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

In a linear program, the constraints are linear in the decision variables, and so is the objective function. In a non linear program, the constraints and/or the objective function can also be non linear function of the decision variables. Example: Gasoline Blending: The qualities of a blend are determined by the qualities of the stocks used in the blend. An optimization is to determine the volume of each input stock in each blend so that the objective function is optimized subject to the output blends satisfying their quality specifications, stock availability constraints, and blend demand constraints. The decision variables are x ij denoting the amount of stock i in blend j. For the most part the constraints can be written as linear functions; but some of the quality constraints are non linear: Distillation Blending: D jk = b k + c k * ln [ Σ i (S ik * VF ij) ] where D jk is the k th distillation point for blend j. S ik is the k th distillation point for stock i. VF ij is the volume fraction of stock i in blend j and is equal to x ij / (Σ i x ij) and b k and c k are constants.

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Non-Linear Programming の計算法について

Currently, the radio spectrum is statically allocated and divided between licensed and unlicensed frequencies. Due to this inflexible policy, some frequency bands are growing in scarcity, while large portions of the entire radio spectrum remain unused independently of time and location. Cognitive Radio is a recent network paradigm that aims a more flexible and efficient usage of the radio spectrum. Basically, it allows wireless devices to opportunistically access portions of the entire radio spectrum without causing any harmful interference to licensed users. The present document surveys the literature on Cognitive Radio. It aims to provide a comprehensive and self-contained description of this research topic area, mainly focusing on communication protocols, spectrum decision issues, and future research directions. It is a tutorial in nature and consequently does not require any previous knowledge about Cognitive Radio. Readers are only required to have some general background on wireless data networks. Emphasis is put on Cognitive Radio genesis, issues that must be addressed, related technologies, standardization efforts, the state of the art, and future research directions according to the vision of the authors.

Cognitive radio: survey on communication protocols, spectrum decision issues, and future research directions

Cognitive radio for M2M and Internet of Things: A survey

The large number of network nodes and the energy constraints make Wireless Sensor Networks (WSN) one of the most important application fields for Cooperative Diversity. Node cooperation increases the spatial diversity of wireless channels and, thus, reduces the transmitted power. In this paper, we propose a multi-hop WSN with nodes grouped in cooperative clusters that exploits transmit and receive cooperation among cluster nodes. Multi-hop transmission is carried out by concatenating single cluster-to-cluster hops, where every cluster-to-cluster link is defined as a cooperative distributed multiple-input-multiple-output (MIMO) channel. Transmit diversity is exploited through a time-division, decoder-and-forward, relaying scheme based upon two time slots: the Intracluster Slot, used for data sharing within the cluster, and the Intercluster Slot, used for transmission between clusters. At the receiver side, a distributed reception protocol is devised based upon a Selection Diversity algorithm. The proposed multi-hop cooperative WSN is optimally designed for minimum end-to-end outage probability by deriving the optimum time and power allocated on the intracluster and intercluster slots of every single hop, given a per-link energy constraint. A simplified suboptimum resource allocation is also proposed, which performs close to the optimal policy. Results show that the proposed scheme achieves diversity equal to the equivalent MIMO system and significantly reduces energy consumption with respect to. the non-cooperative channel

Cooperative distributed MIMO channels in wireless sensor networks

In this article we illustrate the performance of Transmission Control Protocol in an overlay cognitive radio network under dynamic spectrum access. We show that the performance of TCP in overlay CR networks that implement DSA to be quite different from its performance in conventional networks, which do not allow DSA. The key difference is that secondary users in an overlay CR network have to cope with a new type of loss called service interruption loss, due to the existence of primary users. We demonstrate on an NS2 simulation testbed the surprising result: Excessive radio resource usage leads to a decrease in aggregate TCP throughput. This behavior is in contrast to the behavior of TCP in conventional networks, where throughput increases monotonically with the available radio resource.

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Tuning radio resource in an overlay cognitive radio network for TCP: Greed isn't good

We design transmission schemes in cooperative MIMO sensor networks that optimize the choice of rate and cluster for minimizing the energy consumption subject to a given average probability of error. In general it was observed that, the number of clusters to optimize the energy consumption is a non-increasing function of long haul distance. By virtue of this, we conclude that as the long haul distance increases the cooperation among the nodes increases. Also, for large long haul distances the optimal number of clusters is shown to converge to a constant. Next, we propose a provably convergent block coordinate descent algorithm to determine the optimal joint rate and number of clusters. Through our numerical results it was observed that a cluster optimized cooperative MIMO transmission scheme can be more energy efficient than a rate only optimized scheme. Also a joint rate and cluster optimized transmission scheme can yield large scale energy savings for short and medium range applications compared to the rate only or cluster only optimized transmission schemes.

Joint rate and cluster optimization in cooperative MIMO sensor networks

We consider the issue of fair share of the spectrum opportunity for the case of spectrum-overlay cognitive radio networks. Owing to the decentralized nature of the network, we adopt a pricing based game-theoretic approach where the actions of players would be choosing the modulation rate. The resulting game can be verified to be supermodular, and thus has at least one pure strategy Nash equilibrium. Next, we propose a Stochastic Approximation based algorithm for the computation of best response correspondence whose convergence to the Pareto-dominant Nash equilibrium can be established. Furthermore, we also propose a decentralized algorithm for tuning the price factor of the network. Our simulation results demonstrate the gains that can be achieved with pricing.

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Game Theoretic Rate Adaptation for Spectrum-Overlay Cognitive Radio Networks

We present the multiple-input multiple-output (MIMO) capacity results of an orthogonal frequency division multiplexing (OFDM) based system in correlated Ricean fading. Assuming that only the receiver has the channel state information (CSI), the dependence of capacity on the propagation and system parameters are investigated. Our simulation and analytical results show that in general there would be a loss in ergodic capacity because of the line-of-sight (LOS) component, while high cluster angle spread increases the ergodic capacity.

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MIMO capacity of an OFDM-based system under Ricean fading

In this paper, we compare the capacity of multiple-input and multiple-output (MIMO) systems under the dual effects of Rayleigh fading and log-normal shadowing with that of Rayleigh fading alone. Our numerical results suggest that in general, there could be a loss in capacity with both optimal and suboptimal adaptive transmission schemes under the combined effect of fading and shadowing when compared to that of a fading alone case.

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Laxminarayana S. Pillutla

Papers

Tuning radio resource in an overlay cognitive radio network for TCP: Greed isn't good

Joint rate and cluster optimization in cooperative MIMO sensor networks

Game Theoretic Rate Adaptation for Spectrum-Overlay Cognitive Radio Networks

MIMO capacity of an OFDM-based system under Ricean fading

Capacity of MIMO systems in Rayleigh fading and shadowing