Abstract
Power allocation plays a pivotal role in improving the communication performance of interference-limitedwireless network (IWN). However, the optimization of power allocation is usually formulated as a mixed-integernon-linear programming (MINLP) problem, which is hard to solve. Whale optimization algorithm (WOA)has recently gained the attention of the researcher as an efficient method to solve a variety of optimizationproblems. WOA algorithm also has the disadvantages of low convergence accuracy and easy to fall into local optimum.To solve the above problems, we propose Cosine Compound Whale Optimization Algorithm (CCWOA).First of all, its unique cosine nonlinear convergence factor can balance the rate of the whole optimization processand prevent the convergence speed from being too fast. Secondly, the inertia weight and sine vector canincrease the probability of jumping out of the local optimal solution. Finally, the Archimedean spiral can reducethe risk of losing the optimal solution. A representative benchmark function is selected to test the convergencerate of CCWOA algorithm and the optimization performance of jumping out of local optimum. Compared withthe representative algorithms PFP and GAP, the optimization effect of CCWOA is almost consistent with theabove two algorithms, and even exceeds 4% - 6% in numerical value. The advantage of CCWOA is that it haslower algorithm complexity, which has a good advantage when the network computing resources are fixed. Inaddition, the optimization effect of CCWOA is higher than that of WOA, which lays a good foundation for furtherapplication of swarm intelligence optimization algorithm in network resource allocation.
Highlights
The wireless networks have been widely used in civilian and military fields, and people rely on wireless networks to transmit crucial private information
The convergence curves of Cosine Compound Whale Optimization Algorithm (CCWOA) and MPA in f5 and f6 functions are almost the same, and the optimization speed is fast decreasing, which shows that the optimization effect of the two algorithms is very good
The optimization effect of CCWOA on benchmark function is significant, and CCWOA can improve the accuracy of solving the problem and the the same, and the optimization speed is fast decreasing, which shows that the optimization effect of the two algorithms is very good
Summary
The wireless networks have been widely used in civilian and military fields, and people rely on wireless networks to transmit crucial private information. Optimizing security energy efficiency (SEE) is an important research goal indicated by the ratio of secrecy throughput to total network power consumption [22]. The security researches of physical layer are mainly divided into two directions: optimizing secrecy throughput and improving security energy efficiency. In [31, 38], scholars study beamforming technology, which by designing the direction of transmitted signal, signal received by the legitimate receiver is enhanced It improves the throughput of legal channel, and reduces the throughput of eavesdropping channel. The task of this paper is to optimize the minimum secure throughput (MST) of eavesdropper and SEE of transmission link by using CCWOA to control power allocation. SSuuppppooosseseethtthahatatatananneaeevaavevsedserdsodrporpopepreperexreixsetixssitisntisnthitnehctehocemomcmoummnuimcnaui--nicatctiioaontniolinlninlki,kn,kw,whwihchihicchhisiisasalslaoslosroreeccreeeivciveeesivssesiisggnnsaialgl nfraolmfrttorramanstmraitnt--smitttteerrrss. .DDDeenenonototeetebybyhiehciehcanhnnanenelnl geal ingaffirrnoommfrtotrrmaannsstmrmainttsteemrrssititiettroos i to eeaavvveeessdsddrrorooppppepere,rr,r,reercececeiveiveivesesigisgninagalnlaaatlttahthteeteheaevaveesadsvdreorspodppreporepirpsiesr is
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