is used to estimate the statistics of PHEV mobility and energy demand. The performance of the proposed scheme is demonstrated by simulations based on survey and real data collected from Canadian households. Numerical results indicate that our proposed scheme performs closely to a scheme with a priori knowledge of the PHEV mobility and energy demand information. Compared with the existing approaches, the proposed scheme can achieve energy cost reduction, which increases with the battery capacity.

In this paper, we propose a distributed Prediction-based Secure and Reliable routing framework (PSR) for emerging Wireless Body Area Networks (WBANs). It can be integrated with a specific routing protocol to improve the latter's reliability and prevent data injection attacks during data communication. In PSR, using past link quality measurements, each node predicts the quality of every incidental link, and thus any change in the neighbor set as well, for the immediate future. When there are multiple possible next hops for packet forwarding (according to the routing protocol used), PSR selects the one with the highest predicted link quality among them. Specially-tailored lightweight source and data authentication methods are employed by nodes to secure data communication. Further, each node adaptively enables or disables source authentication according to predicted neighbor set change and prediction accuracy so as to quickly filter false source authentication requests. We demonstrate that PSR significantly increases routing reliability and effectively resists data injection attacks through in-depth security analysis and extensive simulation study.

In this paper, radio resource allocation for mobile terminals (MTs) in a heterogeneous wireless access medium is investigated. Unlike the existing solutions in literature, two types of services are considered in this paper, namely single-network and multi-homing services. In single-network services, an MT is assigned to the best available wireless network, while in multi-homing services an MT utilizes all available wireless access networks simultaneously. With the presence of both services in the heterogeneous wireless access medium, the radio resource allocation objective is of twofold: We aim to find the optimal assignment of MTs with single-network service to the available wireless access networks and to determine the corresponding optimal bandwidth allocation to the MTs with single-network and multi-homing services. The radio resource allocation problem is formulated to guarantee the service quality for both service types. Numerical results are presented to demonstrate the performance of the proposed radio resource allocation scheme.

A vehicular ad hoc network (VANET) is an emerging technology which has a great potential of new applications in safety, traffic optimization, and entertainment. The VeMAC [1], [2] is a medium access control protocol recently proposed for VANETs, which can support efficient broadcast service necessary for high priority safety applications. The VeMAC protocol reserves disjoint sets of time slots to vehicles moving in opposite directions and to road side units (RSUs). The protocol has been evaluated for vehicle-to-vehicle (V2V) communications and under balanced vehicle traffic conditions, in which the densities of vehicles moving in opposite directions on a two-way road are approximately equal [2]. In this paper we investigate the effects of the existence of RSUs and the unbalanced vehicle traffic conditions on the VeMAC performance, via simulations in highway and city scenarios in terms of network throughput and transmission collision rate.

Nowadays, with the dramatically increased penetration of wireless access, the conflict between spectrum scarcity and under-utilisation is becoming more and more aggravating. A promising technology to tackle such challenge is cognitive radio, of which spectrum sensing is one of the most important functionalities. In this study, the authors consider an essential problem of energy-efficient spectrum sensing in cognitive radio networks. Although most existing works of spectrum sensing mainly focus on determining an optimal sensing time to maximise the detection probability and/or to minimise the false alarm probability, our problem of how to schedule the power-constrained sensor is much more challenging, because of the trade-off among interests of the primary user, secondary user and sensor. The authors formulate it as a non-linear optimisation problem to maximise the sensor lifetime, with necessary constraints of quality and delay of spectrum sensing, and throughput for performance guarantee of primary and secondary users. Moreover, the authors incorporate the distribution information of channel occupancy/vacancy durations into the problem to yield a desirable solution. They propose a novel framework to obtain the optimal energy-efficient periodic scheduling by adopting both non-linear programming and linear programming. Extensive simulation results are provided to validate our theoretical analysis.

In this paper, radio resource allocation in a heterogeneous wireless access medium is studied. Mobile terminals (MTs) are assumed to have multi-homing capabilities. Both constant bit rate and variable bit rate services are considered. A novel algorithm is developed for the resource allocation. Unlike existing solutions in literature, the proposed algorithm is distributed in nature, such that each network base station / access point can perform its own resource allocation to support the MTs according to their service classes. The coordination among different available wireless access networks' base stations is established via the MT multiple radio interfaces in order to provide the required bandwidth to each MT. A priority mechanism is employed, so that each network gives a higher priority on its resources to its own subscribers as compared to other users. Numerical results demonstrate the validity of the proposed algorithm.

As essential building blocks of the future smart grid, microgrids can efficiently integrate various types of distributed generation (DG) units to supply the electric loads at the minimum cost based on the economic dispatch. In this paper, we introduce a decentralized economic dispatch approach such that the optimal decision on power generation is made by each DG unit locally without a central controller. The prerequisite power generation and load information for decision making is discovered by each DG unit via a multiagent coordination with guaranteed convergence. To avoid a slow convergence speed which potentially increases the generation cost because of the time-varying nature of DG output, we present a heterogeneous wireless network architecture for microgrids. Low-cost short-range wireless communication devices are used to establish an ad hoc network as a basic information exchange infrastructure, while auxiliary dual-mode devices with cellular communication capabilities are optionally activated to improve the convergence speed. Two multiagent coordination schemes are proposed for the single-stage and hierarchical operation modes, respectively. The optimal number of activated cellular communication devices is obtained based on the tradeoff between communication and generation costs. The performance of the proposed schemes is analyzed and evaluated based on real power generation and load data collected from the Waterloo Region in Canada. Numerical results indicate that our proposed schemes can better utilize the cellular communication links and achieve a desired tradeoff between the communication and generation costs as compared with the existing schemes.

In this paper, we investigate on-demand data services for high-speed trains via a cellular/infostation integrated network. Service requests and acknowledgements are sent through the cellular network to a content server, while data delivery is achieved via trackside infostations. The optimal resource allocation problem is formulated by taking account of the intermittent network connectivity and multi-service demands. In order to achieve efficient resource allocation with low computational complexity, the original problem is transformed into a single-machine preemptive scheduling problem based on a time-capacity mapping. As the service demands are not known a priori, an online resource allocation algorithm based on Smith ratio and exponential capacity is proposed. The performance bound of the online algorithm is characterized based on the theory of sequencing and scheduling. If the link from the backbone network to an infostation is a bottleneck, a service pre-downloading algorithm is also proposed to facilitate the resource allocation. The performance of the proposed algorithms is evaluated based on a real high-speed train schedule. Compared with the existing approaches, our proposed algorithms can significantly improve the quality of on-demand data service provisioning over the trip of a train.

In this paper, we investigate data dissemination in delay tolerant networks (DTNs) via roadside wireless local area networks (RS-WLANs). The data dissemination service is destined to a group of nomadic nodes roaming in a large network region with a low node density. The local nodes within the coverage area of an RS-WLAN can provide packet caching and relaying capabilities. We consider a cooperative data dissemination approach where information packets are first pre-downloaded to the local nodes within the RS-WLAN before the visit of a pedestrian nomadic node, and then opportunistically scheduled to transmit to the nomadic node upon its arrival. In order to resolve the channel contention among multiple direct/relay links and exploit the predictable traffic characteristics as a result of packet pre-downloading, a double-loop receiver-initiated medium access control (DRMAC) scheme is proposed. The MAC scheme can achieve spatial and temporal diversity via the outer-loop and inner-loop MAC, respectively. A receiver initiated mechanism is used to reduce the signalling overhead, where the ACK message is used as an invitation of channel contention. An analytical model is established to evaluate the performance of the proposed MAC scheme. Numerical results demonstrate that the proposed MAC scheme can significantly improve the number of delivered packets from an RS-WLAN to a nomadic node as compared with the existing MAC schemes.

In this paper, we propose a distributed asynchronous clock synchronization (DCS) protocol for Delay Tolerant Networks (DTNs). Different from existing clock synchronization protocols, the proposed DCS protocol can achieve global clock synchronization among mobile nodes within the network over asynchronous and intermittent connections with long delays. Convergence of the clock values can be reached by compensating for clock errors using mutual relative clock information that is propagated in the network by contacted nodes. The level of clock accuracy is depreciated with respect to time in order to account for long delays between contact opportunities. Mathematical analysis and simulation results for various network scenarios are presented to demonstrate the convergence and performance of the DCS protocol. It is shown that the DCS protocol can achieve faster clock convergence speed and, as a result, reduces energy cost by half for neighbor discovery.

ZigBee, a unique communication standard designed for low-rate wireless personal area networks, has extremely low complexity, cost, and power consumption for wireless connectivity in inexpensive, portable, and mobile devices. Among the well-known ZigBee topologies, ZigBee cluster-tree is especially suitable for low-power and low-cost wireless sensor networks because it supports power saving operations and light-weight routing. In a constructed wireless sensor network, the information about some area of interest may require further investigation such that more traffic will be generated. However, the restricted routing of a ZigBee cluster-tree network may not be able to provide sufficient bandwidth for the increased traffic load, so the additional information may not be delivered successfully. In this paper, we present an adoptive-parent-based framework for a ZigBee cluster-tree network to increase bandwidth utilization without generating any extra message exchange. To optimize the throughput in the framework, we model the process as a vertex-constraint maximum flow problem, and develop a distributed algorithm that is fully compatible with the ZigBee standard. The optimality and convergence property of the algorithm are proved theoretically. Finally, the results of simulation experiments demonstrate the significant performance improvement achieved by the proposed framework and algorithm over existing approaches.

This paper studies radio resource allocation for mobile terminals (MTs) in a heterogeneous wireless access medium. Unlike the existing solutions in literature, we consider the simultaneous presence of both single-network and multi-homing services in the networking environment. In single-network services, an MT is assigned to the best wireless access network available at its location. On the other hand, in multi-homing services, an MT utilizes all available wireless access networks simultaneously. The objective of the radio resource allocation is of twofold: to determine the optimal assignment of MTs with single-network service to the available wireless access networks, and to find the corresponding optimal bandwidth allocation to the MTs with single-network and multi-homing services. We develop a sub-optimal decentralized implementation of the radio resource allocation, which relies on network cooperation to perform the allocation in a dynamic environment in an efficient manner. The MT plays an active role in the resource allocation operation, whether by selecting the best available wireless network for single-network services or by determining the required bandwidth share from each available network for multi-homing services. Simulation results are presented to demonstrate the performance of the proposed algorithm.

Popular smart wireless devices become equipped with multiple radio interfaces. Multihoming support can be enabled to allow for multiple simultaneous associations with heterogeneous networks. In this study, we focus on video streaming traffic and propose analytical approaches to evaluate the packet-level and call-level performance of a multipath transmission scheme, which sends video traffic bursts over multiple available channels in a probabilistic manner. A probability generation function (PGF) and z-transform method is applied to derive the PGF of packet delay and any arbitrary moment in general. Particularly, we can obtain the average delay, delay jitter, and delay outage probability. The essential characteristics of video traffic are taken into account, such as deterministic burst intervals, highly dynamic burst length, and batch arrivals of transmission packets. The video substream traffic resulting from the probabilistic flow splitting is characterized by means of zero-inflated models. Further, the call-level performance, in terms of flow blocking probability and system throughput, is evaluated with a three-dimensional Markov process and compared with that of an always-best access selection. The numerical and simulations results demonstrate the effectiveness of our analysis framework and the performance gain of multipath transmission.

Node clustering is an effective approach to improve scalability of network protocols in wireless communications. In this paper, we analyze the effect of the cluster structure on the average routing overhead for a wireless ad hoc network where nodes are distributed along a line. The cluster size and the overlapping range between clusters that achieve minimum routing overhead are investigated. Numerical results show that cluster structure for minimum routing overhead is highly dependent on the node density and/or the inter and intra-cluster routing methods.

This paper introduces a statistical model that allows the users of a random access-based cognitive radio network sharing a single wireless channel to evaluate the quality-of-service (QoS) capability of the network. The model captures the variation of the channel service process taking into account the statistics of the channel availability in addition to the randomness of the data traffic of the cognitive radio network users. The proposed model allows the cognitive radio network users to employ the effective bandwidth theory and its dual, the effective capacity concept, in order to evaluate the maximum packet delay bound that can be satisfied with a certain violation probability. As a result, the users can adapt the performance of their real-time applications to fit the cognitive radio network status. Simulation results validate the model and demonstrate its accuracy.

In this paper, we investigate the on-demand data delivery to high-speed trains via trackside infostations. The optimal resource allocation problem is formulated by considering the trajectory of a train, quality of service (QoS) requirements, and network resources. The original problem is transformed into a single-machine preemptive scheduling problem based on a time-capacity mapping. As the service demands are not known a priori, an online resource allocation algorithm is proposed based on the Smith ratio and exponential capacity. The performance of the proposed algorithm is evaluated based on a real high-speed train schedule. Compared with the existing approaches, our proposed algorithm can achieve the best performance in terms of the total reward of delivered services over the trip of a train.

Cognitive Radio Networks (CRNs) provide a solution for the spectrum scarcity problem facing the wireless communications community. To be able to utilize CRNs in practical applications, a certain level of quality-of-service (QoS) should be guaranteed to the secondary users (SUs) in such networks. In this paper, we propose a packet scheduling scheme that orders the SUs' transmissions according to the packet dropping rates and the number of packets queued waiting for transmission. A medium access control (MAC) protocol, based on the mentioned scheduling scheme, is proposed for a centralized CRN. In addition, the scheduling scheme is adapted for a distributed CRN, by introducing a feature that allows SUs to organize access to the available spectrum without the need for a central unit. Extensive simulation results are presented to evaluate the proposed protocols, in comparison with other MAC protocols designed for CRNs. The results demonstrate the effectiveness of our proposed protocols to guarantee the required QoS for voice packet transmission, while maintaining fairness among SUs.

