This paper aims to develop an effective uplink access scheduling strategy for massive Internet-of-Things (IoT) networks. To better reap the benefits of uplink resources, the BS has to adjust the uplink resources relies on the network states available at the BS. However, in massive IoT networks, the acquisition of network states, including traffic arrivals, channel conditions, and energy supply rate, are typically obtained through in-band feedback from devices. Therefore, the network states available at the BS are differently outdated across devices, as the staleness depends on the time elapsed since each device was last scheduled. This motivates us to develop a proactive scheduling scheme that enables the BS to schedule uplink access under differently outdated states information. To combat the performance loss caused by the outdated states information, we propose a novel primal-dual online learning framework. This framework leverages mini-batch gradient descent for dual updates and employs Online Convex Optimization for proactive primal updates, which effectively predicting current network states based on outdated knowledge. We evaluate the performance of the proposed proactive scheduling scheme against the offline optimum, which is optimized using prior knowledge of network states. The performance analysis shows that the proactive scheme asymptotically approaches to the offline optimum. Simulation results further validate the effectiveness of the proposed algorithm by comparing to other benchmarks.

In this paper, we investigate the joint beamforming and power allocation problem in multi-antenna Cellular Vehicle-to-Everything (C-V2X) networks under uncertain channel state information (CSI). Our objective is to minimize the beamforming vector at the base station and the transmit powers of vehicle users while satisfying probabilistic quality-of-service (QoS) constraints. To address the uncertainty of CSI, we first develop an ellipsoid-based uncertainty set learning approach, which models the uncertain CSI as a symmetric ellipsoid. Building on this uncertainty set, we propose a robust counterpart transformation method to reformulate the joint beamforming and power allocation problem into a deterministic semi-definite problem without probabilistic constraints. Through our analysis, the ellipsoid-based uncertainty set exhibits significant conservatism when applied to uncertain CSI with asymmetric distributions. To mitigate this conservatism, we propose a support vector clustering (SVC)-based uncertainty set learning approach, which can tightly enclose the distribution of uncertain CSI. To further simplify the spatial structure of the SVC-based uncertainty set, we develop aK-Medoids-based equivalent set construction (KMSC) approach, significantly reducing the number of variables in the resulting robust equivalent problem. Finally, we conduct extensive simulations to evaluate the performance of our proposed robust approaches and compare them with non-robust methods.

This paper focuses on the robust beamforming for space-air-ground integrated networks (SAGIN) under uncertain channel state information (CSI). In SAGIN, uncertain CSI undermines the precise adjustment of beamforming, posing a major challenge to meeting users’ quality of service (QoS) requirements. To address this challenge, we first formulate the optimization problem as a quadratic program, which ensures a specified outage probability to maintain QoS. We then propose a multiple kernel-based machine learning method to model the uncertain CSI as an asymmetric convex polyhedron set. Leveraging semidefinite relaxation, the quadratic objective function is transformed into a linear function composed of beamforming matrix traces. Under the constructed CSI uncertainty set, a general robust transformation method is developed to linearly approximate the original probability constraints. Finally, we reformulate the joint optimization problem as a standard semidefinite program (SDP) and design an adaptive robust strategy to find its optimal solution. Simulation results show that our proposed method outperforms traditional robust and non-robust methods, effectively addressing limitations in existing SAGIN-related research.

The autonomous scheduling at unsignalized intersections faces a great challenge for ensuring collision-free passing and improving traffic efficiency under the complex intersection environment and heavy traffic density. In this article, we first design an autonomous management model for unsignalized intersections which uses the intersection control center to manage the passage of vehicles instead of traffic light. To avoid vehicles collision, the intersection is divided into multiple collision subzones, and each collision subzone needs to satisfy that the occupancy time of different vehicles is not overlapped. Second, a conflict graph tree search (CGTS) algorithm is developed to attain the optimal passing priority, which has the highest traffic efficiency. The CGTS algorithm reduces the computational complexity by compressing the solution space and reducing repeated calculations. Then, a heuristic threshold-based motion control strategy is proposed, which supports vehicles to reach the intersection at the assigned time by controlling their acceleration. Finally, we perform simulation experiments to show that the effectiveness of our algorithm outperforms comparison algorithms at various traffic densities.

