Wireless Communications and Mobile Computing

Volume 2018, Article ID 4392710, 11 pages

https://doi.org/10.1155/2018/4392710

## Dynamic OFDM Transmission for a Cognitive Radio Device Based on a Neural Network and Multiresolution Analysis

^{1}Metropolitan Autonomous University, Iztapalapa Campus, Mexico City, Mexico^{2}Universidad Distrital Francisco José de Caldas, Bogota, Colombia

Correspondence should be addressed to Enrique Rodriguez-Colina; ten.batnac@cr.euqirne

Received 16 February 2018; Revised 4 June 2018; Accepted 27 June 2018; Published 2 August 2018

Academic Editor: Francisco J. Martinez

Copyright © 2018 Enrique Rodriguez-Colina et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Cognitive radio communications depend on methods for sensing the spectrum as well as adapting transmission parameters to available resources. In this context, this work proposes a novel system that makes use of prediction to dynamically allocate subcarriers to different transmissions in an orthogonal frequency division multiplexing (OFDM) system. To this end, the proposal is comprised of a predictive component which makes use of a neural network and multiresolution analysis and a second component, which uses wavelet analysis and cognitive radio functions to carry out a dynamic allocation of subcarriers in an OFDM system. The use of wavelets allows the system to split the data stream in blocks of information to be transmitted over multiple orthogonal subcarriers. This proposed system makes use of the decision-making functions of a cognitive radio device to select the number and position of the subcarriers used for communications without interference. Although there exist other OFDM systems using wavelets, they are not used in combination with the decision-making functions implemented in cognitive radio devices. In contrast, the proposed OFDM system operates using some of these functions, thus being able to better adapt its operational parameters. The use of wavelets combined with a neural network model improves the prediction of the bandwidth utilization as shown in this work. It is concluded that the proposed system improves spectral efficiency and data rate by using the decision-making functions of cognitive radios to select the appropriate OFDM subcarriers to be used during the data transmissions.

#### 1. Introduction

Cognitive radios must be able to change several of their parameters in order to adapt their operation to the environment where they work and in this way improve spectral usage and enhance communication characteristics (e.g., energy efficiency and data rate). Cognitive radio design makes use of cross-layer and multilayer approaches to face the challenges that opportunistic spectrum allocation poses. For a cognitive radio network to work it is necessary that it be comprised of devices which are both adaptable to the environment (to take advantage of the changing conditions in spectrum availability) and sensitive enough to avoid interference with primary users and other cognitive devices [1, 2]. In the past years several techniques have been proposed to implement dynamic spectrum access (DSA) (e.g., see [3] for a general overview and [4] for the ad hoc networking case), but there are several issues still open to solve. For example, it is necessary to detect and predict spectrum availability quickly enough to avoid interference with licensed users.

Transmissions from cognitive devices can make use of orthogonal frequency division multiplexing (OFDM), which presents several advantages in contrast to the approach of transmitting using a single carrier. For example, OFDM systems can increase spectrum usage and can operate at different frequencies with simple modifications to the frontend radio interface. Furthermore, OFDM can exploit the advantages of diversity by spreading the signal over different subcarriers and improves reception due to the reduction of narrowband noise. However, OFDM is also highly affected by phase noise and it is not suitable for bursty traffic applications.

The inverse fast Fourier transform (IFFT) is commonly used to generate the multiple orthogonal subcarriers that are required for communications in a conventional OFDM system. As an alternative, it is worth mentioning that multiresolution analysis (MRA) using wavelets is able to generate a signal decomposition using different orthogonal frequencies, where each component corresponds to a different resolution [5, 6]. Thus, wavelet-based MRA is able to generate the orthogonal frequencies required by OFDM in order to obtain the set of subcarriers to be used for transmission of a data signal.

A complex synchronization system is required to generate subcarriers of the wavelet-based MRA; however, this approach has the advantage that the wavelet spectral components decay quickly and smoothly, thus favoring the filtering of the signal. Thus, wavelets do not require the use of a significant part of the spectrum in contrast to signal decomposition using sinusoidal functions that have components all over the spectrum, which causes information loss when filtering is applied. The spectral shape of a wavelet offers significant advantages over the noise, as well as the opportunity to increase the bandwidth usage by reducing the need of filtering.

A comparative study between OFDM based on the Fourier transform (FFT-OFDM) and wavelet-based OFDM using the discrete wavelet transform (DWT) is presented in [7]. This study also shows that a system based on DWT-OFDM outperforms the FFT-OFDM in presence of additive white Gaussian noise (AWGN) and Rayleigh fading. Their analysis shows that, under similar conditions, for a channel with AWGN the gain in terms of the ratio between the energy per bit and the average spectral noise density () improves when Haar and Daubechies wavelets are used and when it is compared with the FFT-OFDM system. The DWT-OFDM system shows a similar performance when is less than 10 dB in frequency selective fading; however, DWT-OFDM has a significant improvement in performance when the is higher than 10 dB. This improvement is a consequence of the wavelet properties where the use of a cyclic prefix for synchronization is not required in contrast to FFT-OFDM systems, where it is used to compensate deviations in the delays due to variations in channel conditions [7]. In addition, wavelet packets offer a better definition in the analysis than a simple signal decomposition using wavelets as it is shown in [7]. A wavelet packet is a generalization of the MRA where particular bands of frequencies are split into narrower bands and these are split again repeatedly into narrower bands using a two-to-one ratio [8]. Thus, the signal to be transmitted can be decomposed into multiple wavelet packets which are orthogonal. Furthermore, the signal can be represented by a selection of wavelet packets without using all wavelet packets for a specific resolution. The decomposition procedure improves operation for systems with OFDM and high data rate operating in fading channels. Studies of the OFDM with wavelets can be found in the literature [9, 10] where its effectiveness to compensate noise and fading is shown. Thus, wavelets can substitute the inverse fast Fourier transform (IFFT) which is simpler to implement but has less accuracy in the reconstruction of the received signal than wavelet packets.

