The fully-decoupled radio access network (FD-RAN) is an innovative architecture designed for next-generation mobile communication networks, featuring decoupled control and data planes as well as separated uplink and downlink transmissions. To further enhance energy efficiency, this paper explores a green approach to FD-RAN by incorporating adaptive base station (BS) sleeping and resource allocation. First, we introduce a holistic power consumption model and formulate a energy efficiency maximization problem for FD-RAN, involving joint optimization of user equipment (UE) association, BS sleeping, and power control. Subsequently, the optimization problem is decomposed into two subproblems. The first subproblem, involving UE power control, is solved using a successive lower-bound maximization approach based on Dinkelbach’s algorithm. The second subproblem, addressing UE association and BS sleeping, is tackled via a modified, low-complexity many-to-many swap matching algorithm. Extensive simulation results demonstrate the superior effectiveness of FD-RAN with our proposed algorithms, revealing the sources of energy efficiency gains.

Traffic flow prediction is an essential part of intelligent transportation systems, which enhance traffic mobility and safety while also increasing traffic management efficiency. However, due to the complex factors, such as the nonlinear and dynamic nature of traffic flow data, effectively capturing the spatial and temporal characteristics of the data becomes a major challenge. In addition, accurate predictions are essential to guide traffic travel. Thus, we present a traffic flow prediction network in this paper designed to effectively capture spatial, long-term, short-term, and periodic characteristics. To this end, we use a convolutional neural network and an attention mechanism-based multi-layer LSTM-SNP model to develop a hybrid model, referred to as CAMLP. The model extracts spatial and short-term features effectively and focuses on key information through the attention mechanism, thereby improving prediction accuracy. Secondly, in order to capture long-term and periodic features, we utilize the BI-LSTM-SNP model and attention mechanism to process long-term data and periodic data. The extracted long-term, periodic, and short-term features are then combined for prediction. The design of the model fully considers the dynamic principles of time-varying traffic flow and the physical correlation between different road sections so that the model can more accurately reflect the dynamic changes of traffic flow. Finally, the proposed model is tested on three public datasets, and the experimental results show that the proposed model achieved the best performance among the compared models, thus confirming its effectiveness.

Diverse critical data, such as location information and driving patterns, can be collected by IoT devices in vehicular networks to improve driving experiences and road safety. However, drivers are often reluctant to share their data due to privacy concerns. The Federated Vehicular Network (FVN) is a promising technology that tackles these concerns by transmitting model parameters instead of raw data, thereby protecting the privacy of drivers. Nevertheless, the performance of Federated Learning (FL) in a vehicular network depends on the joining ratio, which is restricted by the limited available wireless resources. To address these challenges, this paper proposes to apply Non-Orthogonal Multiple Access (NOMA) to improve the joining ratio in a FVN. Specifically, a vehicle selection and transmission power control algorithm is developed to exploit the power domain differences in the received signal to ensure the maximum number of vehicles capable of joining the FVN. Our simulation results demonstrate that the proposed NOMA-based strategy increases the joining ratio and significantly enhances the performance of the FVN. Index Terms—Federated Vehicular Network, NOMA

Adopting serverless computing to edge networks benefits end-users from the pay-as-you-use billing model and flexible scaling of applications. This paradigm extends the boundaries of edge computing and remarkably improves the quality of services. However, due to the heterogeneous nature of computing and bandwidth resources in edge networks, it is challenging to dynamically allocate different resources while adapting to the burstiness and high concurrency in serverless workloads. This article focuses on serverless function provisioning in edge networks to optimize end-to-end latency, where the challenge lies in jointly allocating wireless bandwidth and computing resources among heterogeneous computing nodes. To address this challenge, We devised a context-aware learning framework that adaptively orchestrates a wide spectrum of resources and jointly considers them to avoid resource fragmentation. Extensive simulation results justified that the proposed algorithm reduces over 95% of converge time while the end-to-end delay is comparable to the state of the art.

This paper investigates a wireless powered internet of things (IoT) network, where a hybrid access point (HAP) performs both wireless energy transfer (WET) and wireless information transfer (WIT). Specifically, the HAP charges IoT devices via WET and collects their updated information via WIT. To ensure the information freshness, an adaptive cooperative transmission scheme is proposed, where information packets of devices are transmitted either directly or cooperatively with the assistance of another device. To this end, an expected weighted sum of age of information (EWSAoI) minimization problem is formulated to adaptively determine the best device pairs for cooperative transmissions, and schedule the corresponding transmission, i.e., WET, direct WIT, or cooperative WIT. Leveraging the Lyapunov optimization framework, a low-complexity adaptive scheduling scheme is proposed, wherein the device pairing or information transmissions are determined based on the instantaneous AoI and energy status of IoT devices. Furthermore, to reduce signalling overhead, a two-timescale scheduling scheme is proposed, wherein a matching algorithm is incorporated to pair devices for cooperative transmission in a large timescale while the adaptive transmission of energy and information are executed in a small timescale. Simulation results validate that adaptive cooperative scheduling scheme effectively reduces the AoI with low system overhead, and the gain approaches to 43.3%, compared with non-cooperative scheme.

In this paper, we propose a novel beamforming codebook (CB) design for wireless communications with recon-figurable intelligent surface (RIS) in both near-field and far-field scenarios. To this end, we first develop a generic model to analyze spherical waves and explore the boundary between the near-field and far-field regions. Based on this model, we propose a novel beamforming design for both the near-field and far-field areas. We prove that near-field beamforming can be mathematically decomposed into two parts: directional beamforming which is similar to far-field beamforming, and distance beamforming within the near field region. In addition, we propose an algorithm to decide the beamforming CB, taking into account the practical constraint of quantized phase shifts in RIS implementation. Finally, we implement the proposed beamforming CB design in a network scenario, considering both cases with and without the location information of the user equipment (UE). Extensive simulations validate the superior performance of the proposed beamforming design.

In this paper, we aim to minimize the age of information (AoI) for a random access internet of things (IoT) network, where AoI is a metric to measure the freshness of information delivery. Since non-orthogonal multiple access (NOMA) can improve network throughput and connectivity, we exploit an AoI-oriented NOMA-based random access scheme, wherein devices simultaneously access wireless channel over multiple power levels with different access probabilities when their AoIs is not smaller than a threshold. We firstly study the comprehensive steady-state analysis of an AoI-independent NOMA-based random access scheme, which is a special case when the threshold is one. The AoI evolution is formulated as a markov chain based on the analyzed transmission success probability, and the probabilities of AoI states and the achieved AoI under generate-at-will are derived. Then, an AoI minimization algorithm is proposed to optimize the power access probabilities. Concerning stochastic-arrival, the steady-state probabilities of devices’ active state, successful transmission, and number of active devices, are derived to analyze the expected AoI. Finally, the steady-state probabilities of AoI states and the achieved AoI of AoI-dependent NOMA-based scheme are obtained. Simulation results validate our analysis, and demonstrate the significant performance improvement in terms of AoI. In specific, the proposed scheme can achieve AoI reduction by 65%, compared with random access without NOMA.

The canonical solution methodology for finite constrained Markov decision processes (CMDPs), where the objective is to maximize the expected infinite-horizon discounted rewards subject to the expected infinite-horizon discounted costs' constraints, is based on convex linear programming (LP). In this brief, we first prove that the optimization objective in the dual linear program of a finite CMDP is a piecewise linear convex (PWLC) function with respect to the Lagrange penalty multipliers. Next, we propose a novel, provably optimal, two-level gradient-aware search (GAS) algorithm which exploits the PWLC structure to find the optimal state-value function and Lagrange penalty multipliers of a finite CMDP. The proposed algorithm is applied in two stochastic control problems with constraints for performance comparison with binary search (BS), Lagrangian primal-dual optimization (PDO), and LP. Compared with the benchmark algorithms, it is shown that the proposed GAS algorithm converges to the optimal solution quickly without any hyperparameter tuning. In addition, the convergence speed of the proposed algorithm is not sensitive to the initialization of the Lagrange multipliers.

In this paper, we study how to minimize the age of information (AoI) for remote monitoring over a WiFi network, where a tagged node under study needs to deliver the sampling messages to the monitoring application installed at the access point (AP). We consider a very challenging practical scenario where multiple background nodes might incorporate heterogeneous and generic traffic models; all the nodes contend for the transmission channel through the practical IEEE 802.11 based medium access control (MAC) protocol. The existing AoI analyses over distributed MAC protocol are not sufficient for our problem, which are limited to simplified MAC modeling or homogeneous traffic modeling. We propose an AoI optimization algorithm that integrates the AoI queueing analysis with the 802.11 MAC performance analysis. Specifically, we develop an innovative method to address the impact of the MAC channel attention on the message service time of the tagged node and compute the minimal AoI iteratively. Simulation results demonstrate that our algorithm is very accurate and robust crossing a variety of networking scenarios. Moreover, our methods require only local computation and slight probing of the conditional collision probability, making them suitable for practical use.

As a main use case of 5G and Beyond wireless network, the ever-increasing machine type communications (MTC) devices pose critical challenges over MTC network in recent years. It is imperative to support massive MTC devices with limited resources. To this end, Non-orthogonal multiple access (NOMA) based random access network has been deemed as a prospective candidate for MTC network. In this paper, we propose a deep reinforcement learning (RL) based approach for NOMA-based random access network with truncated channel inversion power control. Specifically, each MTC device randomly selects a pre-defined power level with a certain probability for data transmission. Devices are using channel inversion power control yet subject to the upper bound of the transmission power. Due to the stochastic feature of the channel fading and the limited transmission power, devices with different achievable power levels have been categorized as different types of devices. In order to achieve high throughput with considering the fairness between all devices, two objective functions are formulated. One is to maximize the minimum long-term expected throughput of all MTC devices, the other is to maximize the geometric mean of the long-term expected throughput for all MTC devices. A Policy based deep reinforcement learning approach is further applied to tune the transmission probabilities of each device to solve the formulated optimization problems. Extensive simulations are conducted to show the merits of our proposed approach.

Mobile crowdsensing (MCS) booms the implementation of the Internet of Things (IoT) in different areas due to flexibility and low deployment cost. However, collecting sufficient high quality sensing data is crucial for the success of various applications. Incentive mechanism design plays a critical role in the successful implementation of mobile MCS systems. Most of existing work consider that the platform exactly knows the trajectory of mobile users. However, in most cases, it is difficult to obtain the accurate information of the location of mobile users due to either privacy issue or the lack of information. In this article, we consider nondeterministic mobility of mobile users, where only the probability distribution of users’ mobility is available. We design an effective mechanism to achieve the quality data collection with the objective of maximizing the expected social welfare. Simulation results show that the proposed mechanism achieves her expected social welfare compared with four existing schemes, while satisfying truthfulness, individual rationality, and computational efficiency.

