Combining Software-Defined Radio Learning Modules and Neural Networks for Teaching Communication Systems Courses †

Citation: Camuñas-Mesa, L.A.; de la Rosa, J.M. Combining Software-Defined Radio Learning Modules and Neural Networks for Teaching Communication Systems Courses. Information 2023,14, 599. https://doi.org/10.3390/ info14110599 Academic Editor: Emilio Matricciani Received: 8 September 2023 Revised: 20 October 2023 Accepted: 2 November 2023 Published: 4 November 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). information Article Combining Software-Defined Radio Learning Modules and Neural Networks for Teaching Communication Systems Courses † Luis A. Camuñas-Mesa * and José M. de la Rosa * Instituto de Microelectrónica de Sevilla, IMSE-CNM, CSIC, Universidad de Sevilla, 41092 Sevilla, Spain *Correspondence: [email protected] (L.A.C.-M.); [email protected] (J.M.d.l.R.) †This article is a revised and expanded version of a paper entitled Using Software-Defined Radio Learning Modules for Communication Systems, which was presented at the TAEE (Technology, Learning and Teaching of Electronics) Conference in Teruel, Spain, 29 June–1 July 2022. Abstract: The paradigm known as Cognitive Radio (CR) proposes a continuous sensing of the electromagnetic spectrum in order to dynamically modify transmission parameters, making intelligent use of the environment by taking advantage of different techniques such as Neural Networks. This paradigm is becoming especially relevant due to the congestion in the spectrum produced by increasing numbers of IoT (Internet of Things) devices. Nowadays, many different Software-Defined Radio (SDR) platforms provide tools to implement CR systems in a teaching laboratory environment. Within the framework of a ‘Communication Systems’ course, this paper presents a methodology for learning the fundamentals of radio transmitters and receivers in combination with Convolutional Neural Networks (CNNs). Keywords: cognitive radio; software-defined radio; communication systems; modulation; neural networks 1. Introduction The integration of computing, communication, and information technologies into everyday objects, or things, is made possible by the miniaturization of microelectronics. Artificial intelligence (AI), big data, robotics, cloud computing, and IoT are among the disruptive technologies that are increasingly influencing our day-to-day lives. The demands brought on by the COVID-19 pandemic, during which millions of people were forced to interact virtually, have prompted the acceleration of this penetration. In fact, a set of digital layers surround our natural environment, enabling us to experiment with virtual entities and objects in an augmented reality known as the metaverse [1–3]. IoT is one of the most important technological players in the current digital transformation of our society. IoT refers to the interconnection of billions of cyberphysical entities, which can be real, virtual, or use a hybrid software/hardware structure. These cyberphysical entities are able to communicate with one another, sometimes without the need for human intervention, thanks to machine-to-machine communication protocols. In addition, it is anticipated that IoT technologies will have a potential impact on the global economy of USD 11.1 trillion by 2025, which equates to more than 10% of the world’s gross domestic product. There are approximately 30 billion connected devices as of 2023, and this is expected to rise to 350 billion by 2030 [4]. The widespread utilization of electromagnetic spectrum is one of the main outcomes of the IoT boom. Even though new frequency bands such as millimeter wavelengths (mm- Wave) are being included in the most recent 5G and 6G mobile networks generations, data traffic continues to grow [ 5 – 10 ]. By dynamically modifying the transceiver specifications in response to the information that is sensed from the electromagnetic environment, so-called CR [ 11 ] enables communication systems to make better use of the frequency spectrum. Information 2023,14, 599. https://doi.org/10.3390/info14110599 https://www.mdpi.com/journal/information Information 2023,14, 599 2 of 23 Nonetheless, the viable execution of CR presents an enormous number of constraints. These include aspects connected to legal regulations associated with bands being licensed to mobile and internet services along with countless technical difficulties. Examples of the latter include the interference brought about by CR clients as well as spectrum management tasks (access, sensing, band allocation, hand-off, etc.) which must be carried out simultaneously with the signal processing [12–14]. In