The data stream can be divided into blocks and with the use of wavelets these data can be transmitted over multiple orthogonal subcarriers by using OFDM. To this end, two of the main characteristics of cognitive devices can be incorporated to adapt the parameters for the wavelet-based OFDM transmission. These two characteristics are the cognitive capability and the autoconfiguration. The cognitive capability is mainly related to signal perception in order to get information about the environment, which not only considers received power, but also information extracted with techniques based on expert systems, artificial intelligence and machine learning. This capability is fundamental for the optimal use of the spectral availability since it can take into account variations in both time and frequency of the spectrum characteristics. The autoconfiguration of the radio is crucial to determine how the device should adapt itself to environmental changes and how its parameters (e.g., modulation, medium access, and coding) should be changed accordingly. Cognitive radio devices implement their functions (i.e., sensing, decision making, sharing, and spectral mobility) on flexible hardware platforms based on software defined radio (SDR) [2–4, 11].

In this work combining wavelet-based MRA for detection and adaptive OFDM transmission is proposed where the dynamic selection of subcarriers is controlled by the decision-making function of the cognitive device. The purpose is to adapt the operational parameters of the radio in order to appropriately select the subcarriers to be used for communication. Thus, the proposed system is intended to improve both spectral efficiency and data rate. Although in the literature it is possible to find studies of OFDM systems using wavelets, none of them is used in combination with the decision-making functions of the cognitive radio as proposed in this work.

It is important to mention that the use of wavelets helps to accurately detect time-scale relationships for signal transmission and improves the spectral usage by detecting discontinuities in available bandwidth. This information is used in the proposed system to implement a predictive approach where wavelet-based analysis can implement transmission, signal perception, and effective reconstruction of signal patterns. The use of wavelets combined with a neural network increases the prediction capabilities of bandwidth usage [12]. The dynamic spectrum allocation is leveraged by the cognitive radio functions, where the communication between cognitive devices improves the spectral use and minimizes interference to licensed users. This is achieved by accessing the medium opportunistically, which optimizes bandwidth utilization by opportunistically occupying white spaces. This opportunistic approach for using white spaces is known as overlay and allows the coexistence of licensed users and cognitive devices in contrast to the underlay approach which requires that the cognitive devices operate at lower levels of power than the licensed users [13].

The rest of the document is organized as follows. For the sake of clarity Section 2 gives a brief overview on wavelet theory and multiresolution analysis. Section 3 describes the proposed system, Section 4 presents the simulation characteristics and results for the predictive neural network system and wavelets for the decomposition of the power signal, as well as the simulation characteristics and results for the proposed DWT-OFDM system for cognitive radio. Finally, some conclusions are provided in Section 5.

#### 2. Background Concepts on Wavelets and Multiresolution Analysis

Wavelets, which are denoted by , represent an ensemble of functions obtained from a time-shift by and a dilation (scale) of an initial function called mother wavelet ; i.e., . These functions exhibit fast oscillations and decay quickly in a finite time period [14]. The decomposition of a signal with this family of functions yields the continuous wavelet transform (CWT), thus creating the so-called time-scale plane, i.e., . This time-scale plane allows a representation of at multiple scales or resolutions. There is an inverse relationship between scale and frequency (a pseudo frequency to be more precise) which allows a time-frequency representation associated with the . The discrete wavelet transform (DWT) is obtained by discretization of the time-scale plane at values ; for , i.e., . From this sampling, the ensemble becomes a wavelet basis (not necessarily orthonormal).

Due to the multiresolution analysis (MRA), developed by Mallat [15, 16], it is possible to construct a wavelet basis (e.g., Daubechies, Symlets, and Discrete Meyer) using a filter bank. Basically, the MRA consists of a low-pass filter and high-pass filter with a recursive decomposition on the low-pass element as it is depicted in Figure 1. Resulting filtered signals correspond to the so-called approximation () and detail coefficients (). The approximation is just a coarse version of the analyzed signal . The details, besides adding finer information of the signal, correspond to the DWT. These coefficients allow a representation of signal* x*(*n*) aswhere is the projection in the time domain of over the orthonormal base of the approximation space** V** at level and is the projection over the orthonormal base of the wavelet spaces** W**_{j}. Embedded spaces** V**_{j} and** W**_{j} (complementary and orthogonal to** V**_{j}) are the fundamental idea behind the MRA. The initial functions and are called the* scaling* function and the* wavelet* function, respectively. These functions, generating these spaces, are linked to a bank of digital filters by a two-scale relationship given by [16]It turns out that , where* g*(*n*) is a low-pass filter and is the corresponding mirror filter, i.e., a high-pass filter. This two-scale relationship is implemented through a low-pass filter and a high-pass filter in conjunction with decimators by a factor of 2 (see Figure 1).