Non-orthogonal multiple access (NOMA) is a key technology to enable massive machine type communications (mMTC) in 5G networks and beyond. In this paper, NOMA is applied to improve the random access efficiency in high-density spatially-distributed multi-cell wireless IoT networks, where IoT devices contend for accessing the shared wireless channel using an adaptive$p$-persistent slotted Aloha protocol. To enable a capacity-optimal network, a novel formulation of random channel access management is proposed, in which the transmission probability of each IoT device is tuned to maximize the geometric mean of users’ expected capacity. It is shown that the network optimization objective is high dimensional and mathematically intractable, yet it admits favourable mathematical properties that enable the design of efficient data-driven algorithmic solutions which do not require a priori knowledge of the channel model or network topology. A centralized model-based algorithm and a scalable distributed model-free algorithm, are proposed to optimally tune the transmission probabilities of IoT devices and attain the maximum capacity. The convergence of the proposed algorithms to the optimal solution is further established based on convex optimization and game-theoretic analysis. Extensive simulations demonstrate the merits of the novel formulation and the efficacy of the proposed algorithms.

In this paper, we analytically study the performance of non-orthogonal multiple access (NOMA) transmissions in a random access network with truncated channel inversion power control. Specifically, in a slotted ALOHA network in support of NOMA transmissions, a wireless device randomly selects the transmission power with a certain probability, using channel inversion power control yet subject to the upper bound of the transmission power. Taking into consideration the stochastic nature of wireless fading channels, we first quantify two network areas such that devices in different areas have various choices of transmission powers for NOMA transmissions. An analytical model is developed to analyze the successful transmission probability and throughput of wireless devices located in different areas. Based on the analysis, two optimization problems are formulated to maximize the network throughput and the minimum throughput of wireless devices by tuning the transmission probabilities of each device. To solve the formulated combinatorial optimization problems, two heuristic algorithms are proposed. Extensive simulations are conducted to validate the analysis, and verify the efficiency of the proposed algorithm to attain the maximum network throughput and max-min fairness.

In this article, a cybertwin-assisted nonorthogonal random access (RA) system is presented, where the cybertwins of the physical devices at the access point (AP) collect the devices’ information and decide the transmission parameters on behalf of the devices to achieve the maximum system performance. Specifically, the system performance of a$p$-persistent slotted CSMA system with nonorthogonal multiple access (NOMA) is analyzed, in which wireless devices transmit data to the ensure the received signal strength at the AP side is either high power or low power with certain probabilities. We first develop an analytical framework to quantify the successful transmission probability and the sum data rate as a function of the above probabilities. Accordingly, the feasible region of the number of high-power and low-power devices to ensure successful transmission is derived. With the analysis, nonconvex optimization problems are then formulated to maximize successful transmission probability and the sum data rate, respectively. To tackle the nonconvexity, an effective and fast-convergent iterative algorithm is designed to obtain the optimal transmission probabilities for the devices. Extensive simulations are conducted to validate our analytical results and demonstrate the benefits of NOMA in RA networks.

In this article, we exploit nonorthogonal multiple access (NOMA) for simultaneous energy and information transfer in a wireless-powered Internet-of-Things (IoT) network. As double near-far problem causes severe unfairness, we propose a fairness-aware NOMA-based scheduling scheme to enhance the max-min fairness. Specifically, according to the channel conditions, we divide IoT devices into the interference and noninterference groups with relatively good and poor channel qualities, respectively. Energy transfer is concurrently scheduled with data transmissions of devices with good channels. Thus, devices can harvest more energy to achieve higher rates at the cost of reduced rates of devices with good channels due to the interfering energy signals. We then apply order statistics to theoretically analyze the achievable rates of ordered devices. Based on the analysis, devices are optimally categorized into the interference and noninterference groups to achieve the max-min fairness, i.e., the minimum rate of devices in both groups is maximized. An adaptive power allocation algorithm is also proposed to further improve the network fairness when the transmission power of the energy transmitter is controllable. Throughput-aware NOMA-based scheduling is also presented and compared with the fairness-aware NOMA-based scheduling to illustrate the performance tradeoff between the throughput and fairness. The simulation results validate that the proposed NOMA-based scheduling schemes significantly improve the fairness and throughput performance of wireless-powered IoT networks, compared with the existing solutions.

In this paper, we apply the Non-Orthogonal Multiple Access (NOMA) technique to improve the massive channel access of a wireless IoT network where solar-powered Unmanned Aerial Vehicles (UAVs) relay data from IoT devices to remote servers. Specifically, IoT devices contend for accessing the shared wireless channel using an adaptive p-persistent slotted Aloha protocol; and the solar-powered UAVs adopt Successive Interference Cancellation (SIC) to decode multiple received data from IoT devices to improve access efficiency. To enable an energy-sustainable capacity-optimal network, we study the joint problem of dynamic multi-UAV altitude control and multi-cell wireless channel access management of IoT devices as a stochastic control problem with multiple energy constraints. We first formulate this problem as a Constrained Markov Decision Process (CMDP), and propose an online model-free Constrained Deep Reinforcement Learning (CDRL) algorithm based on Lagrangian primal-dual policy optimization to solve the CMDP. Extensive simulations demonstrate that our proposed algorithm learns a cooperative policy in which the altitude of UAVs and channel access probability of IoT devices are dynamically controlled to attain the maximal long-term network capacity while ensuring energy sustainability of UAVs, outperforming baseline schemes. The proposed CDRL agent can be trained on a small network, yet the learned policy can efficiently manage networks with a massive number of IoT devices and varying initial states, which can amortize the cost of training the CDRL agent.

Multi-Unmanned Aerial Vehicle (UAV) control is one of the major research interests in UAV-based networks. Yet few existing works focus on how the network should optimally react when the UAV lineup and user distribution change. In this work, proactive self-regulation (PSR) of UAV-based networks is investigated when one or more UAVs are about to quit or join the network, with considering dynamic user distribution. We target at an optimal UAV trajectory control policy which proactively relocates the UAVs whenever the UAV lineupis about tochange, rather than passively dispatches the UAVsafterthe change. Specifically, a deep reinforcement learning (DRL)-based self-regulation approach is developed to maximize the accumulated user satisfaction (US) score for a certain period within which at least one UAV will quit or join the network. To handle the changed dimension of the state-action space before and after the lineup changes, the state transition is deliberately designed. To accommodate continuous state and action space, an actor-critic based DRL, i.e., deep deterministic policy gradient (DDPG), is applied with better convergence stability. To effectively promote learning exploration around the timing of lineup change, an asynchronous parallel computing (APC) learning structure is proposed. Referred to as PSR-APC, the developed approach is then extended to the case of dynamic user distribution by incorporating time as one of the agent states. Finally, numerical results are presented to demonstrate the convergence and superiority of PSR-APC over a passive reaction method, and its capability in jointly handling the dynamics of both UAV lineup and user distribution.

Channel aggregation (CA) is proposed in IEEE 802.11ax to allow wireless users to aggregate multiple available channels, either contiguous or non-contiguous, to improve the network throughput. In this paper, the performance of CA is extensively investigated. It is shown that a simple CA that aggregates all available channels does not always promote but may degrade the network performance due to the increased inter-channel contentions in a random access wireless local area network (WLAN). Thus, it is of critical importance to select an appropriate set of channels for CA. To this end, we propose an efficient probabilistic channel aggregation scheme to maximize the network throughput under the quality of service constraints. That is, an ax user aggregates each secondary channel with a certain probability based on the traffic load of the secondary channel. A Proximal Policy Optimization (PPO) based approach is further applied to intelligently tune the aggregating probabilities of secondary channels to maximize the network throughput. Numerical results show that the proposed algorithm can greatly improve the network throughput compared with existing CA algorithms in the literature.

It is well known that the Proof-of-Work (PoW) based blockchain scheme, first introduced in Bitcoin system, is not scalable, where the PoW implementation limits the transaction processing rate. In recent years, parallel chain techniques have been proposed to overcome this issue. Chainweb is one of the parallel chains to be studied in this paper with a focus on security related issues. It is worth noting that existing blockchain security studies mainly focus on traditional single-chain based protocols. There are not many security related studies on parallel blockchains. This paper for the first time reveals that selfish mining attack is possible on Chainweb blockchain, to the best of our knowledge. Specifically, we propose a selfish mining attack that exclusively mines blocks on a subset of parallel chains with the same block height and achieves gain through a proper withholding strategy. We develop a mathematical model to quantitatively evaluate the performance and demonstrate the effectiveness of the proposed attack. Our results show that the attacker can gain extra mining reward when his computational power is at least 38% of the total power in Chainweb network. Under the 50% computational power restriction, the attacker's extra gain increases monotonically with its computational power.

Energy-aware control for multiple unmanned aerial vehicles (UAVs) is one of the major research interests in UAV based networking. Yet few existing works have focused on how the network should react around the timing when the UAV lineup is changed. In this work, we study proactive self-remedy of energy-constrained UAV networks when one or more UAVs are short of energy and about to quit for charging. We target at an energy-aware optimal UAV control policy which proactively relocates the UAVs when any UAV is about to quit the network, rather than passively dispatches the remaining UAVs after the quit. Specifically, a deep reinforcement learning (DRL)-based self remedy approach, named SREC-DRL, is proposed to maximize the accumulated user satisfaction scores for a certain period within which at least one UAV will quit the network. To handle the continuous state and action space in the problem, the state-of-the-art algorithm of the actor-critic DRL, i.e., deep deterministic policy gradient (DDPG), is applied with better convergence stability. Numerical results demonstrate that compared with the passive reaction method, the proposed SREC-DRL approach shows a 12.12% gain in accumulative user satisfaction score during the remedy period.

Non-orthogonal multiple access (NOMA) has been considered as a promising solution for improving the spectrum efficiency of next-generation wireless networks. In this paper, the performance of a p-persistent slotted ALOHA system in support of NOMA transmissions is investigated. Specifically, wireless users can choose to use high or low power for data transmissions with certain probabilities. To achieve the maximum network throughput, an analytical framework is developed to analyze the successful transmission probability of NOMA and long term average throughput of users involved in the non-orthogonal transmissions. The feasible region of the maximum number of concurrent users using high and low power to ensure successful NOMA transmissions are quantified. Based on analysis, an algorithm is proposed to find the optimal transmission probabilities for users to choose high and low power to achieve the maximum system throughput. In addition, the impact of power settings on the network performance is further investigated. Simulations are conducted to validate the analysis.

