Cognitive network

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

Outage performance analysis of underlay cognitive RF and FSO wireless channels

Abstract: In this work, the outage performance analysis of a dual-hop transmission system composed of asymmetric radio frequency (RF) channel cascaded with a free-space optical (FSO) link is presented. For the RF link, an underlay cognitive network is considered where the secondary users share the spectrum with licensed primary users. Indoor femtocells act as a practical example for such networks. More specifically, it is assumed that the RF link applies power control to maintain the interference at the primary network below a predetermined threshold. While the RF channel is modeled by the Rayleigh fading distribution, the FSO link is modeled by a unified Gamma-Gamma turbulence distribution. The FSO link accounts for pointing errors and both types of detection techniques (i.e. heterodyne detection as well as intensity modulation/direct detection (IM/DD)). With this model, a new exact closed-form expression is derived for the outage probability of the end-to-end signal-to-noise ratio of these systems in terms of the Meijer's G function and the Fox's H functions under fixed amplify-and-forward relay scheme. All new analytical results are verified via computer-based Monte-Carlo simulations and are illustrated by some selected numerical results.

Cooperative Cognitive Networks: Optimal, Distributed and Low-Complexity Algorithms

Abstract: This paper considers the cooperation between a cognitive system and a primary system where multiple cognitive base stations (CBSs) relay the primary user's (PU) signals in exchange for more opportunity to transmit their own signals. The CBSs use amplify-and-forward (AF) relaying and coordinated beamforming to relay the primary signals and transmit their own signals. The objective is to minimize the overall transmit power of the CBSs given the rate requirements of the PU and the cognitive users (CUs). We show that the relaying matrices have unity rank and perform two functions: Matched filter receive beamforming and transmit beamforming. We then develop two efficient algorithms to find the optimal solution. The first one has a linear convergence rate and is suitable for distributed implementation, while the second one enjoys superlinear convergence but requires centralized processing. Further, we derive the beamforming vectors for the linear conventional zero-forcing (CZF) and prior zero-forcing (PZF) schemes, which provide much simpler solutions. Simulation results demonstrate the improvement in terms of outage performance due to the cooperation between the primary and cognitive systems.

Cognitive radio: An intelligent wireless communication system

Abstract: The radio frequency spectrum is a scarce natural resource and its efficient use is of the utmost importance. The spectrum bands are usually licensed to certain services, such as mobile, fixed, broadcast, and satellite, to avoid harmful interference between different networks to affect users. Most spectrum bands are allocated to certain services but worldwide spectrum occupancy measurements show that only portions of the spectrum band are fully used. Moreover, there are large temporal and spatial variations in the spectrum occupancy. In the development of future wireless systems the spectrum utilization functionalities will play a key role due to the scarcity of unallocated spectrum. Moreover, the trend in wireless communication systems is going from fully centralized systems into the direction of self-organizing systems where individual nodes can instantaneously establish ad hoc networks whose structure is changing over time. Cognitive radios, with the capabilities to sense the operating environment, learn and adapt in real time according to environment creating a form of mesh network, are seen as a promising technology. This report collects the research work carried out in the CHESS and CHESSEXT projects on cognitive radios and networks in 2006-2008. CHESSEXT project included a one year research visit to Berkeley Wireless Research Center (BWRC) in Berkeley, California, which is a research center belonging to the University of California at Berkeley. The report presents an overview of cognitive radios and cognitive radio networks. The report lists enabling techniques for cognitive radios and describes the state-of-the-art in cognitive radio standards, regulation, products and research. Cognitive radio tasks are reviewed with a more detailed discussion on spectrum sensing, and frequency and power management functionalities. Mesh networks are reviewed as their selforganizing structure is appealing for the use of cognitive radios. Some measurements on current spectrum occupancy are described indicating that even with low overall spectrum occupancy figures, the spectrum band usage can still frequent and the temporal characteristics need to be identified to find spectrum opportunities. In addition to the literature review of cognitive radios, the results of the project include link budget calculations, evaluation of the performance of an energy detection scheme with and without cooperation between the nodes, transmitter power control, and intelligent frequency selection. The results include both analysis and computer simulations using Matlab. The availability of spectrum holes, i.e., frequency bands assigned to a primary user but that are vacant in a given place at a given time, can be estimated with spectrum sensing techniques, such as energy detection and feature detection. When little or no knowledge of the primary user signal is available, energy detection is useful while feature detection can exploit a priori information about the used waveforms. We have studied the performance of an energy detection scheme in terms of probability of detection and probability of false alarm without and with cooperation between the nodes. Cooperative detection by combining the observations of several cognitive radio nodes can be used to improve the performance of spectrum sensing. In addition to the estimation of the availability of spectrum holes, the predicted length of the spectrum holes is of interest in selecting suitable communication channels. Frequency and power management selects suitable frequency bands and transmission power levels for the cognitive radio system. We have studied intelligent channel selection for transmission of data and control information based on the spectrum sensing information, which should minimize harmful interference to other users. Cognitive radio can learn temporal characteristic of channels over time which can be exploited in intelligent channel selection to improve the performance. Transmitter power control needs to assure reliable communication in the changing environment without causing harmful interference to other users. A good candidate for cognitive radios is the inverse power control technique that allocates lower transmission power levels to good channel RESEARCH REPORT VTT-R-02219-08 5 (154) realizations and higher power levels to deeper fading, aiming at minimizing the interference and to allow more secondary users to share the spectrum. Moreover, truncation, i.e., cutting off transmission in poor channel realizations, leads to performance benefits. RESEARCH REPORT VTT-R-02219-08 6 (154)

Maximizing Throughput of Cognitive Radio Networks with Limited 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 (BS) controls and supports a set of fixed wireless subscribers. The objective is to maximize the downlink throughput of the cognitive network without affecting the performance of primary users. For this, we propose a mixed distributed/centralized control algorithm that requires minimal interaction between cognitive and primary devices. First, 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 ratios (SINR) of primary transmissions. After that, centralized channel assignment is carried out within the cognitive network to maximize its downlink throughput. Numerical results are obtained for the behaviors and performance of our proposed algorithm.

A cognitive future internet architecture

Abstract: This Chapter proposes a novel Cognitive Framework as reference architecture for the Future Internet (FI), which is based on so-called Cognitive Managers. The objective of the proposed architecture is twofold. On one hand, it aims at achieving a full interoperation among the different entities constituting the ICT environment, by means of the introduction of Semantic Virtualization Enablers, in charge of virtualizing the heterogeneous entities interfacing the FI framework. On the other hand, it aims at achieving an inter-network and inter-layer cross-optimization by means of a set of so-called Cognitive Enablers, which are in charge of taking consistent and coordinated decisions according to a fully cognitive approach, availing of information coming from both the transport and the service/content layers of all networks. Preliminary test studies, realized in a home environment, confirm the potentialities of the proposed solution.