order to dynamically select the optimal set of performance metrics and transmission bands, CR-based terminals may benefit from embedding AI engines in their main subsystems. However, this necessitates new circuits and systems strategies with a high degree of programmability and reconfigurability [ 1 , 6 , 10 , 14 – 16 ]. In this manner, a few recent works have proposed utilizing AI (Machine Learning (ML) and Deep Learning (DL)) methods to improve the management of the electromagnetic spectrum and to facilitate the signal processing and performance of IoT nodes equipped with CR technology [15,17–19]. As a result, the development of effective IoT devices necessitates multidisciplinary expertise in wireless communications, DL, and microelectronics. During the last decades, many different works have proposed multiple ways to use CR concepts and SDR platforms for educational purposes [ 20 – 25 ]. Inspired by these references, initial pedagogical efforts were made to introduce this topic in the framework of the Department of Electronics and Electromagnetism of the University of Seville through two different bachelor theses [26,27] . As a continuation of this preliminary work, a learning methodology based on the use of SDR platforms as a pedagogical tool to improve the quality of teaching and learning in telecommunications and electrical engineering is presented in this paper. In order to accomplish this aim and contribute to the field, two distinct case studies are presented here. These case studies use SDR/CR systems as an application scenario and provide students with the opportunity to acquire both fundamental knowledge and practical insight regarding wireless communications, DL, and signal processing. To illustrate how the presented strategy can be applied to a variety of educational contexts, experimental results are presented. 2. Materials and Methods 2.1. SDR-Based Learning Methodology The mobile terminals prevalent at the beginning of the wireless telecom era were basic electronic devices that mostly only transmitted voice data. Nearly three decades later, 5G mobile telecom is gradually being implemented, reaching data rates of tens of gigabits per second and operating in multiple frequency bands from the sub-6 GHz band to the mm-Wave band [28,29]. 2.1.1. Towards Software Defined Radio Today, the majority of wireless devices have very small radio modules; however, it is challenging to maintain and scale the mechanism when seeking to add new communication modes and services. Dedicated Radio Frequency (RF) chipsets are usually required each time a new communication protocol is created. On the other hand, the rate at which new features are incorporated into handheld devices is beginning to exceed the rate of package reduction and the trend toward Systems on Chip (SoC) that technology downscaling facilitates. Moving away from pure hardware-based devices and towards hybrid hardware/software-based ones is necessary to address this issue. An SDR system, as Mitola envisioned in 1995 [ 30 ], is a universal radio platform that can be programmed to steer any frequency band and process arbitrary communication protocols while guaranteeing privacy and security and providing the necessary service quality [31]. An ideal SDR transceiver, conceptually depicted in Figure 1a, processes all digital information using three main components: a Digital Signal Processor (DSP), Analog-to- Digital (A/D) interface, and antenna. As shown in Figure 1b, an effective interface between RF signals and digital data necessitates at least some analog signal conditioning circuitry, Information 2023,14, 599 3 of 23 meaning that this ideal implementation is not practically feasible due to the significant power consumption of A/D interfaces. ADC DSP DAC (a) Rx#Filter LNA DSP ADC PLL-based# Synthesizer PA DAC LPF Amplifier LPF Amplifier Rx#Filter Duplexer Quadrature Mixer (b) Figure 1. Block description of a generic SDR transceiver: ( a ) ideal blocks and ( b ) programmable multi-standard (direct-conversion) architecture. 