Cognitive network

About: Cognitive network is a research topic. Over the lifetime, 4213 publications have been published within this topic receiving 107093 citations.

Dynamical cognitive network - an extension of fuzzy cognitive map

Abstract: We present the dynamic cognitive network (DCN) which is an extension of the fuzzy cognitive map (FCM). Each concept in the DCNs can have its own value set, depending on how precisely it needs to be described in the network. This enables the DCN to describe the strength of causes and the degree of effects that are crucial to conducting meaningful inferences. The arcs in the DCN define dynamic, causal relationships between concepts. Structurally, DNCs are scalable and more flexible as compared to FCMs. A DCN can be as simple as a cognitive map and FCM, or as complex as a nonlinear dynamic system. To demonstrate the potential applications of DCNs, we present some simulation results. This paper represents our first attempt to develop a dynamic fuzzy inference system using causal relationships. There are many interesting and challenging theoretical and practical issues in DCNs open to further research.

Power Control and Channel Allocation in Cognitive Radio Networks with Primary Users' Cooperation

Abstract: We consider a point-to-multipoint cognitive radio network that shares a set of channels with a primary network. Within the cognitive radio network, a base station controls and supports a set of fixed-location wireless subscribers. The objective is to maximize the throughput of the cognitive network while not affecting the performance of primary users. Both downlink and uplink transmission scenarios in the cognitive network are considered. For both scenarios, we propose two-phase mixed distributed/centralized control algorithms that require minimal cooperation between cognitive and primary devices. In the first phase, a distributed power updating process is employed at the cognitive and primary nodes to maximize the coverage of the cognitive network while always maintaining the constrained signal to interference plus noise ratio of primary transmissions. In the second phase, centralized channel assignment is carried out within the cognitive network to maximize its throughput. Numerical results are obtained for the behaviors and performance of our proposed algorithms.

Sociocognitive Analysis of Group Decision Making among Consumers

Abstract: A sociocognitive perspective is developed to further the understanding of the relation between cognitive and social processes. The approach combines social network analysis with a cognitive network perspective to enable the researcher to study how social structure influences cognitive structure and how shared cognitive structure influences choice. This perspective is applied to how a group (with several subgroups) makes a consumer decision with consequences for the entire group. The results show that social structure influences cognitive structure, that shared knowledge is related to choice, and that the sociocognitive perspective provides new insights to prior literature on group decision making and the relation between group membership and brand choice.

Kernel-Based Learning for Statistical Signal Processing in Cognitive Radio Networks: Theoretical Foundations, Example Applications, and Future Directions

Abstract: Kernel-based learning (KBL) methods have recently become prevalent in many engineering applications, notably in signal processing and communications. The increased interest is mainly driven by the practical need of being able to develop efficient nonlinear algorithms, which can obtain significant performance improvements over their linear counterparts at the price of generally higher computational complexity. In this article, an overview of applying various KBL methods to statistical signal processing-related open issues in cognitive radio networks (CRNs) is presented. It is demonstrated that KBL methods provide a powerful set of tools for CRNs and enable rigorous formulation and effective solutions to both long-standing and emerging design problems.

Convergence of cognitive radio networks

Abstract: In this paper, we examine the conditions and behavior of several common convergence dynamics from game theory and show how they influence the structure of networks of cognitive radios. We then apply these to previously proposed distributed power control algorithms and describe how they impact network complexity.