This paper proposes new utility‐based fairness criteria for radio resource allocation in underlay cognitive radio networks based on harmonic mean concept. Then, joint rate and power allocation problem is considered for secondary users subject to the signal‐to‐interference‐noise ratio and interference threshold constraints. It is shown that the proposed approach yields a better balance between throughput and fairness compared with conventional fairness criteria, namely max–min and proportional fairness. Furthermore, in order to address the requirements of future wireless applications a cross‐layer resource‐allocation is proposed for heterogeneous traffic. A combination of streaming (which requires a maximum guaranteed average delay) and elastic traffic (with flexible rate requirements) is considered. The optimization problem allocates the available resources to the streaming users such that the quality‐of‐service constraints of the streaming users are satisfied. Through extensive simulations, the effect of streaming traffic on the total throughput of elastic users is investigated. Simulation results demonstrate the increase in throughput of elastic users achieved by decreasing number of streaming users.Copyright © 2012 John Wiley & Sons, Ltd.

In this paper, we develop quantized ergodic resource allocation in uplink OFDMA-based relay assisted networks in which both Amplify and Forward (AF) and Decode and Forward (DF) protocols are investigated. The main objective is to maximize the average sum rate subject to the power constraint at the users and relays. Using dual decomposition technique and block coordinate descent algorithm (BCDA), we then obtain near-optimal solution for the proposed problems. The proposed methods significantly decrease the required feedback information which reduce the overhead on the bandwidth as well as cost. Furthermore, we investigate the impact of noisy feedback channel on the system performance. Extensive simulation results also presented which indicate that DF outperform AF in various conditions. It is also observed that by increasing the number of quantization regions, the average sum rate approaches to the case in which full channel state information is available.Index Terms-Amplify-and-Forward relaying, Decode-andForward relaying, dual decomposition, Orthogonal Frequency Division Multiple Access, limited rate feedback.

No abstract

This article investigates the issue of radio resource allocation strategies for cognitive networks based on the underlay approach, while adhering to the interference constraint on the primary user. Joint rate and power allocation problem is considered for secondary users (SUs) with homogeneous and heterogenous traffic subject to the QoS and interference threshold constraints. Two well-known fairness approaches [max-min and proportional fairness (PF)] are compared for the proposed optimization problem. Three scenarios are considered. The first scenario corresponds to elastic traffic in which all the SUs are elastic users. In the second, it is assumed that all the SUs are streaming users. Considering the proportional and max min fairness, it is observed that for both fairness criterion the streaming users achieving higher throughput and fairness, compared with the elastic users due to the fact that for streaming users, stringent transmission rate guarantees are necessary to ensure real-time communication. Moreover, considering the requirements of future wireless networks, a cross-layer resource-allocation is proposed for heterogeneous traffic in the third scenario. A combination of streaming traffic (which requires a maximum guaranteed average delay) and elastic traffic (with flexible rate requirements) is investigated. The optimization problem allocates the available resources to the streaming users such that the delay constraints of the streaming users are satisfied. Through extensive simulations, the effect of streaming traffic, Interference threshold, minimum processing gain, imperfect channel state information and signal-to-interference-noise ratio constraint on the total throughput of elastic users are investigated. Simulation results demonstrate that in all scenarios PF outperforms max min fairness. Furthermore, it is shown that increasing the number of streaming traffic results in lower throughput of elastic users for both PF and max min fairness.

No abstract