Matching Theory for Future Wireless Networks: Fundamentals and Applications

The emergence of novel wireless networking paradigms such as small cell and cognitive radio networks has forever transformed the way in which wireless systems are operated. In particular, the need for self-organizing solutions to manage the scarce spectral resources has become a prevalent theme in many emerging wireless systems. In this paper, the first comprehensive tutorial on the use of matching theory, a Nobelprize winning framework, for resource management in wireless networks is developed. To cater for the unique features of emerging wireless networks, a novel, wireless-oriented classification of matching theory is proposed. Then, the key solution concepts and algorithmic implementations of this framework are exposed. Then, the developed concepts are applied in three important wireless networking areas in order to demonstrate the usefulness of this analytical tool. Results show how matching theory can effectively improve the performance of resource allocation in all three applications discussed

Offloading in Software Defined Network at Edge with Information Asymmetry: A Contract Theoretical Approach

The proliferation of highly capable mobile devices such as smartphones and tablets has significantly increased the demand for wireless access. Software defined network (SDN) at edge is viewed as one promising technology to simplify the traffic offloading process for current wireless networks. In this paper, we investigate the incentive problem in SDN-at-edge of how to motivate a third party access points (APs) such as WiFi and smallcells to offload traffic for the central base stations (BSs). The APs will only admit the traffic from the BS under the precondition that their own traffic demand is satisfied. Under the information asymmetry that the APs know more about own traffic demands, the BS needs to distribute the payment in accordance with the APs' idle capacity to maintain a compatible incentive. First, we apply a contract-theoretic approach to model and analyze the service trading between the BS and APs. Furthermore, other two incentive mechanisms: optimal discrimination contract and linear pricing contract are introduced to serve as the comparisons of the anti adverse selection contract. Finally, the simulation results show that the contract can effectively incentivize APs' participation and offload the cellular network traffic. Furthermore, the anti adverse selection contract achieves the optimal outcome under the information asymmetry scenario.Comment: 10 pages, 9 figure

Matching Theory Framework for 5G Wireless Communications

The prevalence of high-performance mobile devices such as smartphones and tablets has brought fundamental changes to the existing wireless networks. The growth of multimedia and location-based mobile services has exponentially increased the network congestion and the demands for more wireless resources. The extremely high computational complexity and communication overhead resulting from the conventional centralized resource management methods are no longer suitable to capture the scale of tomorrow’s wireless networks. As a result, the resource management in next-generation networks is shifting from the centralized optimization to the self-organizing solutions. The goal of this thesis is to demonstrate the effectiveness of matching theory, a powerful operational research framework, for solving the wireless resource allocation problems in a distributed manner. Matching theory, as a Nobel-prize winning framework, has already been widely used in many economic fields. More recently, matching theory has been shown to have a promising potential for modeling and analyzing wireless resource allocation problems due to three reasons: (1) it offers suitable models that can inherently capture various wireless communication features; (2) the ability to use notions, such as preference relations, that can interpret complex system requirements; (3) it provides low-complexity and near-optimal matching algorithms while guaranteeing the system stability. This dissertation provides a theoretical research of implementing the matching theory into the wireless communication fields. The main contributions of this dissertation are summarized as follows. An overview of the basic concepts, classifications, and models of the matching theory is provided. Furthermore, comparisons with existing mathematical solutions for the resource allocation problems in the wireless networks are conducted. Applications of matching theory in the wireless communications are studied. Especially, the stable marriage model, the student project allocation model and so on are introduced and applied to solve the resource allocation problems, such as the device-to-device (D2D) communication, LTE-Unlicensed, and so on. Both theoretical and numerical analysis are provided to show that matching theory can model complex system requirements, and also provide semi-distributive matching algorithms to achieve stable and close-optimal results. The potential and challenges of the matching theory for designing resource allocation mechanisms in the future wireless networks are discussed.Electrical and Computer Engineering, Department o

Scorpion in Combination with Gypsum: Novel Antidiabetic Activities in Streptozotocin-Induced Diabetic Mice by Up-Regulating Pancreatic PPARγ and PDX-1 Expressions

