Summary We investigate the problem of joint downlink wireless backhaul bandwidth (WBB) and power allocation in heterogeneous cellular networks (HCNs). A WBB partitioning scheme is considered, which allocates the whole bandwidth between the macrocell and small cells for data transmission and backhauling. We formulate an optimization problem to maximize the weighted sum logarithmic utility function by jointly optimizing WBB portion and fronthaul power allocation of each base station with consideration of the backhaul capacity limitation on each small cell. In order to solve this joint optimization problem, we propose a hierarchical two‐level approach and decompose the original problem into two independent subproblems: the WBB allocation at the macrocell base station (MBS) and the power allocation at both the MBS and small cell base stations (SBSs). Accordingly, the optimal WBB portion and power allocation solutions are obtained, respectively. Furthermore, we develop a distributed algorithm to implement the joint WBB and power allocation. Numerical results verify the effectiveness of the proposed approach and analyze the impact of the weighted coefficient and backhaul capacity limitation on the network performance. In addition, significant performance gains can be achieved by the proposed approach over the benchmark.

In heterogeneous cellular networks (HCNs), the growing demand of small cell data traffic puts great pressure on the capacity limited backhaul links. Resource allocation is an efficient approach to mitigate the severe interference and improve the network performance in HCNs. However, differentiated backhaul capacity constraints introduce the inherent nonconvexity to the resource optimization problem, which hinders the development of the optimal solutions. In this article, we investigate the joint resource block (RB) and power allocation problem under two different backhaul capacity limitations. First, we consider the small cell backhaul limitation scenario where the backhaul capacity constraint is imposed on each small cell. Then, we extend the resource optimization to the macrocell base station backhaul limitation scenario which considers the overall backhaul capacity constraint on the whole small cell network. Besides, the quality of service (QoS) requirements of users and the cross‐tier interference mitigation are also considered. This resource allocation problem is formulated as a nonconvex mixed‐integer nonlinear programming (MINLP) problem. By variable substitution and constraints relaxation, we convert the original optimization problem into a convex one and obtain the optimal solutions. Accordingly, joint RB and power allocation algorithms are proposed. Numerical results verify the effectiveness of the proposed algorithms and evaluate the impact of different types of backhaul capacity limitations on system performance.

Summary In heterogeneous networks (HetNets), sharply increasing data traffic exacerbates the rapid growth in the network energy consumption and at the same time brings tremendous pressure on the backhaul links. Aiming at improving the network energy efficiency (EE) and relieving backhaul data volume traffic pressure, in this paper, we investigate energy efficient resource allocation which restricts small cell data traffic by imposing different kinds of backhaul capacity constraints (BCCs) on small cells, including small cell BCC (SBCC) and macrocell BCC (MBCC). We formulate the joint resource block (RB) and power allocation problem as a fractional programming problem. By using variable relaxation and replacement, we convert this nonconvex optimization problem into a concave–convex fractional programming problem, which can further be converted into a parametric convex problem and solved by convex optimization theory. Based on the Dinkelbach and dual decomposition methods, we propose iterative resource allocation algorithms under different BCCs, where RB and power are jointly allocated among users in each cell. Numerical results reveal that the proposed algorithms have good convergence performance and also evaluates the impact of both SBCC and MBCC on network performance.