Low Earth Orbit (LEO) satellite communication is critical for widespread Internet of Things (IoT), especially for small data transmissions. Conventional Access Class Barring (ACB) and Random Access Procedure (RAP) schemes, originally designed for stochastic terrestrial channels, are ill-suited for LEO networks as they fail to exploit the deterministic nature of satellite trajectories. To address this critical gap, we propose Channel and Timing-Predicted (CTP) ACB that dynamically prioritizes User Equipment (UEs) by exploiting the high predictability of LEO satellite channels, thereby optimizing resource allocation and improving energy efficiency. We introduce an optimized Random Access Channel (RACH) period to balance system throughput with individual UE service rates. Through comprehensive simulations, we demonstrate that CTP ACB substantially improves throughput, resource utilization, and energy efficiency. Regarding information freshness, while CTP ACB introduces a marginal scheduling delay in low-density scenarios, it significantly reduces the mean AoI by approximately 30% in high-density massive IoT networks by effectively mitigating access congestion. These findings highlight the necessity of trajectory-aware designs in next-generation NTN-IoT standards.

Modern speech quality prediction models are trained on audio data resampled to a specific sampling rate. When tested on audio with a higher sampling rate, these models can produce biased scores. We present HighRateMOS, the first non-intrusive mean opinion score (MOS) model that explicitly considers sampling rate. HighRateMOS ensembles three model variants that exploit the following information: (i) a learnable embedding of speech sampling rate, (ii) Wav2vec 2.0 selfsupervised embeddings, (iii) multi-scale CNN spectral features, and (iv) MFCC features. In AudioMOS 2025 Track 3, HighRateMOS ranked first in five of eight metrics. Our experiments confirm that modeling sampling rate leads to more robust and sampling-rate-agnostic speech quality predictions.

The increasing adoption of electric vehicles (EVs) brings new challenges in jointly optimizing mobility, charging behavior, and grid stability. This paper proposes a two-stage optimization framework that integrates Ant Colony Optimization (ACO) for EV routing and a grid-aware heuristic scheduler for vehicle-to-grid (V2G) discharging. The system models both static charging stations and dynamic wireless power tracks under time-of-use electricity pricing and regional load constraints. In the first stage, EVs plan energy-efficient routes while satisfying individual constraints. In the second stage, the proposed Heuristic Load-Balancing Scheduler (HLBS) iteratively refines discharging actions based on regional power demands to reduce grid imbalance. Simulation results based on a large-scale Taiwan road network with 1,500 EV trips show that our method achieves lower total energy cost, reduced regional load variance, and improved frequency stability compared to baseline approaches.

The integration of Non-Terrestrial Networks (NTN) and Terrestrial Networks (TN) is a promising solution for 6 G, enabling seamless and ubiquitous connectivity by combining the high data rate capabilities of TN with the extensive coverage of NTN. Spectrum sharing between NTN and TN can significantly improve spectral efficiency but poses challenges related to intersystem interference, especially in fast time-varying NTN channels caused by low Earth orbit (LEO) satellite movement. This paper introduces a novel robust spectrum sharing mechanism that addresses these challenges by dynamically adapting to channel variations and mitigating interference in NTN uplink scenarios. Unlike previous approaches, our method independently manages spectrum sharing mechanisms for NTN and TN, reducing coordination complexity and enhancing system adaptability. By leveraging adaptive bandwidth allocation based on real-time channel conditions, the proposed method ensures efficient radio resource utilization and robust performance. Simulation results show that the robust spectrum sharing mechanism achieves up to a 20% improvement in throughput compared to baseline methods, demonstrating its effectiveness in enabling efficient and robust spectrum sharing for NTN-TN integrated networks.

The Internet of Vehicles (IoV) connects vehicles with surrounding devices to enable advanced services like vehicle-to-vehicle and vehicle-to-infrastructure communication. To support these services, 3GPP introduced the New Radio (NR) Sidelink (SL) standard. A key performance measure in IoV networks is the freshness of data, often evaluated using the Age of Information (AoI), which is especially important for real-time and safety-critical applications. To improve energy efficiency, the NR SL framework includes power-saving features like partial sensing and Sidelink Discontinuous Reception (SL DRX). However, these features may negatively affect AoI performance. This paper studies how SL power-saving strategies influence AoI, analyzes different transmission policies, and proposes solutions to reduce potential performance loss. We support our findings with analytical models that highlight the trade-offs between power saving and information freshness. This is the first in-depth study to analyze the balance between energy efficiency simultaneously for the sidelink transmitter and receiver and AoI performance in vehicular networks.

The variety of low latency applications in 6G makes the issue of maintaining data freshness increasingly important. However, due to battery life limitations, User Equipment (UE) should also apply power-saving procedures, such as Discontinuous Reception (DRX), which may result in additional latency. Therefore, this paper aims to study the relationship between data freshness and power-saving performance. To capture the information freshness, we use Age of Information (AoI) as the performance metric. To the best of our knowledge, unlike previous studies that primarily focus on the trade-off between power saving and delay, this is the first study to consider the impact of DRX on the Age of Information (AoI). We have derived the closed-form solutions for both the peak-average AoI (PAoI) and time-average AoI (TAoI) under the DRX mechanism, along with their respective parameter adjustment strategies. Based on our simulation validation, these proposed strategies enable the UE to maintain the average AoI close to the theoretical lower bound while simultaneously achieving high power-saving efficiency. This positions our approach at the forefront of balancing information freshness and energy conservation in mobile and IoT networks.

The development of Electric Vehicles (EVs) in smart city infrastructure has ushered in new opportunities for vehicle automation but poses challenges to the electricity load (EL) and computational load (CL) on the smart grid. While existing studies utilized Fog/Edge computing as a decentralized solution to mitigate the EL and CL on the smart grid, these often overlook the idle computational resources of EVs. Therefore, Electric Vehicle Edge Computing (EVEC) leverages EVs as the extended computational resources of edge compute nodes called edge servers. Our proposed architecture strategically schedules EVs to targeted electric vehicle parking lots (EVPLs), to minimize the EL and CL of all EVPLs, EVs based on EV preference within the smart grid as a whole. To handle the EV scheduling, we develop a greedy-based algorithm to tackle the complexity of the optimization problem. While the greedy algorithm ensures computational efficiency and effective local optimization, it does not always guarantee global optimality. Upon EV arrival at an EVPL, we develop an algorithm based upon the convex-concave procedure (CCP) and derive the Karush-Kuhn-Tucker (KKT) conditions to determine the optimal devoted computational resources of EVs. Furthermore, our model introduces an incentive mechanism that encourages EVs as vehicular fog computing (VFC) nodes to fully devote their computational resources, while ensuring the profitability of the Edge Computing Orchestrator (ECO). Through comprehensive simulations, we demonstrate the effectiveness of our approach by completely utilizing the charging time of EVs for computation at EVPLs, which brings benefits to EVs, edge servers, and ECO by optimizing the devoted computational resources based upon the CL of edge servers.

In today's ever-growing data traffic landscape, optimizing network power efficiency and performance has become crucial. Discontinuous Reception (DRX) and Bandwidth Parts (BWP) are two key technologies that fulfill this pursuit. DRX is a time-domain power-saving technology that allows user equipment (UE) to switch off their radio frequency module. BWP switching is a frequency domain operation that allows UE to operate on only partial bandwidth for power saving. Investigating the interaction and trade-off between DRX and BWP is a must to optimize network efficiency and enhance network performance. This work proposed a novel BWP-DRX joint mechanism and its analytical model that leverages the concept of “Detect time” with the consideration of BWP switching delay. The model reduces packet loss rate by 50%, packet delay by 36% and increases the energy efficiency rate by 50% when arrival rate is high with the trade-off of 12% power efficiency reduction when arrival rate is low compared to the model without Detect time. The influence of each parameter is further analyzed to reach the best network efficiency under different traffic conditions.

Split learning (SL), a machine learning (ML) technique for collaborative training across devices and servers, partitions the model across entities to leverage computing power while preserving raw data privacy. However, device heterogeneity in terms of computation or communication capabilities causes the presence of stragglers, resulting in significant delays in the training process. This paper addresses the straggler problem in a wireless scenario with one edge server and multiple devices collaborating to train ML models using SL. We introduce a novel, low-complexity Coalition Formation Game (CFG) algorithm for SL in wireless networks. The CFG algorithm clusters work-ers based on their training times, effectively mitigating delays caused by stragglers. Specifically, this solution involves training devices negotiating to form or leave clusters to achieve better accuracy and/or shorter training delays, as well as selecting SL cut layers that best align with their communication and computing resources. Theoretical proof is provided, guaranteeing the termination of the CFG algorithm and the stability of the final clustering, where no devices have the incentive to leave the collaboration. We validate the proposed method on various datasets, and the results show that it strikes a good balance between lower training delay and high accuracy, consistently achieving top accuracy, and converging at the fastest speed.

Facing the emergence of 6G and the rapid increase in electric vehicles (EVs), smart parking lots providing real-time services like EV charging have become essential. Edge computing, due to its proximity to end devices, offers low latency and high bandwidth, but its limited resources necessitate efficient allocation. We present a parking lots scenario with edge computing system offering four key services: Supply Equipment Communication Controller, charging space detection, monitoring, and video streaming, along with their QoE models and cor-responding estimation models. We predict system requests for the next time slot and employ the Maximum-chosen algorithm and Collaborative Optimal Decision Search method to optimize service deployment and assignment, maximizing QoE values and resource efficiency. Simulation results validate that we can obtain request status that is more similar to real requests by prediction and the Collaborative Optimal Decision Search method can generate optimal service assignment strategy within different methods.

Vehicular edge computing (VEC) has become a promising solution in electric vehicle (EV) utilization. However, the uneven geographical distribution of service requests may lead to load imbalances among edge servers in different clusters. Thus, the integration of task offloading (TO) and resource allocation (RA) is pivotal for achieving optimal performance in edge computing systems. In this study, we explore an efficient collaborative scheme for task offloading and resource allocation across multiple edge network areas. Initially, we model the Multi-Edge System Delay (MESD) by considering the average end-to-end delay in the system. Subsequently, we introduce the concept of request redistribution using a load-balancing approach to simplify the joint TO & RA problem into a manageable RA problem. Our algorithm mathematically formulates the MESD model and employs a heuristic method to address the formulated problem. Finally, we have compared our proposed work with several baselines and the results confirm the effectiveness of the proposed mechanism.

