Modulation Recognition using Artificial Neural Network(ANN) Ram Kishore Roy, Bhaben Saikia and Kandarpa Kumar Sarma
Abstract—This work describes a configuration of subsystems for the automatic modulation recognition of analog and digital signals. The system consists of modulation generators, classifier and recognizer formed by an Artificial Neural Network (ANN) and an output stage. The modulated signals are used to train the ANN. The trained ANN is next used to recognize the modulation type with consistently varying environment conditions as observed in wireless channels. The output of the system is a properly the recognized modulation type which can be next passed on to the appropriate receiver for demodulation and recovery of the data. Keywords—Modulation, artificial neural network, modulation recognition
I. I NTRODUCTION
M
ODULATION is the process in which some characteristics of a high frequency wave, called the carrier, is changed in according to the instantaneous value of low frequency wave. The low frequency wave is termed as modulating wave and the resultant wave is referred to as modulated wave. At the receiving end of the system, demodulation is used to recover the message[1]. But when multiple modulation techniques are used, the receiver design complexity increases and requires separate sub-systems for recovery of the specific signal forms. This is a case commonly used in high data rate systems and shall be commonly used in 3-G mobile networks. To remove the complexity associated with such reception, a readily available system is required which can recognize the modulation format and direct the recovery process accordingly. This work is an attempt to formulate a system that has the capacity to recognition the modulation type as a part of upcoming systems like software defined radio (SDR). II. MODULATION RECOGNITION AND SYSTEM DEFINITION The recognition of the modulation format of a detected signal is the intermediate step between signal detection and demodulation. With no knowledge of the transmitted data and many unknown parameters at the receiver, like the signal power, carrier frequency, phase offsets, timing information etc blind identification of the modulation is a difficult task. This becomes more challenging in real-world scenarios where multipath fading is a common occurrence with frequency selective and time varying behaviour of wireless channels[2]. Modulation recognition system must be able to make the correct classification of the modulation schemes of the received R. K. Roy, B. Saikia and K. K. Sarma are with the Department of Electronics and Communication Technology, Gauhati University, Assam, India e-mail: (:
[email protected],
[email protected] and
[email protected]).
signal under interference.Automatic recognition of different modulation schemes can be done by an intelligent receiver [3]. Modulation classifiers are generally subdivided into following two categories: − 1) Decision - Theoretic Approach-: The decision - theoretic approach is a probabilistic solution based on a priori knowledge of probability functions and certain hypothesis [2]. 2) Pattern Recognition Approach-: The pattern recognition approach is based on extracting some basic characteristics of the signal called features. This approach is generally divided into two subsystems, one is the feature extraction subsystem and the other is the classifier subsystem. The second approach is more robust and easier to implement if the proper features set is chosen[2]. III. STAGES OF MODULATION RECOGNITION A modulation recognition system is designed by following a block as depicted in Figure 1. The first stage is a pre processing block. This primarily is a noise removal operation carried out by a low pass filter. The classifier used for the recognition process is an Artificial Neural Network (ANN). The specific type selected for the work is a Multi Layer Perceptron (MLP) which is a feed forward network back propagation algorithm. The ANN is trained with signal samples of six different modulation schemes with SNR variation. The modulation schemes which are taken as samples are: − A. Amplitude Modulation: Consider a sinusoidal carrier wave defined by C(t)=Ac cos(2πfc t) where Ac is the carrier amplitude and fc is the carrier frequency. If m(t) is the message signal then amplitude modulated signal is given by S(t)=Ac (1 + ka m(t)) cos(2πfc t), where ka is the amplitude sensitivity[4]. B. Frequency Modulation: FM modulated signal is given by S(t)=Ac cos[2πfc t + β sin(2πfm t)], where β is the modulation index[1]. C. Phase Modulation: PM modulated signal is given by S(t)=Ac cos[2πfc t + kp m(t)], where kp is the phase sensitivity[1]. D. Amplitude Shift Keying: The ASK can be written as ASK(t)=S(t) sin(2πf t),where S(t) have two different amplitude levels.
V. A work by K. Hassan, I. Dayoub, W. Hamouda and M. Berbineau is related to an algorithm for automatic digital modulation scheme recognition. The proposed algorithm is verified using higher order statistical moments (HOM) of continuous wavelet transform (CWT) as a features set [8].
