mca 14(4): e3

Research Article

Multicast in Femtocell Networks: A Successive Interference Cancellation Approach

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  • @ARTICLE{10.4108/mca.1.4.e3,
        author={Donglin Hu and Shiwen Mao and Xin Su},
        title={Multicast in Femtocell Networks: A Successive Interference Cancellation Approach},
        journal={EAI Endorsed Transactions on Mobile Communications and Applications},
        volume={1},
        number={4},
        publisher={ICST},
        journal_a={MCA},
        year={2014},
        month={9},
        keywords={Cross-layer optimization; femtocell networks; green communications; multicast; superposition coding; successive interference cancellation.},
        doi={10.4108/mca.1.4.e3}
    }
    
  • Donglin Hu
    Shiwen Mao
    Xin Su
    Year: 2014
    Multicast in Femtocell Networks: A Successive Interference Cancellation Approach
    MCA
    ICST
    DOI: 10.4108/mca.1.4.e3
Donglin Hu1, Shiwen Mao2,*, Xin Su3
  • 1: AT&T Labs, Inc., San Ramon, CA, USA
  • 2: Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA
  • 3: Wireless and Mobile Communications Technology R&D Center, Tsinghua University, Beijing, China
*Contact email: smao@ieee.org

Abstract

A femtocell is a small cellular base station (BS), typically used for serving approved users within a small coverage. In this paper, we investigate the problem of data multicast in femtocell networks that incorporates superposition coding (SC) and successive interference cancellation (SIC). The problem is to decide the transmission schedule for each BS, as well as the power allocation for the SC layers, to achieve a sufficiently large SNR for each layer to be decodable with SIC at each user. Minimizing the total BS power consumption achieves the goal of “green” communications. We formulate a Mixed Integer Nonlinear Programming (MINLP) problem, and then reformulate the problem into a simpler form. Upper and lower performance bounds on the total BS power consumption are derived. Finally, we consider three typical connection scenarios, and develop optimal and nearoptimal algorithms for the three scenarios. The proposed algorithms have low computational complexity, and outperform a heuristic scheme with considerable gains in our simulation study.