Abstract
This paper presents an optimal design of low noise amplifier (LNA) using an efficient swarm-based optimizer called firefly algorithm (FA). Many researchers have used firefly algorithm to solve various nonlinear engineering problems and reported outstanding results. In view of this, FA is implemented for the first time in this paper to optimize the parameters of LNA like gain and noise figure (NF). Two case studies have been performed which includes the minimization of NF and maximization of gain. Optimization of these two parameters has been carried out by considering each parameter as a single objective function. Penalty factor method is considered for handling the constraints. Other parameters of LNA like power consumption, linearity, and stability are also discussed for both the cases. The designed LNA has a cascode structure with inductive source degeneration topology and is implemented in UMC 0.18-μm CMOS technology using CADENCE software. LNA is designed for 5.5-GHz frequency. The performance of FA in optimizing the parameters of LNA is also compared with the performance of other similar contemporary algorithms like particle swarm optimization (PSO), human behavior PSO (HB-PSO), backtracking search algorithm, and cuckoo search algorithm (CSA). The optimized value of LNA parameter using FA and other algorithms when simulated in MATLAB environment is compared with the simulated result of CADENCE. Statistical analysis is also performed for each case study, and the results are compared with the above-mentioned optimization algorithms. Simulation results, comparative study, and statistical analysis confirm the superiority of FA over other methods in terms of its computational efficiency, consistency, and robustness.
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Lee TH (2000) X “5-GHz CMOS wireless LANs”. IEEE Trans Microw Theory Tech 50:268–280
Kotti M, Sallem A, Bougharriou M, Fakhfakh M, Loulou M (2010) Optimizing CMOS LNA circuits through multi-objective meta-heuristics. In: XIth international workshop on symbolic and numerical methods modelling and applications to circuit design, pp 1–6
Fakhfakh M, Cooren Y, Sallem A, Loulou M, Siarry P (2010) Analog circuit design optimization through the particle swarm optimization technique. Analog Integr Circ Sig Process 63:71–82
Shams M, Rashedi E, Hakimi A (2015) Clustered-gravitational search algorithm and its application in parameter optimization of a low noise amplifier. Appl Math Comput 258:436–453
Nguyen N, Lim C, Jain LC, Balas VE (2009) Theoretical advances and applications of intelligent paradigms. J Intell Fuzzy Syst 20:1–2
Timar DB, Balas VE(2007) Decision-making in human resources selection methodology. In: 2nd IEEE international workshop on soft computing applications, 21–23 August, Gyula, Hungary–Oradea, Romania pp 123–127
Jones DR, Perttunen CD, Stuckman BE (1993) Lipschitzian optimization without the Lipschitz constant. J Optim Theory Appl 79:157–181
Kaveh A, Share MAM, Moslehi M (2013) Magnetic charged system search: a new meta-heuristic algorithm for optimization. Acta Mech 224:85–107
Khong SZ, Nesic D, Manzie C, Tan Y (2013) Multidimensional global extremum seeking via the DIRECT optimization algorithm. Automatica 49:1970–1978
Kaveh A, Talatahari S (2010) A novel heuristic optimization method: charged system search. Acta Mech 213:267–289
Aarts E, Lenstra K (2003) Local search in combinatorial optimization. Princeton University Press, Princeton
Taherzadeh M, Lotfi R, Zare H, Shoaei O (2003) Design optimization of analog integrated circuits using simulation-based genetic algorithm. Proc IEEE Int Symp Signals Circuits 1:73–76
Kirkpatric S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. J Sci 220:671–680
Dorigo M, Maniezzo V, Colorni A (1996) The ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B 26:29–41
Kennedy J, Eberhart RC (1995) Particle swarm optimization. Proc IEEE Int Conf Neural Netw 4:1942–1948
Hao L, Gang X, Ding G, Sun Y-B (2014) Human Behavior-Based particle swarm optimization. Sci World J 2014:1–14
González E, Álvarez O, Díaz Y, Parra C, Bustacara C (2005) BSA: a complete coverage algorithm. In: Proceedings of the IEEE international conference on robotics and automation, pp 2040–2044
Mohanty CS, Khuntia PS, Mitra D (2014) A modified bacterial foraging optimized PID controller for time-delay systems. Int J Adv Intell Paradig 6(4):255–271
Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inform Sci 179:2232–2248
Yang XS, Deb S (2009) Cuckoo Search via Levy Flights. In: World congress on nature and biologically inspired computing, pp 210–214
Li Y (2009) A simulation-based evolutionary approach to LNA circuit design optimization. Appl Math Comput 209:57–67
Karkhanehchia MM, Naderib S, Jafaria F, Majidifarc S (2014) Design and optimization of a very low noise amplifier using particle swarm optimization technique. Int J Eng Technol Sci 2(2):122–131
Shin LW, Chin NS, Marzuki A (2013) 5 GHz MMIC LNA design using particle swarm optimization. Inf Manag Bus Rev 5(6):257–262
Yang XS (2008) Firefly algorithm, Nature-inspired metaheuristic algorithms, vol 20. Wiley Online, Library, pp 79–90
Razavi B (1997) RF Microelectronics. PTR, Prentice-Hall
Ellinger F (2007) Radio frequency integrated circuits and technologies, 2nd edn. Springer, New York
Rajan A, Malakar T (2015) Optimal reactive power dispatch using hybrid Nelder-Mead simplex based firefly algorithm. Int J Electr Power Energy Syst 66:9–24
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Kumar, R., Rajan, A., Talukdar, F.A. et al. Optimization of 5.5-GHz CMOS LNA parameters using firefly algorithm. Neural Comput & Applic 28, 3765–3779 (2017). https://doi.org/10.1007/s00521-016-2267-y
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DOI: https://doi.org/10.1007/s00521-016-2267-y