Whale Optimization Algorithm With Applications to Resource Allocation in Wireless Networks
TL;DR: This article presents the fundamental backgrounds and the binary version of the WOA as well as introducing a penalty method to handle optimization constraints and presents the adoption of WOA to solve a variety of potential resource allocation problems in 5G wireless networks and beyond.
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Abstract: Resource allocation plays a pivotal role in improving the performance of wireless and communication networks. However, the optimization of resource allocation is typically formulated as a mixed-integer non-linear programming (MINLP) problem, which is non-convex and NP-hard by nature. Usually, solving such a problem is challenging and requires specific methods due to the major shortcomings of the traditional approaches, such as exponential computation complexity of global optimization, no performance optimality guarantee of heuristic schemes, and large training time and generating a standard dataset of machine learning based approaches. Whale optimization algorithm (WOA) has recently gained the attention of the research community as an efficient method to solve a variety of optimization problems. As an alternative to the existing methods, our main goal in this article is to study the applicability of WOA to solve resource allocation problems in wireless networks. First, we present the fundamental backgrounds and the binary version of the WOA as well as introducing a penalty method to handle optimization constraints. Then, we demonstrate three examples of WOA to resource allocation in wireless networks, including power allocation for energy-and-spectral efficiency tradeoff in wireless interference networks, power allocation for secure throughput maximization, and mobile edge computation offloading. Lastly, we present the adoption of WOA to solve a variety of potential resource allocation problems in 5G wireless networks and beyond.
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Figures
![Fig. 4: Performance comparison when the number of users varies from 2 to 10. The WOA-based algorithm performs very close to the solution obtained by the centralized heuristic algorithm HODA in [28].](/figures/fig-4-performance-comparison-when-the-number-of-users-varies-hszqmoje.png)
Fig. 4: Performance comparison when the number of users varies from 2 to 10. The WOA-based algorithm performs very close to the solution obtained by the centralized heuristic algorithm HODA in [28]. ![Fig. 2: Convergence evolution of the and the path-following procedure in [25] (with M = 4 users).](/figures/fig-2-convergence-evolution-of-the-and-the-path-following-24f1ha4o.png)
Fig. 2: Convergence evolution of the and the path-following procedure in [25] (with M = 4 users). 
Fig. 1: Examples of s-shaped transfer functions. 
Fig. 3: Performance evaluation of the WOA-based algorithm.
![Fig. 4: Performance comparison when the number of users varies from 2 to 10. The WOA-based algorithm performs very close to the solution obtained by the centralized heuristic algorithm HODA in [28].](/figures/fig-4-performance-comparison-when-the-number-of-users-varies-hszqmoje.webp)
![Fig. 2: Convergence evolution of the and the path-following procedure in [25] (with M = 4 users).](/figures/fig-2-convergence-evolution-of-the-and-the-path-following-24f1ha4o.webp)
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References
The Whale Optimization Algorithm
Seyedali Mirjalili,Andrew Lewis +1 more
TL;DR: Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods.
11.1K
Next Generation 5G Wireless Networks: A Comprehensive Survey
TL;DR: This survey makes an exhaustive review of wireless evolution toward 5G networks, including the new architectural changes associated with the radio access network (RAN) design, including air interfaces, smart antennas, cloud and heterogeneous RAN, and underlying novel mm-wave physical layer technologies.
3.4K
Energy-Efficient UAV Communication With Trajectory Optimization
Yong Zeng,Rui Zhang +1 more
TL;DR: In this article, a new design paradigm that jointly considers both the communication throughput and the UAV's energy consumption was proposed to maximize the energy efficiency of UAV communications with a ground terminal.
Stochastic ranking for constrained evolutionary optimization
Thomas Philip Runarsson,Xin Yao +1 more
TL;DR: A novel approach to balance objective and penalty functions stochastically, i.e., stochastic ranking, is introduced, and a new view on penalty function methods in terms of the dominance of penalty and objective functions is presented.
User Association for Load Balancing in Heterogeneous Cellular Networks
Ye Qiaoyang,Beiyu Rong,Yudong Chen,Mazin Al-Shalash,Constantine Caramanis,Jeffrey G. Andrews +5 more
TL;DR: In this paper, the authors provide a low-complexity distributed algorithm that converges to a near-optimal solution with a theoretical performance guarantee, and observe that simple per-tier biasing loses surprisingly little, if the bias values Aj are chosen carefully.
1.3K