V. ANN CONFIGURATION OF TRAINING
Fig. 1.
Block diagram of a generic Modulation Recognition System
E. Frequency Shift Keying:
The Multi Layer Perceptron(MLP) is configured as per specification in the Table. It is trained using (error) Back Propagation (BP) depending upon which the connecting weights between the layers are updated. This adaptive updating of the MLP is continued till the performance goal is met. Training the MLP is done in two broad passes -one a forward pass and the other a backward calculation with Mean Square Error (MSE) determination and connecting weight updating in between. Batch training method is adopted as it accelerates the speed of training and the rate of convergence of the MSE to the desired value[9]. The steps are as below. • Initialization:Initialization:Initialize weight matrix W with random values be- tween [0, 1]. • Presentation of training samples:Input is pm = [pm1 , pm2 .....pmL ]. The desired output is dm =[dm1 , dm2 ......dmL ]. – Compute the values of the hidden nodes as:
In FSK,the frequency changes in response to information, one particular frequency for a 1 bit and another frequency for a 0 bit. F SK(t)=sin(2πf1 t), for bit 1 and F SK(t)=sin(2πf2 t),for bit 0.
nethmj =
h mi wji p + ∅hj
(1)
i=1
– Calculate the output from the hidden layer as ohmj = fjh (nethmj )
F. Phase Shift Keying:
(2)
where f(x)= e1x x −x or f(x)= eex −e +e−x depending upon the choice of the activation function. – Calculate the values of the output node as:
Phase shift represents the change in the state of the information. In this case, P SK(t)=sin(2πf t), for bit 1 and P SK(t)=sin(2πf t + π),for bit 0 [5].
oomk = fko (netomj )
IV. SOME OF THE RELEVANT WORKS I. Alexander Iversen, Nicholas K Taylor and Keith E. Brown developed a technique by which the neural network classify those signal for which they trained and also detect which are unknown to them. This unknown formats were detected using Auto- Association neural network [2].
L X
•
Forward Computation:Compute the errors: ejn = djn − ojn
(4)
Calculate the mean square error(MSE) as: PM PL 2 j=1 n=1 ejn M SE = 2M Error terms for the output layer is:
I. M. Richterova proposed a method to identify the class of modulation using ANN. In the proposed modulation classifier (MC), the key extracted features are derived from instantaneous phase, amplitude and frequency [3].
o δmk = oomk (1 − oomk )emn
III. H. Wijanto, Sugihartono, S. Tjondronegoro and Kuspriyanto proposed a method to improve the modulation recognition using HOS [5].
(3)
(5)
(6)
Error terms for the hidden layer: h δmk = ohmk (1 − ohmk )
X
o o δmj wjk
(7)
j
IV. A.E. El-Mahody had proposed a method for automatic classification of intercepted signals into FM or PM signal using sensor arrays [7].
•
Weight Update: – Between the output and hidden layers o o o wkj (t + 1) = wkj (t) + ηδmk omj
(8)
where η is the learning rate(0 < η < 1). For faster convergence a momentum term(α)maybe added as: o o o o wkj (t+1) = wkj (t)+ηδmk omj +α(wkj (t+1)−wkj ) (9) – Between the hidden layer and input layer: h h h wji (t + 1) = wji (t) + ηδmj pi
(10)
A momentum term maybe added as: h h h o wji (t + 1) = wji (t) + ηδmj pi + α(wji (t + 1) − wji (11) One cycle through the complete training set forms one epoch.Repeat the above till MSE meets the performance criteria and keep count of the epoch elapsed[9].
VI. EXPERIMENTAL DETAILS AND DISCUSSION The work is carried out as per the process depicted in Figure 1. During training samples of AM, FM, PM, ASK, FSK and PSK are taken with SNR variation of 21 different ranges between 10dB.The specification for different modulation schemes are as in Table II. The resultant ideal samples, with −3dB and −10dB SNR samples are as in Figure 2 to Figure 4. These samples of the different modulation schemes are taken to train the ANN classifier. Training is carried out and the learning of one session is depicted as in Figure 5. The training shows performance at optimum training state provides 100% success with ideal samples. A summary of the results derived with different samples are included as in Table III. The usefulness of the scheme is that the recognition is carried out despite presence of noise in the received signal content. This is due to the fact that the ANN is robust to noise and similar variations if properly trained.
