Xing Chengwen

Resource allocation for physical layer security in heterogeneous network with hidden eavesdropper

The tremendous performance gain of heterogeneous networks (HetNets) is at the cost of complicated resource allocation. Considering information security, the resource allocation for HetNets becomes much more challenging and this is the focus of this paper. In this paper, the eavesdropper is hidden from the macro base stations. To relax the unpractical assumption on the channel state information on eavesdropper, a localization based algorithm is first given. Then a joint resource allocation algorithm is proposed in our work, which simultaneously considers physical layer security, cross-tier interference and joint optimization of power and subcarriers under fairness requirements. It is revealed in our work that the considered optimization problem can be efficiently solved relying on convex optimization theory and the Lagrangian dual decomposition method is exploited to solve the considered problem effectively. Moreover, in each iteration the closed-form optimal resource allocation solutions can be obtained based on the Karush-Kuhn-Tucker (KKT) conditions. Finally, the simulation results are given to show the performance advantages of the proposed algorithm.

Joint linear processing design for distributed two-way Amplify-and-Forward MIMO relaying networks

Linear transceiver designs are investigated for distributed two-way relaying networks, which aim at minimising the Weighted Mean Square Error (WMSE) of data detections. The forwarding matrices at relays and equalization matrices at destinations are jointly optimised. To overcome the challenging limitations introduced by individual power constraints, a Semi-Definite Relaxation (SDR) called element-wise relaxation is proposed, which can transform the original optimization problem into a standard convex optimization problem. In this research, two-way relaying is understood from a pure signal processing perspective which can potentially simplify the theoretical analysis. Finally, simulation results are used for assessing the performance advantage of the proposed algorithm.

Distributed resource allocation in ultra-dense networks via belief propagation

Ultra-dense networking is widely accepted as a promising enabling technology to realize high power and spectrum efficient communications in future 5G communication systems. Although joint resource allocation schemes promise huge performance improvement at the cost of cooperation among base stations, the large numbers of user equipment and base station make jointly optimizing the available resource very challenging and even prohibitive. How to decompose the resource allocation problem is a critical issue. In this paper, we exploit factor graphs to design a distributed resource allocation algorithm for ultra dense networks, which consists of power allocation, subcarrier allocation and cell association. The proposed factor graph based distributed algorithm can decompose the joint optimization problem of resource allocation into a series of low complexity subproblems with much lower dimensionality, and the original optimization problem can be efficiently solved via solving these subproblems iteratively. In addition, based on the proposed algorithm the amounts of exchanging information overhead between the resulting subprob-lems are also reduced. The proposed distributed algorithm can be understood as solving largely dimensional optimization problem in a soft manner, which is much preferred in practical scenarios. Finally,the performance of the proposed low complexity distributed algorithm is evaluated by several numerical results.

Distributed chunk-based optimization for multi-carrier ultra-dense networks

In this paper, a distributed chunk-based optimization algorithm is proposed for the resource allocation in broadband ultra-dense small cell networks. Based on the proposed algorithm, the power and subcarrier allocation problems are jointly optimized. In order to make the resource allocation suitable for large scale networks, the optimization problem is decomposed first based on an effective decomposition algorithm named optimal condition decomposition (OCD) algorithm. Furthermore, aiming at reducing implementation complexity, the subcarriers are divided into chunks and are allocated chunk by chunk. The simulation results show that the proposed algorithm achieves more superior performance than uniform power allocation scheme and Lagrange relaxation method, and then the proposed algorithm can strike a balance between the complexity and performance of the multi-carrier Ultra-Dense Networks.