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The increasingly growing data traffic has posed great challenges for mobile operators to increase their data processing capacity, which incurs a significant energy consumption and deployment cost. With the emergence of the Cloud Radio Access Network (C-RAN) architecture, the data processing units can now be centralized in data centers and shared among base stations. By mapping a cluster of base stations with complementary traffic patterns to a data processing unit, the processing unit can be fully utilized in different periods of time, and the required capacity to be deployed is expected to be smaller than the sum of capacities of single base stations. However, since the traffic patterns of base stations are highly dynamic in different time and locations, it is challenging to foresee and characterize the traffic patterns in advance to make optimal clustering schemes. In this paper, we address these issues by proposing a deep-learning-based C-RAN optimization framework. First, we exploit a Multivariate Long Short-Term Memory (MuLSTM) model to learn the temporal dependency and spatial correlation among base station traffic patterns, and make accurate traffic forecast for a future period of time. Afterwards, we build a weighted graph to model the complementarity of base stations according to their traffic patterns, and propose a Distance-Constrained Complementarity-Aware (DCCA) algorithm to find optimal base station clustering schemes with the objectives of optimizing capacity utility and deployment cost. We evaluate the performance of our framework using data in two months from real-world mobile networks in Milan and Trentino, Italy. Results show that our method effectively increases the average capacity utility to 83.4% and 76.7%, and reduces the overall deployment cost to 48.4% and 51.7% of the traditional RAN architecture in the two datasets, respectively, which consistently outperforms the state-of-the-art baseline methods.

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We experimentally investigate the drag modification by neutrally buoyant finite-size particles with various aspect ratios in a Taylor–Couette (TC) turbulent flow. The current Reynolds number, $Re$ , ranges from $6.5\times 10^3$ to $2.6\times 10^4$ , and the particle volume fraction, $\varPhi$ , is up to $10\,\%$ . Particles with three kinds of aspect ratio, $\lambda$ , are used: $\lambda =1/3$ (oblate), $\lambda =1$ (spherical) and $\lambda =3$ (prolate). Unlike the case of bubbly TC flow (van Gils et al., J. Fluid Mech., vol. 722, 2013, pp. 317–347; Verschoof et al., Phys. Rev. Lett., vol. 117, issue 10, 2016, p. 104502), we find that the suspended finite-size particles increase the drag of the TC system regardless of their aspect ratios. The overall drag of the system increases with increasing $Re$ , which is consistent with the literature. In addition, the normalized friction coefficient, $c_{f,\varPhi }/c_{f,\varPhi =0}$ , decreases with increasing $Re$ , the reason could be that in the current low volume fractions the turbulent stress becomes dominant at higher $Re$ . The particle distributions along the radial direction of the system are obtained by performing optical measurements at $\varPhi =0.5\,\%$ and $\varPhi = 2\,\%$ . As $Re$ increases, the particles distribute more evenly in the entire system, which results from both the greater turbulence intensity and the more pronounced finite-size effects of the particles at higher $Re$ . Moreover, it is found that the variation of the particle aspect ratios leads to different particle collective effects. The suspended spherical particles, which tend to cluster near the walls and form a particle layer, significantly affect the boundary layer and result in maximum drag modification. The minimal drag modification is found in the oblate case, where the particles preferentially cluster in the bulk region, and, thus, the particle layer is absent. Based on the optical measurement results, it can be concluded that, in the low volume fraction ranges ( $\varPhi =0.5\,\%$ and $\varPhi = 2\,\%$ here), the larger drag modification is connected to the near-wall particle clustering. The present findings suggest that the particle shape plays a significant role in drag modification, and the collective behaviours of rigid particles provide clues to understand the bubbly drag reduction.