Wenmin Ma

Resource allocation with interference mitigation in OFDMA femtocells for co-channel deploymenta

Femtocells have been considered as a promising technology to provide better indoor coverage and spatial reuse gains. However, the co-channel deployment of macrocells and femtocells is still facing challenges arising from potentially severe inter-cell interference. In this article, we investigate the uplink resource allocation problem of femtocells in co-channel deployment with macrocells. We first model the uplink power and subchannel allocation in femtocells as a non-cooperative game, where inter-cell interference is taken into account in maximizing the femtocell capacity and uplink femto-to-macro interference is alleviated by charging each femto user a price proportional to the interference that it causes to the macrocell. Based on the non-cooperative game, we then devise a semi-distributed algorithm for each femtocell to first assign subchannels to femto users and then allocate power to subchannels. Simulation results show that the proposed interference-aware femtocell uplink resource allocation algorithm is able to provide improved capacities for not only femtocells, but also the macrocell, as well as comparable or even better tiered fairness in the two-tier network, as compared with existing unpriced subchannel assignment algorithm and modified iterative water filling-based power allocation algorithm.

QoE-based cross-layer design for video applications over LTE

Video applications such as video-on-demand and videoconferencing over wireless networks require system resources to be allocated dynamically and optimally in accordance with the time-varying environment and video contents. User-perceived quality is tending to be a crucial factor in evaluating the success of video applications. The aim of this paper is to propose a cross-layer design scheme for optimizing resource allocation of video applications over Long Term Evolution (LTE) networks based on Quality of Experience (QoE) evaluation. We propose a novel mapping model between Peak Signal-to-Noise Ratio (PSNR) and Mean Opinion Score (MOS) based on a hyperbolic tangent function, which can reflect the relation between objective system parameters and subjective perceived quality simply and precisely. The cross-layer architecture presented in this paper jointly optimizes the Application (APP) layer, the Media Access Control (MAC) layer and the Physical (PHY) layer of the wireless protocol stack. On the basis of this architecture, we present a QoE prediction function and utilize the Particle Swarm Optimization (PSO) method to solve the resource allocation problem. Simulation results show that the proposed scheme significantly outperforms the traditional scheduling scheme in terms of maximizing user-perceived quality as well as maintaining fairness among users.

MOS-driven energy efficient power allocation for wireless video communications

In this paper, a MOS-driven energy efficient power allocation scheme (MEEPA) is proposed for video transmission over downlink orthogonal frequency division multiple access (OFDMA) networks. In the proposed framework, system power consumption and MOS for users based on end-to-end video distortion have been jointly optimized. We also define a novel energy efficient utility as the unifying metric based on the MOS model and the system power consumption. The main objective is to maximize the energy efficiency by balancing the MOS and the power consumption for each user. The power allocation problem is solved by using constrained particle swarm optimization (CPSO). Moreover, simulation results show that the proposed algorithm can improve energy efficiency greatly in terms of lower power consumption with a comparable video quality performance compared with the traditional algorithm.