text: 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.
  @cite_30: Over the last decade, a significant amount of effort has been invested on architecting agile and adaptive management solutions in support of autonomic, self-managing networks. Autonomic networking calls for automated decisions for management actions. This can be realized through a set of pre-defined network management policies engineered from human expert knowledge. However, engineering sufficiently accurate knowledge considering the high complexity of today's networking environment is a difficult task. This has been a particularly limiting factor in the practical deployment of autonomic systems. ML is a powerful technique for extracting knowledge from data. However, there has been little evidence of its application in realizing practical management solutions for autonomic networks. Recent advances in network softwarization and programmability through SDN and NFV, the proliferation of new sources of data, and the availability of lowcost and seemingly infinite storage and compute resource from the cloud are paving the way for the adoption of ML to realize cognitive network management in support of autonomic networking. This article is intended to stimulate thought and foster discussion on how to defeat the bottlenecks that are limiting the wide deployment of autonomic systems, and the role that ML can play in this regard.
  @cite_4: 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.
  @cite_29: 
  @cite_5: In this paper, we design and test the performance of a distributed and adaptive resource management controller, which allows the optimal exploitation of cognitive radio and soft-input soft-output data fusion in vehicular access networks. The goal is to allow energy and computing-limited car smartphones to utilize the available vehicle-to-infrastructure (V2I) WiFi connections for performing traffic offloading toward local or remote clouds by opportunistically acceding to a spectral-limited wireless backbone built up by multiple roadside units (i.e., cloudlets). We cast the resource management problem into a suitable constrained stochastic network utility maximization problem and derive the optimal cognitive resource manager that dynamically allocates the access time windows at the serving roadside units (i.e., the access points), together with the access rates and traffic flows at the served vehicular clients (i.e., the secondary users of the wireless backbone). The developed controller provides hard reliability guarantees to the cloud service provider (i.e., the primary user of the wireless backbone) on a per-slot basis. Furthermore, it is able to acquire context information about the currently available bandwidth–energy resources to quickly adapt to the mobility-induced abrupt changes in the state of the vehicular network.
  @cite_34: Contributors. Foreword 1. Foreword 2. Preface. Acknowledgements. Introduction. Chapter 1: Biologically Inspired Networking. Chapter 2: The Role of Autonomic Networking in Cognitive Networks. Chapter 3: Adaptive Networks. Chapter 4: Self-Managing Networks. Chapter 5: Machine Learning for Cognitive Networks: Technology Assessment and Research Challenges. Chapter 6: Cross-Layer Design and Optimization in Wireless Networks. Chapter 7: Cognitive Radio Architecture. Chapter 8: The Wisdom of Crowds: Cognitive Ad hoc Networks. Chapter 9: Distributed Learning and Reasoning in Cognitive Networks: Methods and Design Decisions. Chapter 10: The Semantic Side of Cognitive Radio. Chapter 11: Security Issues in Cognitive Radio Networks. Chapter 12: Intrusion Detection in Cognitive Networks. Chapter 13: Erasure Tolerant Coding for Cognitive Radios. Index.
  @cite_20: Cognitive small cell networks have been envisioned as a promising technique for meeting the exponentially increasing mobile traffic demand. Recently, many technological issues pertaining to cognitive small cell networks have been studied, including resource allocation and interference mitigation, but most studies assume non-cooperative schemes or perfect channel state information (CSI). Different from the existing works, we investigate the joint uplink subchannel and power allocation problem in cognitive small cells using cooperative Nash bargaining game theory, where the cross-tier interference mitigation, minimum outage probability requirement, imperfect CSI and fairness in terms of minimum rate requirement are considered. A unified analytical framework is proposed for the optimization problem, where the near optimal cooperative bargaining resource allocation strategy is derived based on Lagrangian dual decomposition by introducing time-sharing variables and recalling the Lambert-W function. The existence, uniqueness, and fairness of the solution to this game model are proved. A cooperative Nash bargaining resource allocation algorithm is developed, and is shown to converge to a Pareto-optimal equilibrium for the cooperative game. Simulation results are provided to verify the effectiveness of the proposed cooperative game algorithm for efficient and fair resource allocation in cognitive small cell networks.
  @cite_17: The lack of available unlicensed spectrum together with the increasing spectrum demand by multimedia applications has resulted in a spectrum scarcity problem, which affects satellite communications (SatCom) as well as terrestrial systems. The goal of this paper is to propose resource allocation (RA) techniques, i.e., carrier, power, and bandwidth allocation, for a cognitive spectrum utilization scenario where the satellite system aims at exploiting the spectrum allocated to terrestrial networks as the incumbent users without imposing harmful interference to them. In particular, we focus on the microwave frequency bands 17.7–19.7 GHz for the cognitive satellite downlink and 27.5–29.5 GHz for the cognitive satellite uplink, although the proposed techniques can be easily extended to other bands. In the first case, assuming that the satellite terminals are equipped with multiple low block noise converters (LNB), we propose a joint beamforming and carrier allocation scheme to enable cognitive space-to-Earth communications in the shared spectrum where fixed service (FS) microwave links have priority of operation. In the second case, however, the cognitive satellite uplink should not cause harmful interference to the incumbent FS system. For the latter, we propose a joint power and carrier allocation (JPCA) strategy followed by a bandwidth allocation scheme, which guarantees protection of the terrestrial FS system while maximizing the satellite total throughput. The proposed cognitive satellite exploitation techniques are validated with numerical simulations considering realistic system parameters. It is shown that the proposed cognitive exploitation framework represents a promising approach for enhancing the throughput of conventional satellite systems.
summary: 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 .