Yong Teng

Cooperative distributed optimization for the hyper-dense small cell deployment

The fifth generation mobile networks will be developed to improve area spectral and energy efficiency, and provide uniform user experience. Hyper-dense small cell deployment can move devices closer to the wireless network and satisfy 5G system requirements. The main challenge of this network deployment results from the random deployment, dynamic on-off, flexible connection to cellular core networks, and flat system architecture of 5G systems. Therefore, conventional network planning and radio resource management, which depend on a central control node, cannot be applied to small cell networks. In this article some cooperative distributed radio resource management algorithms for time synchronization, carrier selection, and power control are discussed for hyper-dense small cell deployment.

Fairness Guaranteed Cooperative Resource Allocation in Femtocell Networks

User-deployed low-power femtocell access points (FAPs) can provide better indoor coverage and higher data rates than conventional cellular networks. However, a major problem in this uncoordinated frequency reuse scenario is the inter-cell interference. In this paper, we propose a graph based distributed algorithm called fairness guaranteed cooperative resource allocation (FGCRA) to manage interference among femtocells. Since the optimal resource allocation is a NP-hard problem, which is difficult to get global optimization in femtocell networks, our proposed FGCRA algorithm provides sub-optimal resource allocation via cooperation among interfering neighbors. First, we propose a specific fairness factor obtained from two-hop interference relations, to determine the lower bound amount of subchannels that each FAP can use and guarantee the fairness among femtocells. Second, we propose scalable rules for distributed resource allocation and the solution to avoid the conflicts among interfering neighbors. Simulation results show that our proposed FGCRA significantly enhances both average user throughput and cell edge user throughput, and provides better fairness.

Cooperative component carrier (Re-)selection for LTE-advanced femtocells

In dynamic HeNB networks, each user-installed HeNB autonomously selects component carriers (CCs) depending on offered traffic and interference relations with other cells. But it will cause that certain HeNB cant select feasible CC. Due to that the performance of BIM based ACCS scheme are sensitive to the predefined BIM threshold, we first introduce an adaptive binary criterion to determine whether two HeNBs can use the same CC. Furthermore, we propose a cooperative component carrier reselection algorithm based on backup CC list, to control inter-cell interference (ICI) and enhance the possibility of successful carrier selection. Simulation results show that CCCS can enhance the system performance both in terms of average user throughput and user outage rate.

McPAO: A Distributed Multi-channel Power Allocation and Optimization Algorithm for Femtocells

Efficient radio resource management is a key issue in a multi-channel femtocell system, where femtocell base stations are deployed randomly and will generate interference to each other. In this research, we formulate multi-channel power allocation as a convex optimization problem, in order to maximize the overall system throughput under complex transmit power constraint. We apply the Lagrangian duality techniques to make the problem decomposable and propose a distributed iterative subgradient algorithm, namely Multi-channel Power Allocation and Optimization (McPAO). Specifically, McPAO consists of two phases: (I) a gradient projection algorithm to solve the optimal power allocation for each channel under a fixed Lagrangian dual cost; and (II) a subgradient algorithm to update the Lagrangian dual cost by using the power allocation results from Phase I. This two-phase iteration process continues until the Lagrangian dual cost converges to the optimal value. Numerical results show that our McPAO algorithm can improve the overall system throughput by 18xa0%, comparing to with fixed power allocation schemes. In addition, we study the impact of errors in gradient direction estimation (Phase I), which are caused by limited or delayed information exchange among femtocells in realistic situations. These errors will be propagated into the subgradient algorithm (Phase II) and, subsequently, affect the overall performance of McPAO. A rigorous analytical approach is developed to prove that McPAO can always achieve a bounded overall throughput performance very close to the global optimum.