An integrated cross-layer framework of adaptive FEedback REsource allocation and Prediction for OFDMA systems
- 作者：Mohammad Abdul Awal ; awal@lri.fr ; [Author Vitae] ; Lila Boukhatem lila@lri.fr ; [Author Vitae] ; Lin Chen chen@lri.fr ; [Author Vitae]
- 关键词：OFDMA ; Cross-layer design ; Adaptive channel feedback ; Channel prediction ; Service differentiation
- 刊名：Computer Networks
- 出版年：2012
- 期刊代码：28_13891286
- 类别：cp
- 出版时间：3 May, 2012
- 卷：56
- 期：7
- 页码：1863-1875
- 文件大小：839 K
摘要
Orthogonal frequency division multiple access (OFDMA) technology has been adopted by 4th generation (a.k.a. 4G) telecommunication systems to achieve high system spectral efficiency. A crucial research issue is how to design adaptive feedback mechanisms so that the base station can use adaptive modulation and coding (AMC) techniques to adjust its data rate based on the channel condition. This problem is even more challenging in resource-limited and heterogeneous multiuser environments such as Mobile WiMAX and long-term evolution (LTE) networks. In this paper, we develop an integrated cross-layer framework of adaptive FEedback REsource allocation and Prediction (FEREP) for OFDMA systems. The proposed framework, implemented at the base station side, is composed of three modules. The feedback window adaptation (FWA) module dynamically tunes the feedback window size for each user based on the received automatic repeat request (ARQ) messages that reflect the current channel condition. The priority-based feedback scheduling (PBFS) module then performs feedback resource allocation by taking into account the feedback window size, the user profile and the total system feedback budget. To choose adapted modulation and coding schemes (MCS), the channel quality indicator prediction (CQIP) module performs channel prediction by using recursive least square (RLS) algorithm for the users whose channel feedback has not been granted for schedule in current frame. Through extensive simulations, the proposed framework shows significant performance gain especially under stringent feedback budget constraints.