期刊
IEEE TRANSACTIONS ON COMMUNICATIONS
卷 67, 期 10, 页码 7043-7057出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCOMM.2019.2936813
关键词
Femtocells; OFDMA; heterogeneous networks; resource allocation; successive convex approximation
资金
- Australian Government under the Endeavour Postgraduate Scholarship Program
- Science and Engineering Faculty, Queensland University of Technology, Australia under the Faculty Write-up Scholarship
- Science Foundation Ireland [17/CDA/4786]
- project Flexible Uplink-Downlink Resource Management for Energy and Spectral Efficiency Enhancing in Future Wireless Networks (FURMESFuN) - Academy of Finland [31089]
- project 6Genesis Flagship - Academy of Finland [318927]
We proposed several energy-efficient resource allocation algorithms for the downlink of an orthogonal frequency-division-multiple-access (OFDMA) based femtocell heterogeneous networks (HetNets). Heterogeneous QoS and fairness in rate are investigated in the proposed resource allocation problem. A dense deployment of femtocells in the coverage area of a central macrocell is considered and energy usage of both femtocell and macrocell users are optimized simultaneously. We aim to maximize the weighted sum of the individual energy efficiencies (WSEEMax) and the network energy efficiency (NEEMax) while satisfying the following: (1) minimum throughput for delay-sensitive (DS) users, (2) fairness constraint for delay-tolerant (DT) users, (3) required constraints of OFDMA systems. The problem is formulated in three different forms: mixed 0-1 integer programming formulation, time-sharing formulation and sparsity-inducing formulation. The proposed resource block (RB) and power optimization problems are combinatorial and highly non-convex due to the fractional form of the objective function, the integer constraint of OFDMA RBs and non-affine fairness. We adopt the successive convex approximation (SCA) approach and transform the problems into a sequence of convex subproblems. With the proposed algorithms, we show that the overall joint RB and power allocation schemes converge to suboptimal solutions. Numerical examples confirm the merits of the proposed algorithms.
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