requires also a good network support. In fact, for defining. Fig. 1. Operative Framework for CBTS an Intelligent Handover, two different procedures should be.
Neural Networks Mode Classification based on Frequency Distribution Features Andrea F. Cattoni, Marina Ottonello, Mirco Raffetto and Carlo S. Regazzoni Department of Biophysical and Electronic Engineering (DIBE) University of Genova Genova, Italy Email: {cattoni, marina, raffetto, carlo}@dibe.unige.it
Abstract—The growing number of new emerging wireless standards is creating regulatory problems in allocating the unlicensed frequencies. A possible solution for increasing the frequency re-usage within the framework of info-mobility cellular systems is the joint exploitation of Smart Antennas and Cognitive Radio. In the paper a Mode Identification algorithm, based on frequency distribution features and multiple neural network classifiers, for a Cognitive Base Transceiver Station is presented. Simulated results, obtained in a simplified framework, will prove the effectiveness of the proposed approach.
I. I NTRODUCTION The growing number of new emerging wireless standards is creating regulatory problems in allocating the unlicensed frequencies. In fact, from a regulatory point of view the electro-magnetic spectrum seems to be overcrowded. Indeed, measurement campaigns and studies made by the US Federal Communication Commission (FCC) [1] prove that only a small sub-portion of the spectrum is used over all US at every moment. Hence an exploitation of the spatial diversity of the radio signals can allow to re-use the same frequencies in different places. In order to increase the frequency re-use in wireless cellular networks, the simplest solution is to reduce the dimension of each elementary cell. This fact has a side effect that is potentially dangerous for the network: it dramatically increases the handover rate, especially in the case of fast mobile users such as vehicular users. Another possible solution is to exploit the benefits of a joint usage of technologies that enable a dynamic and flexible Spatial Division Multiple Access (SDMA) such as Smart Antennas (SAs) [2] and Cognitive Radio (CR) [3]. In fact, having a SA as a perceptive organ controlled by the Cognitive system can allow to heavily exploit SDMA within each single cell, to increase the number of users enabled to communicate, and to keep low the inter-cell handover rate. All these features can be included into a Cognitive Base Transceiver Station (CBTS) for an Intelligent Highway. The CBTS is substantially an intelligent cellular node, able to interact with both standard and Cognitive nodes, designed to work in an high-speed info-mobility framework, as shown in Figure 1. Besides to the Cognitive and adaptive capabilities, SDMA requires also a good network support. In fact, for defining
Fig. 1.
Operative Framework for CBTS
an Intelligent Handover, two different procedures should be considered: a between-cells handover protocol and a withincell channel switching. Common commercial standards already include good between-cells handover procedures but rarely they are designed for dynamic spectrum allocation. Let us consider, for example, the case when two cars, driving from opposite directions use the same communication channel. Until they are at the border of the Field of View (FoV) of the base station, they can be considered quite sufficiently separated. While they are meeting in the middle of the FoV, one of the two needs to change the channel. In order to adapt also commercial standards to flexible spectrum allocation, spectrum awareness techniques, performed at the signal level of the involved standards are required. These procedures need the support of a Mode Discovery within the cell in order to avoid jamming between two or more users. This fact is a further confirmation of the role of the Mode Identification and Spectrum Monitoring (MISM) processes within CR operative frameworks. In the present paper, the above described MISM problems are faced through a physically grounded sub-symbolic representation [4] and classification [5] of the available communication modes. In fact the features extracted from the incoming radio signal represent a sort of spectral signature for each considered transmission modality and they are used as input in a multiclassifier architecture based on Neural Networks (NNs). The paper is organized as follows: in Section II a brief overview on the concept of Cognitive Radio is presented while