2.1.2. Commercial SDR Boards as Learning Tools The majority of signal processing is transferred to the digital domain by SDR transceivers. This feature makes software programming easier and allows SDRs to serve as the foundation for a variety of communication protocols and technologies, including CR; in essence, CR-based SDRs enable wireless networks and handheld terminals to dynamically utilize the RF spectrum. Consequently, both licensed and unlicensed spectrum can be utilized more effectively with lower power consumption and/or less interference. Along the same lines, SDR/CR-based mobile terminals can be prepared to dynamically sense the spectral environment and take advantage of received data to change their transmission/reception parameters on the fly, thereby improving the communication link and decreasing the amount of energy consumed. SDRs’ high level of programmability and adaptability can be used in an educational context to teach students the fundamentals of communication systems with real hardware and RF signal processing. To this end, Analog Devices ADALM-Pluto, Ettus USRP B210, Nuan BladeRF, and a number of other commercially available SDR platforms are offered by vendors. The primary specifications of these SDR boards can be found at [32] , and they include, among other things: frequency tuning range, duplex method, Analog-to- Digital Converter/Digital-to-Analog Converter (ADC/DAC) resolution, FPGA (Field Programmable Gate Array) chipset, Rx (receiver) noise figure, etc. The majority of SDR boards’ performance, at a reasonable hardware cost, can be very useful for teaching and learning. Information 2023,14, 599 4 of 23 The SDR-based learning module setup used in this work is depicted in Figure 2. A commercial SDR board such as the Analog Devices ADALM-Pluto, Nuan BladeRF, or Ettus USRP B210 is connected to a personal computer, where it can be controlled by various programming applications (for example, GNU Radio, URH, Gqrx, etc.) and programming languages (C, Python, and MATLAB). PC/Workstation USB MATLAB (Communications & DL Toolboxes) Python & C programming Commercial SDR boards Figure 2. Proposed setup for a learning module based on commercial SDR boards. 2.2. Neural Networks for Cognitive Radio Several of the tasks performed by CR systems require abilities that resemble those present in the human brain, suggesting that the use of neural networks can be especially suited to implementation of CR systems due to the similarities between artificial neural networks and the brain [17]. These required abilities are: • The ability to deal with incomplete or erroneous data without affecting the results. • The ability to process large amounts of data, given the massively parallel architecture. • The ability to make decisions based on the processed data. In this section, we briefly describe the main reasons why neural networks are so powerful, and summarize the most important applications of neural networks to CR systems. 2.2.1. Neural Networks Based on our understanding of the way in which the human brain processes complex information through a structure formed by layers of neurons [ 33 ], preliminary computing systems have been designed inspired by biological architecture, such as using the weights of the connections to implement learning mechanisms [ 34 ]. After the idea of backpropagation was introduced as a systematic method to train neural networks [ 35 ], it was demonstrated that these bio-inspired processing systems could be used for recognition applications [ 36 ], enabling the impressive growth of the field of neural network research observed during the last decades. The generic structure of a neural network is shown in Figure 3a. Inspired by the human brain, the processing capabilities are distributed in very simple processing units (neurons) organized in different layers. In principle, each layer receives inputs from the previous layer and sends outputs to the next layer (this is the definition of a feed-forward neuron; recurrent networks include backward connections as well). The basic computational model of a neuron is illustrated in Figure 3b. In general, it is a processing unit with multiple inputs and single output. The operation performed by neuron j is a weighted addition of inputs xi , with the weights wij applied to an activation function φ through a threshold θj . Synaptic plasticity allows the weights of the network to be modified, supporting the implementation of learning capabilities through learning algorithms. Under this generic definition of a neural network, the field has evolved towards a great number of specific implementations for many applications. In the next subsection, we focus on several applications of neural networks to CR. Information 2023,14, 599 5 of 23 Figure 3. (a) Generic structure of a neural network and (b) basic computational model of a neuron. 