In recent years, massive IoT networks have become increasingly important for achieving the 6G vision. However, the need for wireless resources for network management also increases, raising the importance of Multicast and Broadcast Services (MBS). With the natural constraint of the battery life of mobile devices, the Third Generation Partnership Project (3GPP) designed the Discontinuous Reception (DRX) mechanism for MBS to improve power efficiency in NR. The additional DRX for MBS is designed to operate independently from the existing UE-specific DRX. In this work, we point out that the independent operation of MBS DRX could severely degrade the power-saving feature of the DRX mechanism. We propose a DRX configuration method called Offset and Length-Aligned Multi-PTM (OLAM-PTM) for mixed unicast-MBS transmission scenarios. The results show that an MBS UE could dormant more at the cost of a slight increase in resource usage and execution time at the gNB side with OLAM-PTM.

Bandwidth part (BWP) and discontinuous reception (DRX) are critical designs to help devices save power. Unlike DRX, a time-domain power-saving mechanism, the BWP is a frequency-domain mechanism newly proposed in the 3GPP NR system. The interaction between the two mechanisms needs investigation for performance optimization. Therefore, this work proposed a joint design for the BWP switching algorithm and analytical models. The simulation results showed that when the DRX cycle is 320 ms, and the packet arrival rate is 0.01/ms, our method improves 31.4% of packet delay or 19.5% of power consumption compared to the baselines. We also showed the range of the traffic arrival rate that a UE with both mechanisms configured is sensitive to the BWP switch strategy.

With the growing demand for latency-sensitive applications in 5G networks, edge computing has emerged as a promising solution. It enables instant response and dynamic resource allocation based on real-time network information by moving resources from the cloud to the network edge. Containers, known for their lightweight nature and ease of deployment, have been recognized as a valuable virtualization technology for service deployment. However, the prolonged startup time of containers can lead to long response time, particularly in edge computing scenarios characterized by long propagation time, frequent deployment, and migration. In this paper, we comprehensively consider image caching, container assignment, and registry selection problem in an edge system. To our best effort, there is no existing work that has taken all the above aspects into account. To address the problem, we propose a novel image caching strategy that employs partial caching, allowing local registries to cache either the least functional or complete version of application images. In addition, a container assignment and registry selection problem is solved by using an edge-based collaborative lazy pulling algorithm. To evaluate the performance of our proposed algorithms, we conduct experiments with real-world app usage data and popular images in a testbed environment. The experimental results demonstrate that our algorithms outperform traditional greedy algorithms in terms of average user response time and cache hit rate.

The advent of 5G-enabled edge servers presents an opportunity to distribute computational tasks to the network edge. This approach helps alleviate the strain on limited central network resources caused by the rapid growth in the number of mobile devices and computation-intensive services. Moreover, it leads to reduced end-to-end delays for users. In this paper, we investigate resource allocation optimization in a dynamic multi-service system, where each service provider (SP) serves geographically dispersed service subscribers (SSs). Each SP can offload tasks to multiple edge servers, while each SS can freely switch between SPs offering homogeneous services. We propose the Multi-Stage Coalition Game Task Offloading (MSCGTO) framework, accommodating scalability, resource heterogeneity, and dynamic conditions. This framework encompasses two distributed algorithms to jointly maximize SP profit and minimize SS end-to-end delay, addressing cost-benefit considerations and user latency acceptance. We conduct extensive simulations and practical experiments with real-world services including augmented reality (AR), online gaming, and live video streaming applications, performed in a controlled testbed environment. The results of our experiments demonstrate that the proposed algorithms yield a 25% increase in system utility considering both the profit of SPs and the end-to-end delay of SSs when compared to existing approaches.

Distributed Denial-of-Service (DDoS) attack is critical to latency-critical systems such as Multi-Access Edge Computing (MEC) as it significantly increases the response delay of the victim service. An intrusion prevention system (IPS) is a promising solution to defend against such attacks. Still, there will be a trade-off between IPS deployment and application resource reservation as IPS deployment will reduce the computational resources for MEC applications. In this work, we propose a game-theoretic framework to study the joint computational resource allocation and IPS deployment in the MEC architecture. Given the expected attack strength and end-user demands, we study the pricing strategy of the MEC platform operator (MPO) and purchase strategy of the application service providers (ASPs). The best responses of both MPO and ASPs are derived theoretically. Based on the best responses, we propose an efficient algorithm to derive the Stackelberg equilibrium. The properties and optimality in the efficiency of the equilibrium are analyzed through simulations. The results confirm that the proposed solutions significantly increase the social welfare of the system.

Reducing the power consumption of User Equipment (UE) has always been a crucial issue in communication systems. As a result, Discontinuous reception (DRX) is introduced in today's New Radio (NR) networks as a power-saving strategy. In addition, the variety of low latency applications in 6G makes the issue of maintaining data freshness increasingly important. This paper aims to study the relationship between freshness and power-saving. To capture the information freshness, we use Age of Information (AoI) as the performance metric. To our best knowledge, unlike previous studies focusing on power saving and delay trade-offs, this is the first study considering the impact of DRX on AoI. We solve the closed-form solution of the peak average AoI with the DRX mechanism. Moreover, we propose a parameters adjustment strategy. Based on the simulation validation, our proposed strategy can enable the UE to keep the AoI close to the theoretical lower bound AoI while achieving high power-saving efficiency.

Mobile edge computing (MEC) and 5G networks can provide ultra-low latency connections. Combining the two, caching services at the network edge can greatly reduce the delay of virtual reality (VR) and augmented reality (AR) services, enhancing the Quality of Service (QoS) for users. In this paper, we investigate an efficient collaborative service caching scheme between multiple service providers (SPs) with a game-theoretical approach. We model SPs as players who care about nothing but their profits and can form coalitions by sharing edge server resources as well as costs with other members. More than one coalition can be formed in an edge server. Our algorithm guarantees to reach a Nash equilibrium, where no one has the incentive to deviate. Simulation results show that through the proposed collaboration scheme, SPs can reach a higher profit compared to several baselines as well as previously proposed schemes.

With the development of telecommunication technologies and the proliferation of network applications in the past decades, the traditional cloud network architecture becomes unable to accommodate such demands due to the heavy burden on the backhaul links and long latency. Therefore, edge computing, which brings network functions close to end-users by providing caching, computing and communication resources at network edges, turns into a promising paradigm. Benefit from its nature, edge computing enables emerging scenarios and use cases, such as augmented reality (AR) and Internet of Things (IowT). However, it also creates complexities to efficiently orchestrate heterogeneous services and manage distributed resources in the edge network. In this survey, we make a comprehensive review of the research efforts on service orchestration and resource management for edge computing. We first give an overview of edge computing, including architectures, advantages, enabling technologies and standardization. Next, a comprehensive survey of state-of-the-art techniques in the management and orchestration of edge computing is presented. Subsequently, the state-of-the-art research on the infrastructure of edge computing is discussed in various aspects. Finally, open research challenges and future directions are presented as well.

Deep learning (DL) applications have attracted significant attention with the rapidly growing demand for Internet of Things (IoT) systems. However, performing the inference tasks for DL applications on IoT devices is challenging due to the large computational demands of DL models. Recently, edge computing has offered us a solution by deploying resources near the end users. However, resources at the edge are still limited; thus, management issues, such as allocating the networking resources as well as the computing capabilities and configuring the devices appropriately for different applications, become essential. For knobs in such edge management, we consider multiple application tasks with different options of DL models and different hyperparameter settings, along with possible decomposition points that utilize the split DL concept to design the configuration tables. Layer-level decomposition in split DL provides greater flexibility by splitting a single DL inference model into parts on different computing devices, and each part consists of several consecutive layers. We then propose the SplitDL-Image and the SplitDL-Video algorithms based on the Vickrey–Clarke–Groves (VCG) mechanism by considering model performance and frames per second (FPS) requirements with the preferences of the heterogeneous IoT devices. The proposed method allocates networking and edge server computing resources according to the designed configuration tables by assigning the appropriate configuration to each IoT device. Simulation results based on real-world applications show that the proposed method indeed allocates more resources to IoT devices with more urgent/important tasks, preference for better accuracy, or higher local computational cost. In addition, other desired properties, such as truthful bidding, individual rationality, and weakly budget balance, are also guaranteed.

Recently, Edge Computing (EC) has become a promising enabler to support emerging applications in 5G mobile networks by offloading compute-intensive tasks from devices to proximate EC servers. Meanwhile, Network Slicing (NS) aims to provide service subscribers (SSs) with dedicated network resources based on virtualization techniques so that the service requirements can be guaranteed. The combination of EC and NS can efficiently utilize dynamic network resources at edge networks while improving the Quality of Service (QoS) of SSs. In this paper, we aim to jointly address the problem of dynamic slice scaling and task offloading from the perspective of profit of service providers (SPs) in the multi-tenant EC system. Specifically, we propose a Deep Q-Learning (DQL) based network slicing framework to dynamically reconfigure the scale of radio and computing resources of a slice reserved for a target SP. Then, by exploiting alternative optimization, we proposed a low-complexity algorithm to optimize the real-time offloading ratio and resource allocation policy of slice requests from SSs. To further verify our proposed framework, we have implemented the network slicing testbed with Docker container and conducted a series of experiments based on a real-world traffic dataset and a sample Augmented Reality (AR) application.

Millimeter wave (mmWave) is a crucial component in 5G and beyond 5G communications. However, the dense deployment of mmWave transceivers would impose a heavy burden on the management of the radio access network (RAN). This challenge increases the need for leveraging intelligent network management techniques. Thanks to edge computing, machine learning (ML) based network management algorithms and other delay-sensitive user applications can operate at the network edge. But, due to the limited resources on edge servers, developing an orchestration scheme for intelligent network management and user applications is necessary. In this paper, we provide an edge-centric resource management framework for intelligent RAN management and applications with the awareness of the users’ quality of experiences (QoE). Specifically, we consider the scenario of a mmWave communication system equipped with an ML-based mmWave beam tracking algorithm. The users under this system request mobile edge gaming services. We formulate a game QoE aware orchestration problem as a non-linear integer programming and prove its NP-hardness. To reduce the complexity, we decompose the original problem into two subproblems, the service placement problem for mobile edge gaming and the configuration selection and placement problem for mmWave beam tracking. Then, we solve the two subproblems consecutively with heuristic approaches. Simulation results demonstrate the effectiveness of the proposed orchestration scheme.

Recently, an increasing amount of application tasks have depended on deep learning (DL) inference models on Internet of Things (IoT) based camera networks. However, it is challenging to perform the inference of such resource-hungry DL models on the computationally limited IoT system. Compared to cloud computing, edge computing deploys resources near the end-users to reduce the transmission delay and retains the raw data on the trusted servers to mitigate privacy concerns. Since resources at the edge are limited, management like user association decisions, edge resources allocation, and device configuration with DL model parameter selection becomes essential. This work proposes a coalition formation game-based algorithm to solve the association problem between IoT-based cameras and the edge nodes. Our goal is to maximize the social welfare that consists of multi-view detection enhancement, the privacy retained preference, and the power savings from the cameras. Besides, we adopt the concept of split-ML to provide more flexibility for networking and computing resources allocation at the edge. The final coalition structure is proved to converge and maintain stability. The simulation results show that each design knob, including association decisions made by coalition formation, DNN layer-level partition, and multi-view detection, is essential under different scenario settings.