Understanding lane changing intention on highways is a challenging task for satisfying the safety requirements and supporting the decision-making of autonomous vehicles in dense traffic environments. We propose a peril set aided lane changing intention inference model (PS-LCIIM) to accurately and timely recognize a driver’s lane changing intention before the vehicle crosses the line. To save computational resources of the onboard processor, a peril set-based vehicle selection strategy is designed to select the target vehicles based on collision risk and perception salience. Then, a dynamic time series Bayesian network is introduced to model time-varying vehicle interactions. The real-time intention classification is conducted based on the outputs of a newly developed population evolutionary particle filter algorithm. A naturalistic vehicle trajectories dataset is applied to train and validate the proposed model. The results demonstrate that the proposed model predicts the lane changing intention earlier with higher accuracy while maintaining a low computation cost.

This paper investigates a robust resource allocation for reconfigurable intelligent surface (RIS) aided vehicle-to-everything (V2X) communications with imperfect channel state information (CSI). To satisfy the diverse quality-of-service (QoS) requirements of V2X communications, we aim at maximizing the sum capacity of cellular user equipments (CUEs) while guaranteeing the outage probability constraints of vehicular user equipments (VUEs). Then, the considered problem is decomposed into the subproblems of power, spectrum and RIS phase shift op-timization. A graph-based power allocation method is presented to transform the non-convex power allocation subproblem into a tractable one and obtain the closed-form solutions. A worst-case conditional value-at-risk (CVaR) approximation-based method is developed to convert the RIS phase optimization subproblem into a convex semidefinite programming (SDP) problem. We propose a low-complexity learning-based alternating optimization approach which alternately optimizes three subproblems to obtain a near-optimal solution. Simulation results demonstrate that the proposed approach outperforms other benchmark methods.

This paper investigates the joint resource allocation and group formation for platoon in vehicle-to-everything (V2X) networks under vehicular channel uncertainty. To achieve the high spectrum efficiency and overcome the platoon head communication range limitation, an adaptive multicast-based group cooperation communication model is developed for the platoon with dynamic topology. Considering the heterogeneous characteristics of different types of links, i.e., high capacity for vehicle-to-infrastructure (V2I) links and ultra-reliability for vehicle-to-vehicle (V2V) links, we attempt to maximize the V2I capacity whilst satisfying a probability constraint for ultra-reliable V2V-supported intra-platoon communication. To handle the intractable probability constraint, a support vector clustering (SVC) based method is developed to capture the distributional geometry of massive uncertain channel samples as a sphere in high-dimensional feature space with asymmetric structure. Based on it, the probability constraint is transformed into a tractable linear convex set. After that, an exploration-selection-alternating-iterative algorithm is developed to solve the formulated problem with coupled optimization variables. Specifically, in the exploration process, a two-stage algorithm is proposed for the resource allocation problem under fixed group formation decision, which includes power control and spectrum allocation. During the selection process, a performance difference-based decision transition rate is designed to optimize group formation solution. Simulation results demonstrate the proposed data-driven approach can overcome the over-conservatism of the traditional symmetric-geometry-based uncertainty sets, and the multicast-based group cooperation communication model corresponds to a higher performance on V2I capacity than other traditional schemes.

