Resource Allocation In Cloud Radio Access Networks With Fronthaul Capacity Constraints

With the explosive growth of mobile service, the traditional cellular network can not satisfy the capacity demand and bring in huge capital,operational and system updating expenditure. The cloud radio access network (C-RAN) as an advanced wireless access technology can reduce the capital expenditure and operational expenditure, and also improve energy efficiency (EE) and spectrum efficiency (SE) effectively. The fronthaul links as the key component of C-RAN are used to transmit all kinds of radio-frequency signal, and the performance gain is limited by the fronthaul capacity constraints.The fronthaul links can be realized by different carrier technologies which lead to different fronthaul cost and capacity, and the research for the tradeoff between capacity and cost is necessary. However the traditional EE and SE can not characterize the tradeoff between fornthual capacity and cost. Hence, an enhanced economical spectral efficiency (ESE)performance metric that presents a tradeoff between the EE and the relationship of fornthual cost and capacity has been proposed in this paper.Aiming at maximizing ESE, a non-convex beamformer design problem with fronthaul capacity and transmit power constraints is formulated, and an algorithm containing an outer loop and an inner loop is proposed to deal with this non-convexity. In particular, in the outer loop,the bi-section search method is adopted to transform the primal problem into an equivalent subproblem; while in the inner loop, the subproblem is solved by the weighted minimum mean square error approach. Simulation results demonstrate that the proposed algorithms can achieve the optimal beamforming and improve the overall network performance.To alleviate the fronthaul capacity and delay constraints, Device-to-Device (D2D) communication and C-RAN are the paradigm arising in this paper. An ESE optimization problem of designing the optimal resource allocation and mode selection is formulated as a non-convex issue. To deal with this problem, an iterative algorithm is proposed to transform the objective function to a convex problem, which is solved by Lagrange dual decomposition method. The simulation results demonstrate that the proposed algorithm converges within a small number of iterations and a near-optimal ESE performance can be achieved.In this paper, ESE performance metric is proposed to evaluate the impact of backhaul or fronthaul links on the performance of wireless networks. The results of the two optimization problems have considerable research significance on the performance evaluation and the corresponding optimization design in future wireless networks.