Fig. 2.
Ideal training samples
Fig. 3.
Ideal training samples with -3dB SNR
TableI.Specifications of ANN used ANN Type Sample size Input size Hidden Layer Activation function Training method Epochs require (average)
MLP Six schemes with SNR 10dB 6100 1 tansig, tansig, logsig Back propagation 148
TableII.Modulation schemes details Modulation Schemes AM FM PM ASK FSK PSK
Specification F s = 500,F c = 30 F s = 800,F c = 200,f r.dev = 1.7π F s = 800,F c = 200,ph.dev = 4π Amplitude level=0, 1 F 2 = 10000,F 1 = 2000 F = 10000,phase = π/2
Fig. 6.
Simulating fading channels
The training is carried out considering fading effects observed in wireless channel which can be generated using the Clarke-Gans model as depicted in Figure 6 [10]. Training continues till the ANN approaches the desired goal. Several configurations of the MLP can be utilized for training. Fig. 4.
Ideal training samples with -10dB SNR
The ANN configurations used have one input layer, one hidden layer and one output layer. A single hidden layered MLP is found to be computationally efficient for the work as 2-hidden layered or a 3-hidden layered MLPs are found to be showing no significant performance improvement at the cost of slowing down training. TableIII. Recognition rate with varying SNR SNR in dB 0 dB ±3 dB ±10 dB
% of success 100% 95% 75%
TableIV. Effect on average MSE convergence after 1000 epochs with variation of activation functions at input, hidden and output layers Case Input layer Hidden Layer Output Layer M SE × 10−4 1 log-sigmoid log-sigmoid log-sigmoid 1.45 2 tan-sigmoid tan-sigmoid tan-sigmoid 1.32 3 tan-sigmoid log-sigmoid tan-sigmoid 1.01 4 log-sigmoid tan-sigmoid log-sigmoid 1.08 5 log-sigmoid log-sigmoid tan-sigmoid 1.15 6 log-sigmoid tan-sigmoid log-sigmoid 1.19 VII. C ONCLUSION Fig. 5.
ANN convergence plot
In an extended form, the system can be modelled to carry out recognition for a wide range of signal and modulations combinations irrespective of SNR and channel condition. It
can be considered for application with system where proper knowledge of signals and modulation types are critical. Such a system can be ideal for 3-G mobile set-ups and SDR systems. R EFERENCES [1] S. Haykin, Communication System , 4th ed. New Delhi, India: Wiley Publication, 2006. [2] M. Richterova, Signal Modulation Recognizer Based on Method of Artificial Neural Networks, China:University of Defence, Czech Republic, Progress in Electromagnetics Research Symposium, August 22-26, pp. 575-578, 2005. [3] A. Iversen, N. K. Taylor, K. E. Brown and J. Krstad, Classification of Communication Signals and Detection of Unknown Formats Using Artificial Neural Networks, Edinburg EH 144 AS, UK:Intelligent Systems Laboratory, Heriot -Watt University, IEEE Nordic Signal Processing Symposium, pp. 6-20, 2006. [4] B. P. Lathi, Modern Analog and Digital Communication, 3rd ed. New Delhi, India:Oxford University Press, 1998. [5] H. Wijanto, Sugihartono, S. Tjondronegoro and Kuspriyanto, The Performance Improvement of Automatic Modulation Recognition Using Simple Feature Manipulation, Analysis of the HOS and Voted Decision, World Academy of Science, Engineering and Technology, pp. 763-767, 2009. [6] B. Sklar, Digital Communication- Fundamentals And Applications, 2nd ed. New Delhi, India:PHI, 2001. [7] A. E. EI-Mahdy, Automatic Modulation Classification of Composite FM / PM Speech Signal, Cairo, Egypt:IET Communication, vol.1 pp.157-164, 2007. [8] K. Hassan, I. Dayoub, W. Hamouda and M. Berbineau, Automatic Modulation Recognition Using Wavelet Transform and Neural Networks in Wireless Systems,EURASIP Journal on Advances in Signal Processing, pp. 1-13, 2010. [9] S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd ed. New Delhi, India:Pearson Education, 2003. [10] T. S. Rappaport, Wireless Communications: Principles and Practice, 2nd ed. New Delhi, India:Pearson Education, 2004.