Seismic fault interpretation is of extraordinary significant for hydrocarbon reservoir characterization and drilling hazard mitigation. In recent years, deep learning-based seismic fault detection methods have been conducted actively. Considering efficiency and fault prediction consistency, the most appealing way is to train a 3D segmentation network using synthetic seismic data with ground truth fault structure. However, the differences in signal-to-noise ratio, resolution, and fault strike distribution between synthetic and real data, can lead to inconsistent and unreliable prediction results. In this paper, we propose a multi-task deep learning-based seismic fault detection method, which takes seismic fault detection as the main task and 3D seismic data reconstruction as the auxiliary task, named MTL-FaultNet. The auxiliary branch can provide suggestive information to the main branch thereby improving its performance. We also designed two levels of multi-scale modules and embedded attention mechanisms in the network, so as to improve the network’s ability to focus on multi-scale fault features and learn stable fault structures. Different weights are assigned to the loss for different tasks, with large and small weights on the main and the auxiliary branch respectively. We apply the proposed method to Netherlands offshore F3 seismic data and a land field seismic data collected from Tarim Basin with mainly strike-slip faults, and Poseidon 3D seismic data. The proposed fault detection method is experimentally demonstrated on the improved network generalization and achieves reliable fault interpretation on field seismic data.

Abstract The authors investigate the adaptive resource allocation problem for cognitive power line communication system. The dynamic optimisation model is employed to maximise the energy‐aware network utility, which is defined as the difference of the weighted network throughput and the power consumption. Then, the authors decompose the stochastic optimisation into a traffic management subproblem and a joint subchannel and power allocation subproblem. Since the joint subchannel and power allocation subproblem is a mixed integer non‐linear optimisation, a continuity relaxation and Lagrange dual method is proposed to obtain the optimal solution of the mixed integer non‐linear optimisation subproblem. After that, the authors develop an adaptive power line communication resource allocation algorithm to cope with the power line communication network dynamics only according to the current network state information. Finally, we derive a trade‐off mechanism between network utility and network delay, in which the network delay is proportional to the control parameter V and the network utility is proportional to the parameter . Simulation results not only show the effectiveness but also verify the theoretical analysis of our proposed algorithm.

In this paper, we study the resource allocation in high mobility vehicle-to-everything (V2X) networks with only slowly varying large-scale channel parameters. For satisfying the diversity requirements of different types of links, i.e., low delay for vehicle-to-infrastructure (V2I) connections and ultra-reliability for vehicle-to-vehicle (V2V) connections, we formulate a joint power, spectrum and vehicle local computing ratio allocation problem to minimize the delay of V2I links whilst satisfying the V2V reliability constraint. For solving the formulated problem, a Feasible Region Transformation Method is firstly developed to convert the probabilistic V2V reliability requirement into a computable constraint. In addition, a Robust Signal to Interference Plus Noise Ratio (SINR) Modified Method is proposed to give the computable expression for the V2I throughput. Then, a parallel Deep Neural Network (DNN) framework is designed for the resource allocation in V2X networks, where one is the transmit power control unit and the other is the local computing ratio allocation unit. After that, a Feedback-oriented Learning Method is proposed to train the parallel DNN-based resource allocation framework, in which the output of DNN is used as feedback to dynamically revise the training loss function along with the training process. Afterwards, the Hungarian method is employed to obtain the optimal spectrum matching. Finally, we conduct the simulations to show that the proposed learning-based algorithm has better performance compared with other general algorithms.

In the edge computing Internet of Things network, we minimize the offloading overhead (caused by the bandwidth cost for data transmission and computation resource consumption for task remote processing) while providing the end-to-end (E2E) delay provisioning. Under the scenario, a tandem queue consisting of a transmission queue and a computing process queue is formed by the tasks offloaded to the edge server via wireless link and then processed through the computing resource. Due to the tandem queue, the offloading decision and computing resource allocation are coupled over the tandem queue. To make the problem tractable, we decouple the above two operations and propose a two-stage offloading filtering and computing resource allocation policy. After decouple, we then investigate the delay bound violation probability of the tandem queue by leveraging large deviation analysis. Further, we reveal that under the same E2E delay provisioning, the offloading overhead under the proposed decoupled policy can approach to the non-decoupled optimum by selecting an appropriate value of control parameter. Simulation results verify the theoretical analysis and show the efficiency of the proposed policy.