In this paper, we propose a stopping rule-driven channel access scheme for a secondary user pair in multichannel cognitive radio networks (CRNs), aiming to achieve a desired tradeoff between channel sensing and channel access. In the proposed approach, we first formulate the sensing-access tradeoff problem as a 1-stage look-ahead stopping problem. We then derive two stopping conditions, namely power-limited stopping and bandwidth-limited stopping, whereby a desired tradeoff between sensing overhead and throughput increase can be achieved. Once a stopping condition is reached, a secondary user pair stops sensing and starts accessing previously sensed free channels for packet transmission. Simulation results show that, in the case of perfect sensing, the proposed approach outperforms a greedy approach by at least 80% in terms of throughput. Imperfect sensing and its impact are also addressed and evaluated.

Successive interference cancellation (SIC) is an effective way of multipacket reception to combat interference. We consider the problem of maximizing the number of successful transmissions based on the physical model in wireless networks with SIC at the physical layer. We propose weighted simultaneity graph to characterize the sequential detection nature of SIC and the accumulative effect of multiple interfering signals. A context-aware metric, transmission price, is defined to measure the interference of a link set. As maximizing the number of supported links is NP-hard, a greedy scheme is proposed to efficiently construct a near-optimal maximal feasible set of links. We show that the approximation performance is bounded by the transmission price of the constructed link set. The performance of the proposed scheme is further verified by simulation.

We consider the greedy scheduling based on the physical model in wireless networks with successive interference cancellation (SIC). There are two major stages in a scheduling scheme, link selection (to decide which link is scheduled next) and time slot selection (to deciding which slot is allocated to a given link). Most available schemes take a first-fit policy in the latter and strive to achieve good performance by careful selection of link ordering with respect to interference. Due to the accumulation effect and sequential detection nature of SIC, however, it is difficult to evaluate the interference of a link. As a result, many existing scheduling schemes become less efficient. In this paper, we take a new look on the problem and focus to the time slot selection stage. We define tolerance margin to measure the saturation of a link set and present two heuristic policies: one is to schedule a link to a slot such that the resulting set of links has a maximum tolerance margin; the other is to choose a slot such that the increase of tolerance margin is minimum. Simulation results show that the performance of the proposed schemes is better than the first-fit policy and is close to the optimal solution.

Cooperative communication has attracted dramatic attention in the last few years due to its advantage in mitigating channel fading. Despite much effort that has been made in theoretical analysis of the performance gain, cooperative relay selection, which is one of the fundamental issues in cooperative communications, is still left as an open problem. In this paper, the tradeoff between improvement and corresponding cost of cooperative communication, focusing on relay selection is addressed. We consider a challenging scenario which takes user mobility into consideration. Based on user mobility pattern, a dynamic relay selection scheme aiming at minimizing the long-term average cost while satisfying the QoS requirement is proposed. For relay selection to achieve maximal performance, an optimization model based on the constrained Markov decision process (CMDP) is formulated and solved by applying the linear programming (LP) technique. Comprehensive analysis and comparison with several other relay selection schemes are presented. Through extensive simulations, our scheme shows its high effectiveness and flexibility in balancing the cost and QoS performance.

Successive interference cancellation (SIC) is an effective way of multipacket reception (MPR) to combat interference in wireless networks. To understand the potential MPR advantages, we study link scheduling in an ad hoc network with SIC at the physical layer. The fact that the links detected sequentially by SIC are correlated at the receiver poses key technical challenges. We characterize the link dependence and propose simultaneity graph (SG) to capture the effect of SIC. Then interference number is defined to measure the interference of a link. We show that scheduling over SG is NP-hard and the maximum interference number bounds the performance of maximal greedy schemes. An independent set based greedy scheme is explored to efficiently construct a maximal feasible schedule. Moreover, with careful selection of link ordering, we present a scheduling scheme that improves the bound. The performance is evaluated by both simulations and measurements in testbed. The throughput gain is on average 40% and up to 120% over IEEE 802.11. The complexity of SG is comparable with that of conflict graph, especially when the network size is not large.

The distributed nature of the CSMA/CA based wireless protocols, e.g., the IEEE 802.11 distributed coordinated function (DCF), allows malicious nodes to deliberately manipulate their backoff parameters and thus unfairly gain a large share of the network throughput. The non-parametric cumulative sum (CUSUM) test is a promising method for real-time misbehavior detection due to its ability to quickly find abrupt changes in a process without any a priori knowledge of the statistics of the change occurrences. While most of the existing schemes for selfish behavior detection depend on heuristic parameter configuration and experimental performance evaluation, we develop a Markov chain based analytical model to systematically study the CUSUM based scheme for real-time detection of the backoff misbehavior. Based on the analytical model, we can quantitatively compute the system configuration parameters for guaranteed performance in terms of average false positive rate, average detection delay and missed detection ratio under a detection delay constraint. Moreover, we find that the short-term fairness issue of the 802.11 DCF impacts the transition probabilities of the Markov model and thus the detection accuracy. We develop a shuffle scheme to mitigate the short-term fairness impact on the sample series, and investigate the proper shuffle period (in terms of observation windows) that can maintain the randomness in each node's backoff behavior while resolving the short-term fairness issue. We present simulation results to confirm the accuracy of our theoretical analysis as well as demonstrate the performance of the developed real-time detection scheme.

This paper studies the multihop packet delivery delay in a low density vehicular ad hoc network (VANET). We address a disrupted vehicle-to-infrastructure communication scenario, where an end-to-end path is unlikely to exist between a vehicle and the nearest road side unit (RSU). We present an analytical framework, which takes into account the randomness of vehicle data traffic and the statistical variation of the disrupted communication channel. Our framework employs the effective bandwidth theory and its dual, the effective capacity concept, in order to obtain the maximum distance between RSUs that stochastically limits the worst case packet delivery delay to a certain bound (i.e., allows only an arbitrarily small fraction of the packets received by the farthest vehicle from the RSU to exceed a required delay bound). Our study also investigates the effect of the vehicle density, transmission range, and speed difference between vehicles on the end-to-end packet delivery delay. Extensive simulation results validate our analytical framework.

In cognitive radio networks (CRNs), effective and efficient channel exploitation is imperative for unlicensed secondary users to seize available network resources and improve resource utilization. In this paper, we propose a simple channel sensing order for secondary users in multi-channel CRNs without a priori knowledge of primary user activities. By sensing the channels according to the descending order of their achievable rates with optimal stopping, we show that the proposed channel exploitation approach is efficient yet effective in elevating throughput and resource utilization. Simulation results show that our proposed channel exploitation approach outperforms its counterparts by up to 18% in a single-secondary user pair scenario. In addition, we investigate the probability of packet transmission collision in a multi-secondary user pair scenario, and show that the probability of collision decreases as the number of channels increases and/or the number of secondary user pairs decreases. It is observed that the total throughput and resource utilization increase with the number of secondary user pairs due to increased transmission opportunities and multi-user diversity. Our results also demonstrate that resource utilization can be further improved via the proposed channel exploitation approach when the number of secondary user pairs approaches the number of channels.

In this paper, homogeneous voice traffic in a single-channel cognitive radio network (CRN) is considered. We analyze the constant-rate voice capacity of a fully-connected network with slot-ALOHA and round-robin channel access, and propose two call admission control (CAC) algorithms for a non-fully-connected network with slot-ALOHA channel access. Different from the existing work in literature, transmission of multiple packets in a single time-slot is considered. Two discrete-time Markov chain based approaches are used for the capacity analysis of the two channel access schemes, respectively. It is shown that the number of voice packets that can be transmitted in a time-slot has a significant impact on the system capacity. The capacity analysis results of the slot-ALOHA scheme is used to develop a CAC procedure when all the voice flows have an identical statistical delay requirement. Further, two CAC algorithms (A1 and A2) are developed for a network with voice traffic flows having different delay requirements in which one (A1) is based on the theory of effective capacity and is considered as a benchmark to compare with the other. Simulation results demonstrate that algorithm A2 performs better than algorithm A1, and that a relaxed delay requirement leads to an increase in the network capacity.

In this paper, we study medium access control (MAC) protocol design for distributed cooperative wireless networks. We focus on beneficial node cooperation by addressing two fundamental issues of cooperative communications, namely when to cooperate and whom to cooperate with, from a cross-layer protocol design perspective. In the protocol design, taking account of protocol overhead we explore a concept of cooperation region, whereby beneficial cooperative transmissions can be identified. We show that a rate allocation in the cooperation region provides higher link utilization than in a non-cooperation region. To increase network throughput, we propose an optimal grouping strategy for efficient helper node selection, and devise a greedy algorithm for MAC protocol refinement. Analysis of a successful transmission probability with cooperative or direct transmission is presented. Simulation results show that the proposed approach can effectively exploit beneficial cooperation, thereby improving system performance. Further, analytical and simulation results shed some light on the tradeoff between multi-user diversity gain at the physical layer and the helper contention overhead at the MAC layer.

Abstract With recent advances in the development of wireless communication networks, wireless mesh networks (WMNs) have been receiving considerable research interests in recent years. The need to support integrated services and ensure quality of service (QoS) satisfaction for various applications is one of the fundamental challenges for successful WMN deployment. In order to provide differentiated services, medium access control (MAC) should have priority management at the link layer. In code division multiple access (CDMA)‐based WMNs, the interference phenomenon and simultaneous transmissions must be considered. We propose two priority schemes for MAC in a distributed CDMA‐based WMN, taking into account interference, multimedia services, QoS requirements, and simultaneous transmissions. The first priority scheme is within a node. Each node has an independent queue for each traffic class. According to QoS requirements, the queue that should be served first is determined. The second priority scheme is among neighbor nodes. It is proposed for multiple simultaneous transmissions in the CDMA network. This scheme gives a larger chance of correct transmission to high priority traffic than low priority traffic. In addition, we propose to use adaptive spreading gain and a frame structure to achieve high resource utilization. Simulation results demonstrate that the proposed schemes can achieve effective QoS guarantee. Copyright © 2009 John Wiley & Sons, Ltd.

In this paper, we address the relation between message delivery delay and reliability for the communication between a vehicle and a road side unit (RSU). We focus on sparse or low density vehicular ad hoc networks (VANETs), where timely message delivery and reliable transmission are of significant importance. We present an exact message delivery delay distribution for a two-lane road, where vehicles in one direction act as message carriers for the ones in the other direction and have the freedom to leave the road from randomly distributed exits with a certain probability. Our analysis offers a tool for an intelligent transportation system (ITS) service provider to determine the minimal separation between two consecutive RSUs for meeting a probabilistic requirement of the message delay. Simulation results show the accuracy of our analysis.

As an emerging networking technology, cognitive radio networks (CRNs) have drawn immense attention in the wireless networking community. Since multimedia services have become widely popular among wireless communication services users, supporting those services over CRNs has become an interesting research topic in recent years. However, due to the random nature of the resource availability in CRNs, providing quality-of-service (QoS) guarantees for multimedia services is a challenging task. In this paper, we consider a secondary system operating over a time-slotted primary system and secondary users accessing the channels at the spectrum holes without interfering with primary users. As the capacity analysis is one of the basic steps to guarantee QoS, we analyze the constant-rate voice capacity of multi-channel fully-connected CRNs under different generic channel access schemes with centralized and distributed control, respectively. The capacity is represented in terms of the number of simultaneous independent voice calls that the secondary system can support, providing stochastic delay guarantee. It is shown that the analytical results closely match with the simulation results, and the number of voice packets that can be simultaneously transmitted in a time-slot per channel has a significant impact on the capacity of the system. With proper medium access control, capacity analysis can help to develop a call admission control policy for QoS provisioning in CRNs.

In this paper, we consider a DTN/WLAN integrated network where high-mobility nomadic nodes form a delay tolerant network (DTN) and low-mobility local nodes reside in the coverage area of wireless local area networks (WLANs). A data dissemination service facilitated by packet pre-downloading is generated by a server in the Internet and destined to a group of nomadic nodes. In order to achieve efficient data dissemination, a double-loop receiver-initiated medium access control (MAC) scheme is proposed. By implementing both outer-loop and inner-loop MAC, the proposed MAC scheme can achieve spatial and temporal diversity while reducing the MAC overhead. Analytical and simulation results are presented to demonstrate the performance of the proposed MAC scheme.

To support seamless communications for roaming users over heterogeneous wireless networks, mobile ad-hoc networks (MANETs) are receiving a special interest in the research community due to its infrastructure-less nature. MANETs can help to achieve a seamless service for users roaming over areas with no coverage from other wireless networks. As a result, MANETs are a key access network component of the super node system that employs the delay tolerant network (DTN) architecture to address user mobility issues. Within the super node system, a dominating-set routing technique is proposed to improve message delivery over MANETs and to achieve better resource utilization. The performance of the dominating set routing technique depends on estimation accuracy of the probability of a future contact between nodes. This paper studies how node mobility can be modeled and used to better estimate the probability of a contact. We derive a distribution for the node-to-node inter-meeting time, and present numerical results to demonstrate that it can be used to improve the dominating-set routing technique performance.

In this paper, we study two fundamental issues of cooperative communications in distributed wireless networks, namely when to cooperate and whom to cooperate with. In specific, we focus on cross-layer medium access control (MAC) protocol design with beneficial node cooperation. To increase long-term network throughput, we propose an optimal grouping strategy for relay node selection, and devise a greedy algorithm for MAC protocol parameter refinement. Quantitative relationships among the channel state, payload length, protocol overhead, and cooperation gain are illustrated. Simulation results show that the proposed approach can effectively exploit beneficial cooperation, thereby improving system performance.

