2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems (iCACCESS) 8-9 March, Dhaka, Bangladesh CNN-Based End-to-End Deeper Autoencoders for Physical Layer of Wireless Communication System Jannatul Ferdous Department of ETE RUET Rajshahi, Bangladesh jannatulferdous1754@gmail.com Md. Aslam Mollah Department of ETE RUET Rajshahi, Bangladesh ruet10aslam@gmail.com Afiquer Rahman Department of ETE RUET Rajshahi, Bangladesh afiq.ruet18@gmail.com Abstract—In this paper, a deeper autoencoder is proposed, which is composed of systematic and strategic convolutional neural network (CNN) layers. The proposed autoencoder can function under various conditions of wireless communication sys- tems, such as AWGN, Rayleigh, and other fading environments. This model with perfect channel state information is compatible with different channel use, data rates, throughput, and variations in block length. The proposed system is an end-to-end task that can optimize the transceiver’s components jointly and inherit the sophisticated features of convolutional neural networks, such as fast training convergence, generalization, classification, and feature learning. In addition, simulation results illustrate that the proposed model outperforms the existing models in terms of block error rate. Another autoencoder is proposed in this paper, where VGG-16 architecture is implemented before feeding to the convolutional layer at the decoder side. A comparison of block error rate performance shows that this scheme performs better than the human expertise models. Index Terms—Autoencoder, convolutional neural network, wireless communication, channel state information, end-to-end task, VGG-16. I. I NTRODUCTION In today’s digitized world, wireless networks and related services have evolved into vital building blocks that have revolutionized how we live, work, and interact. The growing popularity of wireless technology makes wireless communi- cation networks increasingly crowded, making efficient use of wireless resources crucial. Therefore, the management and allocation of resources, such as the transmission power, must be done effectively to maximize the efficiency of wireless communications. The communications field is particularly rich in specialized knowledge derived from information theory, statistics, and solid mathematical modeling proficient in chan- nel modeling, optimal signaling, and detection algorithms for consistent data transfer to compensate for numerous hardware issues, etc., particularly for the physical layer. Ensuring opti- mal communication between the transmitter and receiver end despite the noise and distortions produced separately. One of the latest innovations in the field of artificial intel- ligence and machine learning is deep Learning (DL). Com- puter vision, digital image processing, and natural language processing have already experienced significant advancements thanks to DL methodologies [1]. In recent years, DL has been implemented in physical-layer communications systems such as channel estimation [2] - [4], modulation classification [5], and signal detection [6], [7]. It has also demonstrated impressive outcomes, which are close to or even better than the conventional systems. At the physical layer, to decrease system complexity and concentrate on the explicit function of each processing block, the traditional communication system uses the divide-and- conquer strategy relying on mathematical models. It divides the transmitter and receiver into subtasks, such as modula- tion, equalization, source coding, and channel coding. This model, whose benefit is to enable component-by-component optimization, has proven to be particularly effective in various communications systems, including wireless communications. On the other hand, complex systems, which experience unde- sirable effects, are challenging to be modeled mathematically. Therefore, a more adaptive and flexible paradigm may be required to address these issues. This has led one creative viewpoint to consider wireless communications as a self- learned end-to-end autoencoder [8] that jointly optimizes transmitter and receiver components so that learned weights of neural networks facilitate encoding and decoding. In terms of communications, the main objective of an autoen- coder is to detect representations of the inputs (transmitted signals) at some intermediate layer that are resilient concerning the channel impairments mapping (e.g., noise, fading, and distortion), allowing reconstruction at the output (received signal) with a low probability of error. The self-learning system can be considered weights learned from an optimized neural network using end-to-end loss functions. In the pioneering work of [9], a CNN-based end-to-end autoencoder was proposed where the communication system performed intelligently. However, the idea of using convo- lutional layers as the fundamental blocks of autoencoder (AE) for the communication system was introduced by the 979-8-3503-5028-9/24/$31.00 ©2024 IEEE 2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems (iCACCESS) | 979-8-3503-5028-9/24/$31.00 ©2024 IEEE | DOI: 10.1109/ICACCESS61735.2024.10499465 Authorized licensed use limited to: Rajshahi University Of Engineering and Technology. Downloaded on April 29,2024 at 18:35:48 UTC from IEEE Xplore. Restrictions apply.