In this paper, we study the resource allocation in D2D underlaying cellular network with uncertain channel state information (CSI). For satisfying the minimum rate requirement for cellular user and the reliability requirement for D2D user, we attempt to maximize the cellular user’s throughput whilst ensuring a chance constraint for D2D. Then, a robust resource allocation framework is proposed for solving the highly intractable chance constraint, where the CSI uncertainties are represented as a deterministic set and the reliability requirement is enforced to hold for any CSI within it. Then, a symmetrical-geometry-based learning approach is developed to model the uncertain CSI into polytope, ellipsoidal and box. After that, the chance constraint under these uncertainty sets is transformed into computation convenient convex constraints. To overcome the conservatism of symmetrical-geometry-based approach, we develop a support vector clustering (SVC)-based approach to model uncertain CSI as a compact convex uncertainty set. Based on that, the chance constraint is converted into a linear convex set. Then, we develop a bisection search-based power allocation algorithm for solving the resource allocation in D2D underlaying cellular network with the obtained convex constraints. Finally, we conduct the simulation to compare the proposed robust optimization approaches with the non-robust one.

Coronavirus disease 2019 (COVID-19) has spread rapidly worldwide, causing significant mortality. There is a mechanistic relationship between intracellular coronavirus replication and deregulated autophagosome-lysosome system. We performed transcriptome analysis of peripheral blood mononuclear cells (PBMCs) from COVID-19 patients and identified the aberrant upregulation of genes in the lysosome pathway. We further determined the capability of two circulating markers, namely microtubule-associated proteins 1A/1B light chain 3B (LC3B) and (p62/SQSTM1) p62, both of which depend on lysosome for degradation, in predicting the emergence of moderate-to-severe disease in COVID-19 patients requiring hospitalization for supplemental oxygen therapy. Logistic regression analyses showed that LC3B was associated with moderate-to-severe COVID-19, independent of age, sex and clinical risk score. A decrease in LC3B concentration <5.5 ng/ml increased the risk of oxygen and ventilatory requirement (adjusted odds ratio: 4.6; 95% CI: 1.1-22.0; P = 0.04). Serum concentrations of p62 in the moderate-to-severe group were significantly lower in patients aged 50 or below. In conclusion, lysosome function is deregulated in PBMCs isolated from COVID-19 patients, and the related biomarker LC3B may serve as a novel tool for stratifying patients with moderate-to-severe COVID-19 from those with asymptomatic or mild disease. COVID-19 patients with a decrease in LC3B concentration <5.5 ng/ml will require early hospital admission for supplemental oxygen therapy and other respiratory support.

In this study, we extend traditional (single-target) hybrid systems to multi-target hybrid systems with a focus on the multi-maneuvering-target tracking system. This system consists of a continuous state, a discrete and switchable state, and a discrete, time-constant, and unique state. By defining a new generalized labeled multi-Bernoulli density, we prove that it is closed under the Chapman-Kolmogorov prediction and Bayes update for multi-target hybrid systems. In other words, we provide the exact derivation of a solution to this system, i.e., the multi-model generalized labeled multi-Bernoulli filter, which has been developed without strict proof.

The resource allocation in vehicle-to-everything (V2X) communications face a great challenge for satisfying the heterogeneous quality of service (QoS) requirements of the ultra-reliable safety-related services and the minimum throughput required entertainment services, due to the channel uncertainties caused by high mobility. In this paper, we first consider an optimistic scenario where the distribution of uncertain channel state information (CSI) can be deterministic and accurately known at the eNB. Then, a low-complexity resource allocation approach is developed, in which the probabilistic QoS constraint of V2V is transformed into a computable optimization constraint. To deal with the scenario with unknown uncertain CSI distribution, we develop a distributionally robust resource allocation approach for converting the intractable chance constraint of V2V into a deterministic semidefinite constraint based only on the first- and second-order moments of uncertain CSI. For alleviating the conservatism of above approach, a support-based distributionally robust resource allocation approach is developed to tighten the semidefinite constraint by utilizing the support information of uncertain CSI. Finally, we conduct simulations to show that the effectiveness of the proposed approaches outperforms other state-of-art approaches.