Energy harvesting is a promising solution to enable energy sustainable operation of IoT devices. Especially for under-water IoT network as it is difficult and costly for underwater IoT devices to replace the battery. Unlike traditional power supply, energy harvesting from green sources is a random process and is dependent on the charging environment, which poses new challenges for provisioning quality of services of IoT networks. Due to the high cost for low-powered IoT devices to update its energy status with the scheduler, distributed transmission protocol is more desirable for the IoT networks. In this work, we consider an IoT network where IoT devices use adaptive p-persistent ALOHA for data transmissions. Each IoT device can contend for channel access only when it is ready, i.e., it has a data for transmission and it harvests enough energy for communications. Due to stochastic energy harvesting and random access, the number of ready devices in the network may vary. As such, an analytical framework is first developed using a discrete Markov model to analyze the average number of ready devices. Next, an optimization problem is formulated to maximize the system throughput by tuning the transmission probability. Given that the wireless environment is unknown at different IoT devices, e.g., total number of contending devices, data arrival rates of other IoT devices, a multi-agent reinforcement learning algorithm is introduced for each device to autonomously tune the transmission probability in a distributed manner. In addition, game theory is applied to design the reward function to ensure an equilibrium and to closely approach the optimal parameter setting. Numerical results show that the proposed learning algorithm can greatly improve the throughput performance comparing with other algorithms.

In this paper, we consider the nondeterministic mobility of mobile users, where the platform only has the probability distribution about users' mobility. We design an effective mechanism to achieve high quality data collection with the objective of maximizing the expected social welfare. Simulation results show the better performance of the proposed mechanism compared with four counterparts. In addition, the proposed mechanism also satisfies truthfulness and individual rationality.

In this article, we study an energy sustainable Internet-of-Underwater Things (IoUT) network with tidal energy harvesting. Specifically, an analytical model is first developed to analyze the performance of the IoUT network, characterizing the stochastic nature of energy harvesting and traffic demands of IoUT nodes, and the salient features of acoustic communication channels. It is found that the spatial uncertainty resulting from underwater acoustic communication may cause a severe fairness issue. As such, an optimization problem is formulated to maximize the network throughput under fairness constraints, by tuning the random access parameters of each node. Given the global network information, including the number of nodes, energy harvesting rates, communication distances, etc., the optimization problem can be efficiently solved with the Branch and Bound (BnB) method. Considering a realistic network where the network information may not be available at the IoUT nodes, we further propose a multiagent reinforcement learning approach for each node to autonomously adapt the random access parameter based on the interactions with the dynamic network environment. The numerical results show that the proposed learning algorithm greatly improves the throughput performance compared with the existing solutions, and approaches the derived theoretical bound.

In this paper, we analytically study the performance of opportunistic multi-channel bonding protocol supporting delay-sensitive multimedia services. We consider a multi-channel system shared by IEEE 802.11ac users who can transmit over multiple channels and legacy users who can only transmit over one single channel. By analyzing the channel bonding behavior of IEEE 802.11ac users and the random access of legacy users, bonding probability and successful bonding probability of IEEE 802.11ac users can be derived. Furthermore, the access delays of both legacy and 802.11ac users are analyzed. According to the analytical results, the network capacity which quantifies the maximum number of multimedia flows that can be supported with guaranteed delay is then presented. Additionally, the impacts of different parameters such as traffic data rate on the network capacity are investigated. Our analytical results show that channel bonding is favorable when the secondary channels are underutilized. But channel bonding should be disabled when there are already intense contentions from legacy users. Based on the analytical results, we propose a heuristic bonding policy which can provide important guidelines to control the number of flows to satisfy the QoS requirement and achieve the maximum network capacity. Extensive simulations have been conducted to validate the analytical results.

In this paper, resource allocation is studied for a fully sustainable cooperative IoT network, in which a relay powered by renewable energy forwards data to a destination while charging multiple IoT nodes by radio-frequency (RF) signals. An optimal joint time and power allocation problem is formulated to maximize the long-term sum-throughput of IoT nodes, taking into consideration the bounded transmit power of IoT nodes, the stochastic characteristic of energy harvesting (EH) process, and dynamic wireless channel conditions. To solve the formulated problem, we analyze the time allocation for cooperative communications with EH, considering both data and energy dependency of the two hop transmissions in three cases with different network settings. Based on the analysis, we derive the closed-form solutions of optimal time and power allocation in a network with symmetric links. Then, we extend our solution to a general network with asymmetric links. By employing Lyapunov optimization, an online stochastic resource allocation algorithm is proposed to obtain the maximum network throughput. It has been shown that the proposed algorithm can achieve close-to-optimal network throughput while maintaining the stability of the system. Finally, extensive simulations validate the analysis and demonstrate the effectiveness of the proposed algorithm.

This paper proposes a transmission protocol for massive Internet of Things (IoTs) networks with energy harvesting (EH). Specifically, the IoT devices harvest energy from the renewable natural sources, such as solar and wind, and use the harvested energy to transmit data to a base station (BS). Due to the massive number of IoT devices in the network, it is very challenging, if not impossible, to schedule data transmissions of IoT devices with variable energy supplies. To this end, a hierarchical chain based transmission model is proposed to attain high transmission efficiency of massive IoT devices, considering the stochastic nature of EH and large number of IoT devices. Specially, massive IoT devices are grouped based on their geographic locations; and IoT in one geographic area form a transmission chain to relay the data to the BS. Based on the proposed model, we propose a random chain based transmission protocol, where IoT devices randomly select next hop receiver to relay the data to the BS. The probability density function (pdf) of the size of the random chain is derived, based on which the sustainable energy throughput can be obtained. Finally, extensive simulations validate the analysis and demonstrate that chain based transmission protocol significantly outperform the hierarchal cluster-based transmission protocols.

The near-far problem causes severe throughput unfairness in wireless powered communication networks (WPCN). In this paper, we exploit non-orthogonal multiple access (NOMA) technology and propose a fairness-aware NOMA-based scheduling scheme to mitigate the near-far effect and to enhance the max-min fairness. Specifically, we sort all users according to their channel conditions and divide them into two groups, the interference group with high channel gains and the noninterference group with low channel gains. The power station (PS) concurrently transmits energy signals with the data transmissions of the users in the interference group. Thus, the users in the noninterference group can harvest more energy and achieve a higher throughput, while the users in the interference group degrade their performance due to the interfering signals from the PS. We then apply order statistic theory to analyze the achievable rates of ordered users, based on which all users are appropriately grouped for NOMA transmission to achieve the max-min fairness of the system. Meanwhile, the optimal number of interfered users that determines the set of users in each group, is derived. Our simulation results validate the significant improvement of both network fairness and throughput via the fairness-aware NOMA-based scheduling scheme.

This paper studies resource allocation for a fully sustainable cooperative network, which consists of multiple Internet of Things (IoT) nodes powered by radio-frequency (RF) energy, one relay with renewable energy supplies, and one destination. Specifically, the relay forwards the data received from IoT nodes to the destination and charges the IoT nodes at the same time. A throughput maximization problem is formulated, which takes into consideration the upper bound of transmit power, stochastic energy harvesting (EH) process and channel conditions. To solve the formulated problem, we analyze the time allocation for cooperative communications with EH, considering both data and energy dependency of the two hop transmissions in three cases with different parameters. Based on the analysis, we derive the closed-form solutions of optimal time and power allocation in a network with symmetric links. Extensive simulations validate the analysis and demonstrate the effectiveness of the proposed algorithm.

The proliferation of smartphone and tablet empowers ubiquitous Device-to-device (D2D) networks. Using a locally handled wireless link between devices provides significant convenience to all use cases of Internet of Things, such as home monitors, garage door, and Google home. Nowadays, digital cameras have also adapted Wi-Fi technology to ease the process of transferring pictures and videos to smartphones and laptops. This paper introduces the D2D file transfer mechanisms between cameras and mobile platforms and presents an in-depth empirical security analysis on the D2D network created by those devices. Cameras and applications are close-sourced black boxes, which makes security investigation considerably challenging. In this paper, the analysis concentrates on the most popular camera brands in the market and our team reveals some critical vulnerabilities. We exploit the discovered flaws to construct a proof-of-concept attack to demonstrate how to steal an image from a camera in a busy Wi-Fi environment. We conclude the paper with improvement suggestions and possible solutions. The experimental setup we have developed could be used for future related research.

In most of the existing works about mobile crowd sensing, the service provider collects data from each mobile user separately. However, comparing with the collection of data from individual users, batch trading is more attractive for both service provider and mobile users. On one hand, the service provider prefers to buy a batch of data each time even if it may offer a higher unit price since batch trading can save time and efforts in data collection. On the other hand, batch trading is profitable for mobile users since they can take advantage of volume premium. In this paper, we study how mobile users form a coalition to sell their sensing data together. Based on the concept of majorization, we propose a novel scheme to form a fair and stable coalition. Simulation results show the super performance of the proposed method compared with alternative solutions. In specific, the proposed scheme can improve the achieved utility and fairness by 623.68% and 5.51%, respectively, compared to the scheme with independent sell when the number of users is 90.

Motivated by the increasingly urgent demands for delivering fresh information, the age-of-information (AoI) has recently been introduced as an important metric for evaluating the timeliness performance of information update systems and has shed light on a number of research studies. Nevertheless, the most common goal of the existing works does not characterize the value of information freshness from the users' perspective. In this paper, we introduce the concept of effective AoI (EAoI) to quantify the freshness of the information users utilize for decision-making. We consider a general request-response model, which captures both proactive information update and timely information delivery, for investigating the scheduling problem with respect to EAoI minimization. By decomposing the scheduling problem into multiple computationally tractable subproblems, we propose request-aware scheduling policies for static and dynamic request models, respectively. The numerical results show that serving users requests proactively can reduce time-average EAoI in both scenarios.

Radio frequency (RF) energy harvesting is a promising technology that enables self-sustainable wireless Internet of Things (IoT) networks. In this article, we analyze the energy harvesting performance of a Wi-Fi-based IoT network, where a large number of IoT devices are connected via Wi-Fi for both data communication and energy transfer. Applying probability theory and statistical geometry, we first develop an analytical model to study the energy sustainability of wireless IoT devices with Wi-Fi charging, which operate in an active/charging mode and access the channel using carrier sensing multiple access with collision avoidance (CSMA/CA) protocol. Based on the analysis, we derive the necessary and sufficient conditions for the AP beaconing frequency and the charging period of IoT devices to ensure that a network with a general random topology is long-term energy sustainable. It is shown that transmission collisions due to random access result in too much energy consumption that makes it difficult to achieve energy sustainability of IoT devices. To maximize the total network throughput while ensuring long-term energy sustainability of Wi-Fi IoT devices, a distributed energy-sustainable throughput-optimal algorithm is proposed for user charging period selection. Extensive simulations using NS-3 validate the analysis and demonstrate the efficiency of the proposed algorithm.