2.2.2. Applications to Cognitive Radio The use of neuromorphic techniques within the framework of CR systems can be approached from many different points of view. We first briefly review a number of the most relevant strategies for applying neural networks at the different layers of the protocol stack in a communications system [17]: • In the physical layer, neural networks can be used for interference alignment, to classify the modulation modes, or to design efficient error correction codes. • In the data link layer, neural networks can be used for resource allocation or link quality evaluation. • In the network (or routing) layer, they can help to seek an optimal routing path. • In higher levels, such as the application layer, they can be used to enhance data compression and multi-session scheduling. • Outside of the protocol stack, there are many advantages for using neural networks in other functions, such as security and privacy protection. One of the communication systems tasks in which neural networks can be used is associated with channel resource allocation, that is, dynamically selecting the most appropriate channel and modifying the parameters of the receiver. Many different approaches have been studied to solve this task without neural networks, for example, channel-hopping blind rendezvous protocols [ 37 ] and channel-hopping sequences in the SDR transmitter and receiver, including experiments implemented on Pluto boards [38]. In this section we focus on the application of neural networks for CR. In [ 19 , 39 ], Long Short-Term Memory (LSTM) networks were used to predict the future evolution of the occupation of different frequency bands. Using real-time measurements of the occupation for each band, one LSTM network per channel was used to predict their future evolution, then a decision block used the predicted signals to dynamically select the best band while tuning the receiver filter for that frequency. Information 2023,14, 599 6 of 23 Another application involves spatiotemporal modelling for traffic prediction. In this example [ 40 ], a dataset was collected from a large mobile network in a Chinese city that used nearly 3000 Base Stations. This dataset was then used to analyze the temporal and spatial correlations among neighboring Base Stations. A hybrid neural network model was proposed for spatiotemporal prediction, including an autoencoder-based model and LSTM networks for temporal modelling. The autoencoder model consisted of a Global Stacked Autoencoder and multiple Local Stacked Autoencoders, which was found to offer a good representation of input data, reduced model size, and support for parallel training. One more example illustrates the use of a supervised Deep Neural Network (DNN) system for optimization of routing strategy in heterogeneous networks. This case [ 41 ] involved a heterogeneous network including both wireless and wired connections. The routing strategy was formulated as a combinatorial optimization problem, that is, a shortestpath routing problem in terms of graphs. In this work, the authors proposed a DNN that receives the input traffic pattern of the router and provides the desired output for network traffic control, i.e., the routing paths, showing very good performance. Finally, one last application of neural networks to CR deals with modulation classification. In this example [ 18 ], a convolutional architecture consisting of two convolutional layers and two dense fully connected layers was proposed, for which several synthetic datasets were generated. The most difficult dataset included up to 24 different modulations, with different noise levels and over-the-air (OTA) transmission channels. These labeled signals were used to train different CNNs, and their performance was characterized for different values of Signal-to-Noise Ratio (SNR). This application is used further as a reference to describe the proposed Case Study 2 below. 2.3. Case Study 1: Communication System In the framework of a ‘Communication Systems’ course, we have first proposed a practical methodology to acquire a basic knowledge about radio transmitters and receivers while performing practical experiments using commercial SDR modules specially suited to implement CR applications. This course is part of a Degree in Telecommunications Engineering, and is taught to fourth-year undergraduate students who have previously acquired a background in telecommunications and modulation schemes. Initially, we propose a case study which deals with the implementation of a communications system based on QPSK (Quadrature Phase-Shift Keying) modulation using two ADALM-Pluto SDR boards [ 42 ] controlled by MATLAB Simulink, as described in a Simulink example published on the MathWorks website [ 43 ]. The students are expected to reproduce this example using the available models, modify the parameters as needed, and characterize the quality of the communication. This experiment was designed to be performed in the lab with the available Pluto boards, with the students working in groups of two. The setup illustrated in Figure 4is to be followed by each group, as follows: • Student A implements the QPSK Transmitter module from a PC connected to the Pluto board. • Student B implements the QPSK Receiver module from a PC connected to the other Pluto board. The experiment’s ultimate objective is to establish a communication link between the two students, send fixed configuration messages that the receiver can use to assess the quality of the communication, and adjust the transmission frequency to avoid busy frequency bands, thereby imitating a straightforward CR strategy. The subsequent subsections provide further details. 