The management of diabetes without any side effects remains a challenge in medicine. In this study, antidiabetic activity and the mechanism of action of scorpion combined with gypsum (SG) were investigated. Streptozotocin-induced diabetic mice were orally administrated with scorpion (200 mg kg−1 per day) in combination with gypsum (200 mg kg−1 per day) for 5 weeks. SG treatment resulted in decreased body weight, blood glucose and lipid levels, and increased serum and pancreatic insulin levels in diabetic mice. Furthermore, SG significantly increased the number and volume of beta cells in the Islets of Langerhans and promoted peroxisome proliferator-activated receptor gamma and pancreatic duodenal homeobox 1 expressions in pancreatic tissues. However, scorpion or gypsum alone had no significant effect in this animal model. Metformin showed a slight or moderate effect in this diabetic model, but this effect was weak compared with that of SG. Taken together, SG showed a new antidiabetic effect in streptozotocin-induced diabetic mice. This effect may possibly be involved in enhancing beta-cell regeneration and promoting insulin secretion by targeting PPARγ and PDX-1. Moreover, this new effect of SG offers a promising step toward the treatment of diabetic patients with beta-cell failure as a complementary and alternative medicine

Optimal Channel Selection for Simultaneous RF Energy Harvesting and Data Transmission in Cognitive Radio Networks

In this paper, an RF-powered cognitive radio network is considered, in which the secondary users are powered by an RF energy harvester (Rectenna). Unlike most existing works, we consider a realistic Rectenna characteristic function, and derive the actual amount of harvested energy and thus, the resulting actual energy level of the secondary users. We consider a system architecture at which simultaneous energy harvesting and data transmission for each secondary user is possible. We introduce a strategy to manage the challenge of network throughput decreasing due to lack of the secondary users’ energy, via selecting the best possible channels for energy harvesting and simultaneously by allocating the best channels for data transmission. Therefore, we implement cognition in spectrum utilization and in energy harvesting. We show that the amount of harvested energy affects the available energy of the secondary user and consequently the throughput, therefore, the channels selection to maximize energy harvesting affects the network throughput. To maximize the network throughput, the Hungarian algorithm is employed, and then, an algorithm with lower complexity based on the matching theory is proposed. Finally, we compare our proposed approach with some existing benchmarks and show its high performance in energy harvesting and system throughput

Lifetime Optimization Via Base Station Placement and Power Aware Scheduling in Wireless Sensor Networks

Recent years have witnessed the advent of wireless sensor technologies and proliferating applications of wireless sensor networks (WSNs) in various fields. Network lifetime has been a critical design goal of any battery operated large scale WSNs. Especially for delay-sensitive WSNs, it is challenging to prolong the network lifetime, while meeting the delay requirements of different applications. In this thesis, we fisrt investigate how to prolong the lifetime of large scale WSNs via optimal placement of base stations (BSs). With multiple BSs placed, the sensor nodes can send their data to nearby sinks and may reduce energy consumption ofrelaying packets for other sensor nodes. Due to the high cost of BSs and geographical constraints in WSNs, we can only place a limited number of BSs in a few candidate locations. Considering wireless transmission features and flow routing, we formulate this base station placement (BSP) problem in WSNs into a mixed integer nonlinear programming (MINLP) problem. In view of the NP-hardness of the formulated problem, we develop the heuristic algorithm to pursue feasible solutions. Through extensive 1 2 simulations, we show that the solutions found by the proposed algorithm are close to the optimal one and the proposed scheme is effective in prolonging the lifetime of large scale WSNs. In addition, we investigate how to optimize the network lifetime of delay-sensitive WSNs with respect to energy efficient routing and sleep scheduling. Instead oftreating routing and sleep scheduling as two separate approaches, we have ajoint consideration of them, and mathematically formulate the lifetime maximization problem under multiple constraints (i.e., routing, end-to-end delay bounds, sleep scheduling, energy consumption of transmission, receiving and listening, etc.). Since the formulated problem is a MINLP problem and NP-hard to solve, we relax it into a LP problem and solve the relaxed problem for the upper bound. We also develop a heuristic algorithm for the feasible solution, which yields a lower bound of WSNs\u27 lifetime. Through extensive simulations, we show that the solution found by the proposed algorithm is close to the optimal one and the proposed scheme is effective in prolonging the lifetime of delay-sensitive WSN