Vehicle-to-everything (V2X) communications face a great challenge in delivering not only the low-latency and ultra-reliable safety-related services but also the minimum throughput required entertainment services, due to the channel uncertainties caused by high mobility. This paper focuses on the robust resource management of V2X communications with the consideration of channel uncertainties. First, we formulate a transmit power minimization problem, whilst guaranteeing the different quality-of-service (QoS) requirements. To achieve the robustness of QoS provisions against channel uncertainties, a statistical leaning approach is developed to learn the uncertainties from the data samples of the random channel coefficients as a convex ellipsoid set, which is also called high-probability-region (HPR). Then, the highly intractable power minimization problem is converted into a second-order cone program by the robust optimization approach. Afterwards, we propose a joint set partitioning and reconstruction mechanism to further reduce the total transmit power by pruning the rough HPR into a more precise uncertainty set, which leads to a trackable second-order cone program and a linear program. Finally, we prove that the network performance can be effectively enhanced by the improvement mechanism. Simulation results verify the effectiveness of the robust resource allocation approaches over the non-robust one.

This paper investigates the reliable computation offloading in vehicular edge computing (VEC) systems. Compared with the traditional task replication method in which task replicas are typically assigned to multiple service vehicles at the same time, in our work, a task vehicle allocates the computation tasks and communication resources to its neighboring service vehicles through the vehicle-to-vehicle (V2V) links, and avoids the degradation of delay and computation efficiency. Specifically, an optimization problem is formulated to minimize the task completion delay and ensure offloading reliability. Then, an algorithm based on the penalty and the concave-convex procedure (CCCP) method is proposed to effectively solve the formulated optimization problem. The simulation results show that the task completion delay of the proposed algorithm is only 30% of that in the traditional task replication method.

Long-term evolution (LTE) and wireless local area network (WLAN) are often presented as opposing technologies. Hence, efficient partitioning of the spectrum resources carries critical importance for achieving the coexistence of these on the unlicensed spectrum band. In this paper, we firstly develop an online spectrum partitioning algorithm, which needs little signal transmission and exchange between coordination manager and networks. Then, we focus on the convergence analysis of the online spectrum partitioning algorithm, which is difficult due to the time-varying wireless channels. To overcome this challenge, we model the algorithm and network dynamics as the stochastic differential equations (SDE) and show that the algorithm convergence is equivalent to the stochastic stability of a virtual stochastic dynamic system constructed by the SDEs. Then, we give the sufficient condition about the algorithm convergence and the upper bound on the tracking error of the spectrum partitioning algorithm under exogenous variations of time-varying channel state information (CSI). Based on the insights of the impact of time-varying CSI on algorithm convergence, an online compensative spectrum partitioning algorithm is developed to offset the tracking error caused by the disturbance of time-varying CSI. Through performance evaluation, we show that the coexistence performance efficiency will come at low expense of algorithm complexity and signal overhead.

Energy harvesting with combination of multiple cooperating radio access technologies (multi-RAT) is regarded as a promising network paradigm to improve the energy efficiency of 5G networks. In this paper, we propose a resource-on-demand energy scheduling strategy for multi-RAT wireless networks, where the varying energy demand of the network can be satisfied by both grid power and harvested energy. Due to the high sensitivity to uncertainties of energy harvesting, a dynamic network energy queue model is designed first considering the inherently stochastic and intermittent nature of the harvested energy. Then, to minimize time-averaged grid power consumption and make effective utilization of harvested energy, the energy scheduling is formulated as a stochastic optimization problem subject to data queue stability and harvested energy availability, considering the high dynamics of wireless channel states and renewable energy sources. Following the Lyapunov optimization framework, the stochastic grid power minimization problem is decomposed into a network flow control subproblem, a network energy management subproblem, and a network resource allocation subproblem, respectively. In order to solve these subproblems, we develop a dynamic adaptive resource-on-demand (DAROD) algorithm to effectively reduce the grid power consumption cost by allocating the resource efficiently based on the dynamic demands of multi-RAT networks. Finally, the tradeoff between grid power consumption cost and network delay is achieved, in which the increase of network delay is approximately linear with the network control parameter V and the decrease of grid power consumption cost is at the speed of 1/V. Extensive simulations are conducted to verify the theoretical analysis and show the effectiveness of our proposed algorithm.