in Section III how the MISM problem has been faced in previous literature is depicted. Sections IV and V show more in detail the proposed method. Results obtained through a simulation environment are presented in Section VI and finally Conclusions are drawn in Section VII. II. C OGNITIVE R ADIO T ECHNOLOGY The idea of Cognitive Radio, as a new approach for wireless communication was first presented by Joseph Mitola III [6]. It was thought as the final point of evolution for a softwaredefined radio platform, considered now as a black-box that changes its communication functions depending on network and/or user’s requirements. After Mitola, others researches [7], [8], agencies [9] and private institutions [10] have tried to provide a common definition to the Cognitive Radio concept. But recently a new vision for modeling the intelligence of CRs is emerging: it takes inspiration from neuro-scientific [11] and robotics [4] works. The behavior of the Cognitive Entity so obtained can be seen as the result of the interaction, cooperation and competition of a set of autonomous Agents [12]. The considered CBTS and the MISM procedure here presented can be situated within the above mentioned framework, as showed more in detail in Section IV. III. M ODE I DENTIFICATION AND S PECTRUM M ONITORING The Spectrum Sensing and Mode Identification (MISM) process plays a key role in the Cognitive Radios because it provides an observation of the physical world: this is the knowledge about the channel conditions which allows to take a proper decision for the current context. In the state of the art, different implementations for spectrum sensing are already present. The oldest and simplest one is the Radiometer [13]: it extracts the energy in each sub-band, identifying if the bandwidth is already occupied by a transmitted signal. This approach is characterized by a very low computational load, but it is no able to provide which standard is occupying the examined bandwidth. This feature grew in importance in the last years, in order to perform a fast and optimal re-configuration of the intelligent device. In fact different methods [14], [15] for mode/standard classification have been proposed. In [16] it is possible to see one of the first attempt to introduce bio-inspired signal processing/classification, through the usage of a Neural Network or a Support Vector Machine for mode classification. The proposed method can hence be considered an extension of the ones seen in [16]. IV. C OGNITIVE BTS A RCHITECTURE In Figure 2 the ideal architecture for the proposed CBTS is shown. The CBTS is equipped with two different, smart, antennas. The first one is the perceptual “organ” of the system, while the second one is completely devoted to full-duplex communications with the users. In Figure 2 it is shown how the tasks inside the CBTS are subdivided into a set of autonomous, cooperating and
Fig. 2.
Ideal Architecture for the proposed CBTS
concurrent agents, whose emergent behavior is similar to the one obtained by a subsumption architecture [17], [4]. The Perceptual Agent is devoted to identify the presence, localize and track the users present in the FoV. It also provides the gathered information to the other Agents. Main goal of the Mode Identification and Spectrum Monitoring Agent is to identify which is the transmission modality or communication standard each user is employing. This information is passed to the Agents dedicated to the communication process at any level of the ISO-OSI stack (Physical-PHY, Medium Access Control-MAC, and others) in order to reconfigure the CR. A particular role is played by the Communication Agent that is devoted to merge all the information and all the communication needs in order to choose the appropriate multilobe beamforming strategy for the current context. The Case Based Memory is used to store all the relevant experiences in order to perform a continuous learning phase for all the Agents. On going researches are focusing the attention also on how to use the memory for predicting motion and communication behaviors of the users and how the system can evolve. In the present paper attention is focused on the MISM Agent, on how to model its behavior and how it processes the gathered data in order to enrich sub-symbolic context information with a symbolic, semantic label (i.e. the transmission modality). V. M ODE I DENTIFICATION AND S PECTRUM M ONITORING AGENT Each Agent present in the architecture, and hence the MISM Agent too, can be modeled, in terms of behavior, through an independent Cognitive Cycle [18]. It is a model which describes the behavior of any living being. In fact, it interacts with the external world through four main steps: Sensing, Analysis, Decision and Action. The first stage of the cycle (Sensing or Observation) represents a passive interaction of the terminal with the environment: the Cognitive Terminal (CT) gathers information about both its internal state and the surrounding environment in a
Fig. 4.
Fig. 3.