Providing multimedia services over cognitive radio (CR) networks has become an interesting research topic during past few years. As multimedia applications require specific quality of service (QoS) guarantees, supporting multimedia applications to secondary users over a CR network is a challenging task due to the random nature of resource availability. In this paper, we consider a secondary system operating over a time-slotted primary system with multiple channels and secondary users accessing the channels at the spectrum holes without interfering with primary users. We derive the voice capacity of the CR system based on the theories of effective bandwidth (EB) and effective capacity (EC). The capacity is represented in terms of the number of simultaneous independent voice calls that the system can support, providing stochastic delay guarantee. It is shown that (i) the analytical results match well with simulation results and stays slightly lower than the simulation results due to the conservative nature of the EB and EC theories, and (ii) the mean duration of channel being unavailable to secondary users has a significant impact on the system capacity. With proper medium access control, this analysis can help to develop a call admission control policy for QoS provisioning in CR networks.

As two most popular wireless networks, the third generation (3G) cellular networks and wireless local area networks (WLANs) can be integrated to enhance service provisioning. However, most of the previous studies on cellular/WLAN interworking focus on WLANs in a static indoor environment such as offices, hotels, and cafes. Actually, the two heterogeneous technologies can be integrated to support mobile hotspots, which are usually in and around a moving vehicle, such as a bus, a railway train, and even a flight cabin. Different from traditional single-hop wireless networks, the mobile hotspot can adopt a two-hop relay for wireless access. In this paper, we analyze the delay performance for such a cellular/WLAN integrated mobile hotspot. In particular, we take into account the heavy-tailedness of data file size and self-similarity of aggregate traffic. Based on the analysis, we can determine the cellular channel bandwidth to be reserved for aggregate handoff traffic of the mobile hotspot.

Vehicular ad hoc networks (VANETs) are an extreme case of mobile ad hoc networks (MANETs). High speed and frequent network topology changes are the main characteristics of vehicular networks. These characteristics lead to special issues and challenges in the network design, such as in medium access control (MAC). Due to the high speed and frequent network partitions, it is difficult to design a MAC scheme in VANETs that satisfies quality-of-service (QoS) requirements in all network scenarios. In this paper, we provide an evaluation of the mobility impact on the IEEE 802.11p MAC performance. In this evaluation, we identify a new unfairness problem in the vehicle-to-vehicle (V2V) communications. To achieve better performance, we propose two dynamic contention window mechanisms to alleviate network performance degradation due to high mobility. Simulation results demonstrate the effectiveness of the proposed MAC schemes.

In this paper, we consider a DTN/WLAN integrated network where nomadic nodes with high mobility comprise a delay tolerant network (DTN) while local nodes with low mobility reside in the coverage area of wireless local area networks (WLANs). A message dissemination service is considered, where data traffic is generated by a server in the Internet and destined to a group of nomadic nodes. In order to facilitate message dissemination, a delay tolerant cooperative communication (DTCoop) scheme is proposed. The messages for dissemination are first pre-downloaded to a group of storage local nodes within a WLAN before the visit of a nomadic node, and then scheduled for transmission when a nomadic node comes into the transmission range. Analysis and simulation results are presented to evaluate the performance of the proposed DTCoop scheme. It is shown that our proposed scheme can significantly improve the message delivery performance from a WLAN to a nomadic node as compared with existing schemes without message pre-downloading or message scheduling.

In this paper, we propose a novel end-to-end resource allocation approach for wireless mesh networks with quality-of-service (QoS) assurance. By introducing additional interference tolerability to each multimedia flow, our proposed approach is shown effective in fostering frequency reuse and increasing the number of multimedia flows supported in the system, outperforming its conventional resource allocation counterpart. Further, the proposed approach is of low complexity, leading to a preferred candidate for practical implementation.

In this paper, we investigate packet-level non-altruistic node cooperation in wireless networks with regenerative nodes. Since each node has its own data to transmit, pure relays do not exist. We prove that the split of transmit power has no impact on the diversity performance of non-altruistic cooperative transmissions. Despite the beneficial diversity gain, our results show that non-altruistic cooperative transmissions are not always superior to ordinary direct transmissions. We also evaluate the performance gains due to beneficial packet-level node cooperations in a multi-node wireless network. Simulation results show that favorable packet-level cooperative transmissions provide a substantial gain over ordinary direct transmissions.

Abstract Future mobilead hocnetworks are expected to support voice traffic. The requirement for small delay and jitter of voice traffic poses a significant challenge for medium access control (MAC) in such networks. User mobility presents unique difficulties in this context due to the associated dynamic path attenuation. In this paper, a MAC scheme for mobilead hocnetworks supporting voice traffic is proposed. With the aid of a low‐power probe prior to DATA transmissions, resource reservation is achieved in a distributed manner, thus leading to small packet transmission delay and jitter. The proposed scheme can automatically adapt to dynamic path attenuation in a mobile environment. Statistical multiplexing of on/off voice traffic can also be achieved by partial resource reservation for off voice flows. Simulation results demonstrate the effectiveness of the proposed scheme. Copyright © 2009 John Wiley & Sons, Ltd.

This paper presents mathematical modeling and analysis for the total delay in disseminating safety messages in a vehicular ad hoc network (VANET). Node clustering can help in managing a large network and improving scalability, thus cluster based broadcasting is considered so as to prevent broadcast storms. Traffic flow theories developed in civil engineering are employed, which suggest different mathematical models for different traffic densities. This provides realistic models that account for the mobility and randomness and matches the highly dynamic nature of a VANET. We also investigate the minimum cluster size that achieves acceptable message delivery latency. It is shown that network control and performance parameters are dependent on the traffic density.

This paper studies the multihop packet delivery delay in a disrupted vehicle-to-infrastructure communication scenario, where an end-to-end connected path is not likely to exist between a vehicle and the nearest road side unit (RSU) due to the intermittent connectivity between adjacent vehicles. We present an analytical framework that takes into account the randomness of vehicle traffic and the statistical variation of the disrupted communication channel. Our framework employs the effective bandwidth theory and its dual, the effective capacity concept, in order to obtain the maximum distance between adjacent RSUs that stochastically limits the worst case packet delivery delay to a certain maximum value (i.e., allows only an arbitrarily small fraction of packets received by the RSU from the farthest vehicle to exceed a required delay bound). Simulation results demonstrate that our analytical framework is accurate in determining the separation distance between RSUs that probabilistically limit the worst case delay bound.

Node cooperation has been demonstrated promising in ameliorating system performance for wireless networks. To effectively and efficiently provision quality-of-service (QoS) at the packet level in wireless mesh networks (WMNs) supporting heterogeneous traffic, medium access control (MAC)-layer resource allocation and service differentiation are imperative. In this paper, we propose a low-complexity node cooperative resource allocation approach for WMNs, taking subcarrier allocation, partner allocation, QoS assurance, and service differentiation into account. With beneficial node cooperation, our proposed approach is shown to be promising in provisioning QoS and increasing system throughput. The proposed approach also achieves Pareto optimality, making efficient use of network resources.

In this paper, we consider a rural infostation system where power and bandwidth limited infostations are deployed in a large network area with sparsely populated mobile nodes. Direct transmission services are provided to each mobile node, whereas message dissemination services facilitated by relaying are provided to a subgroup of mobile nodes. We investigate radio resource allocation at infostations, and propose a cross-layer resource allocation scheme with tuneable resource allocation parameters at the network layer and link layer. Analytical models are established to characterize the dependence of system performance on the various parameters of the proposed scheme, and the accuracy is verified by simulations. A cross-layer design example is presented to demonstrate that, by tuning resource allocation parameters at the protocol layers, the performance of direct transmission services can be guaranteed for relay nodes, and the quality of message dissemination services can be improved.

Maintaining user connectivity over heterogeneous wireless networks will be a necessity with the wide spread of wireless networks and limited network coverage. In, we propose a super node system architecture based on the concept of delay tolerant networks (DTN) to overcome roaming user intermittent connection over interconnected heterogeneous wireless networks. Mobile ad hoc network plays a key role in the super node system as it can provide a coverage for areas that lack a network infrastructure to bridge the gaps between wireless networks within the system. Long delays combined with the lack of continuous communication with a network manager introduce new security challenges for mobile nodes in a DTN environment. One of the major open challenges is to prevent unauthorized traffic from entering the network. This paper addresses this problem within the super node system. Two schemes are proposed: one is based on asymmetric key cryptography by authenticating a message sender, and the other is based on the idea of separating message authorization checking at intermediate nodes from message sender authentication. Consequently, the second scheme uses symmetric key cryptography in order to reduce the computation overhead imposed on intermediate network nodes, where one-way key chains are used. A simulation study is conducted to demonstrate the effectiveness of each scheme and compare the performance with and without using an authorization scheme.

As a very promising technique, multi-hop relay has been considered in many wireless networks. It can take advantage of the inherent broadcasting nature of wireless transmission and facilitate cooperative communications. In this paper, we develop an effective analytical framework to study the delay performance of cooperative retransmission strategies. All neighbor nodes overhearing the in-progress transmission cooperate in a distributed manner and contribute to retransmissions. In particular, we focus on the application of cooperative retransmission for delay-sensitive real-time services. Based on the proposed analytical framework, the cumulative distribution function of packet transfer delay can be numerically evaluated. Accordingly, we investigate the delay outage probability (i.e., the probability of violating the maximum delay bound), which is an essential statistical quality-of-service (QoS) metric for real-time services. Further, an enhancement approach is proposed to reduce unnecessary power consumption on retransmissions. It dynamically adapts the transmission probabilities of all participating nodes, depending on current retransmission count. As shown in the numerical results, the adaptive cooperative strategy can achieve a better trade-off between satisfying delay constraint and minimizing total power consumption.

In this paper, we study the joint configuration of routing and medium access control (MAC) parameters in fixed wireless networks. Due to the complexity of the problem, we consider a simple slotted ALOHA MAC protocol for link layer operation. We model the link rate of the slotted ALOHA system under a saturation assumption and use a signal to interference plus noise ratio (SINR) based interference model via the concept of conflict set. We formulate a joint routing and MAC (JRM) optimization problem to determine the optimal max-min throughput of the flows and the optimal configuration of routing and MAC parameters. The JRM optimization problem is a non-convex optimization problem and we solve it by an iterated optimal search technique. We validate our approach via simulation and illustrate the potentially high throughput gains that can be obtained by using our joint configuration technique.

In this paper, we investigate benefits of cooperative communication in mobile ad hoc networks (MANETs). Cooperative communication as an effective way to mitigate channel impairments has attracted much attention, especially on the physical layer. However, without properly designed higher- layer protocols, the cooperation gain can decrease and even disappear, due to factors such as limited payload and nonnegligible overhead. A two-hop interference model from a medium access control (MAC) point of view is proposed to study the performance of a cooperative network. Analysis based on the model demonstrates that cooperation may not be beneficial when the number of blocked nodes increases. Further, a busy- tone based cooperative MAC scheme is presented to investigate the gain from cooperative communication and the relationship among influential factors. Simulation results demonstrate that the node density and traffic load greatly impact the effectiveness of cooperative communication.

As specified by the third-generation (3G) wireless networks such as the universal mobile telecommunication system (UMTS), interactive data services, such as Web browsing, voice messaging, and file transfer, represent a major service class in operation nowadays. In this paper, we develop an analytical approach to evaluate the performance of interactive data services under sharing and preemptive scheduling. Specifically, we take into account user interactions in data sessions and the heavy-tailed data file size. Both the mean and the standard deviation of data transfer delay are investigated for the two representative scheduling disciplines. Numerical results are given to show the validity of the evaluation approach and the impact of the on-off user behavior under the scheduling disciplines.

This paper presents a protocol-independent approach to reveal a new insight into the performance of carrier sense multiple access with collision avoidance (CSMA/CA) protocols: the family of CSMA/CA protocols, independent of implementation details, share the same optimal operation point where the maximum protocol capacity is achieved. The protocol- independent analysis is inspired by the concept of virtual time slot. At the timescale of virtual-slot, all the CSMA/CA protocols show the same behavior pattern and, therefore, a generic virtual- slot based S-G (VS S-G) analysis is developed to compute the optimal operation point. The accuracy of the VS S-G analysis is benchmarked against the precise protocol-specific analysis, in particular, for the 802.11 distributed coordination function (DCF) and the 802.15.4 contention access period (CAP). Furthermore, this paper discusses how to integrate the network-layer queueing analysis with the VS S-G analysis at the medium access control (MAC) layer to form a generic cross-layer framework for call- level network capacity analysis.

This paper studies how to select a path with the minimum cost in terms of expected end-to-end delay (EED) in a multi-radio wireless mesh network. Different from the previous efforts, the new EED metric takes the queuing delay into account, since the end-to-end delay consists of not only the transmission delay over the wireless links but also the queuing delay in the buffer. In addition to minimizing the end-to-end delay, the EED metric implies the concept of load balancing. We develop EED- based routing protocols for both single-channel and multi-channel wireless mesh networks. In particular for the multi-radio multichannel case, we develop a generic iterative approach to calculate a multi-radio achievable bandwidth (MRAB) for a path, taking the impacts of inter/intra-flow interference and space/channel diversity into account. The MRAB is then integrated with EED to form the metric of weighted end-to-end delay (WEED). As a byproduct of MRAB, a channel diversity coefficient can be defined to quantitatively represent the channel diversity along a given path. Both numerical analysis and simulation studies are presented to validate the performance of the routing protocol based on the EED/WEED metric, with comparison to some well- known routing metrics.

In this paper, we propose a model-based quality-of-service (QoS) routing scheme for IEEE 802.11 ad hoc networks. Unlike most of QoS routing schemes in the literature, the proposed scheme provides stochastic end-to-end delay guarantees, instead of average delay guarantees, to delay-sensitive bursty traffic sources. Via a cross-layer design approach, the scheme selects the routes based on a geographical on-demand ad hoc routing protocol and checks the availability of network resources by using traffic source and link-layer channel modeling, taking into consideration the IEEE 802.11 characteristics and node interactions. Our scheme extends the well developed effective bandwidth theory and its dual effective capacity concept to multihop IEEE 802.11 ad hoc networks. Extensive computer simulations demonstrate that the proposed scheme is effective in satisfying the end-to-end delay bound to a probabilistic limit.