Recently, commercial Fifth-Generation (5G) networks have been widely deployed, enabling data traffic to have higher data rates. 5G will be useful in vertical markets, such as railway environments. In such a case, reliability and latency are key performance metrics. This study aims to understand the performance factors related to packet loss and excessive latency in the rail environment. We analyzed measurement data under the metro rail environment and verified that both location and handovers are associated with unsatisfactory performance, such as packet losses and excessive latency. We also observed that some handover events are unnecessary. With these insights, we will better understand 5G metro rail communications and design a better network system in the future.

Federated learning (FL) is a machine learning paradigm in which many users collaboratively train a model under the supervision of a central server (CS). Because the model performance is highly dependent on user quality, user selection becomes a critical issue in FL. In this paper, we develop a system model where the CS aims to select users with high computational power and valuation of the global model. In this regard, we propose an incentive mechanism to motivate users to reveal their computational power and model valuation. Then, we formulate a cost-minimization optimization problem of the CS and propose a polynomial-time dynamic programming algorithm to solve it. The proposed scheme effectively avoids the free-rider problem in which a user with little contribution can obtain the model by joining the FL process. Moreover, utilizing auction theory, our mechanism incentivizes users to report their computational power and model valuation truthfully. Finally, extensive theoretical analysis and numerical simulation validate the superiority of the proposed mechanism compared with two state-of-the-art user selection mechanisms.

This paper develops a resource allocation mechanism of cooperative multicast under the integrated satellite-terrestrial network (ISTN). In cooperative multicast, base stations (BS) form different single frequency networks (SFN), and BSs within each SFN transmit data to mobile users (MU) with the same frequency, thus increasing signal intensity. Under this system architecture, we leverage the bottleneck user problem to characterize the overall system performance, where the bottleneck user is the MU with the lowest data rate inside each SFN. As the bottleneck user has poor signal strength, an SFN suffers a decrease in its Quality of Experience (QoE) provided to all users. Such an issue is alleviated under the ISTN: Users with poor signal strength can connect to the satellite that transmits with a moderate data rate, while the other users in the terrestrial network are unleashed from being grouped with them. In this regard, we propose an SFN-partitioning mechanism with the user-satellite association. In particular, our algorithm employs concepts from cooperative game theory and ensures Nash stability of the system. Finally, the simulation results validate the efficiency of the proposed mechanism.

Real-time environment monitoring is a key application in Industrial Internet of Things, where sensors proactively collect and transmit environmental data to the controller. However, due to limited wireless resources, keeping sensors’ sampled data fresh at the controller is critical. This work aims to investigate the trade-off between the sensor’s data-sampling frequency and long-term data transmission energy consumption while maintaining information freshness. Leveraging the entropic risk measure (ERM), we jointly minimize the global transmission energy’s mean and variance subject to probabilistic constraints on information freshness. Furthermore, while jointly saving the model training energy, we adopt the federated learning (FL) paradigm and propose an FL-based two-stage iterative optimization framework to optimize the aforementioned objective. Specifically, we iteratively learn the sampling frequency via Bayesian optimization and minimize the long-term ERM of the global energy consumption via Lyapunov optimization. Numerical results show that the proposed FL-based scheme saves substantial executing energy with less performance loss. Quantitatively, compared with the centralized learning baseline, the proposed FL-based framework saves up to 69% model training energy at the expense of a mere increased objective outcome, i.e., 6.3% in the global data transmission energy consumption ($9.936\times 10^{-5}$in ERM) under 0.4% bias from the global optimal data-sampling frequency.

The beamforming technique is applied in 5G NR systems to enhance the receiving signal power, especially for millimeter-wave communications. Beamforming complicates the paging procedure and brings extra overhead to the transmission of paging messages. In order to cover the serving area, the gNB should transmit broadcast control messages via beam sweeping. However, beam sweeping is inefficient for paging operation. In this work, an intelligent directional paging framework is proposed. The proposed beam-based paging list enables the gNB to page UEs flexibly. With the implicit gathering of the UE information, the gNB can make intelligent paging decisions without beam sweeping, decrease the paging resources consumption, and optimize the system paging delay. The proposed Markov chain based analytical model is validated by the simulation results. The results show that the proposed schemes outperform the NR baseline in successful paging rate, delay, and system capacity.

With the rapid growth in the demand for internet-of-things (IoT) systems such as factory of future, smart home, smart city, long-term healthcare, deep learning (DL) applications have attracted significant attention from people. However, it is challenging to inference such tasks on computational limited IoT devices due to the massive computational requirements of DL models. The conventional solution is to deliver data collected from IoT devices to remote cloud for computation, while this may not only rely heavily on networking resources but also cause security risks. The rising concept of edge computing gives us another solution. Tasks can be decomposed by different scales. Model-level decomposition is to inference the models in the task pipeline on different computing devices, while layer-level decomposition is to inference the layers in the single DL model on different computing devices. Both scales of decomposition can be inferenced on edge-cloud framework or simply device-edge framework based on different considerations. This would lead to several aspects of management: resource management for both networking resources and computing resources as well as application configuration management. In this work, we first design configuration tables for different application tasks, with different choices of DL models, different parameter settings, and different layer-level partition points, then we apply Vick-rey-Clarke-Groves (VCG) auction to allocate both networking and computing resources by assigning each IoT device a proper configuration. We also show some desired properties such as truthfulness of the mechanism and observe that the VCG truly utilizes both resources better.

The network attack such as Distributed Denial-of-Service (DDoS) attack could be critical to latency-critical systems such as Mobile Edge Computing (MEC) as such attacks significantly increase the response delay of the victim service. Intrusion prevention system (IPS) is a promising solution to defend against such attacks, but there will be a trade-off between IPS deployment and application resource reservation as the deployment of IPS will reduce the number of computation resources for MEC applications. In this paper, we proposed a game-theoretic framework to study the joint computation resource allocation and IPS deployment in the MEC architecture. We study the pricing strategy of the MEC platform operator and purchase strategy of the application service provider, given the expected attack strength and end user demands. The best responses of both MPO and ASPs are derived theoretically to identify the Stackelberg equilibrium. The simulation results confirm that the proposed solutions significantly increase the social welfare of the system.

Discontinuous reception (DRX) is the most critical solution for user equipment (UE) power saving in mobile communications. Inspired by the wake-up radio (WUR) design in IEEE 802.11ba, the 3rd Generation Partnership Project (3GPP) standardized the Downlink Control information of Power saving (DCP) for the DRX mechanism. In this paper, we follow the DCP mechanism in 3GPP standard and derive the quantitative performance models for UE sleep ratio and delay. The proposed model is completely validated by the simulation results. In addition, we investigate the effects of DCP upon DRX mechanism. We also provided the relation between the latency distribution and DRX/DCP configurations. Our work benefits the extension and the improvement of the DCP mechanism.

This work studies a real-time environment monitoring scenario in the industrial Internet of things, where wireless sensors proactively collect environmental data and transmit it to the controller. We adopt the notion of risk-sensitivity in financial mathematics as the objective to jointly minimize the mean, variance, and other higher-order statistics of the network energy consumption subject to the constraints on the age of information (AoI) threshold violation probability and the AoI exceedances over a pre-defined threshold. We characterize the extreme AoI staleness using results in extreme value theory and propose a distributed power allocation approach by weaving in together principles of Lyapunov optimization and federated learning (FL). Simulation results demonstrate that the proposed FL-based distributed solution is on par with the centralized baseline while consuming 28.50% less system energy and outperforms the other baselines.

With the advancement of the 5G network and Internet of Things (IoT) devices, Multi-access Edge Computing (MEC) proposed by ETSI provides multiple devices to access with low latency through heterogeneous networks such as smart factories and vehicular networks. In addition, video streaming and online gaming have become more popular and consume more than half of the traffic on the internet. Thus, there will be more edge servers deployed on the edge of the network for offloading the core network. However, the edge server is more vulnerable because of its proximity to the user equipment (UE). Attackers can quickly launch distributed denial-of-service (DDoS) attacks with plenty of infected IoT devices. In this paper, we propose Tradeoffs Between Security and Application QoE (TBSA) system to solve the security and resource management problems on the edge server. First, we deploy video streaming, online gaming, and network security applications on the edge server. We use Intrusion Detection and Protection Services (IDPS) to perform DDoS mitigation and design resource allocation algorithm to allocate the computing resources to the edge computing applications. Then, we compare different attack rates in the user scenarios and analyze multiple models under the resource limit condition. The experiments show that we can improve the Quality-of-Experience (QoE) of applications by edge computing resources management.

Recently, Multi-access Edge Computing (MEC) has become a promising enabler to support emerging applications in vehicular networks by offloading compute-intensive tasks from vehicles to proximate MEC servers. However, the high mobility of vehicles brings difficulties to provide reliable services in the MEC system due to potential outages of communication in the process of offloading. Also, load balancing of the MEC system is seldom considered in previous offloading schemes, which may increase the risk of system failure and reduce Quality of Service (QoS) of vehicles due to congestions. Currently, we still lack a low-complexity method to address these issues. In this paper, we aim to promote QoS of vehicular applications by taking vehicles' mobility and latency requirements into account while guaranteeing load balancing of the MEC system. Specifically, we first formulate the joint offloading decision and resource allocation problem as a Mixed Integer NonLinear Programming (MINLP) problem. Then, by taking advantage of both Deep Neural Network (DNN) and Particle Swarm Optimization (PSO), we propose a novel framework to effectively address the problem, where PSO accelerates the training by providing high quality labeled data to DNN. Finally, simulation results show that our proposed method outperforms traditional heuristic algorithms in terms of QoS and runtime.

Millimeter wave (mmWave) is a crucial component in 5G and beyond 5G communications. However, the dense deployment of mmWave transceivers imposes a heavy burden on the management of radio access network (RAN). This challenge increases the need for autonomous network management methods leveraging machine learning (ML) techniques. In particular, mmWave beam selection is a critical issue for the management of RAN due to the large training overhead on mmWave transceivers. To this end, a new beam tracking method based on sequence-to-sequence (Seq2Seq) learning is proposed. Besides, thanks to edge computing technologies, network management algorithms and delay-sensitive user applications can be hosted on edge servers in close proximity. Due to limited resources on the edge server, the resource allocation problem for beam tracking and edge gaming is investigated with the aim of maximizing game quality of experience (QoE). Simulation results verify the effectiveness of the proposed orchestration scheme.

