# Wireless Caching: Making Radio Access Networks More than Bit-Pipelines
###### Abstract  
Caching has attracted much attention recently because it holds the promise of scaling the service capability of radio access networks (RANs). We envision that caching will ultimately make next-generation RANs more than bit-pipelines and emerge as a multi-disciplinary area via the union with communications, pricing, recommendation, compression, and computation units. By summarizing cutting-edge caching policies, we trace a common root of their gains to the prolonged transmission time, which is then traded for higher spectral or energy efficiency. To realize caching, the physical layer and higher layers have to function together, with the aid of prediction and memory units, which substantially broadens the concept of cross-layer design to a multi-unit collaboration methodology. We revisit caching from a generalized cross-layer perspective, with a focus on its emerging opportunities, challenges, and theoretical performance limits. To motivate the application and evolution of caching, we conceive a hierarchical pricing infrastructure that provides incentives to network operators and users. To make RANs even more proactive, we design caching and recommendation jointly, showing a user what it might be interested in and what has been done for it. Furthermore, the user-specific demand prediction motivates edge compression and proactive MEC as new applications. The beyond-bit-pipeline RAN is a paradigm shift that brings with it many cross-disciplinary research opportunities. caching; radio access networks; virtual real-time services; AI-empowered networks; cross-layer design; pricing; recommendation; edge compression; proactive computation 2021  
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Wireless Caching: Making RadioMore recently, caching has been found to substantially benefit data transmissions over harsh wireless channels and meet growing demands with restrained radio resources in various ways [5; 6; 7; 8]. For instance, caching is capable of not only exploiting the idle spectrum to offload and reduce the peak-time bandwidth requirement, but also enabling physical-layer multicasting in the content placement phase. With coded caching [9], the physical-layer multicasting gain can be attained in the delivery phase even when users request different files. Caching also enables novel interference cancellation and alignment schemes that hold the potential of substantially improving the device density in RANs [10]. In short, caching exploits scalable storage resources to improve the highly restrained radio resource efficiency. One cost of caching involves the memories at edge nodes and end devices, whose capacities have grown in the past few decades due to technological advances. The various caching gains are attracting increasing attention from both academia and industry in the era of 6G research. For instance, a focus group on machine learning for future networks including 5G (ML5G) was launched by ITU in 2019 with a key purpose of enabling caching. Though considerable literature on the subject of wireless caching exists, there is a need to revisit it from a cross-layer perspective, as shown in Figure 1. Why? A simple answer is that caching relies on both the content placement that transmits on the minus time axis before user requests, and content delivery that transmits upon user requests. Therefore, multiple layers of a RAN have to function together to realize caching. Furthermore, caching relies on the coordination of wireless communication, prediction, and the use of memory units, which should be jointly scheduled in the time domain. It is insufficient to make binary decisions on \"to cache or not to cache\" in practice. By contrast, a RAN should determine when and how long a content item is cached, as well as how it is transmitted in the placement and delivery phases, etc. In summary, a systematic discussion of caching with multi-layer or even multi-unit collaborations is necessary. More importantly, few studies focus on changing the underlying design methodologies of RANs to fully exploit caching.