Joint power-subcarrier-time resource allocation is imperative for wireless mesh networks due to the necessity of packet scheduling for quality-of-service (QoS) provisioning, multi-channel communications, and opportunistic power allocation. In this work, we propose an efficient intra-cluster packet-level resource allocation approach. Our approach takes power allocation, subcarrier allocation, packet scheduling, and QoS support into account. The proposed approach combines the merits of a Karush-Kuhn-Tucker (KKT)-driven approach and a genetic algorithm (GA)-based approach. It is shown to achieve a desired balance between time complexity and system performance. Bounds for the throughputs obtained by real-time and non-real-time traffic are also derived analytically.

Node clustering and subcarrier allocation are imperative to ameliorate system throughput and facilitate quality-of-service (QoS) provisioning by means of effective interference control and maximum frequency reuse. In this paper, we propose a novel node clustering algorithm with effective tax-based subcarrier allocation tailored for wireless mesh networks with QoS support. With increased frequency reuse, our proposed approach is shown to achieve a higher system throughput than a conflict-graph approach and a baseline approach. Also, our approach is demonstrated promising in balancing packet delay and end-to-end transmission rate. By carefully adjusting an upper bound of subcarriers allocated to each cluster, we can achieve improved system performance. The proposed resource allocation achieves the Pareto optimality, demonstrating efficient use of network resources. Further, our analysis reveals that how to allocate resources in a wireless network in a decentralized manner can affect the solution space of a performance tradeoff between QoS provisioning and throughput maximization.

Node cooperation has been demonstrated promising in system performance improvement for wireless networks. To effectively provision packet-level quality-of-service (QoS) in wireless mesh networks (WMNs) supporting heterogeneous traffic, medium access control (MAC) with service differentiation is imperative. In this paper, we study the problem of non-altruistic non-reciprocal node cooperative resource allocation for WMNs with QoS support, taking subcarrier allocation, power allocation, partner selection/allocation, service differentiation, and packet scheduling into account. Due to the NP hardness of our resource allocation problem, we propose two low-complexity yet effective approaches based on the Karush-Kuhn-Tucker (KKT) interpretations, tailored for WMNs with QoS assurance and MAC-layer service differentiation. Further, simulation results show that both proposed approaches can effectively provision packet-level QoS and enhance system performance. Our study also sheds some light on the question of whether and when non-altruistic node cooperation is beneficial to WMNs.

With the interworking between a cellular network and wireless local area networks (WLANs), an essential aspect of resource management is taking advantage of the overlay network structure to efficiently share the multi-service traffic load between the interworked systems. In this study, we propose a new load sharing scheme for voice and elastic data services in a cellular/WLAN integrated network. Admission control and dynamic vertical handoff are applied to pool the free bandwidths of the two systems to effectively serve elastic data traffic and improve the multiplexing gain. To further combat the cell bandwidth limitation, data calls in the cell are served under an efficient service discipline, referred to as shortest remaining processing time (SRPT). The SRPT can well exploit the heavy-tailedness of data call size to improve the resource utilization. An accurate analytical model is developed to determine an appropriate size threshold so that data calls are properly distributed to the integrated cell and WLAN, taking into account the load conditions and traffic characteristics. It is observed from extensive simulation and numerical analysis that the new scheme significantly improves the overall system performance.

Wireless mesh networking is a promising wireless technology for future broadband Internet access. In this paper, a novel collision-free medium access control (MAC) scheme supporting multimedia applications is proposed for wireless mesh backbone. The proposed scheme is distributed, simple, and scalable. Benefiting from the fixed locations of wireless routers, the proposed MAC scheme reduces the control overhead greatly as compared with conventional contention-based MAC schemes (e.g., IEEE 802.11). In addition, the proposed scheme can provide guaranteed priority access to real-time traffic and, at the same time, ensure fair channel access to the routers with data traffic. Unlike most of the existing MAC schemes which focus on single-hop transmissions, the proposed MAC scheme takes the intra-flow correlations between up-stream and downstream hops of a multi-hop flow into consideration. To avoid buffer overflow at bottleneck routers, a simple but effective congestion control mechanism is proposed. Simulation results demonstrate that the proposed scheme significantly improves the delay performance of real-time traffic and the end-to-end data throughput, as compared with IEEE 802.11 and distributed packet reservation multiple access (DPRMA). The performance analysis of the proposed scheme is also presented. The accuracy of the analytical results is verified by computer simulations.

In this paper, we present a fully distributed model-based call admission control (CAC) scheme for IEEE 802.11 multihop ad hoc networks. The scheme predicts the resources that a new call will consume by using both source traffic modeling and link-layer channel modeling, and hence guarantees sufficient network resources for the calls in service. Unlike most of the model-based CAC schemes in the literature, our CAC offers stochastic delay guarantees instead of average delay guarantees for multihop multimedia connections, based on the effective bandwidth theory and its dual effective capacity concept. Simulation results show that our scheme combines both efficient resource utilization and delay guarantees to a certain probabilistic limit.

Multi-user multiple-input multiple-output (MIMO) systems have been emerging and attracting considerable attention recently for its potential to substantially improve system capacity via space division multiple access. In this paper, we propose a distributed multi-user (MU) medium access control (MAC) protocol for wireless local area networks (WLANs) with MIMO capability, using a leakage-based preceding scheme. By exploiting the multi-user degree of freedom in a MIMO system to allow the access point (AP) to communicate with multiple users in the same frequency band simultaneously, the proposed MU MAC can effectively minimize the AP-bottleneck effect in legacy WLANs. We then develop an analytical model to study the performance of the proposed MU MAC, in terms of the maximum number of users that can be supported and the network throughput. The analysis and simulation results show that the proposed MU MAC significantly outperforms the single-user MAC.

In this paper, we propose a novel node clustering algorithm with effective tax-based subcarrier allocation tailored for wireless mesh networks with quality-of-service support. With effective frequency reuse, our proposed approach is shown to achieve a higher system throughput than a conflict-graph approach and a baseline approach. Also, our approach is demonstrated promising in performance tradeoff between packet delay and end-to-end transmission rate.

In this paper, with the cross-layer design principle, a novel cooperative triple busy tone multiple access (CTBTMA) scheme is proposed for wireless networks to achieve cooperative diversity gain. A utility-based algorithm is presented to determine the capability of a node in helping other nodes' transmissions. With the use of three busy-tone channels, not only collisions can be avoided, but also an optimal helper can be determined without disturbing existing transmissions. Simulation results demonstrate that the proposed scheme can effectively increase the throughput in a low SNR environment, as compared with IEEE 802.11a single-hop transmissions. On the other hand, transmit power can be greatly reduced in the proposed scheme in order to achieve the same throughput as in the single-hop transmissions.

In this paper, we consider an interworking architecture of wireless mesh backbone and propose an effective vertical handoff scheme between 802.11 and 802.16 wireless access networks. The proposed vertical handoff scheme aims at reducing handoff signaling overhead on the wireless backbone and providing a lower handoff delay to mobile nodes. The handoff signaling procedure in different scenarios is discussed. Together with call admission control, the vertical handoff scheme directs a new call request in the 802.11 network to the 802.16 network, if the admission of the new call in the 802.11 network can degrade quality-of-service (QoS) of the existing real-time traffic flows. Simulation results demonstrate the performance of the handoff scheme with respect to signaling cost, handoff delay, and QoS support.

The need to support integrated services and provide quality of service (QoS) for various applications is one of the fundamental challenges for successful wireless mesh network (WMN) deployment. In order to provide differentiated services, medium access control (MAC) should have priority management at the link layer. In code division multiple access (CDMA) based WMNs, the interference phenomenon and simultaneous transmissions must be considered. We propose two priority schemes for MAC in a distributed CDMA-based WMN, taking into account interference, multimedia services, QoS requirements, and simultaneous transmissions. In addition, we propose to use an adaptive spreading gain and a frame structure to achieve high resource utilization. Simulation results demonstrate that the proposed schemes can achieve effective QoS guarantee.

The recent years have seen an enormous advance in wireless communication technology and a wide spread of various types of wireless networks. It requires effective inter-networking among the heterogeneous wireless networks in order to support user roaming over the networks while maintaining the connectivity. One of main challenges to achieve the connectivity over a heterogeneous wireless network is potential intermittent connections caused by user roaming. The issue is how to maintain the connection as the user roams and how to ensure service quality in the presence of a long disconnection period. In this paper, we apply the concept of delay tolerant network (DTN) framework to heterogeneous terrestrial wireless networks, and propose a routing scheme to ensure successful communication over an information transport platform that can encounter excessive long delays and intermittent paths. Simulation results demonstrate that our proposed scheme outperforms the epidemic routing based scheme.

In this paper, a novel collision-free MAC scheme supporting multimedia applications is proposed for wireless mesh backbone. The proposed scheme is distributed, simple, and scalable. Benefiting from the fixed locations of wireless routers, the proposed MAC scheme reduces the control overhead greatly as compared with the conventional contention-based MAC protocols (e.g., IEEE 802.11). In addition, the proposed scheme can provide guaranteed priority access to real-time traffic and, at the same time, ensure fair channel access from the routers with data traffic. Unlike most of the existing works which focus on single-hop transmissions, the proposed MAC scheme takes the intra-flow correlations between up-stream and down-stream hops of a multi-hop flow into consideration. To avoid buffer overflow at bottleneck routers, a simple but effective congestion control mechanism is proposed. Simulation results demonstrate that the proposed scheme significantly improves the delay performance of real-time traffic, the fairness of data traffic, and the end-to-end data throughput, as compared with IEEE 802.11.

With the wide spread of user mobility and the existence of various types of wireless networks, effective inter-networking is mandatory in order to support user roaming while maintaining the connectivity. In our previous work [1], we propose a system architecture to achieve the connectivity over a he

In wireless ad hoc networks, in addition to the well-known hidden terminal and exposed terminal problems, the location-dependent contention may cause serious unfairness and priority reversal problems. These problems can severely degrade network performance. To the best of our knowledge, so far there is no comprehensive study to fully address all these problems. In this paper, a new busy-tone based medium access control (MAC) scheme supporting voice/data traffic is proposed to address these problems. Via two separated narrow-band busy-tone channels with different carrier sense ranges, the proposed scheme completely resolves the hidden terminal and exposed terminal problems. Furthermore, with the use of transmitter busy-tones in the node backoff procedure, the proposed scheme ensures guaranteed priority access for delay-sensitive voice traffic over data traffic. The priority is also independent of the user locations, thus solving the priority reversal problem. The fairness performance for data traffic in a non-fully-connected environment is also greatly improved (as compared with the popular IEEE 802.11e MAC scheme) without the need for extra information exchanges among the nodes.

This paper investigates the near-memoryless behavior of the service time for IEEE 802.11 saturated single-hop ad hoc networks. We show that the number of packets successfully transmitted by any node over a time interval follows a general distribution, which is close to a Poisson distribution with an upper bounded distribution distance. The bound on the distribution distance is almost constant and is mainly affected by some system parameters and very slightly by the number of active nodes in the network. We also show that the service time distribution can be approximated by a geometric distribution. We illustrate that the usage of discrete-time queuing analysis (M/Geo/1) near network saturation greatly simplifies the queuing analysis and leads to sufficiently accurate results for both the first order statistics and the probability distribution of the number of packets in the queuing system. Computer simulation results demonstrate that the M/Geo/1 queuing model is very accurate.

This paper presents a new approach to provide stochastic delay guarantees via fully distributed model-based call admission control for IEEE 802.11 single-hop ad hoc networks. We propose a novel stochastic link-layer channel model to characterize the variations of the channel service process in a non-saturated case using a Markov-modulated Poisson process (MMPP) model. We use the model to calculate the effective capacity of the IEEE 802.11 channel. The channel effective capacity concept is the dual of the effective bandwidth theory. Our approach offers a tool for distributed statistical resource allocation in ad hoc networks, which combines both efficient resource utilization and quality-of-service (QoS) provisioning to a certain probabilistic limit. Simulation results demonstrate that the MMPP link-layer model and the calculated effective capacity can be used effectively in allocating resources with stochastic delay guarantees.

Quality-of-service (QoS) provisioning, high system throughput, and fairness assurance are indispensable for heterogeneous traffic in future wireless broadband networks. With limited radio resources, increasing system throughput and maintaining fairness are conflicting performance metrics, leading to a natural tradeoff between these two measures. Balancing system throughput and fairness is desired. In this paper, we consider an interference-limited wireless network, and derive a generic optimization framework to obtain an optimal relationship of system throughput and fairness with QoS support and efficient resource utilization, by introducing the bargaining floor. From the relationship curve, different degrees of performance tradeoff between throughput and fairness can be obtained by choosing different bargaining floors. In addition, our framework facilitates call admission control to effectively guarantee QoS of. multimedia traffic. The solutions of resource allocation obtained from the optimization framework achieve the pareto optimality, demonstrating efficient use of network resources.

Distributed medium access control (MAC) is essential for a wireless network without a central controller. In previous work of the authors, a distributed MAC scheme has been proposed to achieve guaranteed priority and enhanced fairness performance. For a wireless network, the service time distribution at the MAC sub-layer is important for performance analysis (e.g., in terms of packet delay, packet dropping rate, and admission region) at the network layer, because the network layer performance is largely dependent on the high-order time- domain statistics of the service provided by the MAC sub-layer. This paper presents a service time distribution analysis of the previously proposed distributed MAC scheme. Specifically, the respective distributions of the node service time and the system service time are derived. Simulation results verify the accuracy of this analysis.

