다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

On robust estimation of the location parameter

Information Theory and Reliable Communication

https://www.cs.montana.edu/courses/csci566/Ref/WMSN-Survey.pdf

A survey on wireless multimedia sensor networks

Random matrix theory has found many applications in physics, statistics and engineering since its inception. Although early developments were motivated by practical experimental problems, random matrices are now used in fields as diverse as Riemann hypothesis, stochastic differential equations, condensed matter physics, statistical physics, chaotic systems, numerical linear algebra, neural networks, multivariate statistics, information theory, signal processing and small-world networks. This article provides a tutorial on random matrices which provides an overview of the theory and brings together in one source the most significant results recently obtained. Furthermore, the application of random matrix theory to the fundamental limits of wireless communication channels is described in depth.

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Random Matrix Theory and Wireless Communications

In this survey paper, we characterize the learning problem in cognitive radios (CRs) and state the importance of artificial intelligence in achieving real cognitive communications systems. We review various learning problems that have been studied in the context of CRs classifying them under two main categories: Decision-making and feature classification. Decision-making is responsible for determining policies and decision rules for CRs while feature classification permits identifying and classifying different observation models. The learning algorithms encountered are categorized as either supervised or unsupervised algorithms. We describe in detail several challenging learning issues that arise in cognitive radio networks (CRNs), in particular in non-Markovian environments and decentralized networks, and present possible solution methods to address them. We discuss similarities and differences among the presented algorithms and identify the conditions under which each of the techniques may be applied.

A Survey on Machine-Learning Techniques in Cognitive Radios

An energy-efficient virtual multiple-input multiple-output (MIMO)-based communication architecture is proposed for distributed and cooperative wireless sensor networks. Assuming a space-time block coding (STBC) based MIMO system, the energy and delay efficiencies of the proposed scheme are derived using semi-analytic techniques. The dependence of these efficiency values on physical channel propagation parameters, fading coherence time and the amount of required training is also investigated. The results show that with judicious choice of design parameters the virtual MIMO technique can be made to provide significant energy and delay efficiencies, even after allowing for additional training overheads.

Virtual MIMO-based cooperative communication for energy-constrained wireless sensor networks

Hierarchical dynamic spectrum access (DSA) has received the most attention in recent years as the solution for better spectrum utilization. In this paper, on the other hand, we develop a framework for dynamic spectrum leasing (DSL). Power control in hierarchical DSA networks only involves that of controlling secondary user transmissions. Thus, in game theoretic formulations of power control in cognitive DSA networks only secondary users are considered as players of the game. In proposed dynamic spectrum leasing, on the other hand, the primary users are rewarded for allowing secondary users to operate in their licensed spectrum. Thus, in the proposed DSL networks the primary users have an incentive to allow secondary users to access the spectrum whenever possible to the maximum extent. We develop a game theoretic framework for such dynamic spectrum leasing in which primary users actively participate in a non-cooperative game with secondary users by selecting an interference cap on the total interference they willing to tolerate. We establish that the proposed primary-secondary user power control game has a unique Nash equilibrium. Performance of a DSL system based on the proposed game model is compared through simulations under different linear receivers at the secondary base station.

Dynamic spectrum leasing in cognitive radio networks via primary-secondary user power control games

The capacity of multiple-antenna systems operating in Rayleigh flat fading is considered under the assumptions that channel state information (CSI) is available at both transmitter and receiver, and that the transmitter is subjected to an average power constraint. First, the capacity of such systems is derived for the special case of multiple transmit antennas and a single receive antenna. The optimal power-allocation scheme for such a system is shown to be a water-filling algorithm, and the corresponding capacity is seen to be the same as that of a system having multiple receive antennas (with a single transmitter antenna) whose outputs are combined via maximal ratio combining. A suboptimal adaptive transmission technique that transmits only over the antenna having the best channel is also proposed for this special case. It is shown that the capacity of such a system under the proposed suboptimal adaptive transmission scheme is the same as the capacity of a system having multiple receiver antennas (with a single transmitter antenna) combined via selection combining. Next, the capacity of a general system of multiple transmitter and receiver antennas is derived together with an equation that determines the cutoff value for such a system. The optimal power allocation scheme for such a multiple-antenna system is given by a matrix water-filling algorithm. In order to eliminate the need for cumbersome numerical techniques in solving the cutoff equation, approximate expressions for the cutoff transmission value are also provided. It is shown that, compared to the case in which there is only receiver CSI, large capacity gains are available with optimal power and rate adaptation schemes. The increased capacity is shown to come at the price of channel outage, and bounds are derived for this outage probability.

Capacity of multiple-antenna systems with both receiver and transmitter channel state information

Energy efficiency of MIMO techniques in wireless sensor networks is analyzed. Assuming a cooperative sensor network, the energy consumption of MIMO-based wireless sensor networks is compared with conventional SISO sensor networks. The dependence of energy efficiency on coherence time of the fading process and communications distance is considered. Our results show the applicability of MIMO techniques in such sensor networks with judicious system design.

Sudharman K. Jayaweera

Papers

A Survey on Machine-Learning Techniques in Cognitive Radios

Virtual MIMO-based cooperative communication for energy-constrained wireless sensor networks

Dynamic spectrum leasing in cognitive radio networks via primary-secondary user power control games

Capacity of multiple-antenna systems with both receiver and transmitter channel state information

Energy Analysis of MIMO Techniques in Wireless Sensor Networks