Management is a complex task in today's heterogeneous and large scale networks like Cloud, IoT, vehicular and MPLS networks. Likewise, researchers and developers envision the use of artificial intelligence techniques to create cognitive and autonomic management tools that aim better assist and enhance the management process cycle. Bandwidth allocation models (BAMs) are a resource allocation solution for networks that need to share and optimize limited resources like bandwidth, fiber or optical slots in a flexible and dynamic way. This paper proposes and evaluates the use of Case-Based Reasoning (CBR) for the cognitive management of BAM reconfiguration in MPLS networks. The results suggest that CBR learns about bandwidth request profiles (LSPs requests) associated with the current network state and is able to dynamically define or assist in BAM reconfiguration. The BAM reconfiguration approach adopted is based on switching among available BAM implementations (MAM, RDM and ATCS). The cognitive management proposed allows BAMs self-configuration and results in optimizing the utilization of network resources.
As discussed in @cite_30 and @cite_34 , cognitive management using artificial intelligent techniques became more recently a relevant asset for communications management. Cognitive management has been applied using different AI techniques for cellular systems @cite_29 , vehicular networks @cite_5 , satellite communications @cite_17 , small cells mobile systems @cite_20 and, more extensively, to cognitive radio @cite_4 .
@cite_30 @cite_4 @cite_29 @cite_5 @cite_34 @cite_20 @cite_17
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Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a software-defined radio, is defined as an intelligent wireless communication system that is aware of its environment and uses the methodology of understanding-by-building to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: spl middot highly reliable communication whenever and wherever needed; spl middot efficient utilization of the radio spectrum. Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks. 1) Radio-scene analysis. 2) Channel-state estimation and predictive modeling. 3) Transmit-power control and dynamic spectrum management. This work also discusses the emergent behavior of cognitive radio. We evaluate the feasibility of cognitive machine-to-machine communication on cellular bands from engineering and business perspectives. We propose a hierarchical network structure for cognitive M2M communication, where cluster headers gather M2M traffic using cognitive radio and forward it to the cellular networks. This structure can resolve the congestion problem that arises in conventional M2M systems. We obtain the optimal network parameters that minimize congestion at the radio access network. In addition, we investigate the business value of cognitive M2M on cellular bands. Taking into account the network usage fee, service fee, and hardware production costs, we model the profit structure of M2M services and derive the condition under which the CR type of M2M communication is superior to conventional M2M communication. We find that the optimal network design parameters (i.e., the number of cluster headers and cluster size) for business value would be different from the parameters expected from an engineering point of view. We believe that cognitive M2M communication can be a good solution for dealing with excessive M2M traffic in cellular networks, in terms of technical feasibility and business opportunity. Wireless cognitive radio (CR) is a newly emerging paradigm that attempts to opportunistically transmit in licensed frequencies, without affecting the pre-assigned users of these bands. To enable this functionality, such a radio must predict its operational parameters, such as transmit power and spectrum. These tasks, collectively called spectrum management, is difficult to achieve in a dynamic distributed environment, in which CR users may only take local decisions, and react to the environmental changes. In this paper, we introduce a multi-agent reinforcement learning approach based spectrum management. Our approach uses value functions to evaluate the desirability of choosing different transmission parameters, and enables efficient assignment of spectrums and transmit powers by maximizing long-term reward. We then investigate various real-world scenarios, and compare the communication performance using different sets of learning parameters. We also apply Kanerva-based function approximation to improve our approach's ability to handle large cognitive radio networks and evaluate its effect on communication performance. We conclude that our reinforcement learning based spectrum management can significantly reduce the interference to the licensed users, while maintaining a high probability of successful transmissions in a cognitive radio ad hoc network. This paper describes a new class of attacks specific to cognitive radio networks. Wireless devices that can learn from their environment can also be taught things by malicious elements of their environment. By putting artificial intelligence in charge of wireless network devices, we are allowing unanticipated, emergent behavior, fitting a perhaps distorted or manipulated level of optimality. The state space for a cognitive radio is made up of a variety of learned beliefs and current sensor inputs. By manipulating radio sensor inputs, an adversary can affect the beliefs of a radio, and consequently its behavior. In this paper we focus primarily on PHY-layer issues, describing several classes of attacks and giving specific examples for dynamic spectrum access and adaptive radio scenarios. These attacks demonstrate the capabilities of an attacker who can manipulate the spectral environment when a radio is learning. The most powerful of which is a self-propagating AI virus that could interactively teach radios to become malicious. We then describe some approaches for mitigating the effectiveness of these attacks by instilling some level of "common sense" into radio systems, and requiring learned beliefs to expire and be relearned. Lastly we provide a road-map for extending these ideas to higher layers in the network stack. In the past decade, cognitive radio and cooperative communication techniques have been proposed in the literature for efficiently utilizing the radio resources. Cognitive radio is an emerging technology intended to enhance the utilization of the radio frequency spectrum. The cooperative communication system, with the same total power and bandwidth of legacy wireless communication systems, can increase the data rate of the future wireless communication system. A combination of cognitive radio with cooperative communication can further improve the future wireless network performance. Efficient resource allocation in cooperative cognitive radio network (CRN) is essential in order to meet the challenges of future wireless networks. In this article, a survey of resource allocation in cooperative CRN is presented. We discuss the taxonomy of objectives and protocols used in the literature for resource allocation in cooperative CRN. This paper also highlights the use of power control, cooperation types, network configurations and decision types used in cooperative CRN. Finally, directions for future research are outlined. Recently, the CR (cognitive radio) technology is gathering more and more attention because it has the capacity to deal with the scarce of the precious spectrum resource. Within the domain of CR technology, channel management of CR is of utmost importance due to its key role in the performance enhancement of the transmission and the minimum interference to the primary users as well. An 802.11 WLAN based ad-hoc protocol using the cognitive radio has been proposed in this paper. It provides the detection and protection for incumbent systems around the communication pair by separating the spectrum into the data channels and common control channel. By adding the available channel list into the RTS and CTS, the communication pair can know which data sub channels are available (i.e., no incumbent signal). We proposed an ENNI (exchanging of neighbor nodes information) mechanism to deal with the hidden incumbent device problem. The simulation results show that by using our protocol the hidden incumbent device problem (HIDP) can be solved successfully. We propose a probabilistic model based on stochastic geometry to analyze cognitive radio in a large wireless network with randomly located users sharing the medium with carrier sensing multiple access. Analytical results are derived on the impact of the interaction between primary and secondary users, on their medium access probability, coverage probability and throughput. These results can be seen as the continuation of the theory of priorities in queueing theory to spatial processes. They give insight into the guarantees that can be offered to primary users and more generally on the possibilities offered by cognitive radio to improve the effectiveness of spectrum utilization in such networks.