Fair, efficient, and power-optimized spectrum sharing scheme for cognitive radio networks

Cognitive radio network is expected to use flexible radio frequency spectrum sharing techniques for achieving more efficient frequency spectrum usage. In this article, we consider the spectrum sharing problem that one primary user (PU) can share its frequency spectrum by renting this spectrum to multiple secondary users (SUs). The pricing scheme is a key issue for spectrum sharing in cognitive radio network. We first propose a nonlinear one-leader-multiple-follower (NLMF) sharing spectrum scheme as a multi-object optimization problem; the prices are offered by PU to SUs at the same time. This problem can be solved using particle swarm optimization (PSO); SUs gradually and iteratively adjust their strategies respectively based on the observations on their opponents' previous strategies until Nash equilibrium is completed. We then present a general nonlinear bilevel one-leader-multiple-follower (NBMF) optimization problem to further consider the revenue of the PU and a new optimal strategic pricing optimization technique which applies bilevel programming and swarm intelligence. A leader-follower game is formulated to obtain the Stackelberg-Nash equilibrium for spectrum sharing that considers not only revenue of a PU but also the SUs utility. We develop a swarm particle algorithm to iteratively solve the problem defined in the NBMF decision model for searching the strategic pricing optimization. The behaviors of two pricing models have been evaluated, and the performance results show that the proposed algorithms perform well to solve the spectrum sharing in a cognitive radio network.

Section: System Modelmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Two novel price-based algorithms for spectrum sharing in cognitive radio networks

Weng

Lee

Chen

2013

“…The GA performs well for large search space problems because it can work on a population in parallel instead of processing a single solution at a time. The parallel processing allows the GAs to explore several parts of the solution at same time [6] and also many real world problems require simultaneous optimization of several objective functions. Hence, these algorithms become suitable to solve these problems.…”

Section: Application and Backgroundmentioning

confidence: 99%

“…However, a channel that is to be utilized by the PU cannot be used for the transmission of the CR. Therefore, when the PU arrives, the CR should suspend its communication, and it should look for other channel to restart its communication [4][5][6]. Such periodic suspensions in the transmission of the CR user can cause severe degradation in their QoS.…”

Section: Introductionmentioning

confidence: 99%

CSPA: Channel Selection and Parameter Adaptation scheme based on genetic algorithm for cognitive radio Ad Hoc networks

Aslam

Lee

2012

Self Cite

The cognitive radio (CR) is evolved as the potential technology to solve the problem of spectrum scarcity and to meet the stringent requirements of upcoming wireless services. CR has two distinct features, the spectrum sensing and the parameter adaptation. The former feature helps the CR to find the vacant spectrum slots/channels in the radio band while the latter mechanism allows it to adjust the operating parameters (e.g. frequency band, modulation and power, etc.) accordingly. The primary user (PU) activity has serious effects on the overall performance of the cognitive radio network (CRN).The CR should vacate the channel if it detects the arrival of the primary user (PU) in order to avoid the interference. The channel eviction/switching phenomenon severely degrade the quality of service (QoS) of the CR user and it is perhaps the key challenge for the CRN. In this paper, we propose the dynamic channel selection and parameter adaptation (CSPA) scheme based on the genetic algorithm to provide better QoS for the CR by selecting a best channel in terms of the quality, the power and the PU activity. The CSPA deals with the problem of channel switchings and it provides better QoS to the CR user. Simulation results prove that CSPA outperforms the existing schemes in terms of channel switchings, average service time, power and throughput.

“…Cognitive radio which enables spectrum sharing between different wireless services attempts to overcome spectrum scarcity [1][2][3][4]. In cognitive radio networks, unlicensed user or secondary user (SU) is allowed to share spectrum with licensed user, known as primary user (PU).…”

Section: Introductionmentioning

confidence: 99%

Joint power and rate allocation in CDMA-based underlay cognitive radio networks for a mixture of streaming and elastic traffic

Dashti

Azmi

2012

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.