Dynamic Resource Allocation for Virtualized Wireless Networks in Massive-MIMO-Aided and Fronthaul-Limited C-RAN

作者：Parsaeefard Saeedeh^*; Dawadi Rajesh; Derakhshani Mahsa; Tho Le Ngoc; Baghani Mina

来源：IEEE Transactions on Vehicular Technology, 2017, 66(10): 9512-9520.

DOI：10.1109/TVT.2017.2712669

• Cloud-RAN
• complementary geometric programming
• 5G
• massive MIMO
• virtualized wireless networks

This paper considers the uplink dynamic resource allocation in a cloud radio access network (C-RAN) serving users belonging to different service providers (called slices) to form virtualized wireless networks (VWN). In particular, the C-RAN supports a pool of base-station (BS) baseband units (BBUs), which are connected to BS radio remote heads (RRHs) equipped with massive massive multiple input multiple output (MIMO), via fronthaul links with limited capacity. Assuming that each user can be assigned to a single RRH-BBU pair, we formulate a resource allocation problem aiming to maximize the total system rate, constrained on the minimum rates required by the slices and the maximum number of antennas and power allocated to each user. The effects of pilot contamination error on the VWN performance are investigated and pilot duration is considered as a new optimization variable in resource allocation. This problem is inherently nonconvex, NP-hard and, thus, computationally inefficient. By applying the successive convex approximation and complementary geometric programming approach, we propose a two-step iterative algorithm: one to adjust the RRH, BBU, and fronthaul parameters, and the other for power and antenna allocation to users. Simulation results illustrate the performance of the developed algorithm for VWNs in a massive-MIMO-aided and fronthaul-limited C-RAN, and demonstrate the effects of imperfect channel state information estimation due to pilot contamination error, and the optimal pilot duration.

• 出版日期2017-10