Mobile device-to-device (D2D) network has now become a standardized feature in many mobile devices, by which mobile devices can communicate with each other even when commercial Internet access is not available. Because D2D network is expected to be an intrinsic part of the Internet of Things (IoT) and mobile device is the smartest and the most advanced commercial device in everyday usage, the D2D feature and related security protocols it adopts influences the design and implementation of many other IoT devices. While D2D network provides tangible benefits to users, it also raises the security risks of information leaking. This paper presents an in-depth empirical security analysis on mobile D2D network among Android devices. Android apps could establish a mobile D2D network in various ways, including Wi-Fi hotspot, Wi-Fi Direct, and Bluetooth. Those mobile D2D protocols normally take different protection mechanisms, which makes security investigation considerably challenging. In this paper, we focus on most popular apps in the Google Play Store, with aggregated downloads more than 500 million. Our analysis reveals some critical vulnerabilities. The key findings are bi-fold. First, the current mobile D2D network framework enabled by Android has significant flaw of overprivilege issue. Second, we have identified that most data transfer over mobile D2D network is unencrypted. Furthermore, we exploit the identified Android framework flaws to construct three proof-of-concept attacks and we conclude this paper with security lessons and suggestions of possible solutions against the identified security issues.

To support bandwidth-intensive services such as virtual reality video applications, next generation WLANs will allow users to transmit over multiple channels for high data rate transmissions. In this paper, an analytical model is developed to study the performance of the dynamic channel bonding in IEEE 802.11ac, and non-contiguous channel aggregation in IEEE 802.11ax, with coexisting legacy single channel users. By modeling the transmissions of single channel and multi-channel users with and without channel bonding as a two-level renewal process, the bonding probability of multi-channel users, along with the throughput of different users in each channel, are derived. Our analysis shows that multi-channel users can boost their throughput at the cost of degraded throughput of legacy users. Furthermore, it is shown that 802.11ax provides higher spectrum utilization compared to 802.11ac, while 802.11ac provides a friendlier coexistence with single channel users. Based on the analysis, a heuristic algorithm for primary channel selection is further proposed to maximize the throughput of multi-channel users. Extensive simulations using NS-3 validate the analysis and demonstrate the efficiency of the proposed channel selection algorithm.

In this paper, the joint beamforming design for max-min fair simultaneous wireless information and power transfer (SWIPT) is investigated in a green cloud radio access network (Cloud-RAN) with millimeter wave (mmWave) wireless fronthaul. To achieve a balanced user experience for separately located data receivers (DRs) and energy receivers (ERs) in the network, joint transmit beamforming vectors will be optimized to maximize the minimum data rate among all the DRs, while satisfying each ER with sufficient RF energy at the same time. Then, a two-step iterative algorithm is proposed to solve the original non- convex optimization problem with the fronthaul capacity constraint in an l0-norm form. Specifically, the l0-norm constraint can be approximated by the reweighted l1-norm, from which the optimal max-min data rate and the corresponding joint beamforming vector can be derived via semidefinite relaxation (SDR) and bi-section search. Finally, extensive numerical simulations are performed to verify the superiority of the proposed joint beamforming design to other separate beamforming strategies.

Long Term Evolution (LTE) deployment in the unlicensed spectrum is considered a promising solution to overcome spectrum shortage. To ensure fair coexistence among unlicensed users, two Load Based Equipment (LBE) access technologies are introduced in European Telecommunications Standards Institute (ETSI) standard. However, it is not clear whether the two LBE protocols can ensure fair channel access between unlicensed Licensed Assisted Access (LAA) and Wi-Fi users. To this end, renewal theory based analytical models are developed to study the performance of these two LBE random access protocols. Specifically, the throughput performance of Wi-Fi users and LAA users is first derived and compared. Our results show that both options may not achieve throughput fairness among Wi-Fi and LAA users if the key protocol parameters are not fine tuned. Generally option A favors Wi-Fi users, while option B favors LAA users. To improve the fairness performance, channel access parameters in both protocols should be adapted to network conditions in order to achieve the best coexisting performance in terms of both fairness and network throughput. The analysis provides important guidance for the implementation of Listen-before-Talk based access mechanisms in LAA. Extensive simulations using NS-3 are conducted to validate the accuracy of the models.

In this paper, we consider a green cloud radio access network (C-RAN) with simultaneous wireless and power transfer ability. In order to reduce the energy consumed for updating the channel state information (CSI), energy users are divided into two different groups, including the free charge group and the MIMO group. Then a semi-definite programming problem is formulated under the constraints of energy and information transmission requirements. To minimize the total energy consumption, two algorithms are developed to authorize the energy users into two group divisions in single time slot. Then the algorithms are extended to long term scenarios consisting training and long term stages, the CSI of free charge energy users are not required during the long term stage. Simulation and numerical results are presented to demonstrate the efficiency of the proposed algorithms in significantly reducing the energy consumption of C-RAN systems.

In this paper, we investigate the access mechanisms of LTE Licensed Assisted Access (LAA) co-existing with Wi-Fi over the unlicensed band. To this end, we first develop an analytical model to study the performance of existing Load Based Equipment (LBE) MAC for Unlicensed Long Term Evolution (U-LTE), identify the fairness issues, and quantify the reservation overhead of the protocol. To maximize the network throughput and ensure fair spectrum sharing of U- LTE and Wi-Fi, we propose a hybrid MAC protocol that combines the best features of LBE MAC and Frame Based Equipment (FBE) MAC. A two-level renewal process-based model is also developed to analyze the throughput performance of the proposed MAC. By jointly optimizing the sleep period and the contention window size of U-LTE, the best co-existing performance in terms of the total network throughput and throughput fairness of U-LTE and Wi-Fi can be achieved, with minimal reservation overhead. Extensive simulations using NS-3 validate the analysis and demonstrate the efficiency of the proposed MAC protocol.

With the explosive growth of mobile applications and high computation burden on each single device, more and more end users demand to offload expensive computing tasks to external sites via job offloading technologies. Due to the fluctuating nature of jobs from end users, traditional cloud computing paradigm, however, has difficulties in accommodating highly dynamic job requests and meeting heterogeneous user requirements. Locating close to mobile users, edge-clouds have the potential to complement the cloud computing platform by acting as an efficient spot to perform users' deadline-sensitive tasks. In this paper, we study the resource allocation problem for accommodating deadline-sensitive jobs in edge-cloud system. We formulate a revenue maximization problem that captures the SLA-oriented property of job execution, and propose an efficient online multi-resource allocation algorithm that achieves low competitive ratio with moderate resource augmentation.

With the explosive increase of mobile traffic in recent years, cellular networks face enormous challenges in high quality of service (QoS) provisioning for mobile users. Mobile data offloading is a promising way to address this issue, through which a cellular system can reduce its traffic burden by offloading some portion of data to other networks, such as Wi-Fi. However, when the Wi-Fi networks are deployed by different operators, an efficient incentive mechanism is needed to encourage the participation of these networks. However, most of the existing studies on the incentive mechanism design focus on the amount of data offloading from the cellular network, rather than the diverse data patterns and features of different applications. In this paper, we propose a QoS-based incentive mechanism, termed QBIM, to promote the cooperation of multiple offloading networks while achieving high QoS level for mobile users with different applications. Through this mechanism, the cellular network chooses the Wi-Fi access points to offload data traffic of mobile users by jointly considering access point operators' bid vectors and the mobile user utilities of different services. Meanwhile, the corresponding payments are designed as the compensation to the involved Wi-Fi systems. The proposed incentive mechanism can not only achieve the maximum social welfare, but satisfy desirable properties of individual rationality and truthfulness as well. Simulation results show that the proposed mechanism achieves a higher utility with a smaller cost compared to the other counterparts.

In this paper, we investigate joint beamforming for energy-throughput tradeoff in a sustainable cloud radio access network system, where multiple base stations (BSs) powered by independent renewable energy sources will collaboratively transmit wireless information and energy to the data receiver and the energy receiver simultaneously. In order to obtain the optimal joint beamforming design over a finite time horizon, we formulate an optimization problem to maximize the throughput of the data receiver while guaranteeing sufficient RF charged energy of the energy receiver. Although such problem is non-convex, it can be relaxed into a convex form and upper bounded by the optimal value of the relaxed problem. We further prove tightness of the upper bound by showing the optimal solution to the relaxed problem is rank one. Motivated by the optimal solution, an efficient online algorithm is also proposed for practical implementation. Finally, extensive simulations are performed to verify the superiority of the proposed joint beamforming strategy to other beamforming designs.

In this paper, an analytical model is developed to study the performance of distributed and opportunistic multichannel bonding in IEEE 802.11ac WLANs, with co-existing legacy IEEE 802.11a/b/g users. By modeling the transmissions of legacy users and ac users with and without channel bonding in each channel as a two-level renewal process, the channel bonding probability of ac users in each secondary channel is derived. Based on the bonding probability, the throughput of legacy users and ac users can be analyzed respectively. Our analysis shows that ac users with bonding capabilities achieve higher throughput at the cost of degraded throughput of legacy users. The overall network throughput also decreases due to the increased contention level imposed by ac users in secondary channels. Based on the analysis, we further propose a channel selection scheme for ac users to select the best primary channel, in order to mitigate the contentions in the network and attain the maximal network throughput. Extensive simulations using NS-3 validate the analysis and demonstrate the efficiency of the proposed channel selection scheme.

With the development of wireless networks, the scale of network optimization problems is growing correspondingly. While algorithms have been designed to reduce complexity in solving these problems under given size, the approach of directly reducing the size of problem has not received much attention. This motivates us to investigate an innovative approach to reduce problem scale while maintaining the optimality of solution. Through analysis on the optimization solutions, we discover that part of the elements may not be involved in the solution, such as unscheduled links in the flow constrained optimization problem. The observation indicates that it is possible to reduce problem scale without affecting the solution by excluding the unused links from problem formulation. In order to identify the link usage before solving the problem, we exploit deep learning to find the latent relationship between flow information and link usage in optimal solution. Based on this, we further predict whether a link will be scheduled through link evaluation and eliminate unused link from formulation to reduce problem size. Numerical results demonstrate that the proposed method can reduce computation cost by at least 50% without affecting optimality, thus greatly improve the efficiency of solving large scale network optimization problems.

Fog computing is a promising architectural pattern to reduce the amount of data that is transferred to the cloud for processing and analysis. In this paper, we study fog-assisted data streaming scenario in which fog nodes at the network edge share their spare resources to help pre-process raw data of applications hosted in the cloud. A distributed resource sharing scheme is presented where the software defined network (SDN) controller dynamically adjusts the volume of application data that will be directed to fog nodes for pre-processing. The SDN controller makes decisions by coordinating fog nodes and cloud platform to collaboratively solve a social welfare maximization problem. Based on a hybrid alternating direction method of multipliers (H-ADMM) algorithm, computation burden for solving the optimization problem is fully distributed to fog nodes, cloud platform and SDN controller, where local variables of fog nodes are updated in parallel. With proper design of message exchange pattern, the communication overhead of the coordination to SDN controller grows smoothly with increasing number of participating fog nodes.

