ting. Taking into account the end-to-end performance or efficiency, self-learning can be also carried out in the process of cognition in order to help collect more valuable information and also make further proper adjustments. Reference [54] considers Cognitive Networks as a scheme to make up for the shortcomings of current networks and improve performances and status of net- works through approaches of a series of active actions, including observing, learn- ing, decision making, and also adjusting. Reference [55] indicates positive agreements with views above and meanwhile, it also deems Cognitive Networks as a dynamic network which can be self-aware and adaptive for user requirements and dynamic changes in network environments. Cognitive Networks can also dynamically adjust network functions and even net- work topological structures; all those adjustments must strictly follow predefined business rules and corresponding network strategies. Overview of Cognitive NGSDP Model Based on all information discussed above, our understandings of Cognitive Networks are proposed as follows: Cognitive Networks are a kind of intelligent and autonomous network, which can be self-aware and adaptive of information in net- work environments, including network resources, user behaviors, network prob- lems, and SO on. Furthermore, it enables network to adjust according to all these changes in network environments through self-functions and self-actions following predefined service levels and performance strategies. Consequently, network resources can be distributed and utilized more efficiently and flexibly, and the most important is that it is totally possible that Cognitive Networks can enable the whole processes to be nearly fully autonomous without unnecessary human interactions. The ultimate goals aim at flexible adjustments and improvements of network per- formance in order to offer more reliable guarantees of SLA, QoS, and QoE based on predefined business rules and service levels. 6.3.2.3 Cognitive Functions With cleared ind