Cognitive Cycle of the MISM Agent
Representation of a Cognitive Cycle
continuous way. In the second step (Analysis) the acquired data are processed and analyzed in order to provide the system with a symbolic or a sub-symbolic representation of the context. In the Decision stage the Cognitive system has to decide which is the most proper action to the received external stimulus; the choice is based on the embedded internal knowledge, the past experience and the current context. The action represents an active interaction with the external environment because the CT tries to influence the physical context through its actions, in order to gain an “advantage”. The concept of advantage can be translated in engineering terms as a maximization of a functional, defined for the specific application the CT is used for. In the considered case, the MISM Agent, a continuous and stable communication, and hence a confirmation of a good packet exchange in terms of ACK received by the users, is the final goal. Hence cost/merit functionals based on ACKs can be designed. The Cognitive Cycle occurs in a continuous way and during the entire process the CT uses the observations and its decisions in order to dynamically improve its behavior: it can be considered as a continuous learning phase. Let us analyze more in detail how each stage can be implemented for the MISM Agent. In the Sensing stage rough data are acquired through a cooperation with the Perceptual Agent: after it has discovered possible directions of interest where users can reside, it gathers information about the spectrum occupancy and hence MISM Agent performs a classification in each direction of interest as shown in Figure 5. It is interesting to perform MISM especially in the direction θ2 depicted in Figure 5 that can be considered at the border of the FoV, where signals are characterized by low Signal-toNoise Ratios (SNRs). A MISM algorithm that performs good estimations with low SNRs can allow an early discovery of possible frequency superimpositions and hence to activate the
Fig. 5.
Example of Mode Discovery Application
proper channel switching procedure. After the data acquisition, a filtering step, in order to extract only sub-portion of the entire bandwidth considered by the CBTS is hence performed. The Analysis stage, core of the paper, is composed by the feature extractor and the multiple NNs classifier. In the present paper only the Sensing and Analysis stages has been considered, as shown in Figure 4. A. Frequency Distribution Features A more detailed description of the proposed feature extraction method is here depicted. It can be considered as an extension, at fixed time, of the methods already used in TimeFrequency Analysis [19]. Let us now consider a signal s(t) ∈ R. Be S(f ) = F{s(t)} its Fourier transform (FT); given the property of the time signal the transformed one will be characterized by |S(f )|2 = |S(−f )|2 . Given the symmetry of the square modulus of the FT, it is possible to keep into account only the positive part of the frequency axis. If the time signal is complex (e.g. an equivalent low pass signal) the FT is not symmetric but it is possible to consider only the positive frequencies by losing part of the information contained into the square modulus. Let us call, for simplicity |S(f )|2+ the square modulus related to
the positive frequencies. Its energy, defined as: Z ∞ ES+ = |S(f )|2+ df
N 1 X k yi − fˆ(xi ) k2 min α,η N i=1
(1)
0
is a normalization factor used to obtain a normalized quantity that can be considered as a probability density function of the frequencies given the presence of the signal s(t): ps (f |s(t)) =
|S(f )|2+ ES+
(2)
From Equation 2 it is possible to extract two features: the instantaneous (or mean) frequency and its standard deviation that are defined as [18], [20]:
(7)
where X = {x1 , · · · , xN } is the training set and Y = {y1 , · · · , yN } the ground-truth labels of the training set. In order to improve the classification performances of the NNs-based classification, it is possible to adopt strategies like bagging or boosting [21]. In particular, the former one, adopted in the proposed method, consists in dividing the training set X into subsets X j , where: X=
J [
X j and X i ∩ X j = for i 6= j
(8)
j=1
f S = E{f |s(t)}
σ fS =
q
E{(f − f S )2 |s(t)}
(3)
(4)
A pool of J NNs is trained, each one on a different subset, independently. When a new pattern is presented to the system, it is classified by the entire pool of networks and the final classification is taken accordingly to a majority voting algorithm, as shown in Figure 6.
By using ps (f |s(t)) as weighting function for the expectation these two features represent the spectral shape, and hence a spectral signature, of the incoming signal s(t). B. Neural Network Bagged Classification Neural Networks are well known and widely used linear separators. They are considered the first attempt to artificially reproduce the processing capabilities of animals and men. In fact, as in the human brain, they are composed by simple processing units called, for similitude, neurons. As for biological entities, they are able to learn how to classify from a set of training samples. Their discrimination capability is based on an approximation of the optimal Bayesian separation function defined within the training set [5]. In fact, by considering a simple two-layers NN, it is possible to write: fˆ(x) =
L X
(5)
where L is the number of neurons in the first layer, α = {α1 , · · · , αL } is the set of weights learned by the training set, φ = {φ1 , · · · , φL } is th set of base functions and x is the input vector. Augmenting the topological complexity of the network it is possible to refine the estimation: L X
Bagged Multiple NNs
VI. S IMULATIONS AND R ESULTS αk φk (x)
k=1
fˆ(x) =
Fig. 6.
