Pan Zhou

Joint Power and Rate Control in Cognitive Radio Networks: A Game-Theoretical Approach

In cognitive radio networks, power control is necessary to not only decrease the interference among the secondary users (SUs), but also avoid negative impact to the primary users (PUs). Prevalent research works on power control are mainly focus on maximizing SINR as the QoS requirement of SUs under the interference power constraint for PUs. We note that besides achieving a high SINR to guarantee reliable data transmissions, SUs also require to support heterogenous services with different transmission rates. In order to provide flexible transmission rates to each SU, efficient use of networks radio resource requires transmission rate control in addition to transmit power control. In this paper, we consider the problem of joint power and rate control for SUs in cognitive radio network by using non-cooperative game theory. We study how to jointly allocate optimal transmit power and transmission rate given certain QoS requirement of SUs. We analysis of existence, uniqueness and Pareto efficiency of Nash equilibrium for our game. The performance of our proposed joint power and rate control algorithm is investigated by numeral results.

Energy-Efficient Joint Power and Rate Control via Pricing in Wireless Data Networks

Next Generation wireless networks are evolving towards all-data system which are expected to support a variety of application services with diverse transmission rates. Meanwhile, since most of the mobile terminals in wireless networks are battery-powered, to use energy efficiently, each terminal needs to transmit just enough power to achieve the desired transmission rate without causing excessive interference in the network. In this paper, a game-theoretic framework is used to study the joint power and rate control problem on the energy efficiency of wireless data network. A energy-efficient non-cooperative joint power and rate control game is thus introduced in which each user seeks to choose its possible transmit power and transmission rate in order to maximize its own utility while satisfying its target SINR as quality-of service (QoS) requirement. The utility function here we adopt is especially suitable for energy-constrained networks. We introduce pricing of transmit power into the utility function which not only improves the overall system performance, but also obtains Pareto Improvement when compared to the game with no pricing. The existence, uniqueness, best-response strategies and Pareto efficiency of Nash Equilibrium for the proposed game are proved. Based on these analysis, we present a distributive joint power and rate control algorithm. In the simulation part, we investigate the best pricing factor and compare our proposed algorithm with alternative algorithms developed by using game theory.

Randomized Multi-User Strategy for Spectrum Sharing in Opportunistic Spectrum Access Network

In this paper, we consider the problem of opportunistic spectrum access (OSA) for multiple secondary users over multiple channels occupied by primary users whose occupancy state is modeled as discrete-time Markov Process. Taking collisions among secondary users into consideration, we develop a randomized strategy that allows secondary users to seek independently for spectrum opportunities without cooperation, while at the same time maximizing the spectrum utility of the whole system. Our proposed scheme can achieve higher total throughput for all secondary users with no additional message exchange when compared with other similar works. Additionally, our proposed strategy can easily incorporate the spectrum detector in physical layer and interference constraints set by primary users.

An Energy-Efficient Cooperative MISO-Based Routing Protocol for Wireless Sensor Networks

Cooperative transmission technique is now widely considered as a promising approach to combat fading and achieve energy efficiency in wireless networks. In this paper we focus on the routing problem in energy-constrained wireless sensor networks (WSNs), of which a cooperative MISO-based routing strategy is adopted. We first analyze the physical layer energy consumption model of cooperative transmission in the scenarios of one hop and hop-to-hop for energy-efficient routing in order to prolong the network lifetime. Based on this analysis, we disclose how the energy-efficient network routing problem is tightly related to the inter-cluster MISO node and hop-to-hop relay node selection. As we noticed, the problem of energyefficient cooperative routing is NP-hard innately which is difficult to implement in a totally distributive approach. Due to these analysis, a feasible algorithm with minimum cost is thus proposed. In the simulation part, we prove that our protocol can prolong the network lifetime tremendously when choose appropriate transmission parameters. Moreover, as an example, we simulate a typical network scenario which indicates our protocol is more energy efficient when comparing with vMIMO scheme in our previous work.

Clinical decision support for Alzheimer's disease based on deep learning and brain network

Modern e-health systems have undergone rapid development thanks to the advances in communications, computing and machine learning technology. Especially, deep learning has great superiority in image analysis and disease prediction. In this paper, we use Alzheimers Disease (AD) as an example to show advantages of deep learning in diagnosing brain diseases and providing clinical decision support. Firstly, we convert raw functional magnetic resonance imaging (fMRI) to a matrix to represent activity of 90 brain regions. Secondly, to represent the functional connectivity between different brain regions, a correlation matrix is obtained by calculating the correlation between each pair of brain regions. In the next, a targeted autoencoder network is built to classify the correlation matrix, which is sensitive to AD. Finally, the experiment results show that our proposed method for AD prediction achieves much better effects than traditional means. It finds the correlations between different brain regions efficiently, provides strong reference for AD prediction. Compared to Support Vector Machine (SVM), about 25% improvement is gained in prediction accuracy. The e-health field becomes more complete and effective owing to that. Our work helps predict AD at an early stage and take measures to slow down or even prevent the onset of it.