An analytical model is developed to evaluate the network performance of an IEEE 802.11ac Wireless Local Area Network (WLAN) in support of delay sensitive video services over multiple channels. Specifically, the channel bonding probability and the channel access delay of wireless users are analyzed, considering the contentions among legacy and ac users in the same channel and across multiple channels. Based on the analysis, the network capacity region, i.e., the maximum number of traffic flows can be supported with the bounded delay performance in a multi-channel WLAN with and without channel bonding, is then derived. Our analysis shows that channel bonding can greatly improve the network capacity when the channel is under-utilized with a small number of legacy users co-existing with the ac users; yet channel bonding is not always favorable and it may degrade the network capacity when the number of legacy users increases due to the increased contentions in the network. The analysis provides important guidance for effective admission control and channel bonding strategies to guarantee the bonded service delay of realtime applications. Extensive simulations validate the analysis.

LTE over unlicensed spectrum (LTE-U) is one of the promising approaches to further improve LTE network throughput. To maximize the benefit of LTE-U, in this work we study joint resource allocation for LTE over the legacy licensed spectrum and the sharing unlicensed spectrum in a multi-cell scenario. Specifically, we formulate a mixed-integer power-channel allocation problem aiming at maximizing the network throughput, with the constraints of protecting the coexisting Wi-Fi networks and hardware limitation of user equipments in the LTE-U networks. To solve the resource allocation problem efficiently, we exploit delay column generation approach to decompose the original optimization problem and then propose a novel algorithm based KKT conditions. Simulation results show the advantage of LTE-U networking and the effectiveness of the proposed algorithm in terms of convergence speed and network throughput.

In this paper, we consider a fully sustainable cooperative communication system which consists of multiple source nodes with radio-frequency (RF) energy harvesting capabilities, a half-duplex relay node with renewable energy supplies, and a destination node. Specifically, the relay node is powered by the green energy harvested from renewable sources such as solar or wind, while the source nodes are wirelessly charged by the RF energy from the relay node's forwarding signals to the destination node. An optimal joint time scheduling and power allocation problem is formulated to achieve the maximum system sum-throughput of the users over a finite time horizon. To tackle the formulated NP-hard non-convex mixed integer nonlinear programming problem, we first analyze its upper bound by problem reformulation and relaxation, which can be simplified by the directional water filling algorithm and iteratively solved by sequential parametric convex approximation. We then propose an optimal branch-and-bound framework to solve the formulated problem, and develop an efficient sub-optimal offline algorithm and a heuristic online algorithm to reduce the computational complexity. Finally, extensive simulations are conducted to verify the superiority of the proposed solution and demonstrate that the sub-optimal algorithm approaches the performance upper bound with polynomial time complexity.

LTE-Unlicensed has recently captured intense attention from both academic and industrial fields. By integrating the unlicensed spectrum with the licensed spectrum, using carrier aggregation, LTE-Unlicensed users can experience enhanced transmission while maintaining the seamless mobility management and predictable performance. However, due to different transmission regulations, the coordination between LTE and Wi-Fi systems requires careful design. It is especially important to understand how to guarantee the transmission quality for LTE users and reduce Wi-Fi users' performance degradation, under the impact of the co-channel interference. In other words, how can we solve the unlicensed resource allocation problem under both LTE and Wi-Fi transmission requirements? In this paper, we propose a matching theory framework to tackle this problem. Specifically, the coexistence between LTE and Wi-Fi systems, i.e., the interaction between LTE and Wi-Fi users, is modeled as a stable marriage game. The coexistence constraints are interpreted as the preference lists. Two semi-distributed solutions, namely, the Gale-Shapley and the random path to stability algorithms are proposed. In addition, to address the external effect in matching, the inter-channel cooperation algorithm is introduced. Last but not least, the resource allocation problem is studied with network dynamics and the proposed mechanisms are evaluated under two typical user mobility models.

In this paper, we study a buffer-aided collaborative relaying framework for cooperative communication system composed of one source node, multiple half-duplex DF relays with buffers, and one destination node. A two-phase adaptive relaying scheme is proposed,i.e., the source transmits data and the relay buffers receive data in the first phase, and all relays collaboratively transmit the buffered data to the destination node in the second phase. To achieve higher temporal and spatial diversity gains, time slots are dynamically allocated according to the state information of wireless channel (CSI), each node’s energy consumption (ESI), and each relay’s buffer (BSI). Lyapunov optimization theory is utilized to maximize the average achievable throughput under buffer stability and power consumption constraints, and an online buffer-energy-aware adaptive (BEAA) scheduling scheme is proposed to jointly consider relay selection, power allocation, and time allocation. It is disclosed that the proposed BEAA scheduling scheme is able to achieve a higher average network throughput by adapting the transmissions according to the CSI, ESI, and BSI. Moreover, it is unveiled that there exists inherent tradeoff among the transmission delay, power consumption, and the achievable throughput. Extensive simulations are presented to validate the efficiency of the proposed adaptive collaborative relaying protocol.

It is known that the capacity of the cellular network can be significantly improved when cellular operators are allowed to access the unlicensed spectrum. Nevertheless, when multiple operators serve their user equipments (UEs) in the same unlicensed spectrum, the inter-operator interference management becomes a challenging task. In this paper, we develop a multi-operator multi-UE Stackelberg game to analyze the interaction between multiple operators and the UEs subscribed to the services of the operators in unlicensed spectrum. In this game, to avoid intolerable interference to the Wi-Fi access point (WAP), each operator sets an interference penalty price for each UE that causes interference to the WAP, and the UEs can choose their sub-bands and determine the optimal transmit power in the chosen sub-bands of the unlicensed spectrum. Accordingly, the operators can predict the possible actions of the UEs and hence set the optimal prices to maximize its revenue earned from UEs. Furthermore, we consider two possible scenarios for the interaction of operators in the unlicensed spectrum. In the first scenario, referred to as the non-cooperative scenario, the operators cannot coordinate with each other in the unlicensed spectrum. A sub-gradient approach is applied for each operator to decide its best-response action based on the possible behaviors of others. In the second scenario, referred to as the cooperative scenario, all operators can coordinate with each other to serve UEs and control the UEs' interference in the unlicensed spectrum. Simulation results have been presented to verify the performance improvement that can be achieved by our proposed schemes.

The study item: "Feasibility Study on LTE-based V2X Services", approved at 3GPP TSG RAN #68, has aroused the interest in the study of LTE assisted vehicle-to-vehicle (V2V) and vehicle-to- infrastructure (V2I) communications in the networks of connected vehicles. By deploying the direct device-to-device (D2D) technology of traditional cellular networks into the V2X (including both V2V and V2I) communications, performance improvements can be expected, such as better reliability, lower latency, and more efficient content sharing. This paper investigates the content sharing problem in the D2D based V2X communication networks. With both vehicles and eNBs carrying multiple different classes of data, this work studies how to optimize the information exchanged within the network, and in the mean time to guarantee the system quality of service (QoS) requirements. By jointly considering the data diversity and link quality, the interactions between vehicles/eNBs, or in other words, the V2V and V2I link scheduling, is modeled as the stable fixture (SF) matching game. Different from traditional D2D communications, where each node is limited to one link, we allow multiple V2X connections for each vehicle to further optimize the content sharing. More specifically, the formations of such V2X links are independent from each other, thus more flexible than the conventional clustering formation in the Vehicular ad hoc networks (VANETs). The SF game is solved by the proposed Irving's stable fixture (ISF) algorithm. Its advantages over some heuristics are demonstrated through simulation experiments.

In this paper, an analytical framework is developed to study the performance of opportunistic channel bonding in IEEE 802.11 WLANs. Specifically, we consider a WLAN operating on multiple channels shared by both legacy users and IEEE 802.11ac users with channel bonding capability. By capturing the opportunistic channel bonding from the IEEE 802.11ac users in the primary channel and the random access of legacy users in the secondary channels, we derive the successful channel bonding probability, and the throughput of both legacy and IEEE 802.11ac users. Our analysis shows that with multi-channel bonding, ac users achieves a higher throughput at the cost of reduced throughput of legacy users in the secondary channels. The channel bonding achieves a higher total network throughput only when there is no legacy user in the secondary channels, and the total throughput decreases when legacy users exist due to the increased contentions in the secondary channels. The analysis provides important guidance for the deployment of multi-channel WLANs where ac users should select a proper primary channel to maximize its bonding opportunity and to attain the maximum throughput. Extensive simulations are conducted to validate the analysis.

This paper studies the charging problem of plug-in hybrid electric vehicles (PHEVs) with a game theoretic approach. The interplay between PHEVs and smart micro-grid charging stations are modeled as a multi-leader-multi-flower Stackelberg game. In the game, each PHEV needs to select a charging station for maximum utility given the charging prices from each station; and each charging station needs to adjust its charging price for improved utility based on the charging requests received. An important issue being considered in this paper is that the actual cost for charging into a target energy level depends on the travel distance and traffic conditions between the requesting PHEV and the finally selected charging station. In this paper, we adopt a location aware approach to explicitly incorporate the location- related cost into the utility function in the game model. Note that the distance information, traffic information along the roads, and communications between PHEVs and charging stations are to be obtained or enabled by vehicular ad hoc networks (VANET). We develop the algorithms for charging station selection and price adjustment. Simulation results are presented to demonstrate that the utility improvement of the location-aware model over the location-blind model.

In this paper, we investigate the sustainable resource allocation for green Cloud Radio Access Networks (C-RAN) powered by renewable energy. Specifically, the Base Station pool (BS pool) in the C-RAN distributes data to a set of remote radio heads (RRHs) with energy harvesting (EH) capability, and allocates sub-carriers to the selected RRHs for downlink transmissions, by jointly considering the user throughput and energy sustainability performance of RRHs. To this end, we formulate a utility optimization problem, characterizing the stochastic process of energy harvesting (EH) and wireless fading channel. Based on Lyapunov optimization techniques, we decompose the formulated problem into three sub-problems, including energy harvesting, data scheduling, and sub-carrier allocation. We then propose an efficient online algorithm to obtain the maximal aggregate user utility while ensuring the stability of the data buffers and sustainability of the energy buffers. Performance analysis demonstrates that the proposed algorithm can achieve a suboptimal performance with guaranteed upper bounds on data queue and energy queue lengths. Extensive simulations validate the effectiveness and efficiency of the proposed algorithm.

