PSO-Based OFDM Adaptive Power and Bit Allocation for Multiuser Cognitive Radio System

Xu, Shiquan; Zhang, Qinyu; Wei, Lin

doi:10.1109/wicom.2009.5302084

Cited by 7 publications

(6 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The number of subcarriers used in this splitting increases the duration of the symbol, reducing the ISI as a result of multipathing. A cognitive radio system should use OFDM for transmission [13]. OFDM is suitable for CR-based transmission systems due to its features and capabilities.…”

Section: Ofdm For Cognitive Radiomentioning

confidence: 99%

A Novel Particle Swarm Optimization for Channel Allocation in OFDM Based Cognitive Radio Networks

Sharma,

Judith Ratu Tandi Arrang

2023

WJCMS

View full text Add to dashboard Cite

It has become increasingly apparent that bandwidth scarcity is an issue as wireless communications advance. Alternatively, spectrum sensing techniques are used to detect licensed users. A spectrum sensor can detect energy, matched filters, and cyclostationary features. There are, however, some drawbacks to these methods. Energy detector performance is affected by noise power uncertainty. Every primary user needs a dedicated receiver for matched filter spectrum sensing. Computational effort and observation time are required for cyclo-stationary feature detection. Spectrum use is determined using particle swarm optimization (PSO), an algorithm for determining the best frequency allocation and highest accuracy. Using PSO operations, this paper proposes an improved energy detection method compared to conventional energy detection methods. Detecting energy and using the PSO channel allocation technique to detect fading channels is also mathematically described.

show abstract

Section: Ofdm For Cognitive Radiomentioning

confidence: 99%

A Novel Particle Swarm Optimization for Channel Allocation in OFDM Based Cognitive Radio Networks

Sharma,

Judith Ratu Tandi Arrang

2023

WJCMS

View full text Add to dashboard Cite

show abstract

“…So all kinds of complicated problems in CRN, including dynamic spectrum management [15][16][17][18][19] (i.e. spectrum sensing and sharing) and network resource allocation [20,21] are proposed to enforce autonomy and adaptation using PSO.…”

Section: Swarm Intelligence-empowered Crnmentioning

confidence: 99%

“…power allocation and multi- antenna beamforming. For cognitive radio transceivers equipped with multiple antennas, the objective considered in [20] is expected to maximize the transmission capacity of the secondary users subject to minimum interference and maximum quality of service of the primary users with minimum transmission power for secondary users by beamforming approach. According to the well formulated optimization model, PSO is adopted to iteratively adjust the pre and pro beamforming vectors to achieve the goal above.…”

Section: Swarm Intelligence-empowered Crnmentioning

confidence: 99%

A bio-inspired approach for cognitive radio networks

Niu

Qiu

et al. 2012

Chin. Sci. Bull.

View full text Add to dashboard Cite

The recent increasing interest in cognitive radio networks has motivated the study and development of new approaches capable of coping with the intrinsic challenges of this kind of network, such as dynamic spectrum availability, distributed and heterogeneous network architectures, and soaring complexity. The bio-inspired approaches, with appealing characteristics such as autonomy, adaptation and collective intelligence of collaborative individuals, have been extensively studied. This paper presents a comprehensive survey of bio-inspired approaches for cognitive radio networks, emphasizing their domains of application. Specifically, ant colony optimization and particle warm optimization are further investigated with examples and numerical simulation.cognitive radio networks, bio-inspired networking, ant colony optimization, particle swarm optimization Citation:He Z Q, Niu K, Qiu T, et al. A bio-inspired approach for cognitive radio networks. Chin Sci Bull, 2012Bull, , 57: 37233730, doi: 10.1007 In the past decade, the cognitive radio technology, which was a term first coined by Mitola et al.[1], has attracted tremendous attention in the wireless communications to improve the spectrum-scarcity problem in static spectrumassignment policy. It facilitates the coexistence and efficiently exploits available spectrum in heterogeneous networks. However, the cognitive radio networks (CRN) faces many intractable challenges, such as sensing dynamic spectrum availability and complexity of distributed and heterogeneous network architectures, and hardware. These challenges are mainly caused by the discriminatory of the co-existence of secondary network within the primary network and the decentralized physical and access networking mechanisms of secondary network. For the sake of highly capable of autonomy and adaptation, bio-inspired approaches seem to be the promising methods to cope with the challenges in CRN, although they can be rather slow to adapt to environmental changes. Of course, the application of bio-inspired approaches in information technologies is not a new issue. A survey of the bio-inspired networking and its communication protocols and algorithms devised by looking at biology as a source of inspiration, and by mimicking the laws and dynamics governing these systems, is given by [2]. But most of the related works have been focused on optimization problems in network management, especially for the field of swarm intelligence such as ant colony optimization (ACO), particle swarm optimization (PSO) and so on. In order to excavate the scalability, adaptability, robustness and self-organization properties of biological systems, a detailed model description of bio-inspired approaches in every aspects of CRN, e.g. spectrum sensing and spectrum sharing, should be given.In general, this paper intends to introduce a general concept in bio-inspired approaches and show their possible application in the field of CRN. The main objective is to provide better understanding of the current state-of-the-art and the research is...

show abstract

“…To solve this problem, we can resort to the heuristics such as the particle swarm optimization (PSO) [9]. PSO is a H H metaheuristicH H as it makes few or no assumptions about the problem being optimized and can search very large spaces of candidate solutions.…”

Section: B 9 B 9 B Solving the Gamementioning

confidence: 99%

“…Due to the limitation of the space, we do not discuss the issues of the realization of the PSO algorithm here. Interested readers are referred to [9]. In the following simulation part, we will solve (12) using the bisection search algorithm as in [8] and the solution can be regarded as the performance benchmark of the REDG.…”

Section: B 9 B 9 B Solving the Gamementioning

confidence: 99%

Fair and efficient spectrum splitting for cooperative cognitive radio networks

Zhang

Yang

et al. 2012

2012 IEEE Global Communications Conference (GLOBECOM)

View full text Add to dashboard Cite

T AbstractT -This paper considers the network situation where the primary users (PUs) in a cognitive radio network have leased out the idled spectrum to the secondary users (SUs) via pricing-based dynamic spectrum allocations (DSAs). We take into account that a SU can serve as a cooperative relay for a PU, and, then, stimulate the PU to split more spectrum while maintaining the minimum transmission rate of the PU. Without using pricingbased mechanisms again, a resource-exchange based bargaining game is proposed to develop the incentive mechanism. Considering multiple SUs should compete with each other for the newly-obtained spectrum from a PU, the novelty of the game scheme is in taking explicitly account of that each PU and SU have their own minimum rate demands. Simulation results show the game guarantees the minimum rate requirement for the PU, and, at the same time, ensures each SU can get a fair rate-reward from the PU according to the level of contribution that it can make to compensate the PU's rate-loss.

show abstract

PSO-Based OFDM Adaptive Power and Bit Allocation for Multiuser Cognitive Radio System

Cited by 7 publications

References 9 publications

A Novel Particle Swarm Optimization for Channel Allocation in OFDM Based Cognitive Radio Networks

A Novel Particle Swarm Optimization for Channel Allocation in OFDM Based Cognitive Radio Networks

A bio-inspired approach for cognitive radio networks

Fair and efficient spectrum splitting for cooperative cognitive radio networks

Contact Info

Product

Resources

About