In this paper, it is shown that it is challenging to evaluate service fairness in multi-hop wireless networks due to intra-flow contention and unequal channel capacity. The conventional fairness criterion in wireline networks in terms of flow rate is not appropriate in the wireless environment. Thus, the channel time in the maximal clique is proposed here as an alternative criterion. Based on this criterion, a new definition of max-min fairness for wireless networks is given. This definition is shown to be general for both wireless and wireline networks. Under certain conditions, it is seen to be equivalent to the proportional fairness definition.

Most of the existing medium access control (MAC) protocols for wireless local area networks (WLANs) provide prioritized access by adjusting the contention window sizes or inter-frame spaces for different traffic classes. Those MAC protocols can only provide statistical priority access and limited service differentiation. In this paper, a novel token-based scheduling scheme is proposed for a fully-connected WLAN that supports both voice and data traffic. The proposed scheme can provide guaranteed priority access to voice traffic and, at the same time, provide more precise and quantitative service differentiation for data traffic, which provides great flexibility and facility to the network service provider for service class management. Simulation results demonstrate that the proposed scheme can guarantee a small delay for voice traffic. For data traffic, it can effectively achieve proportional differentiation among different classes, while achieving fair resource sharing within the same class. In addition, compared with a contention based scheme and a centralized polling scheme, the proposed scheme significantly improves the channel utilization by avoiding collisions (in the contention based scheme) and the polling overhead (in the polling scheme). The performance analysis of the proposed scheme is also presented. The accuracy of the analytical results is verified by computer simulations.

In this paper, based on a cross-layer design prin- ciple, we propose an interference aware distributed medium access control (MAC) scheme for a code-division multiple access (CDMA)-based wireless mesh backbone. Specifically, benefiting from the fixed location of wireless routers, the power allocation is based on the length of the transmission path, so as to ensure some level of fairness in resource allocation among the routers. For call admission and slot/rate allocation, based on the maximum sustainable interference concept, we propose to estimate the interference from the viewpoint of the receiver (rather than the transmitter). Each receiver estimates its experienced interference level for the hypothesis that one or more new calls are admitted. If the interference is not tolerable, the receiver rejects the new call(s). The main advantages of our proposed scheme are the low control message overhead for easy implementation, and the accurate interference estimation. Simulation results are presented to evaluate the performance of our scheme. router. In such a backbone, fine-granularity QoS provisioning is desired or required. Carrier sense multiple access (CSMA)- based random access schemes, the major stream for traditional ad hoc networks, may not be a choice, due to their limited QoS provisioning capability. Thus reservation-based MAC schemes should be more suitable for the wireless mesh backbone. When resources are reserved for each active flow, fine-granularity QoS can be achieved. This paper presents an effective distributed MAC scheme for the wireless mesh backbone, taking into account the unique networking characteristics. Specifically, we consider a wireless mesh backbone based on code-division multiple access (CDMA) technology, and propose a MAC scheme based on the cross-layer design principle. The merits of our proposed scheme are four-fold: 1) it is fully distributed; 2) each link does not need to have the dynamic information of other links in terms of transmission power, tolerable interference, etc., thus requiring a low information exchange overhead and increasing the robustness and scalability of the MAC scheme; 3) accurate interference estimation can be achieved for each receiver; and 4) fine-granularity QoS can be achieved by burst- based resource reservation. If a traffic burst is admitted into the network, it can use the reserved resources until the completion of the burst.

This paper presents a novel stochastic link-layer channel model for IEEE 802.11 ad hoc networks. The model characterizes the variations of the channel service process in a non-saturated case using a Markov-modulated Poisson process (MMPP) model. It offers a tool for distributed statistical resource allocation in ad hoc networks, which combines efficient resource utilization and quality-of-service (QoS) provisioning to a certain probabilistic limit. We also propose a distributed call admission control algorithm based on the link-layer model. Simulation results demonstrate that the model can be used efficiently in allocating resources with stochastic QoS guarantees.

Mobile ad hoc networks are expected to support voice traffic. The requirement for small delay and jitter of voice traffic poses a significant challenge for medium access control (MAC) in such networks. User mobility makes it more complex due to the associated dynamic path attenuation. In this paper, a MAC scheme for mobile ad hoc networks supporting voice traffic is proposed. With the aid of a low-power probe prior to DATA transmissions, resource reservation is achieved in a distributed manner, thus leading to small delay and jitter. The proposed scheme can automatically adapt to dynamic path attenuation in a mobile environment. Simulation results demonstrate the effectiveness of the proposed scheme.

Multi-service support is an important motivation for interworking between the cellular network and wireless local area networks (WLANs). The complementary strengths of the two networks can be effectively combined to enhance service provisioning. In this paper, we investigate how to properly allocate the overall resources in the integrated network to conversational, streaming, and interactive services. The proposed strategy exploits the essential traffic characteristics such as bursty video streams and elastic data traffic. The system performance is significantly improved over the WLAN-first scheme by applying admission control with service-differentiated session assignment and session migration via vertical handoff.

Distributed channel access is essential for a wireless network without a central controller. In our previous research, we have proposed a distributed channel access scheme to achieve guaranteed priority and enhanced fairness performance. To better understand the properties of the scheme, and also for the sake of the network design, it is important to investigate the time domain statistics of the scheme. In this paper, we derive the distributions of node service time and system service time, respectively, for the distributed channel access scheme in the saturated case. Simulation results verify the accuracy of our analysis.

Most of the existing WLAN MAC protocols can only provide limited service differentiation. In this paper, we propose a novel token-based scheduling scheme for precise and quantitative service differentiation, which can provide great flexibility and facility to the network service provider for service class management. Simulation results demonstrate that the proposed scheme can effectively achieve proportional differentiation among different classes, while achieving fair resource sharing within the same class. In addition, compared with the contention based scheme and the centralized polling scheme, the proposed scheme significantly improves the channel utilization by avoiding collisions (with the contention based scheme) and the polling overhead (with the polling scheme). The performance analysis of the proposed scheme is also presented. The accuracy of the analytical results are verified by computer simulations.

This paper investigates the near-memoryless behavior of the service time for IEEE 802.11 saturated single-hop ad hoc networks. We show that the number of packets successfully transmitted by any node over a time interval follows a general distribution, which is close to a Poisson distribution with an upper bounded distribution distance. We also show that the service time distribution can be approximated by a geometric distribution. We illustrate that the usage of discrete-time queuing analysis (M/Geo/1) near network saturation greatly simplifies the queuing analysis and leads to sufficiently accurate results for both the first order statistics and the probability distribution of the number of packets in the queuing system.

Due to the requisite of multi-channel communications for high-speed data transmissions, power allocation for opportunistically exploiting fading wireless channels, and packet scheduling for quality-of-service provisioning, joint power-frequency-time resource allocation is indispensable. In this paper, we propose a low-complexity intra-cluster resource allocation algorithm, taking power allocation, subcarrier allocation, and packet scheduling into consideration. Numerical results demonstrate that our algorithm is close to optimal, and that our optimality-driven resource allocation algorithm outperforms a greedy algorithm, achieving higher resource utilization and better performance compromise among throughput, packet dropping rate, and packet delay.

In this paper, we demonstrate that traffic modeling with the fractional Brownian motion (FBM) process is an efficient tool for end-to-end performance analysis over a network provisioning differentiated services (DiffServ). The FBM process is a parsimonious model involving only three parameters to describe the Internet traffic showing the property of self-similarity or long-range dependence (LRD). As a foundation for network-wide performance analysis, the FBM modeling can significantly facilitate the single-hop performance analysis. While accurate FBM based queueing analysis for an infinite/finite first-in-first-out (FIFO) buffer is available in the existing literature, we develop a generic FBM based analysis for multiclass single-hop analysis where both inter-buffer priority and intra-buffer priority are used for service differentiation. Moreover, we present both theoretical and simulation studies to reveal the preservation of the self-similarity, when the traffic process is multiplexed or randomly split, or goes through a queueing system. It is such self-similar preservation that enables the concatenation of FBM based single-hop analysis into a network-wide performance analysis.

Resource management is an important aspect for the integrated system of cellular networks and wireless local area networks (WLANs). This study proposed a resource management scheme for the cellular/WLAN integrated network to support multiple service classes, namely, the conversational, streaming, and interactive classes. Resource sharing based on virtual partitioning is employed in the cellular network to overcome the limitation of contention-based WLANs in multi-class support. Further, a dynamic load balancing policy is proposed to distribute the traffic load to the integrated cell and WLAN based on factors such as service class and system utilization. The dedicated resource allocation of the cellular network is exploited to avoid the large overhead paid for WLANs to support realtime conversational and streaming classes. The elastic interactive traffic is manipulated flexibly to minimize the bursty overflow traffic and balance the utilization of both systems. The overall performance of the integrated network is significantly improved by the proposed resource management scheme.

This paper presents an analytical approach to determining the maximum number of on/off voice flows that can be supported over a wireless local area network (WLAN), under a quality of service (QoS) constraint the authors consider multiclass distributed coordination function (DCF) based medium access control (MAC) that can provision service differentiation via contention window (CW) differentiation. Each on/off voice flow specifies a stochastic delay bound at the network layer as the QoS requirement. The downlink voice flows are multiplexed at the access point (AP) to alleviate the MAC congestion, where the AP is assigned a smaller CW compared to that of the mobile nodes to guarantee the aggregate downlink throughput. There are six-fold contributions in this paper: 1) a nonsaturated multiclass DCF model is developed; 2) a cross-layer framework is proposed, which integrates the network-layer queueing analysis with the multiclass DCF MAC modeling; 3) the channel busyness ratio control is included in the framework to guarantee the analysis accuracy; 4) the framework is exploited for statistical multiplexing gain analysis, network capacity planning, contention window optimization, and voice traffic rate design; 5) a head-of-line outage dropping (HOD) scheme is integrated with the AP traffic multiplexing to further improve the MAC channel utilization; 6) performance of the proposed cross-layer analysis and the associated applications are validated by extensive computer simulations.

This paper proposes an approximate yet accurate approach to calculate the loss probabilities in a finite size partitioned buffer system, for the achievement of a quantitative assured service in differentiated services networks. The input is modeled as a fractional Brownian motion (FBM) process including$J$classes of traffic with different packet loss requirements. A first-in first-out (FIFO) buffer partitioned with$J-1$thresholds is used to provide$J$loss priorities. Heuristic expressions of the loss probabilities for all the$J$classes are derived and validated by computer simulations. The proposed loss calculation technique is then extended to a general input process by using the recently proposed traffic substitution technique, where both long-range dependent (LRD) and short-range dependent (SRD) input sources are equivalent to a properly parameterized FBM. We also apply the loss calculation to admission control, where the partition thresholds are optimally configured for quality of service guarantee and maximal resource utilization. Computer simulation results demonstrate that resource allocation based on the accurate finite buffer loss analysis results in much more efficient resource utilization than that based on the classic large-buffer overflow approximation.

This paper proposes an approximate yet accurate approach to calculate the loss probabilities in a finite size partitioned buffer system for the achievement of a quantitative assured service in differentiated services networks. The input is modeled as a fractional Brownian motion (FBM) process including J classes of traffic with different packet loss requirements. A first-in first- out buffer partitioned with J-1 thresholds is used to provide J loss priorities. Heuristic expressions of the loss probabilities for all the J classes are derived, and validated by computer simulations. The proposed loss calculation technique is then extended to a general input process by using the recently proposed traffic substitution technique, where both long-range dependent and short-range dependent input sources are equivalent to a properly parameterized FBM. We also apply the loss calculation to admission control, where the partition thresholds are optimally configured for quality of service guarantee and maximal resource utilization. Computer simulation results demonstrate that resource allocation based on the accurate finite buffer loss analysis results in much more efficient resource utilization than that based on the classic large-buffer overflow approximation.

This paper aims at an effective resource management scheme for ultra-wideband (UWB) networks where the inherent spread spectrum supports simultaneous transmissions. In specific, we present a transmission frame structure tailoring to the UWB characteristics, and develop a novel control message exchange procedure. Furthermore, we propose effective admission control and resource allocation algorithms to achieve high efficiency. The resource management scheme can solve the near-sender-blocking problem and alleviate the negative effect of long acquisition time in UWB transmissions. Extensive simulations demonstrate the superior performance of our proposed scheme.

Although the IEEE 802.11e enhanced distributed channel access (EDCA) can differentiate high priority traffic such as real-time voice from low priority traffic such as delay- tolerant data, it can only provide statistical priority, and is characterized by inherent short-term unfairness. In this paper, we propose a new distributed channel access scheme through minor modifications to EDCA. Guaranteed priority is provided to real time voice traffic over data traffic, while a certain service time and short-term fairness enhancement are provided to data traffic. We also present analytical models to calculate the percentage of time to serve voice traffic and the achieved data throughput. Both analysis and simulation demonstrate the effectiveness of our proposed scheme.

In this paper, with a cross-layer design principle, we propose an interference aware distributed resource management scheme for a code-division multiple access (CDMA)-based wireless mesh backbone (consisting of a number of wireless routers at fixed sites). Specifically, benefiting from the fixed location of wireless routers, the power allocation is based on the length of the transmission path, so as to ensure a certain level of fairness among the routers. For a new call arrival, based on the maximum sustainable interference concept, each existing receiver (rather than the potential sender) estimates its experienced interference level under the hypothesis that the new call is admitted. If the interference is not tolerable, the existing receiver rejects the new call by sending a blocking-signal. The main advantages of our proposed scheme are the low control message overhead for easy implementation, and the accurate interference estimation. Simulation results are presented to evaluate the performance of our scheme.