The proliferation of smart phones enables ubiquitous Mobile Ad-hoc Networks (MANETs) where mobile devices communicate with peers over a wireless channel in an ad hoc mode. In this paper, we introduce a novel method to achieve multi-hop communication among open-source, non-rooted Android devices using Wi-Fi Direct Technology, also known as Wi-Fi Peer-to-Peer (P2P). Then we implement a proactive routing protocol in an MANET using multiple off-the-shelf smart phones to enable efficient message delivery over a multi-hop MANET.

As wireless networks are gaining increasing popularity, the network energy efficiency has become a critical issue. In this paper, we focus on energy-efficient networking in a generic multi-radio multi-channel (MR-MC) wireless network where transmission scheduling, transmit power control, radio and channel assignment are coupled together in a multi-dimensional resource space, thus requiring joint optimization and low complexity algorithms. We propose a novel Decomposed Approach For energy-efficient (DAFEE) networking in MR-MC networks, with the objective to minimize network energy consumption while guaranteeing a certain level of performance. In particular, we leverage a multi-dimensional tuple-link based model and a concept of resource allocation pattern to transform the complex optimization problem into a linear programming (LP) problem. The LP problem however has a very large solution space due to the exponentially many possible resource allocation patterns. We then exploit delay column generation and distributed learning techniques to decompose the problem and solve it with an iterative process. Furthermore, we propose a sub-optimal algorithm to speed up the iteration with constant-bounded performance. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.

In this paper, we propose an energy-efficient location-aware clone detection protocol in densely deployed WSNs, which can guarantee successful clone attack detection and maintain satisfactory network lifetime. Specifically, we exploit the location information of sensors and randomly select witnesses located in a ring area to verify the legitimacy of sensors and to report detected clone attacks. The ring structure facilitates energy-efficient data forwarding along the path towards the witnesses and the sink. We theoretically prove that the proposed protocol can achieve$100$percent clone detection probability with trustful witnesses. We further extend the work by studying the clone detection performance with untrustful witnesses and show that the clone detection probability still approaches$98$percent when$10$percent of witnesses are compromised. Moreover, in most existing clone detection protocols with random witness selection scheme, the required buffer storage of sensors is usually dependent on the node density, i.e.,$O(\sqrt{n})$, while in our proposed protocol, the required buffer storage of sensors is independent of$n$but a function of the hop length of the network radius$h$, i.e.,$O(h)$. Extensive simulations demonstrate that our proposed protocol can achieve long network lifetime by effectively distributing the traffic load across the network.

In IEEE 802.11-based networks, it is well-known that selfish nodes can gain significant performance advantage over other normal nodes by manipulating the medium access control (MAC) protocol parameters. There have been many studies on the detection of such misbehavior, though most of them focus on centralized detection with the assistant of an access point in wireless local-area networks (WLANs). In ad hoc networks, due to the complexity introduced by the hidden terminal issues, many existing misbehavior detection schemes rely on the RTS/CTS (request-to-send/clear-to-send) messages to infer the details of MAC layer behavior of the monitored nodes. However, for networks without the RTS/CTS mechanism (e.g., using the basic access mode), those schemes become inapplicable. In this paper, we propose a novel two-step misbehavior detector for IEEE 802.11-based ad hoc networks, based on observations of successful transmissions and channel conditions, which does not require the RTS/CTS messages. The main idea is to check whether the measured performance matches the model-based expectations, in which neighbors exchange data and cooperate to make detection decisions. Our detector sets two barriers to catch the misbehaving nodes: the first step utilizes neighbor- broadcasted information to establish the relationship between channel availability and transmission rate and checks if the relationship matches the theoretical model, while the second step checks whether the model-based throughput meets the observations. We demonstrate the effectiveness of the our detector through simulations.

LTE, as the advanced mobile telecommunication technology, is serving heavy mobile broadband traffic nowadays. Motivated by the potential boost in performance of LTE utilizing the unlicensed spectrum, significant efforts have been devoted into the commonly referred LTE-Unlicensed technique. In this work, we investigate the carrier aggregation of licensed and unlicensed spectrum by deploying micro-cell base stations, which have access to the unlicensed spectrum, to provide cellular users a more reliable and efficient transmission. We tackle the unlicensed resource allocation problem by modeling it as a student-project allocation matching game. In addition, a postmatching procedure of resource re- allocation is introduced to guarantee unlicensed users' quality of service (QoS), as well as the system-wide stability. The simulation evaluation shows the effectiveness and efficiency of our proposed matching-based approach.

The multimedia communication technologies have been widely used in the anti-money laundering (AML) field to improve the efficiency and security of the business transactions. To reduce the cost of massive multimedia processing and communications, it is desirable to allow multiple financial industries (FIs) to share the AML resources, including human, computation, communication, and storage resources, and cooperate with each other to complete the transaction tasks. In this paper, the optimal AML resource management among peer FIs towards the maximal AML rewards is studied. Specifically, an AML resource allocation model (AMLRAM) based on semi-Markov decision process(SMDP) is proposed, where the system state is represented by a tuple, i.e., the number of High-Risk Operation (HRO), the number of Low or Moderate Risk Operation (L/MRO), and the current event type (i.e., the arrival of HRO or L/MRO suspicious transaction report which needs to be further checked, and the departure of HRO or L/MRO suspicious transaction report which has been checked and releases the occupied AML resource) in the AML field. The maximal long-term rewards of the system is derived, and the optimal AML resource allocation decision among peer FIs is made to achieve the maximal system rewards. Extensive simulations validate our analysis.

In this paper, a new MAC protocol for LTE over unlicensed spectrum (LTE-U) is presented that allows friendly co-existence of LTE-U with other unlicensed wireless networks, including Wi-Fi. Specifically, in a time-slotted LTE-U system, LTE- U users can transmit continuously for a period after a successful channel reservation during the spectrum sensing period. Following each LTE transmission period, a certain duration is reserved for asynchronous Wi-Fi transmissions. By adaptively adjusting the periods of LTE transmissions, Wi-Fi transmissions, and spectrum sensing, different levels of Wi-Fi protection can be achieved. Based on the proposed MAC, an analytical model is developed to study the throughput performance of both LTE-U and Wi-Fi, considering the asynchronous transmission nature of Wi-Fi within the time-slotted MAC structure. Impacts of the protocol parameters, i.e., the periods of LTE/Wi-Fi transmissions and spectrum sensing, on the throughput performance of LTE-U and Wi-Fi are also investigated. Extensive simulation results are provided to validate the analysis.

Allowing cellular operators to offload data traffic to unlicensed spectrum has the potential to significantly increase the capacity of the cellular network systems. This paper considers the spectrum sharing among multiple cellular operators in the unlicensed spectrum. One of the main challenges for this system is how to control the interference between the cellular users and the unlicensed users in other networks, e.g., Wi-Fi, and the interference among cellular users of different operators. As such, we develop a hierarchical game where there is a Kalai-Smorodinsky bargaining game among leaders and a Stackelberg game between operators and mobile users (MU). Accordingly, multiple operators can negotiate with each other for the revenue obtained from the unlicensed spectrum and use a pricing mechanism to control the interference caused by each MU to other operators and users in other unlicensed networks. Simulation results show that our proposed strategy significantly increases the revenue and utility for both operators and MUs.

Exploiting multiple radio interfaces over multiple channels and using directional antennas are promising technologies to enhance the performance of wireless networks. However, in such a multi-dimensional network resource space, assignment of radios and channels and configuration of antenna directions are coupled, making the complexity for network capacity optimization dramatically increase. Existing work has considered either multi-radio multi-channel (MRMC) networks or networks with directional antennas (DA); however, there lacks a generic framework for such complex MRMC-DA wireless networks. In this paper, we employ the tuple concept to define Link- Radio-Antenna-Channel tuple links, which are then utilized as building blocks to construct a multi-dimensional conflict graph (MDCG) of the MRMC-DA network. The MDCG model facilitates mapping the original MRMC-DA network into a simple virtual single-radio single-channel network, on which the capacity optimization problem can be formulated as a linear program. To circumvent searching the exponentially many independent sets, we apply the delayed column generation method to design our algorithm. Simulations demonstrate the performance of the proposed method and analyze the different effects of the numbers of channels, radios and antenna choices.

Green energy sources, such as solar and wind, provide an alternative solution for powering wireless networks. To maximize the utilization of green energy charged from different sources, it is desirable to allow energy trade among neighbor cells. In this paper, the local energy trade issues in a wireless mesh network powered by green energy are studied such that energy can be purchased either from neighbor cells or from electricity grid, based on the energy charging and discharging characteristics in each cell. Our objective is to determine the optimal price and quantity of energy purchase and sale for each cell such that the profits of all cells can be maximized and their energy demands can be fulfilled. To this end, the energy trading problem is formulated as a Stackelberg game. Based on the utility function, the closed-form expressions of the optimal energy quantity and price for trading are derived. Finally, an optimal scheme, namely, Optimal Profits Energy Trading (OPET), is proposed to maximize the profits of all cells. The proposed OPET can achieve the optimal solution with polynomial time complexity. Extensive simulations are conducted to verify the performance of the proposed scheme.

There is a growing interest in the use of renewable energy sources to power wireless networks in order to mitigate the detrimental effects of conventional energy production or to enable deployment in off-grid locations. However, renewable energy sources, such as solar and wind, are by nature unstable in their availability and capacity. The dynamics of energy supply hence impose new challenges for network planning and resource management. In this paper, the sustainable performance of a wireless mesh network powered by renewable energy sources is studied. To address the intermittently available capacity of the energy supply, adaptive resource management and admission control schemes are proposed. Specifically, the goal is to maximize the energy sustainability of the network, or equivalently, to minimize the failure probability that the mesh access points (APs) deplete their energy and go out of service due to the unreliable energy supply. To this end, the energy buffer of a mesh AP is modeled as a G/G/1(/N) queue with arbitrary patterns of energy charging and discharging. Diffusion approximation is applied to analyze the transient evolution of the queue length and the energy depletion duration. Based on the analysis, an adaptive resource management scheme is proposed to balance traffic loads across the mesh network according to the energy adequacy at different mesh APs. A distributed admission control strategy to guarantee high resource utilization and to improve energy sustainability is presented. By considering the first and second order statistics of the energy charging and discharging processes at each mesh AP, it is demonstrated that the proposed schemes outperform some existing state-of-the-art solutions.

Wireless sensor networks (WSNs) play an increasing role in a wide variety of applications ranging from hostile environment monitoring to telemedicine services. The hardware and cost constraints of sensor nodes, however, make sensors prone to clone attacks and pose great challenges in the design and deployment of an energy-efficient WSN. In this paper, we propose a location-aware clone detection protocol, which guarantees successful clone attack detection and has little negative impact on the network lifetime. Specifically, we utilize the location information of sensors and randomly select witness nodes located in a ring area to verify the privacy of sensors and to detect clone attacks. The ring structure facilitates energy efficient data forwarding along the path towards the witnesses and the sink, and the traffic load is distributed across the network, which improves the network lifetime significantly. Theoretical analysis and simulation results demonstrate that the proposed protocol can approach 100% clone detection probability with trustful witnesses. We further extend the work by studying the clone detection performance with untrustful witnesses and show that the clone detection probability still approaches 98% when 10% of witnesses are compromised. Moreover, our proposed protocol can significantly improve the network lifetime, compared with the existing approach.