With the emerging demand for high-definition videos in recent years, Multi-access Edge Computing (MEC) has become a promising solution to leverage Quality of Experience (QoE) of users in the 5G mobile network, which provides computing and cache resource at network edges to serve end users with less latency. Also, since mobile users tend to be influenced by the trends in social media, the performance of video caching will become more effective if we can extract the hidden information from interaction among them. In this paper, we propose a novel Collaborative Social-aware QoE-driven video Caching and Adaption (CSQCA) framework. Specifically, we first design a 2-tier MEC collaborative video caching architecture, which partially caches popular videos among multiple edge servers. Second, we propose a social-aware proactive cache strategy, which embeds interactions of users and video dissemination process in social networks into the caching mechanism. Third, a QoE-driven video adaptation algorithm is presented to dynamically transcode the cached videos into appropriate resolution on edge server for each request. Finally, we conduct our simulation based on real-world datasets. The simulation results show that the proposed CSQCA framework outperforms traditional cache algorithms, in terms of the average hit ratio and QoE.

The industrial Internet of Things (IIoT) has been widely deployed to provide autonomous inspection on current production status and quality of products for modern manufacturing. However, the IIoT sensors generally are short of computing capabilities and therefore could not offer acceptable latency for computation-intensive inspection tasks. Besides, the mission-critical industrial applications are extremely sensitive to inspection failure, which may lead to serious manufacturing problems or accidents. In this paper, we propose a risk-aware cloud-edge computing framework for the delay-sensitive inspections of autonomous manufacturing. Due to the uncertainty of 802.11ax, we utilize the conditional value-at-risk (CVaR) to measure the inspection risk basing on the distribution of channel access delay. We develop a branch-and-check (BNC) approach to optimally and efficiently deploy the decomposable inspection tasks with the minimum operation cost and acceptable latency. The extensive simulations guide the operational use for future IIoT and the results show that the proposed system can save a large amount of unnecessary operation cost by enabling the processor sharing strategy.

Mobile Edge Computing (MEC) is a promising paradigm to ease the computation burden of Internet-of-Things (IoT) devices by leveraging computing capabilities at the network edge. With the yearning needs for resource provision from IoT devices, the queueing delay at the edge nodes not only poses a colossal impediment to achieving satisfactory quality of experience (QoE) for the IoT devices but also to the benefits of the edge nodes owing to escalating energy expenditure. Moreover, since the service providers may differ, computationally competent entities' computing services should entail economic compensation for the incurred energy expenditure and the capital investment. Therefore, the workload allocation mechanism, where we consider flat-rate and dynamic pricing schemes in the multi-layer edge computing structure, is much-needed. We use Stackelberg game to capture the inherent hierarchy and interdependence between the second-layer edge node (SLEN) and first-layer edge nodes (FLENs). A truthful admission control mechanism grounded on the optimal workload allocation is designed for FLENs without violating end-to-end (E2E) latency requirements. We prove that a Stackelberg equilibrium with the E2E latency guarantee and truthfulness exists and can be reached through proposed algorithm. Simulation results confirm the effectiveness of our scheme and illustrate several insights.

With the rapid development of deep learning technology, the modern Internet-of-Things (IoT) cameras have very high demands on communication, computing, and memory resources so as to achieve low latency and high accuracy live video analytics. Thanks to the mobile-edge computing (MEC), intelligent offloading to the MEC nodes can bring a lot of benefits, especially when the decomposable pipeline is adopted in the cloud-edge architecture. In this article, we provide decomposable intelligence on a cloud-edge IoT (DICE-IoT) framework to support joint latency- and accuracy-aware live video analytic services. Specifically, the intelligent framework enables the pipeline-sharing mechanism to reduce MEC resource usage. A Nash bargaining is proposed to incentivize cooperative computing provision between the MEC and the cloud, and a generalized benders decomposition (GBD)-based approach is utilized to optimize the social welfare. The results show that the proposed DICE-IoT framework can achieve a win–win–win solution to the IoT device, the MEC, and the cloud stratum.

Mobile Edge Computing (MEC) is a promising paradigm to ease the computation burden of mobile devices by leveraging computing capabilities at the network edge. With the yearning needs for resource provision from the comparatively limited edge servers, an admission control charging a flat-rate price and resistant from self-interested manipulations is proposed to both utilize the scarce resources to the fullest and guarantee end- to-end latency for served mobile devices. Besides, to achieve energy sustainability and maximize profit, edge servers can lessen the energy consumption, and thus the energy expenditure by offloading certain requests to the cloud with a payment. Nonetheless, the cloud can reduce its energy consumption and enlarge the revenue through raising the service price to maximize its profit as well. The inherent hierarchy and interdependence between edge servers and the cloud are captured by Stackelberg game where the unique Stackelberg equilibrium is reached. The utility maximization problems for determining the optimal offloading ratio of edge servers and setting the optimal pricing of the cloud are investigated by exploiting the strict convexity and KKT conditions. Simulation results confirm the effectiveness of our scheme and several insights are illustrated.

Inter-cell interference coordination (ICIC) has been widely studied for mitigating the effects of severe inter-cell interference (ICI) in cell- edge users. However, based on the scarcity of frequency resources in orthogonal multiple access systems, the ICIC methods proposed in the previous papers have difficulty in maintaining the overall performance and fairness of a system. Non-orthogonal multiple access (NOMA) is a promising radio access technology that can serve multiple users simultaneously with the same frequency resources. However, most previous work has not considered the ICI problem in NOMA systems. We propose a centralized adaptive ICIC framework for downlink NOMA systems, including a distributed clustering algorithm, a distributed power allocation algorithm, and a centralized frequency allocation algorithm. Simulation results demonstrate that the proposed framework outperforms all benchmark frameworks and can improve both the overall performance of a system and fairness among users.

In cellular networks, inter-cell interference is the main factor in the reduction of service quality for users, so intercell interference coordination (ICIC) has been widely studied to mitigate severe interference. However, in some previous work, cell- edge users are sacrificed to improve the performance of the overall system. Apart from this, most previous methods change the ICIC configuration frequently to achieve the optimal results, but in practice, the frequent ICIC reconfiguration results in large overhead for small cells. Thus, a centralized dynamic ICIC scheme is proposed in this work, including Q-learning assisted deep neural network based ICIC framework and Type-Balanced User Grouping algorithm. The simulation results show that the proposed ICIC scheme outperforms the benchmarks in both sparse and dense user distribution.

We develop a new statistical decision making framework to select the optimal subset of sensors to activate, while meeting various quality of service criteria specified by users' queries. The sensor nodes are powered solely by energy harvested from the environment and should be activated in an efficient and economical manner based on the available battery energy, which may not be directly observed by the decision maker. Our decision making framework consists of two aspects: the first is the estimation of the current available battery levels of each of the sensors and the second is a sensor selection policy. The energy estimation step is based on the Cumulative Energy Harvesting Process which is carried out over the collaborative wireless sensor networks. The sensor selection policy is based on the estimated battery levels and uses the Cross-Entropy method which efficiently solves the resulting combinatorial problem to select the sensor set which maximizes the utility function. We demonstrate and provide insights into the robustness and effectiveness of our framework under different operational conditions.

It has been a significant issue to satisfy the rapidly growing data traffic with the limited wireless radio resources. Licensed-assisted access to unlicensed spectrum (e.g., LAA) brings hope for the service provider (SP) to mitigate the deficiency of radio resources. This work contributes on designing a pricing model in a licensed and unlicensed coexisting network, modelled as a two-sided market with content providers (CPs) and end users (EUs) at the SP's two sides. A premium content delivery deal is further designed via the optimal auction in order to efficiently allocate the scarce radio resources for the CPs with higher traffic load and QoS requirement. Thus, the SP and CPs form a prioritized spectrum game, and the SP and EUs form a radio access subscription game. By backward induction, we derive the basic delivery price and the premium delivery price to CPs, the reservation price, and the LTE-only and LAA subscription prices to EUs. Analysis shows that all players benefit from the premium delivery deal and co-existence of unlicensed LTE. When the unlicensed spectrum becomes reliable, all players' payoffs and the subscription ratio increase. In addition, the impact of the subsidies and technology heterogeneity are also addressed in this article.

Recent studies have experimentally shown the gains of full-duplex radios. However, due to its relatively higher cost and complexity, we can envision a more practical step in the network evolution is to have a full-duplex access point (AP) but keep the clients half-duplex. Unfortunately, the full-duplex gains can hardly be extracted in practice as the uplink transmission from a half-duplex client introduces inter-client interference to another downlink client. To address this issue, we present the design and implementation of IC2 (Inter-Client Interference Cancellation), the first physical layer solution that exploits the AP's full-duplex capability to actively cancel the interference at the downlink client. Such active cancellation not only improves the achievable capacity, but also better tolerates imperfect user pairing, simplifying the MAC design as a result. We build a prototype of IC2 on USRP-N200 and evaluate its performance via both testbed experiments and large-scale trace-driven simulations. The results show that, without IC2, about 60% of client pairs produce no gain from full-duplex transmissions, while, with IC2 the median gain of the achievable rate over conventional half-duplex networks can be $1.65\times $ and $1.47\times $ for 1- and 2-antenna scenarios, respectively, even when clients are simply paired randomly.

360-degree videos for Virtual Reality (VR) applications are getting more popular because of their diverse applications. However, VR videos usually need more bandwidth than conventional videos to provide the same quality of experience (QoE). Tiled videos can help save bandwidth by selecting lower quality encoding for the tiles with lower probability of viewing. Dynamic Adaptive Streaming over HTTP (DASH) enables the adaptive rate selection of tiles based on the channel conditions. Multicasting also can help save bandwidth, since many users share the spectrum when they request the same video contents. In this paper, we formulate the utility maximization problem to find which tiles should have which video representations to satisfy the most users in multicasting groups using limited resources. A cross-layer optimization framework, which includes user grouping, resource allocation, and the tile-based rate-selection algorithms, is proposed to maximize the total utility among all users. Simulation results show that the proposed cross-layer optimization framework can achieve a higher utility than the broadcasting solution or existing multicast solutions.

Vehicular fog computing (VFC) is a promising approach to provide ultra-low-latency service to vehicles and end users by extending the fog computing to conventional vehicular networks. Parked vehicle assistance (PVA), as a critical technique in VFC, can be integrated with smart parking in order to exploit its full potentials. In this paper, we propose a VFC system by combining both PVA and smart parking. A single- round multi-item parking reservation auction is proposed to guide the on-the-move vehicles to the available parking places with less effort and meanwhile exploit the fog capability of parked vehicles to assist the delay-sensitive computing services. The proposed allocation rule maximizes the aggregate utility of the smart vehicles and the proposed payment rule guarantees incentive compatible, individual rational and budget balance. The simulation results confirmed the win-win performance enhancement to fog node controller (FNC), vehicles, and parking places from the proposed design.

