This article presents a novel information criterion based optimal model parameter selection algorithm for behavioral modeling of Radio Frequency Power Amplifiers (RF PAs). The proposed approach uses Particle Swarm Optimization (PSO) along with the Information Criterion (IC) based cost functions for determining the most parsimonious model from all the available combinatorial models. The proposed technique thereby helps in deriving complexity reduced models without compromising modeling accuracy. The validation of the proposed approach was carried out by modeling a GaAs based PA driven by a 20-MHz generic random input signal. It was shown that, the model performance was maintained while its complexity in terms of number of coefficients was reduced by around 35% in the considered cases. In addition, the proposed PSO based approach helps in deriving the most parsimonious PA model in a very short amount of time compared to the conventional sweep technique.
L.Ding,G.Tong Zhou, R.Morgan, Z.Ma, J.Stevenson Kenney, J.KimandC.R.Giardina, A robust digital baseband predistorter constructed using memory polynomials, IEEE Trans Commun52(1) (2004), 159–165.
2.
A.Zhu, M.Wren and T.J.Brazil, An efficient Volterra based behavioral modeling of wideband RF power amplifiers, IEEE MTT-S International Microwave Symposium, Philadelphia, USA, 2003.
3.
A.Ahmed, E.R.Srinidhi and G.Kompa, Neural network and memory polynomial methodologies for PA modeling, Proc IEEE 7th International Conference on Telecommunications, 2005.
4.
R.V.Sanjika Devi and D.G.Kurup, Behavioral modeling of RF power amplifiers for designing energy efficient wireless systems, Proc IEEE International Conference on Wireless Communications, Signal Processing and Networking, Chennai, India, 2017.
5.
P.Crama and Y.Rolain, Broadband measurement and identification of Wiener-Hammerstein model for an RF amplifier, 60th ARFTG Conf Dig, 2002, pp. 49–57.
6.
R.V.Sanjika Devi, B.Manasa Nandini, M.Niharika, P.Anusha and D.G.Kurup, Broadband RF power amplifier modeling using an enhanced Wiener model, Proc IEEE International Conference on Computational Intelligence and Computing Research, Coimbatore, India, 2017.
7.
T.Liu, S.Boumaiza and F.M.Ghannouchi, Augmented Hammerstein predistorter for linearization of broadband wireless transmitters, IEEE Trans Microw Theory Techn54(4) (2006), 1340–1349.
8.
M.E.Gadringer, D.Silveira and G.Magerl, Efficient power amplifier identification using modified parallel cascade Hammerstein models, Proc IEEE Radio Wireless Symp Dig, 2007, pp. 305–308.
9.
R.V.Sanjika Devi, C.S.P.Kumar, M.Krishna Chaitanya, M.Vishnu Deepak and D.G.Kurup, Modeling of RF power amplifiers using a modified Hammerstein model, Proc IEEE International Conference on Communication and Signal Processing, Chennai, India, 2018.
10.
H.Ku and J.Stevenson Kenney, Behavioral modeling of non linear RF power amplifiers considering memory effects, IEEE Trans Microw Theory Techn15(12) (2003), 2495–2504.
11.
M.R.Dennis, Z.Ma, J.Kim, M.G.Zierdt and J.Pastalan, A Generalized Memory Polynomial model for digital predis-tortion of RF power amplifiers, IEEE Trans Signal Process54(10) (2006), 3852–3859.
12.
R.V.Sanjika Devi and D.G.Kurup, Sparse identification of memory effects and nonlinear dynamics for developing parsimonious behavioral models of RF power amplifiers, IEEE MTT-S International Microwave and RF Conference, Ahmedabad, India, 2017.
13.
A.S.Tehrani, H.Cao, S.Afsardoost,T.Eriksson, M.Isaksson and C.Fager, A comparive analysis of the complexity/accuracy tradeoffs in power amplifier behavioral models, IEEE Trans Microw Theory Techn58(6) (2010), 1510–1520.
14.
M.Younes, O.Hammi and F.M.Ghannouchi, Algorithm for model dimension estimation of memory polynomial-base RF transmitters/power amplifiers behavioral models, International Journal of Microwave and Optical Technology4(4) (2009), 242–247.
15.
R.Mondal, T.Ristaniemi and M.Doula, Genetic algorithm optimized memory polynomial digital pre-distorter for RF power amplifiers, IEEE International Conference on Wireless Communication and Signal Processing, China,2013.
16.
S.Wang, M.A.Hussein, O.Venard and G.Baudoin, Optimal sizing of Generalized Memory Polynomial structure based on hill - climbing heuristic, Proc IEEE 46th European Microw Conf, 2016, pp. 190–193.
17.
A.H.Abdelhafiz, O.Hammi, A.Zerguine, A.T.Al-Awami and F.M.Ghannouchi, A PSO based memory polynomial predistorter with embedded dimension estimation, IEEE Trans Broadcast59(4) (2013).
18.
A.Abdelhafiz, L.Behjat and F.M.Ghannouchi, Generalized memory polynomial model dimension selection using Particle Swarm Optimization, IEEE Microwave and Wireless Components Letters28(2) (2018), 96–98.
19.
R.Eberhart and J.Kennedy, A new optimizer using Particle swarm theory, Proc IEEE 6th International Symposium on Micro machines and Human Science28(2) (1995), 96–98.
20.
P.Stoica and Y.Selen, Model-order selection: A Review of Information criterion rules, IEEE Signal Processing Magazine21(4) (2004), 36–47.
21.
R.Doraiswami, L.Cheded and H.M.Khalid, Model Order Selection Criterion with application to Physical Systems, 6th Annual IEEE Conference on Automation Science and Engineering, Canada, 2010.
22.
H.Bozdogan, Akaike's information criterion and recent developments in information complexity, Journal of Mathematical Psychology44(1) (2000), 62–91.
23.
G.Schwarz, Estimating the dimension of a model, The Annals of Statistics6(2) (1973), 461–464.
24.
S.Hosseini, S.Mohammad Saberali and M.Farzan Sabahi, Model Order Selection based on different Information Criteria For PDF Estimation Using Maximum Entropy Method and Application in Cognitive Radio Systems, IEEE International Symposium on Telecommunications, 2014, pp. 1239–1244.