Ultra-wideband (UWB) transmission is an emerging wireless technology, and medium access control (MAC) with quality of service (QoS) provisioning is essential to coordinate the access among competing devices in UWB-based wireless networks. In this paper, we study the exclusive region concept (which was previously proposed) to determine the active set of senders at a time. We find out that, different from the previous work, the exclusive region for a specific link should be a system-level concept, and should depend on system factors such as interference from/to other active links. Based on the findings, two MAC packet scheduling schemes are proposed to exploit the system capacity and, at the same time, to achieve a certain level of fairness in UWB wireless networks. As the long acquisition time in UWB transmission can significantly reduce the system efficiency, the proposed schemes can be modified to alleviate the negative effect of a long acquisition time. Computer simulations demonstrate the effectiveness and efficiency of our proposed schemes

In this paper, we study voice and data service provisioning in an integrated system of cellular and wireless local area networks (WLANs). With the ubiquitous coverage of the cellular network and the disjoint deployment of WLANs in hot-spot areas, the integrated system has a two-tier overlaying structure. As an essential resource allocation aspect, admission control can be used to properly admit voice and data calls to the overlaying cells and WLANs. A simple admission scheme is proposed in this study to analyze the dependence of resource utilization and the impact of user mobility and traffic characteristics on admission parameters. Both admission control and rate control are considered to limit the input traffic to the WLAN, so that the WLAN operates in its most efficient states and effectively complements the cellular network. The call blocking/dropping probabilities and data call throughput are evaluated for effective and accurate derivation of the admission parameters. It is observed that the utilization varies with the configuration of admission parameters, which properly distributes the voice and data traffic load to the cells and WLANs. Mobility and traffic variability have a significant impact on the selection of the admission parameters.

In the interworking between a cellular network and wireless local area networks (WLANs), a two-tier overlaying structure exists in the WLAN-covered areas. Due to the heterogeneous underlying quality-of-service (QoS) support, the admission of traffic in these areas has a significant impact on QoS satisfaction and overall resource utilization, especially when multiple services are considered. In this paper, we analyze the performance of a simple admission strategy, referred to as WLAN-first scheme, in which incoming voice and data service requests always first try to get admission to the WLAN whenever it is available. It is observed that the overall resource utilization can be maximized when the admission regions for voice and data services in a cell and a WLAN are properly configured

Voice over wireless local area network (VoWLAN) is an emerging application taking advantage of the promising voice over Internet Protocol (VoIP) technology and the wide deployment of WLANs all over the world. The real-time nature of voice traffic determines that controlled access rather than random access should be adopted. Further, to fully exploit the capacity of the WLAN supporting voice traffic, it is essential to explore statistical multiplexing and to suppress the large overhead. In this paper, we propose mechanisms to enhance the WLAN with voice quality of service (QoS) provisioning capability when supporting hybrid voice/data traffic. Voice multiplexing is achieved by a polling mechanism in the contention-free period and a deterministic priority access for voice traffic in the contention period. Header overhead for voice traffic is also reduced significantly. Delay-tolerant data traffic is guaranteed an average portion of service time in the long run. A session admission control algorithm is presented to admit voice traffic into the system with QoS guarantee. Analytical and simulation results demonstrate the effectiveness and efficiency of our proposed solutions.

This paper presents an analytical model for evaluating the capability of wireless LANs (WLANs) to provision quantitative quality of service (QoS) guarantees. We consider a distributed medium access control (MAC) with class differentiation, where mobile nodes belonging to different classes may have heterogeneous traffic arrival processes or different contention windows. With on/off inputs, our analysis shows that the WLAN admission region under the QoS constraint can be significantly improved, when the statistical multiplexing effect is taken into account. Moreover, the statistical multiplexing gain can be further improved by aggregating the downlink flows at the access point (AP). We also demonstrate that the proper selection of contention windows plays an important role in improving the WLAN QoS capability, while the optimal contention window for each class and the maximum admission region can be jointly solved in our analytical model.

This paper investigates distributed medium access control (MAC) to achieve rate guarantee in pulse-based time- hopping ultra-wideband (UWB) wireless networks, where the inherent spread spectrum supports simultaneous transmissions. In specific, we propose a transmission frame structure for the distributed MAC tailoring to the UWB characteristics, and develop a novel control message exchange procedure. Furthermore, we propose an effective distributed resource allocation algorithm to achieve high efficiency. The proposed distributed MAC can solve thenear-sender-blockingproblemand alleviate the negative effect of long acquisition time in UWB transmissions. Extensive simulations demonstrate the superior performance of the distributed MAC.

In this paper, we study the voice and data service provisioning in an integrated system of cellular and wireless local area networks (WLANs). To maximize the overall resource utilization of the integrated system, complementary quality of service (QoS) support capabilities of the two networks are exploited to serve voice and data traffic. As an essential resource allocation aspect, admission control can be used to properly admit voice and data calls to the overlaying cellular cells and WLANs. In this study, a generalized admission scheme is analyzed to investigate the dependence of resource utilization on admission parameters, which vary with user mobility and traffic variability. By applying an effective QoS evaluation approach, the admission parameters can be determined using a search algorithm.

In wireless ad hoc networks, in addition to the well-known hidden terminal and exposed terminal problems, the location-dependent contention may cause serious unfairness and priority reversal problems. These problems can severely degrade network performance. In this paper, a new busy- tone based medium access control (MAC) scheme supporting voice/data traffic is proposed to address these problems. Via two separated narrow band busy-tone channels with different carrier sense ranges, the proposed scheme completely resolves the hidden terminal and exposed terminal problems. Furthermore, by extending busy-tones in the transmitter busy-tone channel, the proposed scheme ensures guaranteed priority access for delay-sensitive voice traffic independent of the user locations. The long- term and short-term fairness performance for data traffic in a multi-hop environment is also greatly improved as compared with the popular IEEE 802.11e MAC scheme.

Ultra-wideband (UWB) wireless communication is a promising spread spectrum technology that supports very high data rates and provides precise position information of mobile users. In this paper, we present a position-based quality-of-service (QoS) routing scheme for UWB ad-hoc networks. The scheme applies call admission control and temporary bandwidth reservation for discovered routes, taking into consideration the medium access control (MAC) interactions. Via cross-layer design, it exploits UWB advantages by using the position information in routing and bandwidth reservation. Simulation results demonstrate that the proposed routing scheme is effective in end-to-end QoS support.

Although the IEEE 802.11e enhanced distributed channel access (EDCA) can differentiate high priority traffic such as real-time voice from low priority traffic such as delay-tolerant data, it can only provide statistical priority, and is characterized by inherent short-term unfairness. In this paper, we propose a new distributed channel access scheme through minor modifications to the EDCA. Guaranteed priority is provided to real-time voice traffic over data traffic, while a certain service time and short-term fairness enhancement are provided to data traffic. We also present analytical models to calculate the percentage of time to serve voice traffic and the achieved data throughput. Both analysis and simulation demonstrate the effectiveness of our proposed scheme.

Call admission control plays an important role in quality of service (QoS) provisioning in the interworking between the cellular network and wireless local area network (WLAN). Within the WLAN coverage, a service request can be admitted into the cellular network or the WLAN. Due to the heterogeneous underlying QoS support of the cellular network and WLANs, the admission of traffic in the WLAN coverage has a significant impact on QoS satisfaction and overall resource utilization, especially when multiple services are considered. A popular admission strategy (referred to as WLAN-first scheme) is to admit the incoming service requests into the WLAN whenever it is available so as to take advantage of the low cost and large bandwidth of the WLAN. In this paper, we investigate the performance of the WLAN-first scheme. It is observed that the overall resource utilization can be maximized when the admission regions for voice and data services in a cell and a WLAN are properly configured.

Voice over wireless local area network (VoWLAN) is an emerging application taking advantage of the promising voice over Internet Protocol (VoIP) technology and the wide deployment of WLANs all over the world. To fully exploit the capacity of WLAN supporting voice traffic, it is essential to explore statistical multiplexing and to suppress the large overhead. In this paper, we propose mechanisms to enhance the WLAN with voice quality of service (QoS) provisioning capability in supporting hybrid voice/data traffic. Voice multiplexing is achieved by a polling mechanism in the contention-free period and a deterministic priority access for voice traffic in the contention period. Header overhead for voice traffic is also reduced significantly. Delaytolerant data traffic is guaranteed an average portion of service time in the long run. A session admission control algorithm is presented to admit voice traffic into the system with QoS guarantee. Analytical and simulation results demonstrate the effectiveness and efficiency of our proposed solutions.

In a single-channel wireless ad hoc network, the collisions caused by hidden terminals can severely reduce the network capacity. In this paper, a new busy-tone based scheme is proposed, which can completely avoid collisions (including DATA packet and RTS packet collisions) caused by hidden terminals. This is achieved by adding dual busy-tone channels, and setting a larger carrier sense range of the transmitter busy-tone channel than those of the information and receiver busy-tone channels. The proposed scheme also resolves the exposed terminal problem incurred by the increased carrier sense range of the transmitter busy-tone channel. The simulation results demonstrate that the proposed scheme has an improved performance in terms of throughput in the hidden terminal scenario as compared with the traditional busy-tone solution, and also achieves a high channel utilization in the exposed terminal scenario.

Ultra-wideband (UWB) transmission is an emerging wireless technology, and medium access control (MAC) with quality of service (QoS) provisioning is essential to coordinate the access among competing devices in UWB-based wireless networks. In this paper, we study the exclusive region concept (which was previously proposed) to determine the active set of senders at a time. We find out that, different from the previous work, the exclusive region for a specific link should be a system-level concept, and should depend on system factors such as interference from/to other active links. Based on the findings, two MAC packet scheduling schemes are proposed to exploit the system capacity and, at the same time, to achieve a certain level of fairness in UWB wireless networks. As the long acquisition time in UWB transmission can significantly reduce the system efficiency, the proposed schemes can be modified to alleviate the negative effect of a long acquisition time. Computer simulations demonstrate the effectiveness and efficiency of our proposed schemes

This paper presents an analytical model for evaluating the statistical multiplexing effect, admission region, and contention window design in multiclass wireless LANs (WLANs). We consider a distributed medium access control (MAC) which provisions service differentiation via contention window differentiation, where mobile nodes belonging to different service classes have different quality of service (QoS) requirements. With bursty input traffic, we show that the WLAN admission region under the QoS constraint can be significantly improved by exploiting the statistical multiplexing gain. Moreover, the statistical multiplexing gain can be further improved by aggregating the downlink flows at the access point (AP). We also demonstrate that the selection of contention windows plays an important role in improving the WLAN's QoS capability, while the optimal contention window for each class and the maximum admission region can be jointly solved from our analytical model. The analysis accuracy and the resource utilization improvement are demonstrated by extensive numerical results.

Throughput and fairness are conflicting performance metrics, leading to a natural tradeoff between these two measures. In this paper, we derive a generic optimization framework to obtain a relationship of system throughput and fairness, by introducing the bargaining floor. From the relationship curve, different degrees of performance tradeoff between throughput and fairness can be obtained by choosing different bargaining floors. The solutions of resource allocation obtained from the optimization framework achieve the Pareto Optimality, demonstrating efficient use of network resources.

For applications requiring frequent data collections from a remote wireless sensor network, it is a challenging problem to design an efficient routing scheme for comprehensively, accurately and timely delivering data packets in each round of data collection over a long period of time. Unlike previous work targeting at maximizing energy efficiency and network lifetime, we propose and analyze in this paper a new routing scheme, called Minimum Energy Spanning Tree for Efficient Routing (MESTER), which is developed under the design objective of maintaining a high quality in data collection for as long as possible. Compared with the existing Minimum Spanning Tree (MST) based schemes like PEDAP and PEDAP-PA, MESTER can achieve comparable but more balanced performance at a much lower complexity. In addition, we define "throughput efficiency" to characterize our quality-oriented design objective. As a new concept with low granularity (packet level), throughput efficiency is found a fair and stable performance metric to different network sizes, node densities and routing schemes. It provides us an additional insight into the network behavior under different resource and capability constraints.

Ultra-wideband (UWB) wireless communication is a promising spread-spectrum technology that supports very high data rates and provides precise position information of mobile users. In this paper, we present a position-based quality-of-service (QoS) routing scheme for UWB mobile ad hoc networks. The scheme applies call admission control and temporary bandwidth reservation for discovered routes, taking into consideration the medium access control interactions. Via cross-layer design, it exploits UWB advantages at the network layer by using the position information in routing and bandwidth reservation and by supporting the multirate capability. Simulation results demonstrate that the proposed routing scheme is effective in end-to-end QoS support.

One of the major challenges in supporting multimedia services over Internet protocol (IP)-based code-division multiple-access (CDMA) wireless networks is the quality-of-service (QoS) provisioning with efficient resource utilization. Compared with the circuit-switched voice service in the second-generation CDMA systems (i.e., IS-95), heterogeneous multimedia applications in future IP-based CDMA networks require more complex QoS provisioning and more sophisticated management of the scarce radio resources. This paper provides an overview of the CDMA-related QoS provisioning techniques in the avenues of packet scheduling, power allocation, and network coordination, summarizes state-of-the-art research results, and identifies further research issues.

Abstract-In a wireless multimedia code division multiple access (CDMA) system, the resources in terms of transmission rate and power should be efficiently distributed to each user to guarantee its quality-of-service (QoS) requirements. In, this paper, a resource allocation algorithm which combines packet scheduling and power assignment is proposed to achieve efficient resource utilization under QoS constraints. The packet scheduling is based on the fair packet loss sharing (FPLS) principle, and the power assignment is determined by the received power limited (RPL) scheme. The basic idea of FPLS is to schedule the transmission of multimedia packets in such a way that, all the users have a fair share of packet loss according to their QoS requirements, which maximizes the number of the served users with QoS satisfaction. The RPL scheme minimizes the received power for each packet. Given the propagation path loss, it in turn minimizes the transmitted power as well. The intercell interference from the scheduled packets is also limited in order to increase the system capacity.

