Distributed Resource Allocation in an OFDMA-based Femtocell Overlay Network

• Main Authors: Huang, Hung-Hsi, 黃泓錫
• Other Authors: Yu T. Su
• Format: Others
• Language: en_US
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/93897664362830947903

碩士 === 國立交通大學 === 電信工程研究所 === 100 === The deployment of femtocells is an attractive solution to cope with the capacity and coverage limitation of the existing macrocellular networks. For femtocells to overlay an existing macrocellular network, it is important that the signal qualities of macrocell users (mMSs) be maintained, i.e., the femto-users induced interference be limited to a tolerable level. In this thesis, we discuss the capacity enhancement and interference control issues in a closed access two-tier orthogonal frequency division multiple access (OFDMA) based cellular network. We use the average outage probability as the measure of mMS signal quality and employ an iterative distributed resource allocation (RA) based interference control approach. The former measure avoids the need for a femto base station (fBS) to know the exact locations of mMSs and the related signal and interference link gains while the distributed approach assumes that each fBS has access to the link gains or signal to noise-plus-interference ratio (SINR) associated with all downlinks within its coverage only. At each iteration of the distributed RA algorithm, each fBS selects a femto mobile station (fMS) for every subcarrier according to the link gain and SINR and uses a water-filling procedure for power allocation. The average outage probability requirement, however, impose a peak power constraint on each subcarrier. We thus suggest a modified water-filling procedure to maximize the sum capacity of a femtocell. As we observe that the proposed method does not necessarily converge, we propose several remedies to improve the convergence probability. Regarding the distributed RA as a noncooperative game and use a known sufficient condition for the existence of a Nash equilibrium point, we try to interpret the convergence behavior of our algorithm and its remedies.