## Optimized Two-Tier Caching with Hybrid Millimeter-Wave and Microwave Communications for 6G Networks
### Related Works  
There are features such as signal propagation and penetration that differentiate mmWave from \(\upmu\)Wave networks due to hardware and channel characteristics. Since both the mmWave and \(\upmu\)Wave have their own benefits, mmWave and \(\upmu\)Wave cellular networks are anticipated to coexist within cellular networks. In [22], a heterogeneous network (HetNet) environment containing both the mmWave and \(\upmu\)Wave SBSs is considered. The authors maximized data access via cache resources and network success probability considering the data popularity profile and data sizes. In [23], the authors proposed a collaborative edge resource model where dual connectivity is exploited through the deployment of \(\upmu\)Wave and mmWave base stations empowered by edge capability. The authors focused on joint caching and computation optimization to improve the reliability. Then, a best-first branch-and-bound algorithm was devised to achieve high reliability. The obtained results demonstrate a satisfactory virtual reality delivery.  
In [24], the authors focused on cost minimization by integrating data caching and switching between mmWave and \(\upmu\)wave signals to improve network coverage. The complexity of cost calculations was reduced by introducing an approximation approach and the results were validated numerically. The obtained results demonstrate a substantial reduction in cost. In the same vein, the authors in [25] considered evolving the data popularity profile and modeled this through the Markov decision process (MDP). The authors deployed a mmWave link for the fronthaul in order to achieve a higher capacity. Moreover, the \(\upmu\)Wave backhaul link was used to control the network performance degradation due to blockage. The authors proposed a dynamic programming technique to optimize caching in order to minimize the cost. The results demonstrate a substantial improvement in caching and data fetching decisions while adapting to time-varying data popularity. In [26], the authors minimized the average long-term cost by improving caching decisions to enhance content fetching via cache. The authors exploited spectrum switching between \(\upmu\)Wave and mmWave and introduced a chain point detection-assisted reinforcement learning (RL) approach to observe an evolving environment in order to optimize learning and the caching policy accordingly.  
In [27], the authors investigated content pushing in HetNets that are equipped with mmWave hotspots. The content can be cached by UE when it is in contact with the mmWave hotspot. The authors proposed a unified policy to optimize throughput. The obtained results demonstrate a substantial gain in throughput due to the increased content access via the cache resources. In [28], the authors focused on the optimization of cache hits in a D2D environment operating on mmWave signals. The authors proposed a group caching scheme to optimize the objective function. Moreover, the hard-cored-based caching algorithm is designed to avoid redundant caching. The obtained results demonstrate substantial improvement in cache access. In [29], the authors introduced a novel user association-based caching policy where highly popular content is cached on mmWave BSs. Moreover, \(\upmu\)Wave BSs store a portion of the highly popular content that is stored at mmWave BSs and the content that is not stored by mmWave BSs. The results demonstrate that network performance can be optimized by enhancing the cooperation size of \(\upmu\)Wave BSs and array size of mmWave BSs. Moreover, the proposed caching approach demonstrates a substantial benefit in data access success probability.