基于次用户分级的PRP M/G/m排队论频谱切换模型

为满足次用户不同时延需求并提高信道利用率,该文提出一种基于次用户分级的PRP M/G/m排队论频谱切换模型。首先,根据次用户主要业务的时延需求不同,对次用户进行分级评价,将次用户分为时延敏感型和非时延敏感型用户,时延敏感型次用户具有更高的优先级来接入信道。其次,采用PRP M/G/m排队论来建立基于用户分级的频谱切换模型。同时推导和分析了该模型下不同优先级次用户扩展数据传输时间,进而提出了面向各级次用户自适应频谱切换策略。仿真结果表明,相较于已有频谱切换策略,该模型能有效降低次用户切换时延以及扩展数据传输时间,保证高优先级次用户的服务质量,提升次用户频谱切换的综合性能。     

基于网络编码的感知无线网多中继机制

多中继感知协作通信技术是无线通信领域中,利用时空分集提升网络吞吐量的热门研究方向。该文首次提出了感知无线网中分布式译码转发后的中继冗余问题,并在理论与仿真上证明了现有传输协议不仅不足以支持多中继感知通信,而且由于其衍生了大量冗余而降低了网络吞吐量。针对该问题,该文提出了在缓存队列中结合随机网络编码的中继机制,从而有效地避免了中继冗余,使得多中继感知的吞吐量接近理想上界,减少缓存空间的占用,仿真结果表明该文提出的机制是一种高效实用的感知中继传输机制。     

Policies for the reconfiguration of cognitive wireless infrastructures to 3G Radio Access Technologies

The ceaseless evolution of wireless communications is reflected nowadays on the introduction of Beyond-3G (B3G) systems, characterized by the coexistence and cooperation of various Radio Access Technologies (RATs), over a common infrastructure. Major facilitator of this convergence is the advent of cognitive networks, which deploy elements (base stations and mobile terminals) that are able to proactively adapt to environmental stimuli, so that to optimize their performance. Part of the adaptation action takes place in cognitive base stations that own several reconfigurable transceivers, which are controlled by appropriate management functionality and may dynamically change their operating parameters. Each reconfiguration set includes a specific RAT, carrier frequency, as well as demand volume to be allocated per transceiver. Accordingly, proper evaluation of the various candidate reconfiguration sets appears to be of high significance. To this effect, in this paper we consider a cognitive network segment with transceivers operating at 3G RAT/carrier and we solve the DAMC problem (Demand Allocation into Multiple Carriers problem), which aims at evaluating and selecting the optimum policy to allocate the demand into the available 3G carrier frequencies. Optimality is expressed in terms of minimizing the total transmitted/received power per base station, thus deciding for the reconfigurations with the least impact on network interference. Indicative simulation scenarios and results are also presented for the validation and verification of the proposed functionality.

Measuring Lineup Difficulty By Matching Distance Metrics With Subject Choices in Crowd-Sourced Data

Graphics play a crucial role in statistical analysis and data mining. Being able to quantify structure in data that is visible in plots, and how people read the structure from plots is an ongoing challenge. The lineup protocol provides a formal framework for data plots, making inference possible. The data plot is treated like a test statistic, and lineup protocol acts like a comparison with the sampling distribution of the nulls. This article describes metrics for describing structure in data plots and evaluates them in relation to the choices that human readers made during several large Amazon Turk studies using lineups. The metrics that were more specific to the plot types tended to better match subject choices, than generic metrics. The process that we followed to evaluate metrics will be useful for general development of numerically measuring structure in plots, and also in future experiments on lineups for choosing blocks of pictures. Supplementary materials for this article are available online.     

The value of cognitive foundations for dynamic social theory

It is possible to develop models of social behavior that are predicated on detailed mechanical models of cognition. Cognitively based social models are potentially unified theoretical frameworks that can be used to explain a wide variety of social phenomena. Moreover, if a knowledge representation scheme and a knowledge acquisition scheme are specified in the underlying cognitive model then it is possible to produce a dynamic social model. The resulting social model can thus be used to predict and explain not only conditions for specific behaviors but changes in those behaviors over time.

Constructuralism is a theory of social behavior that rests on a cognitive model. The cognitive model specified has a knowledge representation scheme, knowledge acquisition procedures, and control procedures for shifting cognitive attention. The resulting social model is a dynamic model that can be used to explain both conditions for the occurrence of a behavior and social and individual changes that accrue do to a series of behaviors. The explanatory breadth of the model is illustrated by looking at predictions about a variety of social phenomena including: development of shared knowledge, identical behavior by members of the society, foreign language acquisition, clique formation, civil disobedience, and diffusion of innovative information.     