A major task in next-generation wireless cellular networks is provisioning of quality of service (QoS) over the bandwidth limited and error-prone wireless link. In this paper, we propose a cross-layer design scheme to provide QoS for voice and data traffic in wireless cellular networks with differentiated services (DiffServ) backbone. The scheme combines the transport layer protocols and link layer resource allocation to both guarantee the QoS requirements in the transport layer and achieve efficient resource utilization in the link layer. Optimal resource allocation problems for voice and data flows are formulated to guarantee pre-specified QoS with minimal required resources. For integrated voice/data traffic in a cell, a hybrid time-division/code-division medium access control (MAC) scheme is presented to achieve efficient multiplexing. Theoretical analysis and simulation results demonstrate the effectiveness of the proposed cross-layer approach.

The next generation wireless networks need to support video traffic. A major challenge in video services over wireless networks is quality of service (QoS) provisioning. Service differentiation is a good approach for QoS provisioning to video traffic. In this paper, we propose cross-layer protocol stack architecture for wireless video transmission with service differentiation. In the cross-layer architecture, the application layer provides the lower link layer with the video compression information. Using the information, a dynamic-weight generalized processor sharing (DWGPS) discipline is proposed for the link layer resource allocation. The link layer tries to provide the application layer with a stringent delay bound and strong protection to high priority traffic in the case of resource shortage. Acceptable level of fairness can be achieved by DWGPS. A scheduling procedure for DWGPS is presented, which avoids complex per-packet virtual time calculation. It is shown that DWGPS can automatically adapt to multiuser diversity without many modifications. Simulation results demonstrate the effectiveness and efficiency of the link-layer DWGPS resource allocation.

In wireless cellular communication systems, call admission control (CAC) is to ensure satisfactory services for mobile users and maximize the utilization of the limited radio spectrum. In this paper, we propose a new CAC scheme for a code division multiple access (CDMA) wireless cellular network supporting heterogeneous self-similar data traffic. In addition to ensuring transmission accuracy at the bit level, the CAC scheme guarantees service requirements at both the call level and the packet level. The grade of service (GoS) at the call level and the quality of service (QoS) at the packet level are evaluated using the handoff call dropping probability and the packet transmission delay, respectively. The effective bandwidth approach for data traffic is applied to guarantee QoS requirements. Handoff probability and cell overload probability are derived via the traffic aggregation method. The two probabilities are used to determine the handoff call dropping probability, and the GoS requirement can be guaranteed on a per call basis. Numerical analysis and computer simulation results demonstrate that the proposed CAC scheme can meet both QoS and GoS requirements and achieve efficient resource utilization.

Abstract This paper investigates the forward‐link peak and average data rates, throughput, and coverage of a cellular CDMA system for delivering high‐speed wireless data services. The analysis takes into account major aspects commonly found in the forward data channel and applies the generalized Shannon capacity formula for multi‐element antenna (MEA) systems. The study focuses on the physical layer and is flexible for various propagation environments, antenna configurations, multicode allocations, user distributions, and cell site configurations. Numerical results for various multicode allocations are presented for a system model with two‐tier interfering cells operating under a frequency selective slow fading channel with propagation environments specified in the Recommendation ITU‐R M.1225. Copyright © 2006 John Wiley & Sons, Ltd.

Abstract Wireless mesh networking is an emerging technology for future broadband wireless access. The ad hoc manner of wireless mesh networks (WMNs) determines that distributed medium access control (MAC) protocols are desired. Multimedia traffic with heterogeneous quality of service (QoS) requirements is expected to be supported in small‐, medium‐, and large‐scale WMNs. Wireless mesh routers in WMNs are located in fixed sites with low (or no) mobility and no power constraints, thus comprising a robust and reliable wireless mesh backbone. Different networking characteristics between the mesh backbone and various mesh client networks give rise to the demand of heterogeneous MAC design. Due to new design purposes and new networking structures, existing MAC protocols designed for mobile ad hoc networks may not be effective or efficient for multi‐purpose WMNs. This paper provides an overview of distributed MAC protocols based on their underlying design objectives and methodology, discusses their features and suitability for WMNs, and identifies potential challenges and open research issues. Copyright © 2006 John Wiley & Sons, Ltd.

This paper proposes efficient resource allocation techniques for a domain-based wireless/wireline interworking architecture. Resource allocation is driven by the service level agreement (SLA). Each wireless domain can freely choose its internal resource management schemes to guarantee the customer access SLA (CASLA), while the border-crossing traffic is served by a DiffServ/MPLS core network according to the transit domain SLA (TRSLA). Specifically, we propose an engineered priority scheme for a cellular wireless domain, where the CASLA for each service class is met with efficient resource utilization and the interdomain TRSLA bandwidth requirement can be obtained conveniently. In the transit domain, the traffic load fluctuation from upstream access domains is tackled with an inter-TRSLA resource sharing technique, where the spare capacity from underloaded TRSLAs can be exploited by the overloaded TRSLAs to improve resource utilization.

It is widely recognized that the next-generation wireless networks will integrate heterogeneous wireless technologies. The complementary characteristics of cellular networks and wireless local area networks (WLANs) make the interworking a promising trend. Admission control is one of the essential mechanisms to enhance QoS provisioning in a cellular/WLAN integrated network and utilize the overall resources efficiently. In this paper, we propose an effective admission control scheme for both voice and data services. The admission region is derived, taking into account the two-tier overlay structure, non-uniform traffic distribution and user mobility in the integrated network. The impact of traffic and mobility parameters on the admission scheme is evaluated, which provides some insights for further extension.

This paper presents an efficient resource sharing scheme for a network supporting multiple service level agreements (SLAs). Specifically, an overloaded SLA can borrow bandwidth from those underloaded SLAs based on a call level service differentiation concept. While flows admitted with the SLA nominal capacity are considered as in profile flows, flows admitted with borrowed bandwidth are tagged as out profile flows and may be preempted later when the original bandwidth owner needs to claim back the resources. Such preemption is considered as the quality of service (QoS) differentiation between the in profile and out profile flows. Through the implementation design and computer simulations, we show that high resource utilization and SLA compliance can be simultaneously achieved by bandwidth borrowing and call level differentiation.

The future IP (Internet protocol)-based code-division multiple access (CDMA) wireless cellular networks require realtime service support. One major challenge in realtime service is quality-of-service (QoS) provisioning over the wireless link. In this paper, we propose two cross-layer resource allocation schemes for voice and video realtime services over IP-based CDMA cellular networks. For voice traffic, the transport layer model and the link layer resource allocation are combined to guarantee the high layer QoS requirements and achieve efficient resource utilization in the low layer. For video traffic, with the aid of video compression information, in-flow service differentiation can be provided to each video sequence. Simulation results demonstrate the effectiveness of our proposed schemes.

There are two essential ingredients for any telecommunications system to be able to provide quality-of-service (QoS) guarantees: admission control (CAC) and service differentiation. In wireless local area networks (WLANs), it is essential to carry out these functions at the MAC level. The original version of IEEE 802.11 medium access control (MAC) protocol for WLANs does not include either function. The IEEE 802.11e draft standard includes new features to facilitate the provision of QoS, but no specific mechanisms are identified in it to make them possible. This paper introduces specific mechanisms into the relevant MAC protocol to avoid over saturating the medium (via CAC) and to decide how to assign the available resources (via service differentiation through scheduling). The main contributions of this work are a novel CAC algorithm for leaky-bucket constrained traffic streams, an original packet scheduling mechanism called DM-SCFQ, and a performance study of a WLAN including these features.

Recent research based on traffic measurements shows that Internet traffic flows have a fractal nature (i.e., self-similarity property), which causes an underestimation of network engineering parameters when using the conventional Poisson model. Preliminary field measurements demonstrate that packet data traffic in wireless communications also exhibits self-similarity. In this paper, we investigate the queuing behavior of self-similar traffic flows for data applications in packet-switching wireless networks. The traffic is generated by an on-off source with heavy-tailed on periods. We extend a previous relationship among the asymptotic distribution of loss probability, finite buffer size, traffic specifications, and transmission rate for a wireline system to a wireless system, taking into account wireless propagation channel characteristics. We also investigate the multiplexing of heavy-tailed traffic flows with a finite buffer for the downlink transmission of a cellular network. Computer simulation results demonstrate that assumptions made in the theoretical analysis are reasonable and the derived relationship is accurate.

The next generation wireless networks need to support video traffic. A major challenge in video services over wireless networks is quality of service (QoS) provisioning. We propose a cross-layer protocol stack architecture for wireless video transmission. In the architecture, the application layer provides the link layer with video compression information. Using this information, a dynamic-weight generalized processor sharing (DWGPS) discipline is proposed for the link layer resource allocation, which is aimed at providing the application layer with strong protection to high priority traffic. The weights in DWGPS are selected based on an optimization problem. Over time-varying wireless channels, a multiuser diversity gain can be achieved without much modification to DWGPS. Simulation results demonstrate the effectiveness and efficiency of DWGPS.

The location of mobile terminals has received considerable attention in the recent years. The performance of mobile location systems is limited by errors primarily caused by nonline-of-sight (NLOS) propagation conditions. We investigate the NLOS error identification and correction techniques for mobile user location in wireless cellular systems. Based on how much a priori knowledge of the NLOS error is available, two NLOS mitigation algorithms are proposed. Simulation results demonstrate that with the prior information database, the location estimate can be obtained with good accuracy even in severe NLOS propagation conditions.

Abstract This paper proposes a distributed multimedia delivery mobile network for video streaming in 3rd generation (3G) mobile communications. The joint design of layered coding (LC) and multiple description coding (MDC) is employed to address the bandwidth fluctuations and packet loss problems in the wireless network and to further enhance the error resilience tools in MPEG‐4. A new Internet protocol (IP) differentiated services (DiffServ) video marking algorithm is presented to support an unequal error protection of the LC components. Both intra‐RAN (radio access network) handoff and inter‐RAN handoff procedures are discussed, which provide path diversity to combat streaming video outage due to handoff in the universal mobile telecommunications system (UMTS). Computer simulation results demonstrate that: (1) the newly proposed IP DiffServ video marking algorithm is more suitable for video streaming in an IP mobile network as compared with the previously proposed algorithm, and (2) the proposed handoff procedures have better performance in terms of handoff latency, end‐to‐end delay and handoff scalability than that in UMTS. Copyright © 2005 John Wiley & Sons, Ltd.

In the differentiated services model, provisioning quantitative assured services is a challenging topic, as it requires loss probability calculation for a partitioned buffer. In this paper, we study such a loss analysis problem with self-similar input traffic, which has never been studied in the open literature. The input is modeled as a fractional Brownian motion process including J classes of traffic. Each class has its unique requirement on packet loss probability. A first-in-first-out buffer partitioned with J-1 thresholds is used to provide J loss priorities. Heuristic expressions of the loss probabilities for all the J classes are derived, and simulation results demonstrate that the heuristic expressions provide an accurate estimate for all the loss probabilities over the entire buffer range.

The next generation wireless networks need to support video traffic. A major challenge in video services over wireless networks is quality of service (QoS) provisioning. We propose a cross-layer protocol stack architecture for wireless video transmission. In the cross-layer architecture, the MPEG4 compression layer provides the lower link layer with the video compression information. Using this information, a dynamic-weight generalized processor sharing (DWGPS) discipline is proposed for the link layer resource allocation which tries to provide the video compression layer with an acceptable video quality. The proposed DWGPS can achieve lower computational complexity and smaller signaling overhead than previous packet-based GPS implementations. Simulations demonstrate that DWGPS can improve the received video quality as compared with previous work.

The location of mobile terminals has received considerable attention in the recent years. The performance of mobile location systems is limited by errors primarily caused by nonline-of-sight (NLOS) propagation conditions. In this paper, we investigate the NLOS error identification and correction techniques for mobile user location in wireless cellular systems. Based on how much a priori knowledge of the NLOS error is available, two NLOS mitigation algorithms are proposed. Simulation results demonstrate that, with the prior information database, the location estimate can he obtained with good accuracy even in severe NLOS propagation conditions.

This paper proposes a call admission control (CAC) policy for a cellular system supporting voice and data services, and providing a higher priority to handoff calls than to new calls. A procedure for searching the optimal admission region is given. The traffic flow is characterized by a three-dimensional (3-D) birth-death model, which captures the complex interaction between the on/off voice and best-effort data traffic sharing the total resources without partition. To reduce complexity, the 3-D model is simplified to an exact (approximate) 2-D model for voice (data). The mathematical expressions are then derived for the performance measures and for the minimal amount of resources required for quality-of-service (QoS) provisioning. Numerical results demonstrate that: 1) the proposed CAC policy performs well in terms of QoS satisfaction and resource utilization; 2) the approximate 2-D model for data traffic can achieve a high accuracy in the traffic flow characterization; and 3) the admission regions obtained by the proposed search method agree very well with those obtained by numerically solving the mathematical equations. Furthermore, computer simulation results demonstrate that the impact of lognormal distributed data file size is not significant, and may be compensated by conservatively applying the Markovian analysis results.

In the third-generation (and beyond) wireless communication systems, there will be a mixture of different traffic classes, each having its own transmission rate characteristics and quality-of-service (QoS) requirements. In this paper, a QoS-oriented medium access control (MAC) protocol with fair packet loss sharing (FPLS) scheduling is proposed for wireless code-division multiple access (CDMA) communications. The QoS parameters under consideration are the transmission bit error rate (BER), packet loss, and delay requirements. The MAC protocol exploits both time-division and code-division statistical multiplexing. The BER requirements are guaranteed by properly arranging simultaneous packet transmissions and controlling there transmit power levels, whereas the packet loss and delay requirements are guaranteed by proper packet scheduling. The basic idea of FPLS is to schedule the transmission of multimedia packets in such a way that all the users have a fair share of packet loss according to their QoS requirements, which maximizes the number of the served users under the QoS constraints. Simulation results demonstrate effectiveness of the FPLS scheduler, in comparison with other previously proposed scheduling algorithms.

