Resource Allocation in Cloud Radio Access Networks with Device-to-Device Communications

To alleviate the burdens on the fronthaul and reduce the transmit latency, the device-to-device (D2D) communication is presented in cloud radio access networks (C-RANs). Considering dynamic traffic arrivals and time-varying channel conditions, the resource allocation in C-RANs with D2D is formulated into a stochastic optimization problem, which is aimed at maximizing the overall throughput subject to network stability, interference, and fronthaul capacity constraints. Leveraging on the Lyapunov optimization technique, the stochastic optimization problem is transformed into a delay-aware optimization problem, which is a mixed-integer nonlinear programming problem and can be decomposed into three subproblems: mode selection, uplink beamforming design, and power control. An optimization solution that consists of a modified branch and bound method as well as a weighted minimum mean square error approach has been developed to obtain the close-to-optimal solution. Simulation results validate that the D2D can improve throughput, decrease latency, and alleviate the burdens of the constrained fronthaul in C-RANs. Furthermore, an average throughput-delay tradeoff can be achieved by the proposed solution

Power Allocation for Massive MIMO-based, Fronthaul-constrained Cloud RAN Systems

Cloud radio access network (C-RAN) and massive multiple-input-multiple-output (MIMO) are two key enabling technologies to meet the diverse and stringent requirements of the 5G use cases. In a C-RAN system with massive MIMO, fronthaul is often the bottleneck due to its finite capacity and transmit precoding is moved to the remote radio head to reduce the capacity requirements on fronthaul. For such a system, we optimize the power allocated to the users to maximize first the weighted sum rate and then the energy efficiency (EE) while explicitly incorporating the capacity constraints on fronthaul. We consider two different fronthaul constraints, which model capacity constraints on different parts of the fronthaul network. We develop successive convex approximation algorithms that achieve a stationary point of these non-convex problems. To this end, we first present novel, locally tight bounds for the user rate expression. They are used to obtain convex approximations of the original non-convex problems, which are then solved by solving their dual problems. In EE maximization, we also employ the Dinkelbach algorithm to handle the fractional form of the objective function. Numerical results show that the proposed algorithms significantly improve the network performance compared to a case with no power control and achieves a better performance than an existing algorithm

Efficient Virtual Network Function Placement Strategies for Cloud Radio Access Networks

The new generation of 5G mobile services places stringent requirements for cellular network operators in terms of latency and costs. The latest trend in radio access networks (RANs) is to pool the baseband units (BBUs) of multiple radio base stations and to install them in a centralized infrastructure, such as a cloud, for statistical multiplexing gains. The technology is known as Cloud Radio Access Network (CRAN). Since cloud computing is gaining significant traction and virtualized data centers are becoming popular as a cost-effective infrastructure in the telecommunication industry, CRAN is being heralded as a candidate technology to meet the expectations of radio access networks for 5G. In CRANs, low energy base stations (BSs) are deployed over a small geographical location and are connected to a cloud via finite capacity backhaul links. Baseband processing unit (BBU) functions are implemented on the virtual machines (VMs) in the cloud over commodity hardware. Such functions, built-in software, are termed as virtual functions (VFs). The optimized placement of VFs is necessary to reduce the total delays and minimize the overall costs to operate CRANs. Our study considers the problem of optimal VF placement over distributed virtual resources spread across multiple clouds, creating a centralized BBU cloud. We propose a combinatorial optimization model and the use of two heuristic approaches, which are, branch-and-bound (BnB) and simulated annealing (SA) for the proposed optimal placement. In addition, we propose enhancements to the standard BnB heuristic and compare the results with standard BnB and SA approaches. The proposed enhancements improve the quality of the solution in terms of latency and cost as well as reduce the execution complexity significantly.Comment: E-preprin