Price-Based Resource Allocation in Massive MIMO H-CRANs With Limited Fronthaul Capacity

• Published in: IEEE transactions on wireless communications Vol. 17; no. 11; pp. 7691 - 7703
• Main Authors: Hao, Wanming, Muta, Osamu, Gacanin, Haris
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bandwidth; Bandwidths; Channel allocation; Clusters; Computational geometry; Convex analysis; Convexity; Downlink; heterogeneous cloud radio access network; Interference; Iterative algorithms; Iterative methods; massive MIMO; millimeter wave; Millimeter waves; MIMO (control systems); Optical network units; Optimization; Optimization techniques; Power management; Resource allocation; Resource management
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2018.2869749

In this paper, we investigate the bandwidth and power allocation problem in remote radio head cluster (RRHC)-based millimeter wave (mm-wave) massive MIMO heterogeneous cloud radio access networks with limited fronthaul capacity. The coordinated multipoint transmission is applied in each RRHC for cancelling the intra-cluster interference. To avoid the inter-tier interference, distinct bandwidths are allocated to macro base station and RRHs. Following this, we formulate a bandwidth and power allocation optimization problem to maximize the downlink weighted sum rate of the system subject to per-RRHC power and fronthaul capacity constraints, which is a non-convex optimization problem and is difficult to directly solve. Next, we fix the bandwidth allocation and the original problem can be divided into two independent optimization problems, i.e., the weighted sum rate maximization problems of MUs and RRH users, respectively. For the former, the convex optimization technique can be used to solve it. As for the latter, a two-loop iterative algorithm is proposed to deal with it. Specifically, we propose the price-based outer iteration to control the fronthaul capacity and the weighted minimum mean square error-based inner iteration to obtain the power allocation. To this end, a 1-D search method is adopted to find the optimal bandwidth allocation. Finally, numerical results are conducted to verify the effectiveness of the proposed algorithms under different parameters.