Guiding Data Analysts with Visual Statistical Strategies

Abstract

The concept of statistical strategy is introduced and used to develop a structured graphical user interface for guiding data analysis. The interface visually represents statistical strategies that are designed by expert data analysts to guide novices. The representation is an abstraction of the expert's concepts of the essence of a data analysis. We argue that an environment that visually guides and structures data analysis will improve data analysis productivity, accuracy, accessibility, and satisfaction in comparison to an environment without such aids, especially for novice data analysts. Our concepts are based on notions from cognitive science, and can be empirically evaluated. The interface consists of two interacting windows—the guidemap and the workmap. Each window contains a graph that has nodes and edges. The guidemap graph represents the statistical strategy for a specific statistical task (such as describing data). Nodes represent potential data analysis actions that can be taken by the system. Edges represent potential actions that can be taken by the analyst. The guidemap graph exists prior to the data analysis session, having been created by an expert. The workmap graph represents the complete history of all steps taken by the data analyst. It is constructed during the data analysis session as a result of the analyst's actions. Workmap nodes represent data sets, data models, or data analysis procedures that have been created or used by the analyst. Workmap edges represent the chronological sequence of the analyst's actions. One workmap node is highlighted to indicate which statistical object is the focus of the strategy. We illustrate our concepts with ViSta, the Visual Statistics system that we have developed.     

Cognitive Radio Dynamic Access Techniques

The ever growing demand for wireless services has placed enormous burden on valuable resources such as spectral bandwidth. This has resulted in a major rethinking in resource allocation policies culminating in an explosion of research activity in the field of Cognitive Radio (CR) towards optimum resource usage. In this tutorial paper the physical layer design and transmission techniques for CR in the context of efficient spectrum utilization are discussed. Spectrum sensing as the key element of CR awareness is described. Orthogonal frequency division multiplexing (OFDM) as a spectrally efficient modulation scheme is discussed and the rationale for its use in the CR system is explained. Spectrum pooling for efficient use of spectrum is studied and the role of adaptive OFDM techniques in this method is highlighted. Wavelet basis function as a replacement of Fourier transform in OFDM is evaluated. MIMO system as an added value to the CR performance is described. Adaptive Waveform and beamforming as alternative techniques in CR are reviewed.

Performance analysis of cognitive radio networks using channel-prediction-probabilities and improved frame structure

This paper focuses on potential issues related to the random selection of a sensing channel that occurs after the prediction phase in a Cognitive Radio Network (CRN). A novel approach (Approach-1) for improved selection is proposed, which relies on the probabilities of channels by which they are predicted idle. Further, closed-form expressions are derived for the throughput of Cognitive Users (CUs) in the conventional and proposed approaches. In addition to this, a fundamental approach for computing the prediction probabilities is also proposed. Moreover, a new challenging issue named “sense and stuck” was observed in the conventional approach. The proposed approach is validated by comparing the results achieved with the results of the conventional approach. However, to achieve the prediction probabilities, the pre-channel-state-information is a prerequisite, but it may be unavailable for particular scenarios; therefore, a modified selection method is introduced to avoid the sense and stuck problem. An algorithm to evaluate the throughput using the random, improved, and modified selection methods is presented with its space and time complexities. Furthermore, for additional improvement in the throughput of the CU, a new frame structure is introduced, in which the spectrum prediction and sensing periods are exploited for simultaneous transmission of data via the underlay spectrum access technique (Approach-2). The simulated results of Approach-2 are compared with our pre-obtained results of Approach-1, which confirm significant improvement in the throughput.     

Optimal spectrum utilisation in cognitive radio networks based on processor sharing techniques

Cognitive radio networks have achieved higher efficiency in terms of spectrum usage; however they do not readily solve any competition for access among secondary users. Optimisation is applied to an underlay network to obtain the optimal solution for at least two secondary users operating simultaneously on the same channel. Performance measures are used as the target for optimisation. However, the objective function is difficult to obtain in closed form. For the performance measures, queueing theory, particularly weighted processor sharing techniques are employed to model the system dynamics and behaviour. Transmission power and the interference temperature limit are used to allocate weights to the secondary users. Queue length and waiting time functions obtained from the queuing models are used for optimisation. After establishing that the objective function can be considered to be pseudo‐convex, convex programming is then deployed to obtain the optimised solution. The results suggest that there is indeed an improvement in network performance after optimisation. The immediate benefits of such a system are firstly improved spectrum utilisation through adding multiple secondary users and secondly, through optimisation, higher performance that can be achieved by the secondary users.     

Enhanced spectrum sensing using a combination of energy detector,matched filter and cyclic prefix

In this paper, we propose a sensing scheme based on energy detection, matched filter and cyclic prefix. Both Equal Gain Combining (EGC) and optimal combination of the aforementioned detectors are investigated in cooperative and non-cooperative spectrum sensing scenarios. In packet transmission systems such as OFDM (Orthogonal Frequency Division Multiple access) systems, the proposed scheme takes advantage of utilizing more samples than individual detectors, i.e., cyclic prefix, training or pilot samples, and data payload samples. The proposed combine-sensing scheme offers higher detection probability and lower false alarm probability, as compared with the performance of individual detectors over the same frame duration. Simulation results are congruent with the theoretical curves and confirm the validity of our derivations.