In cloud radio access networks (CRANs), mobile users are serviced by a massive number of remote radio heads (RRHs) that are acted as distributed antennas and controlled by cloud-based baseband units (BBUs) via wired or wireless fronthaul links. To improve spectral efficiency, cloud-based cooperative signal processing techniques can be executed centrally at the BBUs. However, despite the ability of CRAN systems to run such complex signal processing functions centrally, their performance remains limited by the capacity of the fronthaul (users to BBU) and backhaul (BBUs to core) links. Indeed, given the massive nature of a CRAN, relying on fiber fronthaul and backhaul links may be infeasible. Consequently, capacity-limited wireless backhaul and fronthaul connections are being studied for CRANs. To overcome these limitations, one can make use of content caching techniques in which users can directly obtain contents stored at the cloud or RRHs. However, deploying caching strategies in a CRAN environment faces many challenges that include optimized cache placement, cached content update, and cached content delivery. In this chapter, we first study the application of echo state network to predict the users' mobility and content request distribution for determining optimal cached contents in CRAN. Then, we study the deployment of cache-enabled UAVs to service ground mobile users.

Inspec keywords: mobile robots; cloud computing; autonomous aerial vehicles; distributed antenna systems; mobile computing; signal processing

Other keywords: UAVs; BBUs; signal processing techniques; CRAN systems; distributed antennas; cloud radio access networks; remote radio heads; cloud-based baseband units; wireless fronthaul links; unmanned aerial vehicles; RRHs

Subjects: Antennas; Signal processing and detection; Mobile, ubiquitous and pervasive computing; Mobile robots; Digital signal processing; Internet software; Aerospace control