A Semi-centralized Approach for Optimized Multihop Virtual MIMO Wireless Sensor Networks

In this paper, a novel cluster-based multihop virtual MIMO communication protocol is proposed by consideration of energy efficient cooperative MIMO transmission and STBC training overhead of the MIMO channel in wireless sensor networks. In the protocol, we use a semi-centralized approach to form clusters and select optimal routing of the mutihop cooperative transmission. The communication cost of these processes is discussed. Based on this protocol, we analysis the overall data communication energy consumption and established an optimization model to find the optimum network parameters. The performance of the proposed protocol is compared with LEACH, LEACH-Centralized (LEACH-C), and another virtual MIMO based multihop protocol in our previous work. Simulation results show that tremendous energy saving is achieved and the network lifetime is prolonged, with judicious choice of the protocol parameters.

A Joint Utility-Lifetime Optimization Algorithm for Cooperative MIMO Sensor Networks

Cooperative MIMO transmission technique is considered as one of the effective solutions to reduce the energy consumption in wireless sensor networks. However, the existing cooperative MIMO based protocols only focused on how to reduce the energy consumption, but for some applications such as audio/video surveillance sensor network, a large amount of gathered data (formulated as network utility function) and long network lifetime are both required. In those cases, the two performance parameters should be considered and jointly optimized during the protocol design. In this paper, we first model and analyze the energy consumption and channel capacity of Multi-hop cooperative MIMO transmission. Then, we propose a joint network lifetime and utility optimization model based on NUM (network utility maximization) approach. Finally, we use the dual decomposition technique to solve the primal optimization problem and get a distributed algorithm. Simulation results show that, by using our distributed algorithm, the lifetime and utility of cooperative MIMO sensor network can converge to Pareto optimal trade-off.

Shortest Path Routing in Unknown Environments: Is the Adaptive Optimal Strategy Available?

We consider the shortest path routing (SPR) problem of a network with time varying link metrics in unknown environments. Due to potential denial of service attacks, the distributions of link states could be stochastic (benign or i.i.d.), contaminated or adversarial (non-i.i.d.) at different temporal and spatial locations. Without any a priori, designing an adaptive SPR protocol to cope with all possible situations in practice optimally is a very challenging issue. In this paper, we present the first solution by formulating it as a multi-armed bandit problem. By introducing novel control parameters to explore link conditions, our proposed algorithms could automatically detect features of the environment within a unified framework and find the optimal SPR strategies with almost optimal learning performance in all possible cases over time. Moreover, we study important issues related to the practical implementation, such as decoupling route selection with multi-path route probing, cooperative learning among multiple sources, the cold-start issue and delayed feedback of our algorithm. Nonetheless, the proposed SPR algorithms can be implemented with low complexity and they are proved to scale very well with the network size. The efficacy of the proposed solutions is verified by simulations from the real tracedriven datasets. Comparing to existing approaches in a typical network scenario, our algorithm has a 65.3 percent improvement of network delay given a learning period and a 81.5 percent improvement of learning duration under a specified network delay.

Distributed private online learning for social big data computing over data center networks

With the rapid growth of Internet technologies, cloud computing and social networks have become ubiquitous. An increasing number of people participate in social networks and massive online social data are obtained. In order to exploit knowledge from copious amounts of data obtained and predict social behavior of users, we urge to realize data mining in social networks. Almost all online websites use cloud services to effectively process the large scale of social data, which are gathered from distributed data centers. These data are so large-scale, high-dimension and widely distributed that we propose a distributed sparse online algorithm to handle them. Additionally, privacy-protection is an important point in social networks. We should not compromise the privacy of individuals in networks, while these social data are being learned for data mining. Thus we also consider the privacy problem in this article. Our simulations shows that the appropriate sparsity of data would enhance the performance of our algorithm and the privacy-preserving method does not significantly hurt the performance of the proposed algorithm.

Differential privacy and distributed online learning for wireless big data

Distributed sensor networks (DSN) have been widely applied in daily life. Many sensor network applications can be regarded as big-data optimization, since the sensor nodes collect large volume of data in a long time. However, subject to limited computation and communication capabilities of sensor nodes, how we process such a large scale of data is a great challenge. In this paper, we give the sensor nodes the ability of online learning, which reduces the size of data storage by “using” the data. Then, each sensor node is able to save much flash memory to handle more data. Furthermore, the communications among sensor nodes may lead to privacy breaches. Hence, we use differential privacy to solve the privacy-preserving problem. Numeric results show the performance of our proposed differentially private distributed online learning algorithm used in DSN.