In this paper, a concurrent transmission scheduling algorithm is proposed to enhance the resource utilization efficiency for multi-Gbps millimeter-wave (mmWave) networks. Specifically, we exploit spatial-time division multiple access (STDMA) to improve the system throughput by allowing both non-interfering and interfering links to transmit concurrently, considering the high propagation loss at mmWave band and the utilization of directional antenna. Concurrent transmission scheduling in mmWave networks is formulated as an optimization model to maximize the number of flows scheduled in the network such that the quality of service (QoS) requirement of each flow is satisfied. We further decompose the optimization problem and propose a flip-based heuristic scheduling algorithm with low computational complexity to solve the problem. Extensive simulations demonstrate that the proposed algorithm can significantly improve the network performance in terms of network throughput and the number of supported flows.

In vehicular ad hoc networks (VANETs), improving uploading efficiency is crucial to enabling the copious applications such as reporting sensed data for traffic management or environment monitoring. Depending on the applications, the contents to be uploaded can be of large volumes. Therefore, there exist the fundamental demands of the delivery with high throughput. In this paper, we derive the achievable throughput capacity scaling law for such applications in VANETs as Θ(1/log n), with the number of road-side units scaling as Θ(n/log n). Furthermore, by exploring the mobility diversity among vehicles, we propose a novel two-hop forwarding scheme to improve the throughput performance approaching the throughput capacity. Specifically, the source vehicle distributes the contents to multiple relay vehicles with the largest mobility diversity so that the number of concurrent transmissions can be increased. The simulation results demonstrate the effectiveness of the proposed transmission scheme in terms of the increased throughput performance.

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

With Internet environment is getting optimized and users preferring mobile communications, Cloud Service Providers (CSP) aim to provide services to users depending on their geographic locations with higher service availability and faster access speed. Mobile cloud computing falls into this category, where mobile users can move around and request cloud services at any given geographic locations. To build such a geographic-based mobile cloud services, an effective mobile cloud resource allocation and service request scheduling scheme is highly desired. To this end, the presented service request scheduling scheme takes a comprehensive approach by considering system parameters from both CSP and mobile users such as computation, energy, connectivity, service payment, mobile users' satisfaction, etc. Finally, the performance evaluation of the proposed scheduling scheme is evaluated through simulations where the results show that the presented scheme achieves better system overall gain compared to traditional over-provisioning approaches.

In this paper, cognitive routing coupled with spectrum sensing and sharing in a multi-channel multi-hop cognitive radio network (CRN) is investigated. Recognizing the spectrum dynamics in CRN, we propose an opportunistic cognitive routing (OCR) protocol that allows users to exploit the geographic location information and discover the local spectrum access opportunities to improve the transmission performance over each hop. Specifically, based on location information and channel usage statistics, a secondary user (SU) distributedly selects the next hop relay and adapts its transmission to the dynamic spectrum access opportunities in its neighborhood. In addition, we introduce a novel metric, namely, cognitive transport throughput (CTT), to capture the unique properties of CRN and evaluate the potential relay gain of each relay candidate. A heuristic algorithm is proposed to reduce the searching complexity of the optimal selection of channel and relay. Simulation results are given to demonstrate that our proposed OCR well adapts to the spectrum dynamics and outperforms existing routing protocols in CRN.

Green energy is emerging as a promising alternative energy source to power network devices in next-generation wireless networks. Different from traditional energy, green energy is replenished from nature, e.g., solar and wind, and is highly dependent on the capacities and locations of the electronic devices. As such, the fundamental design criterion in the network deployment and management is shifted from energy efficiency to energy sustainability due to the sustainable nature of green energy. In this paper, we study the network resource management issues in next-generation wireless networks with sustainable energy supply. Our objective is to deploy the minimal number of green RNs, i.e., RNs powered by green energy, and optimize resource allocation to ensure full network connectivity and users' Quality of Service (QoS) requirements can be fulfilled with the harvested energy based on the cost threshold. To this end, the RN placement and sub-carrier allocation (RNP-SA) issues are jointly formulated into a mixed integer non-linear programming problem. Two low-complexity heuristic algorithms, namely RNP-SA with top-down/bottom-up algorithms (RNP-SA-t/b), are presented to solve the non-linear programming problem in different network scenarios. Extensive simulations show that the proposed algorithms provide simple yet efficient solutions and offer important guidelines on network deployment and resource management in a green radio network with sustainable energy sources.

Next generation communication networks are anticipated to make use of renewable energy sources, e.g., solar and wind power, to reduce carbon footprints and achieve an environmentally sustainable system. However, renewable energy sources have the limitation of unstable availability and capacity, which introduces new challenges for network planning and resource management. In this paper, adaptive resource management is introduced for wireless mesh networks that are powered by sustainable energy sources. The objective is to address the unreliability of the energy supply and to maximize the energy sustainability of the network, or equivalently, minimize the probability that mesh access points (APs) deplete their energy and go out of service. Specifically, the energy buffer of a mesh AP is modeled as a G/G/1 queue and a diffusion approximation is applied to analyze the transient evolution of the queue length and energy depletion duration. Based on the analysis, a resource management scheme is proposed to adaptively distribute traffic over various relay paths across the network and a distributed admission control strategy is applied to further guarantee high resource utilization under the energy sustainability constraint. By considering the first and second order statistics of the energy charging and discharging processes, it is demonstrated that the proposed scheme outperforms some existing state-of- the-art solutions.

The performance of opportunistic forwarding in the multi-hop cognitive radio networks (CRN) is dependent on the efficient detection of spectrum opportunities and the diversity of relay links. To find appropriate spectrum opportunities, effective spectrum sensing is essential in a CRN, especially under highly dynamic channel conditions. In this paper, a novel channel metric is proposed to determine the sensing sequence at the node based on its history channel observations. This metric is then applied in the design of node residence strategy for relay nodes over the channels. Each node distributively calculates the metric and determines the sensing and residence channel to exploit better spectrum opportunities for packet forwarding. By allowing relay nodes to aggregate in the channel with better transmission opportunity, a larger multi-user diversity gain can be exploited and a lower access delay can be achieved. Simulation results demonstrate that the proposed approach well adapts to the channel dynamics and outperforms other solutions, especially in a CRN with low node density.

It is anticipated that future wireless networks will make use of more renewable energy sources, e.g., solar, wind, and hydro, etc., in order to sustain the ever-growing traffic demands, while mitigating the effects of increased energy consumption. The most critical issue of developing a sustainable communications network is how to cost-effectively deploy access points (APs) with sustainable energy supplies and allocate network resources to meet the quality of service (QoS) requirements of users. In this paper, the traditional AP placement problem is revisited with sustainable power supplies. First, a constrained AP placement optimization problem is formulated. The objective is to determine the optimal placement of APs on a set of candidate locations such that the number of APs is minimized, subject to the constraints that QoS requirements of users can be fulfilled with the harvested energy. To further improve the sustainable network performance, joint power control and rate adaptation at APs is considered, based on different user demands and charging capabilities of the APs. After that, an efficient heuristic algorithm with polynomial time complexity is proposed. Extensive simulation results show that the proposed algorithm approaches the optimal solution under a variety of network settings with significantly reduced time complexity.

In this paper, we exploit the heterogeneity of wireless channels and propose an efficient opportunistic cognitive routing (OCR) scheme for dynamic spectrum access (DSA) networks. We first introduce a novel routing metric by jointly considering physical characteristics of spectrum bands and diverse activities of primary users (PU) in each band. To effectively explore the spectrum opportunities, a proper channel sensing sequence for fast and reliable message delivery is determined by secondary users (SU) in a distributed way. We then develop a greedy forwarding scheme that SUs can select the next hop relay based on the geometry information and channel access opportunity of their one hop neighbors. For the proposed OCR, as routing control messages are locally exchanged, SUs can efficiently make the routing decision and opportunistically access the available channels. We further evaluate the performance of OCR via extensive simulations. It is shown that our proposed scheme outperforms existing opportunistic routing schemes in DSA networks by exploiting the heterogeneity of spectrum bands for opportunistic channel access.

The copious social and user generated contents, like Facebook and Youtube, are re-shaping the way people share, access, and digest information. Although flourishing in Internet, content sharing services are still considered expensive and not ready for mobile users of vehicular networks. In this paper, we propose VTube, an autonomous and cost-effective infrastructure, to facilitate the localized content publish/subscribe in an urban area. VTube relies on the distributed low-cost light-weight storage buffers, namely roadside buffer, installed in the city facilities, such as stores, museums, cafeteria, etc., to cache and publish contents for mobile users. The contents at different storage buffers are then transported to different locations by moving vehicles and cached collaboratively in both vehicles and storage buffers across the city. In this work, we unfold the design of VTube by first presenting the detailed design principles and practices of VTube. Given the content availability and capacity of the buffer storage, we then develop a mathematical model to evaluate the mean download delay of mobile users. Using the delay as an input, we formulate the content replication problem in roadside buffers as a stochastic programming problem to attain the mean system-wide minimum download delay. Finally, we propose a fully distributed random walk based algorithm to solve the optimization problem. Extensive simulations demonstrate that VTube can minimize the download delay of users because of the exploitation of vehicle mobility and distributed buffer storage at different locations.

Millimeter-wave (mmWave) communications is a promising enabling technology for high rate (Giga-bit) multimedia applications. However, because of the high propagation loss at 60 GHz band, mmWave signal power degrades significantly over distance. Therefore, a traffic flow being transmitted over multiple short hops can attain higher throughput than that over a single long hop. In this paper, we first design a hop selection metric for the piconet controller (PNC) to select appropriate relay hops for a traffic flow, aiming to improve the flow throughput and balance the traffic loads across the network. We then propose a multi-hop concurrent transmission (MHCT) scheme to exploit the spatial capacity of mmWave WPANs by allowing nodes to transmit concurrently in communication links without causing harmful interference. The analysis of concurrent transmission probability and time division multiplexing demonstrates that the MHCT scheme is capable of improving the time slot utilization. Extensive simulations are conducted to validate the analytical results and demonstrate that the proposed MHCT scheme can improve the average traffic flow throughput and network throughput.

