Convex Optimization
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Resource allocation for non-real-time services in OFDM-based cognitive radio systems
IEEE Communications Letters
Resource allocation in an OFDM-based cognitive radio system
IEEE Transactions on Communications
Cognitive radio game for secondary spectrum access problem
IEEE Transactions on Wireless Communications
OFDM for cognitive radio: merits and challenges
IEEE Wireless Communications
Radio Resource Allocation Algorithms for the Downlink of Multiuser OFDM Communication Systems
IEEE Communications Surveys & Tutorials
Adaptive resource allocation in multiuser OFDM systems with proportional rate constraints
IEEE Transactions on Wireless Communications
Resource Allocation for Multicast Services in Multicarrier Wireless Communications
IEEE Transactions on Wireless Communications
Resource Allocation for Delay Differentiated Traffic in Multiuser OFDM Systems
IEEE Transactions on Wireless Communications
Spectrum pooling: an innovative strategy for the enhancement of spectrum efficiency
IEEE Communications Magazine
Multicarrier communication techniques for spectrum sensing and communication in cognitive radios
IEEE Communications Magazine
Multiuser OFDM with adaptive subcarrier, bit, and power allocation
IEEE Journal on Selected Areas in Communications
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
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In this paper we study the resource allocation problem for the multiuser orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems with proportional rate constraints. The mutual interference introduced by primary user (PU) and cognitive radio user (also referred to secondary user, SU) makes the optimization problem of CR systems more complex. Moreover, the interference introduced to PUs must be kept under a given threshold. In this paper, the highest achievable rate of each OFDM subchannel is calculated by jointly considering the channel gain and interference level. First, a subchannel is assigned to the SU with the highest achievable rate. The remaining subchannels are always allocated to the SU that suffers the severest unjustness. Second, an efficient bit allocation algorithm is developed to maximize the sum capacity, which is again based on the highest achievable rate of each subchannel. Finally, an adjustment procedure is designed to maintain proportional fairness. Simulation results show that the proposed algorithm maximizes the sum capacity while keeping the proportional rate constraints satisfied. The algorithm exhibits a good tradeoff between sum capacity maximization and proportional fairness. Furthermore, the proposed algorithm has lower complexity compared with other algorithms, rendering it promising for practical applications. Copyright © 2011 John Wiley & Sons, Ltd. (By jointly considering the channel gain and the interference level of the OFDM-based cognitive radio systems, the highest achievable rate of each subchannel can be calculated, which indicates the ability of carrying bits of the subchannel. Using the normalized cost index list, an efficient algorithm is proposed to maximize the sum capacity of the cognitive OFDM systems while keeping the proportional rate constraints satisfied.)