# The Challenges and Compatibility of Mobility Management Solutions for Future Networks
## 6 Proposed 6G MM Framework  
According to the discussion in the above sections and in the light of defined parameters (dependability, extensibility, adaptability and power consumption), we have presented our vision for 6G MM architecture in Figure 2. This architecture is characterized at the access, core and extreme edge network levels based on their impact on the network. The architecture mentioned in Figure 2 presents the strategies such as the RAN as service and multi-connectivity with the application of different access points and RATs efficiently at the access level. Next, it is also revealed that the RAT selection procedure may occur at the device or access level. This RAT selection process is also called the cell-less selection process, in which no overhead of handoff occurs from the adaptability, flexibility, and QoS point of view. Further, the core network techniques in Figure 2 are different from previous generations (G1 to G5) due to addition of the following features: (i) intelligent CN signaling, (ii) the mixture of distributed MM for NFV and SDN, (iii) network slicing pattern, (iv) 3D network paradigm, and (iv) low power consumption network operations (using energy harvesting circuit). Such features provide dependability, adaptability, extensibility and efficient use of battery power in a broader sense. Furthermore, the mentioned core network techniques/paradigms/methods must be accompanied by efficient CN (core network) signaling techniques.  
Similarly, some other techniques or paradigms are used in the core or access network or collaboration of both (access level and core network level). Those techniques (core network or access network or both) can fulfill dependability and adaptability criteria. Likewise, D2D methods are expected to deliver additional support for mobility at the device level (edge network) with the help of CP and DP options. Along with these methods (core, access, and extreme edge network level), as described in Figure 2, the named data networking and information-centric support will also be offered at all levels; therefore, it helps to maintain IP addresses/prefixes during mobility while resolving destinations via names [119,120]. It has been observed that traditional IP address/prefix allocation techniques are not supposed to be altered by the addition of NDN-ICN ideas at all the levels of layers (core, access, and extreme edge); instead, they provide support at the top of this IP address allocation technique. Further, the deep learning and data selection using (machine learning (ML) techniques again provide support across the various levels by learning the complex characteristics of the user's mobility, the perspective of the network,  
Figure 2: Future 6G MM structure.  
and an on-demand MM of the users. Furthermore, the cross-layer technique is covered across multiple levels. It applies the strategies by using the existing information at each level and supporting optimum MM decisions across the network.  
To summarize the finding and discussion in the preceding sections, we employ the possible solutions for bridging the technology gap, as identified in Section 5.4, and specific strategies from state-of-the-art and legacy MM mechanisms, as specified in Sections 4 and 5. We have presented Tables 2-5. These Tables indicate the fulfillment of our proposed framework for the 6G network concerning the satisfaction of extensibility, adaptability, power consumption and dependability conditions. Finally, the suggested architecture in Figure 2 will also meet all of the requirements described in Table 1, hence offering a comprehensive solution. With this in mind, we summarize the key results of this work and conclude this paper as follows.