Nowadays, dozens of low-latency required application are emerging, traditional mobile network architecture would not be able to support such applications anymore in the future. Cloud radio access network (C-RAN) combined with Multi-access/mobile edge computing (MEC) seems to be one of the most feasible new RAN architectures to fulfill the requirement. With the assistance of MEC, the computing resource could be allocated more efficiently. In this paper, firstly the advantage of generalized-processor-sharing model (GPS) compared with first-in-first-out (FIFO) and processor-sharing (PS) are discussed in order to figure out the practical queueing behavior in MEC system. Next, the relationship between theoretical traffic intensity factor and realistic system CPU utilization condition is correlated. Finally, based on the discussion, a two threshold forwarding policy (TTFP) algorithm is proposed to dynamically arrange the data traffic according to current system traffic states. The result of the simulation articulates that TTFP algorithm could efficiently fulfill the applications who requires low entire waiting time as possible in high intensity traffic condition.

An excellent self-driving car is expected to take its passengers safely and efficiently from one place to another. However, different ways of defining safety and efficiency may significantly affect the conclusion we make. In this paper, we give formal definitions to the safe state of a road and safe state of a vehicle using the syntax of linear temporal logic (LTL). We then propose the concept of safe driving throughput (SDT) and safe driving capacity (SDC) which measure the amount of vehicles in the safe state on a road. We analyze how SDT is affected by different factors. We show the analytic difference of SDC between the road with perception-based vehicles (PBV) and the road with cooperative-based vehicles (CBV). We claim that through proper design, the SDC of the road filled with PBVs will be upper-bounded by the SDC of the road filled with CBVs.

With the large expected demand of wireless communication, Device-to-Device (D2D) communication has been proposed as a promising technology to enhance network performance. Nevertheless, the selfish nature of potential D2D users may impale the performance of D2D-enabled network. In this paper, we propose a D2D-enabled cellular network framework, which support a novel group D2D mode under overlay D2D communication. The group-based design is derived from the discussions of two common D2D modes, divided and shared D2D modes, regarded as special cases. The proposed framework provides a pricing-based dynamic Stackelberg game for optimal mode selection and spectrum partitioning. We propose the incentive compatible pricing strategy to provide proper incentive for these selfish potential D2D pairs to make optimal choices in mode selection. Our results show that the pricing and spectrum partition strategy effectively prevents selfish potential D2D users from harming the system performance while fully exploits the potential of D2D communication.

Recent studies have experimentally shown the gains of full-duplex radios. However, due to its relatively higher cost and complexity, we can envision a more practical step in the network evolution is to have a full-duplex access point (AP) but keep the clients half-duplex. Unfortunately, the full-duplex gains can hardly be extracted in practice as the uplink transmission from a half-duplex client introduces inter-client interference to another downlink client. To address this issue, we present the design and implementation of IC2 (Inter-Client Interference Cancellation), the first physical layer solution that exploits the AP's full-duplex capability to actively cancel the interference at the downlink client. Such active cancellation not only improves the achievable capacity, but also better tolerates imperfect user pairing, simplifying the MAC design as a result. We build a prototype of IC2 on USRP-N200 and evaluate its performance via both testbed experiments and large-scale trace-driven simulations. The results show that, without IC2, about 60% of client pairs produce no gain from full-duplex transmissions, while, with IC2 the median throughput gain over conventional half-duplex networks can be 1.65× even when clients are simply paired randomly.

Millimeter-wave (mmWave) communications is a promising technology which supports high datarates (multi-Gbps) by utilizing high bandwidth and the directional antenna. While the directionality reduces interference significantly and compensates the high propagation loss, it brings about two major problems. Firstly, mmWave links are easily blocked by obstacles like human bodies and buildings. Secondly, user mobility can frequently cause misalignments between transmitter and receiver beams, which is known as the deafness problem. In this paper, these problems are addressed and a joint scheduling and power allocation framework is proposed to reduce the outage probability during user movement. Extensive simulations are done to demonstrate the pros and cons of the proposed algorithms and the improvement of system performance.

How to enhance the Machine-to-Machine (M2M) communication with improved software services becomes an essential issue in the future fifth generation (5G) systems. Considering the enormous number of connected devices located in a specific direction relative to the base station, Millimeter-Wave (mmWave) communication with the beamforming technology plays a crucial role in meeting the high data transmission requirements of the devices. To achieve the requirements, it is necessary for the devices to integrate the Discontinuous Reception (DRX) mechanism with the mmWave communication. We propose a Beam-Aware DRX mechanism that is adaptive to a periodic beam pattern in mmWave communication. Compared with the original LTE DRX mechanism in mmWave communication.

The millimeter-wave (mmWave) bands, roughly referred to 30-300 GHz, have been widely recognized as a promising candidate for dense deployment of small- cells backhaul network. The dense small-cell deployment produces a huge amount of backhaul traffic, and directional communication poses a significant challenge. In this paper, we propose a dynamic frame reconfiguration scheme which provides greater flexibility for dynamic traffic adaptation in multi-hop mmWave relay backhaul. This allows better exploitation of the traffic dynamics in smallcell. In addition, we present a traffic load and link-quality aware multi-hop relay backhaul scheduling algorithm to maximize the overall system performance. The extensive simulation results demonstrate the superiority of our proposed algorithm when compared with other schemes.

Device-to-Device (D2D) communication is a promising concept used to improve user experience and enhance resource utilization in cellular networks, enabling two close-by D2D devices to establish a direct local link and bypass a base station. The proximity of two D2D devices allows for high data rate, low latency, and low energy consumption. D2D communication for proximity-based services (ProSe) is one of the most popular issues discussed in the 3rd Generation Partnership Project (3GPP). D2D devices require to discover each other (i.e. D2D discovery) before D2D communication. It can be applied to various important fields such as the environment and habitat monitoring, disaster management, and emergency response. In this work, we consider D2D communication is adopted for Internet-of-Things (IoT) devices for local operation. These devices may be randomly deployed and expose to uncontrolled, harsh and hostile environments. Thus, energy-harvesting technology is considered to enable devices to gather energy from the ambient sources and recharge their batteries. We investigate the constraints of an LTE-A system to serve energy-harvesting D2D devices. We develop a low complexity algorithm for D2D discovery resource allocation. Moreover, Sleep-Coordinative Mechanism (SCM) and Saving-before-Activity Mechanism (SBA) are proposed to realize energy-harvesting D2D discovery under the resource and energy constraints. Simulation results show that the combination of SCM and SBA effectively achieves almost 100% discovery rate with fewer time slots and lower energy consumption.

The development of indoor small cell provides network operators with new opportunities to address surging mobile data traffic, coverage infill and capacity boost scenarios, and finally results in growing expectations relative to quality of experience (QoE). However, the deployment of indoor small cell base station also raises some resource allocation issues. This study aims to propose an max‐utility resource allocation mechanism that maximises all users’ utility for indoor small cell networks. The objective of the proposed max‐utility resource allocation algorithm is to optimise the total user's QoE while guaranteeing as many as possible users can enjoy their request service at an acceptable quality. This is achieved by considering user's QoE state and signal strength into the resource allocation algorithm. Multiservice scenario is also considered in the proposed max‐utility resource allocation algorithm. To compare the performance of the proposed max‐utility resource allocation mechanism with round‐robin, max‐throughput and max‐minMOS approach, a simulation analysis is implemented. The simulation result showed that the proposed strategy achieves the best average utility compared with other approaches. Result also showed that the proposed strategy is suitable for indoor small cell networks under various kinds of scenarios with different user behaviours and network topologies.

Device-to-device (D2D) communications is an emerging service model that is currently under standardization by 3GPP. While D2D offloading has a great potential to relieve increasingly congested cellular networks, its benefits, however, come at a cost, namely interference. Most of the prevailing D2D designs conservatively avoid interference via either spectrum resource allocation or power control. These designs, however, do not exploit spatial degrees of freedom (DoF), which are inherently supported by multi-antenna devices. In this work, we present$\sf {MD2D}$, a multiuser D2D system that embraces concurrent D2D transmissions, while leveraging MIMO techniques to actively eliminate interference across D2D pairs.$\sf{MD2D}$has a systematic methodology that checks whether the antenna combination in a D2D network is capable of eliminating cross-pair interference and, thereby, ensuring interference-free concurrent transmissions. If the interference can be eliminated, then$\sf{MD2D}$applies abucket-based DoF assignment algorithmto determine an effective antenna usage configuration that handles the interference. We evaluate our design via testbed experiments and large-scale simulations. The results show that, as compared to the traditional interference avoidance scheme,$\sf{MD2D}$improves the throughput by 87.39 and 218.84 percent in a three-pair testbed and in large-scale simulations, respectively.

A full-duplex radio can transmit and receive simultaneously, and, hence, is a natural fit for realizing an in-band relay system. Most of existing full-duplex relay designs, however, simply forward an amplified version of the received signal without decoding it, and, thereby, also amplify the noise at the relay, offsetting throughput gains of full-duplex relaying. To overcome this issue, we explore an alternative: demodulate-and-forward. This paper presents the design and implementation of DelayForward (DF), a practical system that fully extracts the relay gains of full-duplex demodulate-and-forward mechanism. DF allows a relay to remove its noise from the signal it receives via demodulation and forward the clean signal to destination with a small delay. While such delay-and-forward mechanism avoids forwarding the noise at the relay, the half-duplex destination, however, now receives the combination of the direct signal from a source and the delayed signal from a relay. Unlike previous theoretical work, which mainly focuses on deriving the capacity of demodulate-and-forward relaying, we observe that such combined signals have a structure similar to the convolutional code, and, hence, propose a novel viterbi-type decoder to recover data from those combined signals in practice. Another challenge is that the performance of full-duplex relay is inherently bounded by the minimum of the relay's SNR and the destination's SNR. To break this limitation, we further develop a power allocation scheme to optimize the capacity of DF. We have built a prototype of DF using USRP software radios. Experimental results show that our power-adaptive DF delivers the throughput gain of 1.25×, on average, over the state-of-the-art full-duplex relay design. The gain is as high as 2.03× for the more challenged clients.

Massive M2M (Machine-to-Machine) Communications has been envisioned as a corner stone for next-generation 5G Communications. In addition, LTE has been evolving to accommodate increasing number of IoT devices with Machine Type Communications (also known as LTE-M) and Narrow-Band IOT (NB-IOT). Current cellular network systems are originally engineered for human-to-human communications. So the system allocates major portion of the radio resource to transmit the data packets. However, when devices with different traffic types access the LTE network, this might be a problem. For example, when massive M2M devices access the LTE network, the overhead of control signals might be too high for the system to support. Previous cellular M2M research focused mostly on control signaling in the first step of network entry process (i.e. RACH procedure). Nevertheless, a more complete study on the control signaling overhead for massive M2M devices need to be investigated. There are multiple control signaling steps and several types of channel resources are consumed when an idle state device enters active state. System bottleneck might happen in any of these steps. An inter-channel dynamic resource allocation method is proposed to improve the efficiency of control signaling and to reduce the control signaling storm caused by massive M2M devices. The proposed model estimates the traffic and calculates the demand on each channel. Reallocating of these channel resources leads to efficient resource utilization and thus the system could serve a greater number of M2M devices. In this paper, the investigation is conducted under LTE control plane procedure. Nevertheless, the same design principle could be applied to future 5G M2M.

