Immune Parallel Artificial Bee Colony Algorithm For Spectrum Allocation In Cognitive Radio Sensor Networks

Tian, Min; Deng, Hongtao; Xu, Min

doi:10.1109/cits49457.2020.9232476

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…On the other hand, a wide range of modern Bio-inspired Evolutionary Algorithms [21] have been developed for more difficult optimization problems. As examples, we find Genetic Algorithms (GA) [22][23][24][25], Neural Networks (NN) [26,27], Particle Swarm Optimization (PSO) [28,29], Ant Colony Algorithms (ACO) [30], Grey Wolf algorithms (GWO) [31,32], Artificial Bee Colony Algorithms (ABC) [33,34], Firefly Algorithms (FA) [35,36], Whale Algorithms (WOA) [37], Quantum-based Avian Navigation Algorithms (QANA) [38], Zebra Optimization Algorithms (ZOA) [39], and the more general Swarm Intelligence (SI) [40].…”

Section: State-of-the-artmentioning

confidence: 99%

The F/DR-D-10 Algorithm: A Novel Heuristic Strategy to Solve the Minimum Span Frequency Assignment Problem Embedded in Mobile Applications

Páez-Rueda,

Fajardo,

Pérez

et al. 2023

Mathematics

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Wireless communication supports various real-world applications, such as aeronautical navigation, satellite and TV broadcasting, wireless LANs, and mobile communications. The inherent characteristics of the electromagnetic spectrum impose constraints on telecommunication channels and their frequency bandwidths within mobile networks. A persistent challenge in these applications is providing high-demand services to mobile users, where frequency assignment problems, also known as channel assignment problems, assume significance. Researchers have developed several modeling approaches to address different facets of this problem, including the management of interfering radio signals, the assessment of available frequencies, and optimization criteria. In this paper, we present improved algorithms for solving the Minimum Span Frequency Assignment Problem in mobile communication systems using the greedy optimization approach known as F/DR. We solved and evaluated twenty well-known benchmark cases to assess the efficacy of our algorithms. Our findings consistently demonstrate that the modified algorithms outperform the F/DR approach with comparable computational complexity. The proposed algorithm notably achieves the following benchmarks: The modified algorithms consistently produce at least one local optimum better than the traditional algorithm in all benchmark tests. In 95% of the benchmarks evaluated, the probability of discovering a local optimum value (calculated by the modified algorithm) that is better than or equal to the one found by the conventional algorithm exceeds 50%.

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Section: State-of-the-artmentioning

confidence: 99%

The F/DR-D-10 Algorithm: A Novel Heuristic Strategy to Solve the Minimum Span Frequency Assignment Problem Embedded in Mobile Applications

Páez-Rueda,

Fajardo,

Pérez

et al. 2023

Mathematics

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“…Many algorithms have been developed to systematically solve such problems and find either the global optimum solution or sub-optimal solution, e.g., [24], [68]- [71] . Such algorithms include, fractional programming (FP) [67], [72], Weighted Minimum Mean Square Error (WMMSE) [67], [72], evolutionary algorithms (e.g., particle swarm optimization (PSO) [73], [74], genetic algorithm [75], [76], ant/bee colony optimization algorithm [77], [78]), among others. These algorithms are extremely computationally-extensive and typically executed in a central RNC with full and real-time information about network statistics and CSI.…”

Section: B) Optimization-based Techniquesmentioning

confidence: 99%

The Frontiers of Deep Reinforcement Learning for Resource Management in Future Wireless HetNets: Techniques, Challenges, and Research Directions

Alwarafy

Abdallah

Çiftler

et al. 2022

IEEE Open J. Commun. Soc.

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Next generation wireless networks are expected to be extremely complex due to their massive heterogeneity in terms of the types of network architectures they incorporate, the types and numbers of smart IoT devices they serve, and the types of emerging applications they support. In such large-scale and heterogeneous networks (HetNets), radio resource allocation and management (RRAM) becomes one of the major challenges encountered during system design and deployment. In this context, emerging Deep Reinforcement Learning (DRL) techniques are expected to be one of the main enabling technologies to address the RRAM in future wireless HetNets. In this paper, we conduct a systematic in-depth, and comprehensive survey of the applications of DRL techniques in RRAM for next generation wireless networks. Towards this, we first overview the existing traditional RRAM methods and identify their limitations that motivate the use of DRL techniques in RRAM. Then, we provide a comprehensive review of the most widely used DRL algorithms to address RRAM problems, including the value-and policy-based algorithms. The advantages, limitations, and use-cases for each algorithm are provided. We then conduct a comprehensive and in-depth literature review and classify existing related works based on both the radio resources they are addressing and the type of wireless networks they are investigating. To this end, we carefully identify the types of DRL algorithms utilized in each related work, the elements of these algorithms, and the main findings of each related work. Finally, we highlight important open challenges and provide insights into several future research directions in the context of DRL-based RRAM. This survey is intentionally designed to guide and stimulate more research endeavors towards building efficient and fine-grained DRL-based RRAM schemes for future wireless networks.

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“…These versatile tools and their variants have been extensively applied to solve diverse optimization problems. Some of their prominent applications in CRNs include [16,17] for GA, [18,19] for PSO, and [20,21] for ABC.…”

Section: Introductionmentioning

confidence: 99%

Computational-Intelligence-Based Spectrum-Sharing Scheme for NOMA-Based Cognitive Radio Networks

Sultan

2023

Applied Sciences

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The integration of non-orthogonal multiple access (NOMA) technology and cognitive radio networks (CRNs) promises to enhance the spectrum utilization efficiency of 5G and beyond-5G (B5G) mobile communication systems. In this article, a NOMA-based spectrum-sharing scheme is proposed for dual-hop CRNs in which a primary transmitter separated by a long distance from the primary receiver communicates via NOMA-based CRN. In this scenario, we mathematically formulate a constrained optimization problem to maximize the sum rate of all secondary users (SUs) while maintaining the total transmit power of the system. Inspired by the effectiveness of computational intelligence (CI) tools in solving non-linear optimization problems, this article proposes three CI-based solutions to the given problem aiming to guarantee quality of service (QoS) for all users. In addition, an enhanced version of the classic artificial bee colony (ABC) algorithm, referred to here as the enhanced-artificial-bee-colony (EABC)-based power allocation scheme, is proposed to overcome the limitations of classic ABC. The comparison of different CI approaches illustrates that the minimum power required by the secondary NOMA relay to satisfy the primary rate threshold of 5 bit/s/Hz is 20 mW for EABC, while ABC, PSO and GA achieve the same target at 23 mW, 27 mW and 32 mW, respectively. Thus, EABC reduces power consumption by 13.95% compared to ABC, while 29.78% and 46.15% power-saving is achieved compared to PSO and GA, respectively.

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Immune Parallel Artificial Bee Colony Algorithm For Spectrum Allocation In Cognitive Radio Sensor Networks

Cited by 7 publications

References 10 publications

The F/DR-D-10 Algorithm: A Novel Heuristic Strategy to Solve the Minimum Span Frequency Assignment Problem Embedded in Mobile Applications

The F/DR-D-10 Algorithm: A Novel Heuristic Strategy to Solve the Minimum Span Frequency Assignment Problem Embedded in Mobile Applications

The Frontiers of Deep Reinforcement Learning for Resource Management in Future Wireless HetNets: Techniques, Challenges, and Research Directions

Computational-Intelligence-Based Spectrum-Sharing Scheme for NOMA-Based Cognitive Radio Networks

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