Energy-Per-Bit Minimized Radio Resource Allocation in Heterogeneous Networks

• Published in: IEEE transactions on wireless communications Vol. 13; no. 4; pp. 1862 - 1873
• Main Authors: Kim, Seonwook, Lee, Byeong Gi, Park, Daeyoung
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-04-2014; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Energy consumption; Energy efficiency; Energy-per-bit; Exact sciences and technology; fractional programming; heterogeneous network; Information, signal and communications theory; Iterative methods; Lagrangian dual; Mathematical models; multi-homing; Multiplexing; Networks; Optimization; orthogonal frequency division multiple access (OFDMA); Power demand; Programming; radio resource allocation; Resource allocation; Resource management; Signal and communications theory; Systems, networks and services of telecommunications; Telecommunications; Telecommunications and information theory; Transmission and modulation (techniques and equipments); Wireless LAN; Wireless networks; Performance evaluation; allocation; Optimal algorithm; Wireless telecommunication; Resource allocation; Frequency division multiple access; Updating; Iterative method; Non linear programming; Optimization; multi-homing; Energy-per-bit; fractional programming; Algorithm performance; Optimal allocation; Energetic efficiency; hetergeneous network; Electric power consumption; Orthogonal frequency division multiplexing; Numerical simulation; orthogonal frequency division multiple access (OFDMA); Resource management; Lagrangian dual; radio resource
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2014.022114.130443

In this paper, we present an energy-per-bit minimized radio resource allocation scheme in heterogeneous networks equipped with multi-homing capability, simultaneously connecting to different wireless interfaces. Specifically, we formulate an optimization problem related to minimization of energy-per-bit which takes a form of nonlinear fractional programming. Then we derive a parametric optimization problem out of that fractional programming and solve the original problem by using a double-loop iteration method. In each iteration, we derive the optimal resource allocation policy by applying Lagrangian duality and an efficient dual update method. In addition, we present suboptimal resource allocation algorithms using the properties of the optimal resource allocation policy. Numerical results reveal that the optimal allocation algorithm improves energy efficiency significantly over the existing resource allocation algorithms designed for homogeneous networks and its performance is superior to suboptimal algorithms in reducing energy consumption as well as in enhancing network energy efficiency.