A novel Fog Radio Access Network architecture based on General Purpose Processor(GPP) Platform is proposed. Traditionally, people consider fog computing service as an independent computing unit in the Radio Access Network(RAN). Our design, on the contrary, considers both the computing resource for RAN and for fog computing application as an integrated computing resource pool. Based on the observation of the CPU load measurement on the Baseband Unit(BBU) testbed, we further scrutinize our proposed model and prove that it is feasible in the realistic RAN system.

Random access channel (RACH) contention issue has drawn great attention due to the prospering development of machine-to-machine (M2M) and Internet-of-Things (IoT) applications. Since a high preamble transmission rate is the direct cause of RACH contention, in this paper, we propose a two-stage scheme to control preamble transmission in multiple periods. In stage I, we design an auction method to balance and allocate the RACH transmission traffic among periods. In stage II, we propose an RACH attempt estimation method to control the preamble transmission rate decided by stage I. Through the two-stage scheme, we can efficiently handle a great number of random access request coming from diverse M2M applications.

While the huge number of machine-to-machine (M2M) devices connects to the LTE system, the bursty random access attempts from them are potential to cause severe random access collisions and degrade the successful probability as well as delay of network connection establishment. To address this issue, current LTE spec has specified access class barring (ACB) and enhanced access barring (EAB), but both methods lack a contention detection method to decide when to activate it as well as a proper way to dynamically adjust the parameter. In this work, we first proposed a contention detection method that can work with current long-term evolution (LTE) standard and is easy to be implemented. To further improve the random access performance in dynamic world, we proposed a context-aware dynamic resource allocation (CADRA) mechanism, which is a two-phase method to resolve random access contention: Phase I for the estimation of random access attempts, and Phase II for resource allocation. By CADRA, high resource efficiency and low random access delay can be achieved without prior knowledge about random access arrival traffic, and thus this approach is competent in diverse applications and scenarios of Internet of Things (IoT). Simulation results show that the proposed contention detection method works great with ACB and EAB. Our proposed CADRA has good performance in resource efficiency and delay while satisfying success probability.

While MIMO technologies are rapidly adopted in 802.11, mobile devices increasingly have different numbers of antennas. Several multiuser MIMO (MU-MIMO) MAC protocols have recently been proposed to allow concurrent transmissions across different links. Though those protocols better utilize the available degrees of freedom, they however do not provide each data stream any receive diversity. This paper introduces multiplexing-diversity medium access (MDMA), a distributed MU-MIMO MAC protocol that achieves both the multiplexing and receive diversity gains at the same time. Instead of letting a node pair use its full degrees of freedom, MDMA allows as many contending node pairs as possible to transmit concurrently and share all the available degrees of freedom. By doing this, MDMA exploits more antennas equipped at different receiving nodes to provide concurrent streams more receive diversity, without sacrificing the multiplexing gain. We show via testbed experiments and simulations that MDMA achieves a similar multiplexing gain, but extracts more diversity gains as the number of antennas at any nodes increases. It hence improves the throughput by up to 48.8 percent as compared to the protocol enabling only spatial multiplexing.

Carrier aggregation is introduced in LTE-Advanced to aggregate multiple bands of spectrum into a virtual carrier. User equipment (UE) with carrier aggregation capability can increase peak data rates by transmitting through an aggregated virtual carrier that provides greater transmission bandwidth. Nevertheless, further study is needed to determine how carrier aggregation should best be implemented and configured to effectively address the range of UE carrier quality and their heterogeneous quality of service (QoS) requirements. In addition, most existing resource allocation methods rely on the assumption that UE always reports information truthfully, which may be unrealistic when UEs act rationally from a game-theory perspective. To address these concerns, we provide a strategy-proof auction approach to carrier aggregation design in an LTE-Advanced system. We first formulate the resource allocation problem in carrier aggregation as a non-linear optimization problem, which is proved to be NP-hard. We then propose a strategy-proof auction with a greedy resource allocation algorithm to 1) find an efficient carrier activation and resource allocation solution under the QoS requirements of UEs, and 2) guarantee that all rational UEs truthfully report their QoS requirements. Finally, we conduct extensive simulations to evaluate system performance for the proposed auction design.

An overlay macrocell-femtocell system aims to increase the system capacity with a low-cost infrastructure. To construct such an infrastructure, we need to solve some existing problems. First, there is a tradeoff between femtocell coverage and overall system throughput, which we defined as the cell-breathing phenomenon. In light of this, we propose a femtocell downlink cell-breathing control framework to strike a balance between the coverage and data rate. Second, due to the selfish nature of mobile stations, the system information collected from them does not necessarily reflect the true status of the system. Thus, we design FEmtocell Virtual Election Rule (FEVER), a voting-based direct mechanism that only requires users to report their channel quality information to the femtocell base station. Not only is it proved to be truthful and has low implementation complexity, but it also strikes a balance between efficiency and fairness to meet the different needs. The simulation results verify the enhanced system performance under the FEVER mechanism.

When it comes to machine-to-machine (M2M) communications in advanced cellular networks, the resource allocation scheme should be re-examined to satisfy both low-cost M2M and mission-critical M2M. Because most M2M applications are uplink-dominated, we propose a mixed waiting-time auction and price-based dedicated uplink resource allocation framework for the low-cost and mission-critical M2M. The prioritized framework guarantees resources for traditional human-to-human (H2H) communications while meeting the needs of low-cost and mission-critical M2M devices on the basis of either time bids or direct price. In addition, the scheme ensures the existence and uniqueness of the Bayesian Nash equilibrium and the interregional and waiting-time-based truth-telling properties. This indirect mechanism holds with Bayesian-Nash incentive compatibility, interim efficiency, interim individual rationality, and weak budget balance. The results show that low-cost M2M devices with lower energy awareness are more willing to participate in the waiting-time auction, while mission-critical M2M with higher energy awareness turn to directly pay for guaranteed access. The delay in connected mode and the optimal price vary according to the M2M/H2H traffic loads and resource pool partitions. This paper contributes insights that with proper mechanism design, low-cost M2M and mission-critical M2M can be served together, while the operator is financially compensated.

Since Machine-to-Machine (M2M) communications is going to be realized in advanced cellular networks, the resource allocation scheme should be re-examined to satisfy both traditional Human-to-Human (H2H) communications (e.g., voice calls) and M2M communications. Because most M2M applications are delay-tolerant and uplink-dominated, we propose a waiting-line based uplink resource allocation framework in the M2M/H2H co-existence scenario. The proposed scheme guarantees resources for H2H communications while meeting the needs of M2M communications on a first-come first-served basis. In addition, the scheme ensures the existence and uniqueness of the Bayesian Nash equilibrium and the truth-telling property. Results show that M2M devices with lower energy opportunity cost are more willing to participate in the waiting-line based scheme, and the delay in the connected mode varies according to the H2H traffic load and the total number of competitors. However, M2M devices with higher energy opportunity cost may not join this time bid resource allocation scheme. This work contributes insights into the types of resource allocation schemes, bidding with time or bidding with money, for M2M devices with different levels of energy awareness.

Multi-user MIMO (MU-MIMO) has recently been specified in wireless standards, e.g., LTE-Advance and 802.11ac, to allow an access point (AP) to transmit multiple unicast streams simultaneously to different clients. These protocols however have no specific mechanism for multicasting. Existing systems hence simply allow a single multicast transmission, as a result underutilizing the AP's multiple antennas. Even worse, in most of systems, multicast is by default sent at the base rate, wasting a considerable link margin available for delivering extra information. To address this inefficiency, we present the design and implementation of HybridCast, a MU-MIMO system that enables joint unicast and multicast. HybridCast efficiently leverages the unused MIMO capability and link margin to send unicast streams concurrently with a multicast session, while ensuring not to harm the achievable rate of multicasting. We evaluate the performance of HybridCast via both testbed experiments and simulations. The results show that HybridCast always outperforms single multicast transmission. The average throughput gain for 4-antenna AP scenarios is 6.22× and 1.54× when multicast is sent at the base rate and the best rate of the bottleneck receiver, respectively.

Since uplink (UL) and downlink (DL) traffic loads are time-variant in femtocells, it is essential to adopt dynamic time-division duplexing (TDD) to effectively adjust the uplink and downlink transmission resources. However, the cross-link interference between dynamic TDD femtocells decreases the throughput gain of dynamic TDD. In this paper, we propose an evolutionary game-based distributed approach to choose the UL-DL configuration in order to minimize interference and maximize the system throughput in a large-scale femtocell network. A multiple populations evolutionary game is formulated to model femtocells with different traffic loads. We prove that the evolutionarily stable strategy (ESS) of the considered multiple populations evolutionary game is the optimal configuration which maximizes the system throughput. Simulation results confirm the effectiveness of the proposed evolutionary game-based approach for system throughput optimization in femtocells employing dynamic TDD.

Scalable Video Coding (SVC) is an extension of H.264/Advanced Video Coding (AVC), which has good characteristics for video transmission over networks. SVC encodes a video into a base layer (BL) and multiple enhancement layers (ELs). With the degree of importance of each layer, in addition to different modulation and coding schemes (MCSs), we can assign different retry attempt limits to each layer to improve quality of experience (QoE) metrics, such as average playback bitrate and buffering ratio. For example, we can assign a slower MCS and a higher retry limit to the BL, to reduce the loss rate and maintain the playback smoothness; whereas, at the same time, we can also assign faster MCSs and lower retry limits to each of the ELs, to reduce the buffering ratio. In this paper, we present a QoE-based link adaptation (QLA) scheme for H.264/SVC video streaming over IEEE 802.11 b/g wireless LANs. We then present a multicast extension of the QLA scheme, multicast QLA (MQLA). We implemented our QLA and MQLA schemes on a Linux-based Wi-Fi protocol driver, mac80211, and built a testbed to conduct our experiments. Experiment results show that both our schemes exhibit an improved QoE performance over the default link adaptation scheme provided by the Linux wireless driver, minstrel.

Wireless multicast is a promising technology for delivering information from a server to multiple users. Through wireless multicast, the server can fulfill the data requirement of multiple users by simply transmitting a single copy of data contents, which increases the efficient usage of radio resources. This paper considers a multi-flow multicast scenario where a base station (BS) is capable of sending multiple data flows to multiple multicast groups. To configure the multicast and achieve optimal resource allocation, the BS may require the feedback of the channel-quality information (CQI) from the users. The CQI is, in general, the users' private information as only the users can directly measure their channel qualities. However, the selfish users may manipulate the multicast configuration through untruthful feedback to increase their own performance. Regarding this issue, we propose a multicast resource allocation mechanism with the designs of the pricing scheme and the weighted water-filling resource allocation. Our analysis shows that the proposed mechanism can elicit the true CQI from the users (strategy-proofness), avoiding any manipulation of multicast configuration and thereby guaranteeing efficient and fair network operation.