We propose a distributed quality of service (QoS)-aware MAC protocol for multi-channel cognitive radio networks supporting multimedia applications. Specifically, based on the channel usage patterns of primary users (PUs), secondary users (SUs) determine a set of channels for channel sensing and data transmissions to satisfy their QoS requirements. We further enhance the QoS provisioning of the proposed cognitive MAC by applying differentiated arbitrary sensing periods for various types of traffic. An analytical model is developed to study the performance of the proposed MAC, taking the activities of both PUs and SUs into consideration. Extensive simulations validate our analysis and demonstrate that our proposed MAC can achieve multiple levels of QoS provisioning for various types of multimedia applications in cognitive radio networks.

In order to achieve cooperative driving in vehicular ad hoc networks (VANET), broadcast transmission is usually used for disseminating safety-related information among vehicles. Nevertheless, broadcast over multihop wireless networks poses many challenges due to link unreliability, hidden terminal, message redundancy, and broadcast storm, etc., which greatly degrade the network performance. In this paper, we propose a cross layer broadcast protocol (CLBP) for multihop emergency message dissemination in inter-vehicle communication systems. We first design a novel composite relaying metric for relaying node selection, by jointly considering the geographical locations, physical layer channel conditions, moving velocities of vehicles. Based on the designed metric, we then propose a distributed relay selection scheme to guarantee that a unique relay is selected to reliably forward the emergency message in the desired propagation direction.We further apply IEEE802.11e EDCA to guarantee QoS performance of safety related services. Finally, simulation results are given to demonstrate that CLBP can not only minimize the broadcast message redundancy, but also quickly and reliably disseminate emergency messages in a VANET.

Millimeter-wave (mmWave) communications is a promising enabling technology for high rate (Giga-bit) multimedia applications. However, because oxygen absorption peaks at 60 GHz, mmWave signal power degrades significantly over distance. Therefore, a traffic flow transmitting over multiple short hops is preferred to improve flow throughput. In this paper, we first design a hop selection metric for the piconet controller (PNC) to select appropriate relay hops for a traffic flow, aiming to improve the flow throughput and balance the traffic load across the network. We then propose a multi-hop concurrent transmission (MHCT) scheme to exploit the spatial capacity of the mmWave WPAN. Extensive simulations show that the proposed MHCT scheme can significantly improve the traffic flow throughput and network throughput.

We develop an analytical framework to investigate the impacts of network dynamics on the user perceived video quality. Our investigation stands from the end user's perspective by analyzing the receiver playout buffer. In specific, we model the playback buffer at the receiver by a$G/G/1/\infty$and$G/G/1/N$queue, respectively, with arbitrary patterns of packet arrival and playback. We then examine the transient queue length of the buffer using the diffusion approximation. We obtain the closed-form expressions of the video quality in terms of the start-up delay, fluency of video playback and packet loss, and represent them by the network statistics, i.e., the average network throughput and delay jitter. Based on the analytical framework, we propose adaptive playout buffer management schemes to optimally manage the threshold of video playback towards the maximal user utility, according to different quality-of-service requirements of end users. The proposed framework is validated by extensive simulations.

Millimeter-wave (mmWave) transmissions are promising technologies for high data rate (multi-Gbps) Wireless Personal Area Networks (WPANs). In this paper, we first introduce the concept of exclusive region (ER) to allow concurrent transmissions to explore the spatial multiplexing gain of wireless networks. Considering the unique characteristics of mmWave communications and the use of omni-directional or directional antennae, we derive the ER conditions which ensure that concurrent transmissions can always outperform serial TDMA transmissions in a mmWave WPAN. We then propose REX, a randomized ER based scheduling scheme, to decide a set of senders that can transmit simultaneously. In addition, the expected number of flows that can be scheduled for concurrent transmissions is obtained analytically. Extensive simulations are conducted to validate the analysis and demonstrate the effectiveness and efficiency of the proposed REX scheduling scheme. The results should provide important guidelines for future deployment of mmWave based WPANs.

Millimeter-wave (mmWave) communication technologies can achieve up to several gigabit/sec data rate over a small range, using directional antenna. To enable high data rate wireless connectivity in a large area, a multi-hop routing protocol is needed. The rate-adaptiveness of mmWave link and the use

This paper proposes a multi-channel token ring media access control (MAC) protocol (MCTRP) for inter-vehicle communications (IVC). Through adaptive ring coordination and channel scheduling, vehicles are autonomously organized into multiple rings operating on different service channels. Based on the multi-channel ring structure, emergency messages can be disseminated with a low delay. With the token based data exchange protocol, the network throughput is further improved for non-safety multimedia applications. An analytical model is developed to evaluate the performance of MCTRP in terms of the average full ring delay, emergency message delay, and ring throughput. Extensive simulations with ns-2 are conducted to validate the analytical model and demonstrate the efficiency and effectiveness of the proposed MCTRP.

Ultra-wideband (UWB) communication is a promising enabling technology for future broadband wireless services. A simple, scalable, distributed, efficient medium access control (MAC) protocol is of critical importance to utilize the large bandwidth UWB channels and enable numerous new applications and services cost-effectively. In this paper, by investigating the characteristics of UWB communications, we propose a Distributed, EXclusive region (DEX) based MAC protocol. The proposed DEX protocol capitalizes on the spatial multiplexing gain of UWB networks by reserving exclusive regions (ER) surrounding the sender and receiver for data and acknowledgment (ACK) transmissions, so that users can efficiently and fairly share network resources in a distributed and asynchronous manner. We further quantify the network performance bounds and derive the optimal ER size to maximize the expected network transport throughput for a dense, multi-hop UWB network. Extensive simulation results demonstrate the efficiency and effectiveness of the DEX protocol. This work explores how to effectively utilize the wireless spatial capacity of distributed, multi-hop wireless networks by optimizing protocol parameters, instead of depending on more complicated control messages.

Multicast communications is an efficient mechanism for one-to-many transmissions over a broadcast wireless channel, and is considered as a key technology for supporting emerging broadband multimedia services in the next generation wireless networks, such as Internet Protocol Television (IPTV), mobile TV, etc. Therefore, it is critical to design efficient multicast scheduling schemes to support these multimedia services. In this paper, we propose a cooperative multicast scheduling scheme for achieving efficient and reliable multicast transmission in IEEE 802.16 based wireless metropolitan area networks (WMAN). By exploiting the multi-channel diversity across different multicast groups and user cooperation among group members, the proposed scheme can achieve higher throughput than existing multicast schemes, for subscriber stations in both good and bad channel conditions. In addition, it has good fairness performance by considering the normalized relative channel condition of each multicast group. An analytical model is developed to evaluate the performance of the proposed scheme, in terms of service probability, power consumption, and throughput of each group member and multicast groups. The efficiency of the proposed scheme and the accuracy of the analytical model are corroborated by extensive simulations.

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

Exploiting the broadcast nature of wireless communications, multicast transmission is an efficient way to improve the network throughput by transmitting the same contents to multiple receivers simultaneously. It has been considered as a key technology for supporting emerging services in next-generation IEEE 802.16 based wireless metropolitan area networks (WMANs), such as Internet Protocol TV (IPTV) and mobile TV. Therefore, it is critical to devise efficient multicast scheduling schemes to support these multimedia services. In this paper, we propose a novel multicast scheduling scheme, using downlink cooperative transmission for achieving high throughput not only for all multicast groups but also for each group member. Extensive simulations are conducted to demonstrate the effectiveness and efficiency of the proposed scheme.

By considering the characteristics of Ultra- wideband (UWB) communications networks, ie., short transmission range, accurate ranging, and low transmission/interference power, we propose a Distributed, Exclusive region (DEX) based MAC protocol for multi-hop UWB based wireless networks. DEX can effectively explore the spatial multiplexing gain of UWB networks and allow users to efficiently and fairly share network resources in a distributed manner by reserving exclusive regions (ER) around the sender and receiver for data and acknowledgment (ACK) transmissions. We further quantify the network performance bounds and derive the optimal ER size to maximize the expected network transport throughput for a dense multi-hop UWB network. Extensive simulation results demonstrate the efficiency and effectiveness of the DEX protocol.

Communications at 60GHz millimeter-wave (mmWave) band is a promising technology for future wireless personal area networks (WPAN) supporting high data rate applications. Internet Protocol TV (IPTV) is anticipated to be one of the next killer applications, which requires high data rate and st

In this paper, we investigate the unique characteristics of millimeter-wave (mmWave) communications and propose an exclusive region (ER) based resource management scheme to explore the spatial multiplexing gain of mm Wave WPANs. We develop an analytical model to study the performance of mm Wave WPANs in terms of the average number of concurrent transmissions and the spatial multiplexing capacity, considering the use of omni-directional and directional antennae. Extensive simulations are conducted to demonstrate the accuracy of the analytical model and the efficiency of the ER based resource management scheme. The analysis and simulation results should provide important guidelines for future deployment of mm Wave based WPANs. Our findings can also be extended to other wireless communications networks in general.

IEEE 802.15.3c has recently been formed for developing a millimeter-wave (mmWave)-based alternative physical layer (PHY) for the existing 802.15.3 wireless personal area network (WPAN) standard, using the unlicensed 57-64 GHz band. However, the existing resource management schemes are inherently inefficient and insufficient for mmWave-based WPANs, without the consideration of the unique features of mm Wave communications: high Oxygen absorption rate and atmospheric attenuation, limited communication range, stringent power control for unlicensed usage, and the use of directional antennae. In this paper, by capturing the unique physical characteristics of mm Wave communications and based on the use of omni-or directional antennae, we derive the exclusive regions (ER) to allow efficient concurrent transmissions and develop an ER based scheduling algorithm to improve the network throughput of mm Wave based WPANs by several folds. Extensive simulations are conducted to demonstrate the effectiveness and efficiency of the proposed ER scheduling algorithm. The analysis and simulation results can provide important guidelines for future deployment of mm Wave based WPANs.

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

We analyze the capacity of UWB networks supporting multimedia services by calculating the number of multimedia connections that can be supported in a UWB network based on IEEE 802.15.3 Medium Access Control (MAC) protocol, taking into consideration the overheads from different layers. We then propose how to increase the capacity by improving the MAC protocol design. To fully explore the potential of UWB technologies which favor concurrent transmissions if the interference is appropriately controlled, we study the capacity of cellular-like UWB networks. Our findings, which should provide important guidelines for UWB network planning, are a) the inter-cell interference of UWB networks is closely related to the Riemann Zeta function, and to guarantee the bounded inter-cell interference of UWB networks, the path loss exponent α must be larger than 2; b) the total throughput in an area is a concave function of the cell size; c) the best distance between adjacent cells is a function of path loss exponent, background noise level, and cross-correlation of the target signal and the interfering signal; and d) with the optimal cell size, a single flow's throughput is reduced by 2/α due to inter-cell interference. Simulation results are given to demonstrate the accuracy of the analysis.

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