Cognitive network

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

Maximizing Spectrum Utilization of Cognitive Radio Networks Using Channel Allocation and Power Control

Abstract: We consider a cognitive radio network in which a set of base stations make opportunistic unlicensed spectrum access to transmit data to their subscribers. As the spectrum of interest is licensed to another (primary) network, power and channel allocation must be carried out within the cognitive radio network so that no excessive interference is caused to any primary user. We are interested in spectrum-allocation/power-control schemes that maximize the spectrum utilization of the cognitive network while appropriately protecting primary users. While doing so, the control schemes must also meet the required signal to interference plus noise ratio (SINR) of each subscriber of the cognitive network. This problem can be formulated as a linear mixed (0-1) integer programming. Due to the high complexity in obtaining optimal spectrum-allocation/power-control schemes, we propose a suboptimal scheme that can be obtained at lower complexity while still achieving good spectrum utilization. This suboptimal scheme is constructed based on the idea of a dynamic interference graph that captures the interfering effects. Numerical studies of our control scheme are presented.

Standardization and research in cognitive and dynamic spectrum access networks: IEEE SCC41 efforts and other activities

Abstract: Spectrum crowding, spectrum management, quality of service, and user support are the topics of vigorous research in the cognitive and dynamic spectrum access network communities. As research matures, standardization provides a bridge between research results, implementation, and widespread deployment of such networks. This article reports recent developments within the IEEE Standardization Coordinating Committee 41, "Dynamic Spectrum Access Networks." It outlines possible future standardization topics for IEEE SCC41, in the framework of other related standardization activities, and discusses open research issues that present future challenges for the standardization community.

Brain-computer interfacing based on cognitive control.

Abstract: Objective: Brain– computer interfaces (BCIs) translate deliberate intentions and associated changes in brain activity into action, thereby offering patients with severe paralysis an alternative means of communication with and control over their environment. Such systems are not available yet, partly due to the high performance standard that is required. A major challenge in the development of implantable BCIs is to identify cortical regions and related functions that an individual can reliably and consciously manipulate. Research predominantly focuses on the sensorimotor cortex, which can be activated by imagining motor actions. However, because this region may not provide an optimal solution to all patients, other neuronal networks need to be examined. Therefore, we investigated whether the cognitive control network can be used for BCI purposes. We also determined the feasibility of using functional magnetic resonance imaging (fMRI) for noninvasive localization of the cognitive control network. Methods: Three patients with intractable epilepsy, who were temporarily implanted with subdural grid electrodes for diagnostic purposes, attempted to gain BCI control using the electrocorticographic (ECoG) signal of the left dorsolateral prefrontal cortex (DLPFC). Results: All subjects quickly gained accurate BCI control by modulation of gamma-power of the left DLPFC. Prelocalization of the relevant region was performed with fMRI and was confirmed using the ECoG signals obtained during mental calculation localizer tasks. Interpretation: The results indicate that the cognitive control network is a suitable source of signals for BCI applications. They also demonstrate the feasibility of translating understanding about cognitive networks derived from functional neuroimaging into clinical applications. ANN NEUROL 2010;67:809 – 816

Power Control for Cognitive Radio Ad Hoc Networks

Abstract: While FCC proposes spectrum sharing between a legacy TV system and a cognitive radio network to increase spectrum utilization, one of the major concerns is that the interference from the cognitive radio network should not violate the QoS requirements of the primary users. In this paper, we consider the scenario where the cognitive radio network is formed by secondary users with low power personal/portable devices and when both systems are operating simultaneously. A power control problem is formulated for the cognitive radio network to maximize the energy efficiency of the secondary users and guarantee the QoS of both the primary users and the secondary users. The feasibility condition of the problem is derived and both centralized and distributed solutions are provided. Because the co-channel interference are from heterogeneous systems, a joint power control and admission control procedure is suggested such that the priority of the primary users is always ensured. The simulation results demonstrate the effectiveness of the proposed schemes.

Cognitive Radio Network setup without a Common Control Channel

Abstract: The concept of cognitive radio networks has introduced a new way of sharing the open spectrum flexibly and efficiently. However, there are several issues that hinder the deployment of such dynamic networks. The common control channel problem is one of such issue. Cognitive radio networks are designed by assuming the availability of a dedicated control channel. In this paper, we identify and discuss the network setup problem as a part of the common control channel problem. Probabilistic and deterministic ways to start the initial communication and setup a cognitive radio network without the need of having a common control channel in both centralized and multi-hop scenarios are suggested. Extensive MATLAB simulations validate the effectiveness of the algorithms.