Heterogeneous multimedia content delivery over wireless networks is an important yet challenging issue. One of the challenges is maintaining the quality of service due to scarce resources in wireless communications and heavy loadings from heterogeneous demands. A promising solution is combining multicasting and scalable video coding (SVC) techniques via cross-layer design, which has been shown to effectively enhance the quality of multimedia content delivery service in the literature. Nevertheless, most existing works on SVC multicasting system focus on the static scenarios, where a snapshot of user demands is given and remains the same. In addition, the economic value of the SVC multicasting system, which is an important issue from the service provider's perspective, has seldom been explored. In this paper, we study a subscription-based SVC multicasting system with stochastic user arrival and heterogeneous user preferences. A stochastic framework based on the multidimensional Markov decision process (M-MDP) is proposed to study the negative network externality existing in the proposed system and theoretically evaluate the corresponding system efficiency. A game-theoretic analysis is conducted to understand the rational demands from heterogeneous users under different subscription pricing schemes. By transforming the original dynamic and complex M-MDP revenue optimization problem into a traditional average-reward MDP problem, we show that the optimal pricing strategy that maximizes the expected revenue of the service provider can be derived efficiently. Moreover, the overall user's valuation on the system, e.g., social welfare, is maximized under such an optimal pricing strategy. Finally, the efficiency of the proposed solutions is evaluated through simulations.

To explore the energy efficient Machine-Type Communication (MTC) for 5G cellular systems, we developed a simulation platform to investigate the complete uplink procedures of energy-harvesting MTC devices in LTE-A (Long Term Evolution Advanced) system. Though the 3rd Generation Partnership (3GPP) has worked on LTE-A cellular standards in support of MTC transmission, the standards have not taken energy-harvesting MTC devices into their scope. Energy-harvesting technology can be the candidate to support the MTC features by allowing the devices to harvest ambient energy and support their own power usage without manual upgrade. The proposed Energy-Aware LTE-A scheme, serving as power control and admission control, prevents the devices from building the network connections greedily but ending up with energy shortage and packet loss. This scheme not only can reduce the contention level of control channels but also can decrease energy wastage on network entry procedures, thereby improving the overall energy efficiency.

Mobility enhancements of small-cell networks in the IEEE/3GPP standard meeting is an important issue, where the mobility robustness is aimed at the prevention and solution of connection failure/exceptions that occur as a result of uncertain channel condition or aggressive mobility. Although exception handling, such as uncontrolled handover and connection re-establishment, is supported in a 4G wireless network system, it still results in a lengthy yet acceptable handover duration. Notably, the results from commercial 4G cellular systems show that the preparation phase period accounting for 19% of the controlled handover duration is long and prone to uncompleted handover. In this work, we propose a Context Prefetching handover Scheme (CoPS) for 4G broadband wireless access networks in order to effectively reduce the lengthy preparation phase period in the controlled handover procedure without any modifications on user side. We also propose two triggering mechanisms according to the mobility scenario for a trade-off between the overhead and the benefit of prefetching. We implement CoPS and the triggering mechanisms on a WiMAX small-cell network platform of outdoor small cell base-stations. Our field trial results show significant improvements of CoPS in a reduction of the preparation phase period by 78% as well as a reduction of handover duration by 17% and in negligible resource occupation overhead. The proposed solutions also can be introduced into existing LTE infrastructures.

Heterogeneous multimedia content delivery over wireless networks is an important yet challenging issue. A promising solution is combining multicasting and scalable video coding (SVC) techniques via cross-layer design which has been shown to be effectively solution in the literature. Nevertheless, most existing works on SVC multicasting system focus on the static scenarios. In addition, the economic value of SVC multicasting system has seldom been explored. In this work, we study a subscription-based SVC multicasting system with stochastic user arrival and heterogeneous user preferences. A stochastic framework based on Multi-dimensional Markov Decision Process (M-MDP) is proposed to study the negative network externality existing in the proposed system. A game-theoretic analysis is conducted to understand the rational demands from heterogeneous users the subscription economic model. We show that the optimal pricing strategy which maximizes the expected revenue of the service provider can be derived through dynamic iterative updating techniques. Moreover, the overall user's valuation on the system is maximized under such an optimal pricing strategy. Finally, the solution efficiency is evaluated through simulations.

Rare catastrophic events, like earthquakes, can cause substantial damage in a short span of time. Data on the level of stress sustained by buildings and other critical infrastructure acquired during the event can significantly help in post-disaster recovery and assessment of buildings' structural integrity. While installing sensors to acquire such data is not difficult, ensuring that there is power to drive the sensors at the critical moment of the event is a challenge. In this paper, we propose an event-driven energy-harvesting (EDEH) wireless sensor network (WSN) in which the sensors are powered by the energy harvested from the consequence of the event, e.g. buildings shaking during an earthquake. The scarce amount of energy harvested during the short event occurrence time poses great challenges for the medium access control (MAC) design, which is the focus of our research. Furthermore, when all sensors harvest energy from the event, they become active simultaneously leading to serious channel contention problems. As such, we first examine the amount of harvestable energy and then show analytically that our MAC protocol is able to provide higher packet delivery ratio than conventional wireless technology, e.g. IEEE802.15.4.

In existing multiuser MIMO (MU-MIMO) MAC protocols, a multi-antenna node sends as many concurrent streams as possible once it wins the contention. Though such a scheme allows nodes to utilize the multiplex gain of a MIMO system, it however fails to leverage receive diversity gains provided by multiple receive antennas across nodes. We introduce Multiplex-Diversity Medium Access (MDMA), a MU-MIMO MAC protocol that achieves both the multiplex gain and the receive diversity gain at the same time. Instead of letting a node pair use all the available degrees of freedom, MDMA allows as many contending node pairs to communicate concurrently as possible and share all the degrees of freedom. It hence can exploit the antennas equipped on different receivers to further provide some of concurrent streams more receive diversity, without losing the achievable multiplex gain. We implement a prototype on software radios to demonstrate the throughput gain of MDMA.

Most service providers offer an unlimited data service plan under a flat, fixed-rate contract to meet the huge demand. However, because service quality and user experience can vary dramatically in wireless communications, such a contract design is unable to provide equal service quality for all users, which greatly limits the profit potential of service providers. As a result, mobile industries look to femtocell technology to improve service quality and increase profit by attracting customers. Meanwhile, differentiated contracts for different types of users also show great potential for profit increase. In this paper, we investigate unlimited data service plans in terms of enhancements from both femtocell systems and differentiated contracts. The incentive compatibility (IC) issue in differentiated contract design is considered under the overlay macrocell-femtocell system in both split-spectrum and shared-spectrum models. The profits under optimal differentiated contracts, with and without the IC condition are compared to traditional flat fee contracts, and numerical results show that optimal differentiated contracts indeed generate more profits and serve more users.

In this paper, we study the reliable broadcast transmission in energy harvesting networks. In addition to employ the erasure-based coding scheme to cope with the packet loss, we deal with the main challenge posed to a reliable transmission in energy harvesting networks: energy deficiency. It refers that the energy harvesting node cannot work when its battery is exhausted. Hence, we take both transmission error and energy deficiency into account and propose a theoretical analysis and a system design for reliable broadcast transmissions. We also investigate the performance trade-off between reliability and throughput. For different requirements, we propose the reliability-first policy and the throughput-first policy to determine the system parameters.

As future M2M (Machine-to-Machine) communications aim at supporting wireless networks which feature long range, long operating duration and large device number, the IEEE 802.11ah Task Group is going to specify a global WLAN standard that utilizes carrier frequencies below 1GHz. To power the M2M devices, harvesting energy from ambient environment has attracted attentions from researchers. Although applying the IEEE 802.11 PSM (Power-Saving Mode) scheme reduces energy consumption, devices go to sleep only when the traffic buffer is empty, staying awake unnecessarily, which wastes energy in overhearing the uplink traffic transmitted to the AP. In this paper, we propose DeepSleep, a novel energy-aware scheme, which grants higher channel access priority for low energy level devices dynamically. Moreover, applying DeepSleep scheme alleviates the channel congestion by randomly deferring the wake up time of the devices, thereby achieving higher energy-efficiency, which saves nearly 75 % of energy per delivered packet. Additionally, the overall performance improvement when DeepSleep devices co-exist with 802.11 devices is also verified.

Research on energy harvesting networks has attracted an increasing attention lately. Nevertheless, few research of energy harvesting network follows the IEEE 802.11 protocol, whose medium access control (MAC) mechanism, called distributed coordination function (DCF), is based on the carrier sense multiple access with collision avoidance (CSMA/CA) algorithm and binary exponential random backoff. In this paper, we propose a modified DCF integrating the IEEE 802.11 MAC and the characteristic of device recharging model. A three-dimensional Markov chain is then constructed to evaluate the network performance of the modified DCF, such as throughput and delay. The proposed model addresses essential characteristics of an energy harvesting network and contributes to further study on MAC protocol in energy harvesting networks.

Machine-to-Machine (M2M) communications characterize a huge number of devices and small data transmission, which render admission control and resource allocation two key components to guarantee similar quality of service (QoS) for homogeneous devices. In view of this, we propose a pricing model based on option pricing and auction, aiming to guarantee the required QoS (i.e., throughput in this study) in the successive reserved time slots. We also introduce the concept of TotalPayment, referring to the price paid by the device when entering a M2M network, based on which we further suggest the optimal value of TotalPayment and its corresponding optimal range of strike price. The combination of QoS guarantee, admission control and market modelling in M2M network is an innovative business concept.

No abstract available.

We use a Bayesian game-theoretic approach to model transmission control in energy-harvesting wireless sensor networks. In general, the energy state of an energy-harvesting sensor varies more dramatically with time as compared to traditional battery-powered sensors. Therefore, each energy-harvesting sensor is aware of its instantaneous energy state, which is modeled as its private information. Each sensor decides its transmission strategy according to its belief of its opponents' energy states. There exists a Bayesian Nash equilibrium (BNE) where a sensor with energy higher than its energy threshold will decide to transmit at fixed power, and wait otherwise. We show how each sensor determines its threshold to maximize its utility function. Moreover, we show via simulations that the performance of the Bayesian game model is close to that of a perfect-information game where energy states are common information to all sensors. In addition, since the proposed Bayesian game has the advantage of requiring less information exchange overhead, it seems to be more feasible to implement than the perfect-information game.