2.3.1. QPSK Modulation In this case study, we asked the students to reproduce a Simulink example [ 43 ] to implement a QPSK modulator and demodulator in the transmitter and receiver blocks, respectively, despite the fact that this setup can be used to study and characterize various Information 2023,14, 599 7 of 23 modulation schemes. Utilizing a variety of characteristics of the carrier signal, it is possible to encode the transmitted bits using several kinds of digital modulation techniques: • ASK methods (Amplitude-Shift Keying) rely on the amplitude. • FSK methods (Frequency-Shift Keying) rely on the frequency. • PSK methods (Phase-Shift Keying) use the phase. Figure 4. Description of the proposed experimental setup for Case Study 1. In this work, we consider a particular kind of PSK modulation scheme, namely, QPSK (Quadrature Phase-Shift Keying), with four distinct phases equispaced, providing the chance to encode two bits for transmitted symbol. However, any other modulation can be used in the setup that is being proposed. In the years to come, we intend to introduce various modulations for use in the lab. For the proposed modulation, the general expression for a QPSK signal is s(t) = I(t)cos(wct) + Q(t)sin(wct) , where I(t) and Q(t) represent the in-phase and quadrature components of the modulated signal. Figure 5shows an illustration of an ideal constellation diagram with all four different transmitted symbols, with the I and Q signals represented on x and y axis, respectively. The figure shows that there is a 90 ◦ shift between adjacent symbols [44]. Figure 5. Typical constellation diagram for QPSK modulation. 2.3.2. Transmitter MATLAB Simulink, which includes blocks designed to communicate with the ADALM- Pluto board, is used to physically implement both the transmitter and receiver modules. Information 2023,14, 599 8 of 23 The block diagram of the transmitter module available in this Simulink example [43] is shown in Figure 6. First, the “Bits Generation” block uses a ‘Hello world’ message as input and encodes it in bits. These bits are handled by the ‘QPSK Modulator’ in baseband utilizing Gray mapping. The block labeled ‘ADALM-Pluto Transmitter’ receives this baseband signal after it has been filtered and oversampled. The main parameters which must be controlled by the students are: • Radio ID: used to identify each Pluto board. • Center frequency: the signal is modulated in baseband and afterwards translated to a certain transmission frequency, which must be within the range of 70 MHz to 6 GHz. This parameter should have the same value at both the transmitter and receiver in order to obtain the best possible quality of the communication. • Gain: the attenuation of the signal while being transmitted, which must be with the range of −50 to 0 dB. • Constellation ordering: the way in which the input bits are mapped into the QPSK constellation diagram. • Phase offset: the phase associated with the first symbol in the constellation. Constellation Diagrams for observing the transmitted symbols, Spectrum Analyzers for observing the frequency spectrum of the signals, and Time Scopes for observing the corresponding signals in time domain are among of the visualization blocks included in the module to enhance comprehension of the system, as depicted in Figure 6. Figure 6. Transmitter block diagram implemented in Simulink. 2.3.3. Receiver The ‘ADALM-Pluto Receiver’ block available in this Simulink example [ 43 ] is directly connected to the board Rx (receiver) antenna, while the ‘QPSK Receiver’ demodulates the obtained signal and calculates the Bit Error Rate (BER). This is shown in the block diagram of the receiver module in Figure 7. The red dashed rectangle below shows the complexity of the ‘QPSK Receiver’ block. First, an AGC (Automatic Gain Control) is included in the proposed receiver block to maintain a constant phase gain and timing error and to stabilize the amplitude of the received signal. Next, the estimation quality is improved using an oversampling filter applied to the signal. Using correlation