Recently, satellite networks have played an increasingly important role in both military and civilian fields. With the continual growth of the network size, the assessment of link criticality is of great significance to protect or attack satellite networks. With regard to the dynamic topologies and store-carry-forward transmission paradigm in satellite networks, detecting critical links should fully consider the relationship of consecutive snapshots and the key performance of the traffic, which raises great challenges. In this paper, we explore critical link sequence of satellite networks from the perspective of delay. We first formulate the problem based on the time-expanded graph model and discuss its convexity. Then, by exploring the space-time relationship between the criticality of different link at different slots, a heuristic critical link sequence detection algorithm (CLSD) is proposed. The simulation proves that deleting the critical link sequence given by the algorithm can effectively prolong the minimum transmission delay of the network and verifies the importance of network vulnerability assessment from the perspective of delay.

This paper investigates the resource allocation and user association for time-varying heterogeneous wireless network (HetNet), where the multi-homed user equipment (UE) utilizes multiple access options (AOs) simultaneously. Firstly, a stochastic optimization model is proposed to maximize the long-term energy efficiency (EE), which is defined as the ratio of long-term total throughput to the long-term corresponding energy consumption. A modified fractional programming method is proposed to convert the long-term EE maximize problem into an instance throughput-minus- energy optimization problem, which is proved as a mixed integer nonlinear optimization (MINO). The continuity relaxation and Lagrange dual method are proposed to solve the MINO problem. Then, a utility-based selection algorithm is developed to determine the optimal associated AO sets for each UE. After that, the dynamic EE-based resource allocation algorithm is developed to allocate the AOs radio resource energy efficiently, which depends only on the current network state information. Our simulation results show that the proposed algorithm achieves EE performance improvement compared to other general algorithms.

To satisfy the high requirements on operation efficiency in the 5G network, self-organizing network (SON) is envisioned to reduce the network operating complexity and costs by providing SON functions, which can optimize the network autonomously. However, different SON functions have different time scales and inconsistent objectives, which leads to conflicting operations and network performance degradation, raising the needs for SON coordination solutions. In this paper, we devise a multiple time-scale coordination management scheme (MTCS) for densely deployed SONs, considering the specific time scales of different SON functions. Specifically, we propose a novel analytical model named M time-scale Markov decision process, where SON decisions made in each time-scale consider the impacts of SON decisions in other M - 1 time scales on the network. Furthermore, in order to manage the network more autonomously and efficiently, a Q-learning algorithm for SON functions in the proposed MTCS scheme is proposed to achieve a stable control policy by learning from history experience. To improve energy efficiency, we then evaluate the proposed MTCS scheme with two functions of mobility load balancing and energy saving management with designed network utility. The simulation results show that the proposed SON coordination scheme significantly improves the network utility with different quality of experience requirements while guaranteeing stable operations in wireless networks.

Deploying a radio frequency (RF) access point (AP) to the visible light communication (VLC) system is a promising strategy to overcome the VLC's limitations, such as limited coverage, strictly line-of-sight transmission, and mobility robustness, etc. In this paper, we focus on the energy-aware design of network selection and resource allocation for a heterogeneous network combining with RF and VLC APs. For adapting to different timescale network states and stochastic data arrival, we propose an on-line two-timescale adaptive network resource optimization (ANRO) framework by employing the Lyapunov optimization technique. At the large timescale, we first develop a closed-form solution for the subproblem of network selection for user equipment. Second, we design a cost-effective and easy-to-realize algorithm for VLC's joint transmission scheduling and power control subproblem, which is a nonconvex optimization. While at the small timescale, we obtain the optimal solution for RF's joint resource block and power allocation subproblem, which is proven a mixed integer nonlinear optimization. Simulation results demonstrate that the ANRO can achieve a tradeoff between network power consumption and delay. Furthermore, it not only can stabilize the network but also can significantly reduce the energy consumption compared with other existing schemes.