We investigate via computer simulations the statistical multiplexing and admission control for multiclass Markovian sources in a buffer partitioned with J - I thresholds to provide the J loss priorities. Through heuristic conjecture and numerical analysis, the effective bandwidth concept has been extended to the partitioned buffer system, where the traffic is generated by multiclass Markov-modulated fluid sources [Y. Cheng et al., Nov. 2002]. In this paper, the packet loss probabilities in a partitioned buffer system are estimated by computer simulations. The simulation results verify that the effective bandwidth proposed in [Y. Cheng et al., Nov. 2002] can be used for efficient resource allocation while satisfying the quality of service (QoS) requirements. We use importance sampling whenever applicable to improve the simulation accuracy.

A major challenge in establishing the wireless Internet is provisioning of quality of service (QoS) to different Internet applications. In this paper, we propose a vertically layered scheme to provide QoS for the assured service with a committed information rate (CIR). By controlling the delay and packet loss performance seen from the transport layer, we can achieve the required CIR. Based on the proposed scheme, the resources needed to meet the rate requirement of the assured service can be determined. The resource allocation problem is also formulated as an optimization problem. Numerical results demonstrate that efficient resource utilization can be achieved.

In this paper, we propose a call admission control (CAC) scheme for wireless cellular networks supporting self-similar data traffic, which is rarely discussed in previous literature. The grade of service (GoS) at the call level is represented by handoff call dropping probability, while the quality of service (QoS) at the packet level is represented by transmission accuracy and delay. The effective bandwidth for the data traffic is derived to support the QoS requirements. The user and call transition functions are derived to compute the handoff call dropping probability. The proposed CAC scheme is to ensure satisfaction of both QoS and GoS, and to achieve maximal resource utilization.

In multimedia code division multiple access (CDMA) system, the network performance depends on the success and efficiency in allocating system resources. The system resources in terms of bandwidth and power should be efficiently distributed to each mobile to guarantee its quality of service (QoS) requirements. In this paper, we propose a received power limited (RPL) power assignment, so that the fair packet loss sharing (FPLS) scheduling can be implemented in the multi-cell resource allocation. The basic idea of FLPS is to schedule the transmission of multimedia packets in such a way that all the users have a fair share of packet loss according to their QoS requirements, which maximises the number of the served users under the QoS constraints. The RPL minimizes the received power for each packet. With known path loss, in turn it minimizes the transmitted power as well. The intercell interference caused by the scheduled packets is also limited by the scheduling to increase the system capacity.

We investigate the statistical multiplexing and admission control for a partitioned buffer, where the traffic is generated by multiclass Markov-modulated fluid sources. Each of the sources has J (>1) classes at each state. The quality of service (QoS) is described by the packet loss probability for each class. The buffer is partitioned with J-1 thresholds to provide the J loss priorities. Extending the effective bandwidth concept to such a buffer system is a challenging topic. We find the minimal effective bandwidth in the asymptotic regime of large buffers and small loss probabilities by optimally setting the partition thresholds. The minimal effective bandwidth achieves efficient resource utilization and can be used to do admission control for heterogeneous multiclass Markovian sources in an additive way. The buffer partition thresholds are dynamically adjusted according to the input traffic load to guarantee QoS. Numerical analysis and simulation results verify the QoS satisfaction and the obvious improvement of resource utilization compared with previously published results, when the minimal effective bandwidth is used for resource allocation with the proposed dynamic buffer partitioning techniques.

Abstract Ultra‐wideband (UWB) communication techniques have attracted a great interest in both academia and industry in the past few years for applications in short‐range wireless mobile systems. This is due to the potential advantages of UWB transmissions such as low power, high rate, immunity to multipath propagation, less complex transceiver hardware, and low interference. However, tremendous R&D efforts are required to face various technical challenges in developing UWB wireless systems, including UWB channel characterization, transceiver design, coexistence and interworking with other narrowband wireless systems, design of the link and network layers to benefit from UWB transmission characteristics. This paper is to provide an overview of UWB communications, summarize the previous research results, and identify further research issues that need to be tackled. The emphasis is placed on the commercial wireless communications. Copyright © 2003 John Wiley & Sons, Ltd.

We investigate the statistical multiplexing and admission control for a partitioned buffer, where the traffic is generated by multiclass Markov-modulated fluid sources. Each of the sources has J (> 1) QoS classes at each state. The QoS is described by the packet loss probability for each class. The buffer is partitioned with J - 1 thresholds to provide the J loss priorities. In the asymptotic regime of large buffers and small loss probabilities, the effective bandwidth is defined and derived based on fluid model analysis and buffer partitioning optimization, which is the minimal channel capacity required to serve a multiclass Markovian source while guaranteeing the QoS requirements of all the classes. For heterogeneous multiclass Markovian sources, numerical studies demonstrate that the proposed effective bandwidth can be used for admission control in an additive way.

We evaluate the variance of the union performance bound for a rate-1/3 turbo code over all possible interleavers of length N, under the assumption of a maximum-likelihood (ML) decoder. Theoretical and simulation results for turbo codes with two-memory component codes indicate that the coefficient of variation of the bound increases with the signal-to-noise ratio and decreases with the interleaver length. Theoretical analysis for large interleaver lengths shows that the coefficient of variation asymptotically approaches a constant value. The results also demonstrate that the majority of the interleavers have performance bounds very close to the average value of the bound. This phenomenon is more palpable for larger interleaver lengths.

This paper proposes a scheme of optimal resource management for reverse-link transmissions in multimedia wideband code-division multiple-access (WCDMA) communications. It is to guarantee quality-of-service (QoS) by resource (transmit power and rate) allocation and to achieve high spectral efficiency by base-station assignment. This approach takes the form of a nonlinear-programming large-scale optimization problem: maximizing an abstraction for the profit of a service provider subject to QoS satisfaction. Solutions for both single-cell and multicell systems are investigated. The single-cell solution has the advantage of low complexity and global convergence in comparison with the previous work. Maximum achievable throughput (capacity) of a single cell is mathematically evaluated and used as the benchmark for performance measure of multicell systems. For multicell systems, due to its max-max structure, solving the optimization problem directly entails a high-computational complexity. Instead, the problem is reformulated to a mixed integer nonlinear-programming (MINLP) problem. Then, binary variables indicating base-station assignments are relaxed to their continuous analogs to make a computer solution feasible. Furthermore, approximations can be made to make the resource-management scheme less computationally complex and allow its partial decentralization. The sensitivity of the proposed scheme to path-gain estimation error is studied. Simulation results are presented to demonstrate the performance of the proposed scheme and the throughput improvement achieved by combining resource allocation with base station assignment.

This paper proposes a mobile user location scheme for wideband code-division multiple-access (CDMA) wireless communication systems. To achieve high location accuracy and low cost of the mobile receiver, the location scheme combines the time difference of arrival (TDOA) measurements from the forward link pilot signals with the angle of arrival (AOA) measurement from the reverse link pilot signal. High chip rates in wideband CDMA systems facilitate accurate TDOA measurements, and a smart antenna used at the home base station (BS) can provide accurate AOA measurement in a macrocell environment. A two-step least square location estimator is developed based on a linear form of the AOA equation in the small error region. Numerical results demonstrate that the proposed hybrid TDOA/AOA location scheme gives a much higher location accuracy than TDOA only location, when the number of base stations is small and/or when the TDOA measurements have a relatively poor accuracy.

In this paper, we propose an algorithm for optimal buffer partitioning which requires the minimal channel capacity to satisfy the quality of service (QoS) requirements of input traffic. The traffic is generated by a Markov-modulated fluid source and has J (larger than 1) QoS classes at each state. The QoS is described by a packet loss probability requirement for each class. The buffer is partitioned with J-1 thresholds to provide the J loss priorities, thus the J classes of service. Traffic is admitted or rejected based on the buffer occupancy and its service class. We also present an approach for the buffer partitioning for heterogeneous Markov-modulated sources. Numerical results demonstrate that the proposed algorithm achieves a higher resource utilization efficiency than previously published results.

We investigate the non-line-of-sight (NLOS) propagation identification and correction for time difference of arrival (TDOA) based mobile user location in wireless communication systems. Based on the defined TDOA residual, an NLOS base station identification algorithm is proposed. Different choices of the reference location for the residual calculation are compared via simulation. To correct the NLOS error with a certain distribution, we propose a maximum likelihood (ML) estimator for TDOA location systems. Simulation results demonstrate that the proposed NLOS recovering algorithm performs better than that using only LOS measurements, especially when the number of available base stations is small and/or the LOS base stations have an undesirable geometric layout.

This paper proposes a call admission control (CAC) policy for a wireless communication system supporting both voice and data calls. The total available resources are shared without partitioning between the two types of the calls. Under the CAC, the traffic model for voice calls is an M/M/m/m queue and that for data calls is an M/G/m/m queue. For data calls, the service time distribution is related to the message length distribution and the number of voice and data users respectively in the system. The modeling is more accurate and practical than previously proposed approaches. Numerical results demonstrate that the proposed CAC policy is able to maintain all the specified QoS requirements to the mobile users.

We investigate the practicality of a proposed network-level resource-management scheme for reverse-link transmissions in multimedia wideband code-division multiple-access (WCDMA) communications. The inherently centralized algorithm manages all transmitted powers and rates and base-station assignments to guarantee quality of service while achieving a high spectral efficiency. Approximations make it less computationally complex and allow its partial decentralization. Preliminary simulation experiments and analysis of its sensitivity to path-gain estimation error show reasonable performance. Of interest is its potential combination with closed-loop power control in IMT-2000.

We propose a scheme of optimal resource management for reverse-link transmissions: in multimedia wideband code-division multiple-access (WCDMA) communications. It is to guarantee quality of service (QoS) by resource allocation (transmitted power and rate) and achieve high spectral efficiency by base-station assignment. The approach takes the form of a nonlinear-programming large-scale optimization problem: maximizing an abstraction for the profit of a service provider subject to QoS satisfaction. Due to its max-max structure, solving the optimization problem directly entails a high computational complexity. Instead, the problem is reformulated to: a mixed integer nonlinear-programming (MINLP) problem. Then, binary variables indicating base-station assignments are relaxed to their continuous analogs to make a computer solution feasible. Preliminary simulation results show the effectiveness in a multicell network.

This paper proposes a packetized direct sequence code division multiple access (DS-CDMA) multimedia wireless communication system that allows for seamless interfacing to asynchronous transfer mode (ATM) broadband networks. The issue of reverse link power control is examined in the context of an indoor environment characterized by a slowly Rayleigh fading channel. Three algorithms are discussed: open loop, channel estimation, and closed loop power control. It is shown that closed loop power control with channel estimation achieves the best performance. The system capacity with perfect power control is calculated. The effect of power control imperfection on the capacity and received signal statistics is studied based on computer simulation. It is shown that actual cell capacity using a two-bit power control command is approximately 40% greater than that using a one-bit power control command at the expense of twice the feedback overhead.

This paper proposes a packetized direct sequence code division multiple access (DS-CDMA) system model that supports integrated voice and data traffic in a slowly Rayleigh fading environment and allows for seamless interfacing to an asynchronous transfer mode (ATM) broadband network. Forward error correction (FEC) coding and an automatic retransmission request (ARQ) protocol are applied to data packets. A queueing model is used for servicing data transmission requests. The reverse link power control utilizes a closed loop algorithm with channel estimation. A one-bit power control command is used for delay insensitive data packets and a two-bit command for delay sensitive voice packets. The cell capacity for data users is analyzed and is extended to include voice users by assigning a fixed number of DS-CDMA channels for data traffic and using all the remaining resources for voice traffic. It is shown that there is a linear relation between the capacity for data users and that for voice users.

Theoretical evaluation of the bit error rate (BER) performance of digital communications systems in fading channels may be very difficult or impossible. Computer simulation is an essential approach to assess the performance for mobile and portable communications. However, in the case of a low BER value or a slowly fading channel, computer simulation time can be prohibitively long in order to obtain an accurate BER estimate. This paper proposes a new adaptive importance sampling (AIS) technique for estimating the BER performance in an indoor wireless environment. The technique adaptively searches for optimal biased probability densities during the course of the simulation. The procedure of determining the optimal biased probability densities is simplified and the computer simulation time is considerably reduced.

The bit error rate (BER) performance of a mobile or personal radio digital communications system at a high bit rate can be increased significantly by intersymbol interference (ISI) due to multipath propagation. In this paper, a novel adaptive channel precoder is proposed to improve the system performance with-out increasing the complexity of the portable unit. In the forward link, both the amplitude and phase of the signal carrier are predistorted at the transmitter of the base station, so that the signal received at the portable unit is ISI free (in the ideal case). The precoding is based on channel information estimated (from the reverse link) at the receiver of the base station. Computer simulation results demonstrate that, over a two-path Rayleigh fading channel, differential QPSK with the precoder has significantly lower BER values than those with a conventional decision feedback equalizer (DFE), because the precoder is not subject to error propagation.

In this paper, adaptive channel equalization is investigated in order to reduce the bit error rate of trellis-coded continuous-phase frequency shift keying (CPFSK) in an indoor radio environment. A new equalizer structure is proposed, which makes use of fractionally spaced soft-decision signal samples instead of symbol-spaced hard decisions on transmitted symbols in the decision feedback loop, in order to remove the intersymbol interference (ISI) resulting from the frequency-selective fading channel.>

A global positioning system (GPS) receiver has been modelled and implemented in software. A digital full-time delay lock loop (DDLL) is designed for the pseudorange time delay measurement and a digital phase-locked loop (DPLL) is applied for measurements of the carrier beat phase and Doppler shift. The closed form expressions of the detection and false-alarm probabilities for the code phase acquisition process and the variance of the code phase tracking error for the code phase fine synchronization process are derived. The performance of the modelled static receivers is validated by computer simulations.>