algorithms, the ‘Coarse Frequency Compensation’ system estimates and compensates for the frequency offset. The ‘Image Synchronizer’ then utilizes a PLL (Phase-Locked Loop) to reduce the mismatch related to the sampling rate Information 2023,14, 599 9 of 23 between transmitter and receiver. Using a different PLL, the ’Carrier Synchronizer’ creates a frequency compensation that is more precise. The ‘Preamble Locator’ is then utilized to recognize the header included in the transmitted message, and the ‘Data Decoding’ block demodulates the message. As in the transmitter framework, this module incorporates various types of visualization blocks (Constellation Diagrams, Time Scopes, and Spectrum Analyzers) to assist students in fine-tuning the demodulator, debugging it, and better better understanding its behavior, which is of course the primary goal of this learning experience. Figure 7. Receiver block diagram implemented in Simulink. 2.4. Case Study 2: Modulation Recognition In the same framework described before, we define a second case study based on the combination of the proposed SDR boards with the implementation of neural networks capable of identifying the modulation of the received signal [ 18 ]. Although the targeted students may not necessarily have a deep background in neural networks, we consider this case study to be a good opportunity for them to become familiar with the main basic concepts from a practical point of view just by following certain guidelines. The goal of this experiment is to transmit signals using different modulation schemes and implement a neural network at the receiver that performs a classification task to identify the modulation. From a teaching perspective, this experiment can be very useful to illustrate an important aspect of CR systems, namely, that the receiver has to re-configure automatically in real time in order to be able to demodulate signals using different modulation schemes (although a deeper analysis of these modulation schemes is beyond the scope of this work). Thus, we Information 2023,14, 599 16 of 23 3.2.1. Effect of Learning Rate For a first experiment, the CNN described in Section 2.4.2 was implemented in MATLAB with six layers and sixteen filters. The first parameter to be characterized was the learning rate, which defines the magnitude of change applied to the network weights during the training stage. If the learning rate is too small, the training algorithm evolves very slowly and requires a long time to converge to an efficient solution. If the learning rate is too large, the algorithm evolves faster; however, it might converge to an incorrect solution. In order to find an optimum value, the students were asked to train the network with different values of the learning rate and evaluate the performance of the CNN. Two measurements were used to characterize the performance: • Training time: how long it takes the algorithm to converge. • Accuracy: the percentage of input samples which are correctly classified by the network. Table 2includes the results obtained when applying different values for the learning rate; it can be seen that 0.01 provides the best accuracy (although very similar to 0.02) without leading to an important difference in the training time. Therefore, this is the value used in the other experiments. Although it might be expected that a larger training time would be obtained for lower learning rates, there are no important differences in the applied values. This might be due to the implementation of the training algorithm provided by MATLAB, which probably limits the training time even when it does not converge properly. For this reason, the accuracy degrades with a learning rate of 0.001 while having a very similar training time. However, the limited available time in the lab made it impossible to carry out any deeper analysis of the obtained results, as the main objective of this work was to help students develop their experimental skills while improving their knowledge of basic concepts, not to obtain the most precise results. Figure 14 illustrates the training progress for a learning rate of 0.01, where the horizontal axis represents the number of training iterations and the blue line indicates the accuracy. As can be seen, the network converges in less than 1200 iterations. Figure 14. Training progress of the neural network with a learning rate of 0.01. Table 2. Characterization of the effect of the learning rate for six layers and sixteen filters. Learning Rate Accuracy Training Time 0.001 57.27% 4 min 55 s 0.01 84.09% 5 min 36 s 0.02 83.18% 5 min 31 s 0.1 70.45% 4 min 55 s Information 2023,14, 599 17 of 23 A very common way to