Cognitive network

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

Probabilistic Analysis of Mutual Interference in Cognitive Radio Communications

Abstract: Mutual interference plays decisive role in successful operation of a cognitive radio (CR) network. In order to evaluate the interference experienced by a primary user, we have considered a realistic communication scenario where the transmission of any secondary user is not only constrained by the primary network but also constrained from the secondary network itself so that harmful interference to other secondary users can be avoided. The transmission probability for each secondary user has been derived using joint density function of the distance from secondary user to the primary receiver and the distance between adjacent secondary users. Based on this analysis, a closed-form expression for expected mutual interference experienced by a primary receiver is derived. In addition, a cognitive network is simulated to evaluate the aggregated interference experienced by the primary receiver along with constraints from both primary and secondary networks. The proposed interference modeling is validated by comparing theoretical and simulated results of aggregated interference experienced by the primary receiver.

Optimal scheduling and power allocation in cooperate-to-join cognitive radio networks

Abstract: In this paper, optimal resource allocation policies are characterized for wireless cognitive networks under the spectrum leasing model. We propose cooperative schemes in which secondary users share the time-slot with primary users in return for cooperation. Cooperation is feasible only if the primary system's performance is improved over the non-cooperative case. First, we investigate a scheduling problem where secondary users are interested in immediate rewards. Here, we consider both infinite and finite backlog cases. Then, we formulate another problem where the secondary users are guaranteed a portion of the primary utility, on a long-term basis, in return for cooperation. Finally, we present a power allocation problem where the goal is to maximize the expected net benefit defined as utility minus cost of energy. Our proposed scheduling policies are shown to outperform non-cooperative scheduling policies, in terms of expected utility and net benefit, for a given set of feasible constraints. Based on Lyapunov optimization techniques, we show that our schemes are arbitrarily close to the optimal performance at the price of reduced convergence rate.

Are All Social Networks Structurally Similar

Abstract: The modern age has seen an exponential growth of social network data available on the web. Analysis of these networks reveal important structural information about these networks in particular and about our societies in general. More often than not, analysis of these networks is concerned in identifying similarities among social networks and how they are different from other networks such as protein interaction networks, computer networks and food web. In this paper, our objective is to perform a critical analysis of different social networks using structural metrics in an effort to highlight their similarities and differences. We use five different social network datasets which are contextually and semantically different from each other. We then analyze these networks using a number of different network statistics and metrics. Our results show that although these social networks have been constructed from different contexts, they are structurally similar.

A Cognitive Network Controller Based on Spiking Neurons

Abstract: Cognitive networks plan, decide, and act at different layers of the protocol stack, based on perceived conditions of the network state and assigned rules. We introduce a Cognitive Network Controller (CNC) that relies on a spiking neural network to implement artificial cognition. Based on the outcome of prior actions, the CNC learns how to improve future decisions to maximize its assigned objective. We present the design of the CNC and associated spiking neural network model, a decision encoding strategy based on the time-to- fire of spikes, a learning rule, and synapse weight management. Then, we evaluate the effectiveness of the proposed method using an event-driven simulation of the online selection of end-to-end paths for file transfers over a network of mixed performance. The results indicate that the CNC effectively learns how to dynamically select paths for file transfers that produce lower latency than conventional methods for a broad range of network conditions. The proposed CNC potentially facilitates the integration of cognition and future network applications that require autonomous performance optimization.