Optimal Energy-Efficient Power Allocation for OFDM-Based Cognitive Radio Networks

• Published in: IEEE communications letters Vol. 16; no. 9; pp. 1420 - 1423
• Main Authors: Wang, Yan, Xu, Wenjun, Yang, Kewen, Lin, Jiaru
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.09.2012; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Allocations; Applied sciences; Chromium nitride; Cognitive radio; convex optimization; Energy-efficient; Exact sciences and technology; Information, signal and communications theory; Interference constraints; Iterative methods; Maximization; Measurement; Multiplexing; Networks; OFDM; Optimization; Orthogonal Frequency Division Multiplexing; Programming; Radiocommunication specific techniques; Radiocommunications; Resource management; Signal and communications theory; Systems, networks and services of telecommunications; Telecommunications; Telecommunications and information theory; Transmission and modulation (techniques and equipments); convex opti; Optimal algorithm; Information rate; Iterative method; Power allocation; Convex programming; Information transmission; Energy-efficient; cognitive radio; Energetic efficiency; OF; Electric power consumption; Orthogonal frequency division multiplexing; Numerical simulation; Minimum energy; Metric; Convexity; Low-power electronics; Radio communication; Software radio
• ISSN: 1089-7798; 1558-2558
• DOI: 10.1109/LCOMM.2012.070512.120662

This letter investigates the energy-efficient power allocation for orthogonal frequency division multiplexing (OFDM) based cognitive radio networks (CRNs). The problem is to maximize the energy-efficiency measured using the "throughput per Joule" metric subject to the total transmit power and interference constraints. It is then transformed into an equivalent convex problem using parametric programming. Furthermore, an optimal iterative algorithm based on convex optimization theory and parametric programming is proposed. The numerical results show that the proposed optimal algorithm can achieve higher energy-efficiency than that obtained by solving the original problem directly because of its non-convexity. Energy-efficiency maximization can also achieve a good tradeoff between capacity and energy in CRNs.