represent the accuracy of a network on classification tasks in greater detail is the confusion matrix; each row in the matrix indicates the true class for each input, while each column indicates the predicted class, as shown in Figure 15. Ideally, this matrix should only present non-zero values on the principal diagonal, meaning that the predicted class is always the same as the true one. However, in practice we always obtain an error, resulting in a total accuracy lower than 100%. Figure 15a depicts the confusion matrix for a learning rate of 0.1, showing poor classification results for the 16QAM, 64QAM, 8PSK, B-FM, and QPSK modulations. Figure 15b shows the confusion matrix for a learning rate of 0.01, showing poor classification results for only the 16QAM and QPSK modulations. Figure 15. Confusion matrices obtained when classifying nine types of modulation with learning rates of 0.1 (a) and 0.01 (b). 3.2.2. Effect of Number of Layers In a second experiment, we changed the number of layers in the network in order to obtain the best accuracy. Table 3presents the characterization of this effect, where four layers results in the best performance in terms of accuracy (the training time is not as critical here, as the network has to be trained only once at the beginning, and the obtained values are all considered reasonable). These results show that increasing the number of layers is not always the best way to improve performance, as too many layers can produce an overfitting effect. Figure 16 shows the confusion matrix obtained when the number of layers is four, with only two of modulations presenting a recognition rate lower than 70% . Table 3. Characterization of the effect of the number of layers for sixteen filters and a learning rate of 0.01. Number of Layers Accuracy Training Time 6 84.09% 5 min 36 s 5 90.00% 10 min 30 s 4 92.73% 10 min 8 s 3 89.41% 9 min 54 s 3.2.3. Effect of Number of Filters In a similar way, we proposed optimizing the number of filters in the convolutional layers while keeping a total of four layers, obtaining the results presented in Table 4. In this case, it can be seen that a larger number of filters results in higher accuracy. However, when the number of filters is increased up to 64, we observe an important increment in the training time ( > 20 min); thus, it was considered impractical to continue increasing the number of filters. Figure 17 presents the confusion matrix obtained for 64 filters, which provides the best performance. As can be seen, we obtain a recognition rate larger than 80% for all modulation types, and six of them reach 100%. Information 2023,14, 599 18 of 23 Figure 16. Confusion matrix obtained when classifying nine types of modulation with a neural network consisting of four layers. Table 4. Characterization of the effect of the number of filters for four layers and a learning rate of 0.01. Number of Filters Accuracy Training Time 16 92.73% 10 min 8 s 32 93.64% 10 min 20 s 64 96.82% 21 min 11 s Figure 17. Confusion matrix obtained when classifying eleven types of modulation with a neural network consisting of four layers with 64 filters. 3.2.4. Effect of Communication Distance As a final experiment, we proposed implementing the CNN together with the SDR platform in order to validate the complete setup proposed in Figure 8. For this experiment, Information 2023,14, 599 19 of 23 we used the original network model with six layers and sixteen filters; even though this network presents slightly lower accuracy, it can be trained faster, which is convenient for a repetitive experiment. We implemented the model on the receiver side, while the input dataset was sent from the transmitter side. The goal of this experiment was to measure the effect of the communication distance, i.e., the distance between the transmitting and receiving Pluto boards. For this, we established five different separations: 0 cm, 10 cm, 20 cm, 1 m, and 3 m. Larger distances could not be tested in the lab environment. For each separation, we repeated the experiment ten times in order to obtain more statistics. The results are depicted in Figure 18, where the error bars represent the dispersion of the measured accuracy. As can be seen, there is no clear influence of the distance on the mean accuracy, which is always around 85%, though larger separations produce a higher dispersion in the behavior of the system. Figure 18. Effect of the distance between SDR boards on classification of the modulation type. 4. Discussion In this paper, we propose two different experimental setups based on commercial SDR boards for use in practical lessons in the framework of a ‘Communication Systems’ course. These