Matching theory for wireless networks

This book provides the fundamental knowledge of the classical matching theory problems. It builds up the bridge between the matching theory and the 5G wireless communication resource allocation problems. The potentials and challenges of implementing the semi-distributive matching theory framework into the wireless resource allocations are analyzed both theoretically and through implementation examples. Academics, researchers, engineers, and so on, who are interested in efficient distributive wireless resource allocation solutions, will find this book to be an exceptional resource.

ED50 of Intranasal Dexmedetomidine Sedation for Transthoracic Echocardiography in Children with or without a History of Cardiac Surgery for Cyanotic Congenital Heart Disease

Background and Objective. Dexmedetomidine (DEX) can provide adequate sedation during short procedures. However, the median effective dose (ED50) of intranasal DEX sedation has not been well established in children with a history of correction surgery for cyanotic congenital heart disease (cCHD). This study was to determine ED50 of intranasal DEX sedation for transthoracic echocardiography (TTE) in young children with a history of correction surgery for cCHD. Methods. This prospective single-blinded clinical trial included 72 ASA I-II stage children aged 1-36 months with cCHD who were scheduled to undergo TTE under sedation. Children were assigned to group A (n=37) with a previous history of cardiac surgery and group B (n=35) with no history of cardiac surgery. Doses of intranasal DEX were analyzed by up-down sequential allocation at an initial dose of 2.3 μg/kg and an increase in steps of 0.2 μg/kg. Intranasal DEXED50 values were analyzed by the up-and-down method of Dixon-Massey and probit regression to determine ED50 and 95% confidence interval (CI) for sedation. The time to effective sedation, time to regaining consciousness, vital signs, oxygen saturation, time of performing TTE, clinical adverse effects, and characteristics of regaining consciousness were compared between the two groups. Results. ED50 of intranasal DEX sedation was 2.530 μg/kg (95% CI, 1.657-4.156) in group A and 2.500 μg/kg (95% CI, 1.987-3.013) in group B. There was no significant difference in sedation onset time and time to regaining consciousness between the two groups. Additionally, no significant adverse hemodynamic or hypoxemic effect was observed. There was no significant difference in sedation-onset time and wake-up time between the two groups (15±4 min vs.16±5 min; 50±11 min vs.48±10 min). This trial is registered with the China Clinical Trials Registry (ChiCTR-IOR-1800015038). Conclusions. ED50 of intranasal DEX sedation for TTE is similar in children with and without a history of cardiac surgery for cCHD

Joint Radio and Computational Resource Allocation in IoT Fog Computing

The current cloud-based Internet-of-Things (IoT) model has revealed great potential in offering storage and computing services to the IoT users. Fog computing, as an emerging paradigm to complement the cloud computing platform, has been proposed to extend the IoT role to the edge of the network. With fog computing, service providers can exchange the control signals with the users for specific task requirements, and offload users’ delay-sensitive tasks directly to the widely distributed fog nodes at the network edge, and thus improving user experience. So far, most existing works have focused on either the radio or computational resource allocation in the fog computing. In this work, we investigate a joint radio and computational resource allocation problem to optimize the system performance and improve user satisfaction. Important factors, such as service delay, link quality, mandatory benefit, and so on, are taken into consideration. Instead of the conventional centralized optimization, we propose to use a matching game framework, in particular, student project allocation (SPA) game, to provide a distributed solution for the formulated joint resource allocation problem. The efficient SPA-(S,P) algorithm is implemented to find a stable result for the SPA problem. In addition, the instability caused by the external effect, i.e., the interindependence between matching players, is removed by the proposed user-oriented cooperation (UOC) strategy. The system performance is also further improved by adopting the UOC strategy.peerReviewe