Design and implementation of a cognitive engine functional architecture

Abstract

Intelligence is the most important characteristic for cognitive wireless networks. A cognitive engine built on reconfigurable wireless networks is the key to implementing this characteristic. The design and implementation of a cognitive engine is important in research on the theory of initiative cognition for cognitive wireless networks. This paper first discusses research on cognitive loops, then investigates cognitive functions in the loop through the design of a universal cognitive engine functional architecture, and finally verifies the architecture on the platform of a cognitive engine prototype system. cognitive radio, cognitive wireless network, cognitive loop, cognitive engine, prototype system "Cognition" is the scientific term for the process of thinking. Mitola et al.[1] introduced "cognition" to wireless communication and coined the phrase "Cognitive Radio" in 1999.From then on, cognitive radio and cognitive networks have attracted increased attention from researchers and research institutions [2][3][4], and have become a hotspot in the communication domain [5]. A cognitive network is able to sense information from the outside world and its internal state, and can then adjust the wireless network (including working frequency, air access, data protocols, and so on) by parsing and orienting the sensing information to adapt to changes in the environment. Moreover, a cognitive network can still learn to form new knowledge. Thus, a cognitive network is an intelligent network that can sense, make decisions, and learn like humans. Implementing such a wireless network requires a functional architecture as the brain to control the whole cognitive process, that is, a cognitive engine. The cognitive engine integrates various cognitive functions (such as sensing, learning, and reasoning) so that it can intelligently control a wireless network to implement a cognitive cycle. Rieser [6], a researcher at Virginia Tech, first modeled the biologically inspired cognitive engine called BioCR. Later, his colleague Rondeau [7] further developed the theory of cognitive engines. He evolved cognitive engine modeling as a multi-objective optimization through genetic algorithms and built a cognitive engine architecture composed of a cognitive controller, sensors, a decision maker, optimizers, and interfaces. However, as this cognitive engine was purely designed for waveform optimization, it has limitations. In this paper, we set out to design a universal cognitive engine functional architecture by studying the cognitive loop.

Cross-Layer Analysis in Cognitive Radio—Context Identification and Decision Making Aspects

“…They proposed a structure for the cognitive core consisting of a data base, a learner, a reasoner and an optimizer. Also, in this case, our paper complements [21] since we follow similar architectural principles for the CE (trying to keep it as generic as possible to allow its applicability in many different scenarios and use cases) and we further show a hierarchical view of the related information elements in each of the lower communication stack layers that can be used by the various sub-blocks of the CE. Considering the aforementioned analysis, it is apparent that an effective and precise parameter exchange i.e.…”

“…Our paper builds on such concepts since it shows in detail how information from different communication stack layers can be used by appropriate CEs or metaCEs that will act as intelligent agents to process this crosslayer information and provide thus the expected performance optimization. In [21] the authors proposed a universal CE functional architecture design by studying the cognitive loop OOPDAL (Observe, Orient, Plan, Decide, Act, and Learn) by also considering a Knowledge Base. They integrated a cognitive core, a scheduler, a user interface, a sensor interface and a network interface that could make the necessary reconfiguration of the different OSI layer techniques according to the specific parameters, requirements and constraints.…”

In the development of wireless cognitive science

“…Furthermore, a novel concept of resource flow has been introduced for an intelligent and autonomous cognitive resource management under various types of resource [4]. Besides, other key technologies and applications have also been discussed in this special topic, such as the cognitive engine design [5], delay performance analysis [6], reconfiguration decision making [7], bio-inspired approaches and applications [8] and energy-efficient spectrum-aware clustering for cognitive radio sensor networks [9]. Therefore, this special topic will provide a comprehensive introduction to cognitive wireless network theory and technology, reduce the gap between science and engineering communities and promote innovations for future wireless network evolution.…”