setups were especially appropriate for implementing basic approaches to CR systems, establishing radio connections between two users with dynamic parameters, and experimenting with important concepts associated with the software implementation of neural networks. As a first case study, we implemented a transmitter–receiver system using QPSK modulation and characterized the quality of the communication. The results include observing the critical effect of interference when working in saturated frequency bands and the degradation in quality produced by misalignment between the transmitter and receiver frequencies. In a second case study, we implemented a transmitter–receiver system with a CNN on the receiver side used to identify the modulation of the transmitted signal. This application is specially suited for CR, as it allows for dynamic reconfiguration in order to adapt to the specific modulation used by each signal. The lessons learned from the presented education experience are that students become more motivated and satisfied than when following a traditional lab course, translating into higher grades on the part of a majority of students. From the limited experience obtained thus far, we have compared the average grades of students over the last five Information 2023,14, 599 20 of 23 years previous to implementing the proposed experiments to the one year completed incorporating the program presented in this paper. In previous years, the average grade in the ‘Communication Sytems’ course was 6.84, while the average grade when implementing the proposed methodology was 7.91, both on a scale from 0 to 10. We intend to continue monitoring these results in the coming years. At the end of each year, we collect anonymous surveys in which students can provide feedback about their perception of the course along with a final evaluation between 0 and 5. In the five previous years, the average evaluation was 4.1, while in the year after implementing the practical sessions we obtained an average evaluation of 4.7. We have found that as students keep working on the same project for several lab sessions, they develop a deeper interest in the topic and improve their ability to connect different theoretical concepts, especially those related to the implementation of both neural networks and communication systems. Author Contributions: Conceptualization, L.A.C.-M. and J.M.d.l.R.; Methodology, L.A.C.-M. and J.M.d.l.R.; Software, L.A.C.-M.; Validation, J.M.d.l.R.; Investigation, L.A.C.-M.; Writing-original draft, L.A.C.-M. and J.M.d.l.R.; Writing-review and editing, L.A.C.-M.; Supervision, J.M.d.l.R.; Project administration, L.A.C.-M. and J.M.d.l.R.; Funding acquisition, J.M.d.l.R. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported in part by MCIN/AEI/10.13039/501100011033 under Grants PID2019-103876RB-I00, PID2022-138078OB-I00 and PID2019-105556GB-C31; in part by the European Union “ESF Investing in Your Future” and in part by “Junta de Andalucía”, Spain, under Grant P20-00599 and PROYEXCEL-00060. Data Availability Statement: No new data were created or analyzed in this study. Data sharing is not applicable to this article. Conflicts of Interest: The authors declare no conflict of interest. Abbreviations 5G Fifth Generation 6G Sixth Generation 8PSK Eight Phase-Shift Keying A/D Analog-to-Digital ADC Analog-to-Digital Converter ADALM Advanced Active Learning Module AGC Automatic Gain Control AI Artificial Intelligence AM Amplitude Modulation AM-SSB Amplitude Modulation with Single-Sideband AM-DSB Amplitude Modulation with Double-Sideband ASK Amplitude-Shift Keying BER Bit Error Rate BPSK Binary Phase-Shift Keying CNN Convolutional Neural Network COVID-19 Coronavirus Disease 2019 CPFSK Continuous-Phase Frequency-Shift Keying CR Cognitive Radio DAC Digital-to-Analog Converter dB Decibel DL Deep Learning DNN Deep Neural Network DSP Digital Signal Processor FM Frequency Modulation FPGA Field-Programmable Gate Array FSK Frequency-Shift Keying GFSK Gaussian Frequency-Shift Keying GHz GigaHertz Information 2023,14, 599 21 of 23 ID Identification IoT Internet of Things LSTM Long Short-Term Memory MHz MegaHertz ML Machine Learning mm-Wave Millimeter-Wavelength NN Neural Network OTA Over-The-Air PAM Pulse Amplitude Modulation PLL Phase-Locked Loop PSK Phase-Shift Keying QAM Quadrature Amplitude Modulation QPSK Quadrature Phase-Shift Keying ReLU Rectified Linear Unit RF Radio Frequency Rx Receiver SDR Software-Defined Radio SNR Signal-to-Noise Ratio SoC System-on-Chip Tx Transmitter URH Universal Radio Hacker USRP Universal Software Radio Peripheral References 1. 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