We propose a dynamic auction mechanism to solve the allocation problem of computation capacity in the environment of cloud computing. Truth-telling property holds when we apply a second-priced auction mechanism into the resource allocation problem. Thus, the cloud service provider (CSP) can assure reasonable profit and efficient allocation of its computation resources. In the cases that the number of users and resources are large enough, potential problems in second-priced auction mechanism, including the variation of revenue, will not be weighted seriously since the law of large number holds in this case.

In-station Wi-Fi AP deployment provides opportunistic Wi-Fi access in underground Mass Rapid Transit (MRT) system. But such vehicular network faces the obstacle of limited connection time from the MS on the train to the BS at the station. Therefore, maximizing the throughput during the tens of second intervals becomes crucial to overcome such hindrance. To achieve the goal, we propose Accelerometer-Assisted Rate Adaptation (AARA) to divide the motion of the train into four phases; each adopts a specific rate adaptation mechanism. The experiments show that the average throughput of AARA outperforms that of the conventional scheme.

Applying network coding on broadcasting service is known to reduce times of transmission in the process of recovering the loss packets. In previous design, coding coefficients are put in the packet headers so that MSs can decode the coded packets. However, it causes extra overhead. Moreover, since many mechanisms depend on feedback information to encode packets, they may cease operating once they are out of feedback support. To address these problems, we propose a codebook-based network coding scheme. The codebook defines the coding coefficients of the transmission packets; therefore, we only need to put the index of the codebook in the packet header to decode packets. Furthermore, it can operate without feedback. The simulation result shows that PRNC decodes more packets and has higher decode ratio compared with other network coding schemes.

Adaptive modulation and coding (AMC) is attracting research interest in wireless multicast broadcast services (MBS). AMC increases the system throughput by utilizing possible good channel states instead of using robust modulation and coding schemes. A model for the AMC in an MBS system with multiple MBS groups is provided, and stochastic analysis using Markov chain is also applied for modeling analysis. Simulation results show extremely high correspondence to the proposed mathematical model.

In recent years, the rapid development of wireless communication allows us to enjoy more multimedia services via wireless network. However, due to lack of QoS support and characteristics of wireless channel, there are still many challenges in wireless video streaming. In this paper, we implement a cross-layer architecture to enhance the QoS transmission of h.264/AVC video stream in IEEE 802.11e wireless environment. Cross-layer Adaptive Video Prioritization (CAVP) provides Application layer Video Frame Prioritization (VFP), which prioritizes packet according to PSNR influence level, and MAC-layer Adaptive Prioritization (MAP), which estimates the delay time of each access category (AC) and chooses the faster one. We also show the results of the experiments on real testbed. Our cross-layer architecture has better performance than the works before, especially when the channel is congested.

Game theory has been applied to model power control in wireless systems for years. Conventional power control games tend to consider unlimited backlogged user traffic. Different from the conventional methodology, this paper aims to investigate limited backlogged data traffic and construct a game- theoretic model tackling both one-shot and repeated power control problem in which variable user traffic demand needs to be taken into consideration. To improve the network performance in such situation, we devise a new SINR pricing scheme and propose an algorithm to calculate an optimal price. With this optimal price, we prove that the Nash equilibrium is Pareto efficient and max-min fair.

This paper proposes a network coding based data distribution mechanism in WiMAX. Packets are sent by the BS and then a network coded packet is sent for the data reliability. Different schemes of coding and transmission method are also provided for the data distribution mechanism.

Vehicular ad hoc network protocol with hybrid relay architecture is proposed for improving the success ratio. Access gateway estimation and a probability table based on the routing information are developed and applied in the backhaul-connected infrastructure network in order to estimate the access gateway region where the destination node locates and reduce the transmission flooding in wireless and wired network. The proposed Access Gateway Discovery mechanisms and Access Gateway Selection scheme have been shown effective by the significant improvement of success ratio in NS-2 simulation based on realistic vehicular mobility models.

Mesh networking is a feasible and effective way to route between nodes. The reliability and versatility of wireless mesh networks makes it a critical part of the future Wireless Internet. We apply a tree routing structure to formulate the multihop relay system topology. Wireless mesh network has a gateway node that connects to wired-line Internet. There are several kinds of node characteristics in the system, either being selfish or altruistic. We model bandwidth sharing and multihop relaying problem in wireless mesh networks with Nash Equilibrium. The novelty of this work is in modeling mixed selfish and altruistic user behaviors in multihop relay networks. In addition, the bandwidth sharing strategy space in our fixed pricing network access model differs from the previous variable pricing network model.

Interference in unlicensed band is a severe problem due to sharing of the limited spectrum resource and uncoordinated power transmission of wireless access points. To solve this problem, we aim for minimizing interference among Access Points'coverage while preserving throughput fairness. We investigate this problem by modelling the spectrum sharing WLAN networks as a cooperative game. A bargaining game model is desirable in this frequently changing WLAN networking environment. Our solution is developed based on Nash Bargaining Solution, which can derive desirable properties such as Pareto Efficiency and proportional fairness. In this paper, we first derive the Nash Bargaining Solution for a 2-AP case and a 3-AP case. Then we provide a general solution for scenario with arbitrary number of APs. Simulation results show that the total networking throughput in maximum in the proposed scheme, compared with all other possible transmission power strategies. The interference between WLAN APs is minimal. Our work can lead to a cooperative spectrum sharing WLAN networking design with allocation fairness and optimal throughput performance.

We consider the problem of supporting VoIP calls over a wireless mesh network. We particularly consider the call admission control (CAC) decision problem for VoIP calls on a mesh network. The goal of the call admission decision is to maintain the quality of VoIP calls as given by the R-score measure. In this work, we define a notion of interference capacity model that can be efficiently used to design a CAC algorithm. We show through extensive simulation of 802.11 mesh using ns-2, that our CAC performs well in multi-hop scenarios. Specifically, the proposed CAC provides less than 20% incorrect decisions for different sizes of a multihop linear topology.

In this paper, we present the design of the WiMAX based radio access network to provide data coverage to 802.16 enabled devices. The design provides a multihop solution to provide wide area coverage to users and targets maximizing the radio resource utilization. We propose efficient route construction, data scheduling for increasing the utilization of the backhaul network and base station selection algorithm for end users for efficient load balancing. Through extensive solution, we should attain the benefits by each of the proposed design methods

Non-cooperative behaviors in communication networks can significantly adversely affect the entire network. The WWAN/WLAN two-hop-relay system (Wei, H.-Y. and Gitlin, R.D., 2004) integrates two types of wireless technologies to improve wireless access throughput and coverage. Relay nodes in the two-hop-relay system can be wireless relay routers deployed by wireless service providers, or dual-mode users who voluntarily relay traffic for other users. However, it is likely that all dual-mode terminals are selfish and are not willing to relay for other users without an incentive. We propose a proper scheduling algorithm as an incentive mechanism for the hybrid wireless relay network. We use the basic concepts of game theory, especially the Nash equilibrium concept, to design our scheduling algorithms. Several scheduling algorithms, including the maximum rate C/I scheduler, the proportional fair scheduler, and the round robin scheduler, are examined to understand performance while operating under the assumption that all users are selfish. Under the C/I scheduler or the proportional fair scheduler, Nash equilibriums exist at the operating points where no user relays for other users - an undesirable situation. Under the round robin scheduler, selfish users are indifferent on relaying voluntarily or not relaying. Therefore, we are inspired to design a novel incentive scheduling algorithm to encourage relay. By applying the proposed incentive scheduler at the base station, all selfish users relay cooperatively at the Nash equilibrium.

Non-uniform coverage is a major concern in cellular data networks based on HSDPA/HDR access technologies. Poor coverage lowers the overall utilization of the cell and results in location-dependent downlink throughput for mobile users. We focus on the planning of ad hoc relay network (ARN) in providing an improved cellular coverage. Specifically, we present and discuss issues and approaches for relay node placement in cellular space. Through extensive simulation modeling, we provide the evaluation of the improvement in the location dependent cellular data rate by employing the ARN.

The integration of 3rd generation (3G) cellular networks and the IEEE 802.11 wireless local area networks has drawn considerable attention from the research and commercial communities. Emerging dual-mode mobile terminals could provide flexibility and system performance enhancement by providing seamless roaming between 3G WWANs and 802.11 WLANs. In this paper, we proposed a novel integrated WWAN/WLAN two-hop-relay architecture that both enhances the system capacity of 3G cellular systems and extends the system coverage area of 802.11 terminals. The proposed two-hop-relay architecture utilizes temporal channel quality variation to achieve increased system capacity. Typically, the system throughput is increased by 200-400% in a HDR system.

The huge demand and growth of high-speed mobile data applications drive the development of the next generation wireless systems. The emerging 3G cellular networks provide new wireless access technologies for high-speed downlink packet access. The variable nature of wireless channel quality brings up challenges and opportunities for wireless system design. By utilizing channel state information, the next generation wireless packet systems provide high data rate communications with adaptive modulation and coding. To provide quality of service (QoS) in such systems is an important engineering issue. In this paper, we propose a novel scheduling algorithm that supports assured QoS. Due to the unpredictability and volatility of wireless medium, wireless QoS design tends to focus on soft QoS guarantee instead of hard one. The proposed scheme can effectively guarantee soft rate reservation and provide high-speed connections with channel-aware radio resource allocation without compromising fairness.

This paper introduces a fast handoff mechanism, NeighborCasting, for use in wireless IP networks that utilize neighboring foreign agent (FA) information. NeighborCasting is based on the policy of utilizing, or perhaps even wasting, wired bandwidth between foreign agents, while minimizing RF (radio frequency) bandwidth exchanges, so that handoff latency is minimized. We demonstrate that the handoff latency is substantially reduced, while the typical overhead is minimally increased. Handoff latency is minimized by initiating data forwarding to the possible new foreign agent candidates (i.e., the neighbor foreign agents) at the time that the mobile node initiates the link-layer handoff procedure. NeighborCasting builds upon the Mobile IP handoff procedure by adding a small number of additional message types. The handoff mechanism is a unified procedure for inter-domain, intra-domain and inter-technology (e.g., LAN to WAN or TDMA to CDMA) handoffs and provides flexible choices to the network, while maintaining transparency to the mobile node. The neighbor FA discovery process is a distributed and dynamic mechanism, and the fast handoff schemes are scalable and reliable.

A mechanism for the positioning of mobile user location is presented in this paper. Based on the three-point problem from land surveying, this positioning mechanism makes use of network delay to estimate user locations by trigonometry and analytical geometry. Location-sensitive wireless services and applications can be provided without the need of obtaining access to the physical layer information such as signal strength, time of arrival (TOA), angle of arrival (AOA) or adding GPS (Global Positioning System) hardware. It also provides better protection for user privacy by a user initiation mechanism. The novel concept of virtual location based on network latency is introduced to investigate the relative topologies among mobile users and position known servers.