In order to improve the multi-target tracking performance of Doppler radar in the presence of Doppler blind zone (DBZ), the detection probability model with minimum detectable velocity (MDV) is incorporated into Gaussian mixture cardinality balanced multi-target multi-Bernoulli (GM-CBMeMBer) filter. After integrating the new detection probability model into the multi-target posterior density of the standard GM-CBMeMBer, we derive the updated density for a novel GM-CBMeMBer filter where the MDV and Doppler information are fully exploited. It is demonstrated through numerical studies that the proposed filter outperforms the GM-CBMeMBer filter which doesn't incorporate the Doppler and MDV information in the presence of the DBZ.

In this paper, we design an adaptive multi-homing resource allocation algorithm for time-varying heterogeneous wireless networks (HetNet), where the algorithm iteration timescale is the same to the network state acquisition timescale. First, the network utility maximization is characterized by a stochastic optimization model. Second, the multi-homing resource allocation (MHRA) algorithm is developed to accommodate the dynamic wireless network states, i.e., time-varying wireless channels between the access points (AP) and mobile terminals and as well the queuing dynamics at the APs. Then, we investigate the tracking error between the MHRA algorithm output and the target optimal resource allocation solution. Based on these results, an adaptive-compensation multi-homing resource allocation (AMRA) algorithm is proposed to offset the tracking error so as to enhance the network utility. Specifically, we give a sufficient condition that the AMRA algorithm asymptotically tracks the moving equilibrium point with no tracking errors. Finally, we derive a tradeoff between network utility and media transmission delay, where the increase of average delay is approximately linear in V and the increase of network utility is at the speed of 1/V with the control parameter V. Simulation results validate the theoretical analysis of our proposed scheme.

In this paper, we design an energy-efficient cross-layer optimization framework for media streaming over time-varying wireless network. The energy efficiency (EE) is characterized by the stochastic optimization model subject to the network stability, which is also used to depict the average media delivery delay. In harmony with the hierarchical architecture of the wireless network, the problem of stochastic optimization of media streaming is decomposed by the Lyapunov drift theory into two subproblems, associated with the flow control in transport layer and the power allocation in physical (PHY) layer. Specifically, the dynamic cross-layer control algorithm for media streaming is developed for adapting to the time-varying network state information, i.e. time-varying channel state information (CSI) of mobile terminal (MT)-access points (AP) links and dynamic queue state information (QSI) at APs. We derive a tradeoff between EE and media streaming delay, where the increase of average delay is approximately linear in V and the increase of EE is at the speed of 1/V with the control parameter V. Simulation results validate the theoretical analysis of our proposed scheme.

In this paper, a fast content distribution scheme is proposed with QoS provisions and user cooperation in cellular networks. A group of mobile terminals (MTs), which are interested in the same content, can cooperate to reduce the content distribution time (CDT). The Nash bargaining solution was derived to formulate the content distribution problem, where each player of the game can maximize its individual payoff. Simulation results demonstrate that the MTs endowed with cooperative scheme achieve a significant gain in terms of decreasing the CDT compared with those without cooperation.

Association rule mining (ARM) finds all the association rules in data, that match some measures of interest such as support and confidence. In certain situations where high support is not necessarily of interest, fixed-consequent association-rule mining for confident rules might be favored over traditional ARM. The need for fixed consequent ARM is becoming more evident in a number of applications such as market basket research (MBR) or precision agriculture. Highly confident rules are desired in all situations; however, support thresholds fluctuate with the applications and the data sets under study, as we shall show later. We propose an approach for mining minimal confident rules in the context of fixed-consequent ARM that relieves the user from the burden of specifying a minimum support threshold. We show that the framework suggested herein is efficient and can be easily expanded by adding new pruning conditions pertaining to specific situations.

No abstract available.