ηk ψk (Φ(x))
(6)
k=1
for a three-layers network, where Φ(x) = {φ1 (x), · · · , φH (x)} is the set of outputs of the first layer, ψ = {ψ1 , · · · , ψL } is the set of base functions for the second layer and η = {η1 , · · · , ηL } is the set of weights; α and η are jointly optimized in the learning phase through a common back-propagation algorithm that uses, as a cost function, the mean square error on the training set:
Given the info-mobility framework described above, in order to test the proposed method, a simplified framework consisting in two different communication standards has been set up. The chosen communication modalities are the Complementary Code Keying (CCK) signal described by the IEEE 802.11b standard and the Orthogonal Frequency Division Multiplexing signal defined for the IEEE 802.11g. A single channel for each standard has been considered. Particular characteristic is that, in both cases, channels have the same central carrier frequency, even if they can not be simultaneously superimposed. This assumption can be made looking at the channel allocation policies described in the two standards. The simulation architecture is shown in Figure 7. Simulations have been performed in order to test the system in different conditions, in terms of SNR and Doppler shift. Simulations have been designed for equivalent low-pass signals, in order to reduce the computational complexity and hence the computational time required for each simulation. In
Fig. 7.
Simulation Architecture
particular, SNR in the range 5dB÷40dB while Doppler shift in the range 20Hz÷220Hz have been simulated. Three classes of signals have been considered: in fact the proposed method should be able to identify if the current observation is IEEE 802.11b, IEEE 802.11g or Noise.
Fig. 9.
Distribution of Features for CCK and OFDM for 40dB of SNR
A. Results: Feature Space The proposed method generates a bi-dimensional feature space. In Figure 8 the distribution of features, for the three classes, in the best case, i.e. 40 dB of SNR, is presented.
Fig. 10. Fig. 8.
Migration of the OFDM class from 15dB to 40dB
Feature Space for 40dB of SNR
Noise class is evidently well separated from the others, and this behavior can be noticed for all the simulated SNR. Let us focus the attention on the remaining two classes. Figure 9 shows a zoom on the region containing OFDM and CCK, in the same case presented by Figure 8. In this particular case, due to the high value of the SNR the two classes can be considered quite well separated, but decreasing the SNR a migration of the clusters and a superimposition is present. Decreasing the SNR it is possible to see in Figure 10 how the OFDM class changes, not only its barycenter, but also the spreading around it (and hence the shape). The same effect is suffered by the CCK class too as it is possible to deduce from Figure 11. Under 15dB of SNR preliminary tests have proved that the two classes are superimposed and hence it is not possible to identify clearly the transmission modality, independently from the adopted classifier.
Fig. 11.
Migration of the CCK class from 15dB to 40dB
B. Results: Classification Performances To prove the effectiveness of the proposed feature space, a quite simple classifier has been used. In fact, if with a simple, rough classification method good performances are obtained, a finer and more complex classifier could perform better. All the trained networks are characterized by a two-layers topology. In the first input layer two neurons, each one defined by a tan-sigmoidal base function, are present. The output layer is a single neuron-layer whose activation function is purely linear. The pools of bagged NNs are always composed by 21 networks. Two different strategies for training the NNs have been adopted: in the former, a pool of NNs has been trained for each value of simulated SNR. In the latter only one pool of networks has been trained with subsets composed by randomly selected samples from each SNR. All the pools have finally been tested with test samples characterized by all the simulated SNRs. The nets trained with 15dB samples have provided bad and useless results, probably due to the simple nature of the classifiers. More interesting results also with noisy data have been obtained starting from 20dB (see Table I), where O,C and N mean OFDM, CCK and Noise respectively. TABLE I C LASSIFICATION ACCURACY O BTAINED WITH 20 D B T RAINED N ETWORKS
Net 20dB OFDM CCK Noise
O [%] 71,4 0 0
Test 20dB C N [%] [%] 28,6 0 99,9 0,1 0 100
O [%] 99,9 0,1 0
Test 30dB C N [%] [%] 0,1 0 99,9 0 0 100
O [%] 99,9 0,1 0
Test 40dB C N [%] [%] 0,1 0 99,9 0 0 100
Similar results have been obtained also for higher values of the SNR of the training samples as shown in Table II for a pool of networks trained with 30dB SNR samples. TABLE II C LASSIFICATION ACCURACY O BTAINED WITH 30 D B T RAINED N ETWORKS
Net 30dB OFDM CCK Noise
O [%] 35,8 0 0
Test 30dB C N [%] [%] 64,2 0 99,9 0,1 0 100
O [%] 99,5 0 0
Test 30dB C N [%] [%] 0,5 0 100 0 0 100
O [%] 99,8 0 0
Test 40dB C N [%] [%] 0,5 0 100 0 0 100
It is interesting to notice that with noisy data (samples with 20dB SNR) the network has worst performances respect to the ones trained with 20dB SNR samples. It is hence more interesting to show the performances of the randomly trained pool of networks. Table III shows the obtained accuracies: This training method provides a good solution in terms both of classification accuracy and technical simplicity. In fact, in order to allow an on-line usage of multiple pools of networks (one for each SNR) an additional SNR estimator should be required.
TABLE III C LASSIFICATION ACCURACY O BTAINED WITH R ANDOMLY T RAINED N ETWORKS
OFDM CCK Noise
O [%] 87,5 0 0
Test 20dB C N [%] [%] 12,5 0 100 0 0 100
O [%] 99,9 0,2 0
Test 30dB C N [%] [%] 0,1 0 99,8 0 0 100
O [%] 99,9 0,4 0
Test 40dB C N [%] [%] 0,1 0 99,6 0 0 100
VII. C ONCLUSION After a brief introduction to Cognitive solutions for the info-mobility framework, the proposed method for MISM, based on frequency distribution features has been proposed. Features extracted from simulated signals have hence been classified by multiple-NNs architectures trained in different ways. Results prove that the proposed feature space can lead to good classification performances also with simple classifiers. Ongoing researches are focused on radio-frequency simulations of the signals inside a more complete info-mobility simulator. Other future researches will try to improve classification performances for example through the usage of Support Vector Machines or Evolutionary Neural Networks. ACKNOWLEDGMENT The research work has been developed under the framework of the PRIN-SMART project, financed by the Italian Ministry of the University and Research (MIUR). R EFERENCES [1] “Spectrum policy task force report,” Tech. Rep., Federal Communication Commission, 2002. [2] Michael Chryssomallis, “Smart antennas,” IEEE Antenna and Propagation Magazine, vol. 42, pp. 129–136, 2000. [3] J. Mitola, Software Radio Architecture: Object-Oriented Approaches to Wireless Systems Engineering, John Wiley and Sons, New York, NY, USA, 2000. [4] Rodney A. Brooks, Elephants do not play chess, chapter in “Designing Autonomous Agents”, pp. 3–15, MIT press, 1991. [5] S. Haykin, Neural Networks. A Comprehensive Foundation, Macmillan College Publishing, New York, 1994. [6] J. Mitola, “Cognitive radio: making software radio more personal,” IEEE Pers. Comm., vol. 6, no. 4, pp. 48–52, August 1999. [7] S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, pp. 201–220, 2005. [8] Bruce A. Fette, Cognitive Radio Technology (Communications Engineering), Newnes, 2006. [9] Federal Communications Commission, “Notice of proposed rule making and order, tech. rep. et docket 03-322,” Tech. Rep., December. [10] “Ieee 802.22 standardization committee web site,” http://www.ieee802. org/22/. [11] R.R. Llinas, I of the Vortex, Bradford Book, MIT Press, Cambridge, MA, 2001. [12] L. Steels and R.A. Brooks, The Artificial Life Route to Artificial Intelligence: Building Embodied Situated Agents, Lawrence Erlbaum Associates, Inc., Hillsdale, NJ, 1995. [13] H. Urkowitz, “Energy detection of unknown deterministic signals,” Proceedings of IEEE, vol. 55, no. 4, pp. 523–531, April 1967. [14] J. Palicot, C. Roland, “A new concept for wireless reconfigurable receivers,” IEEE Communications Magazine, vol. 41, no. 7, pp. 124 – 132, July 2003. [15] G. Vardoulias and J. Faroughi-Esfahani, Mode Identification and Monitoring of Available Air Interfaces, chapter in Software Defined Radio; Architectures, System and Functions, pp. 329–352, John Wiley and Sons Ltd, April 2003.
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