Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication

Abstract

The limitation of the available channel bandwidth and availability of a sustainable energy source for battery feed sensor nodes are the main challenges in the underwater acoustic communication. Unlike terrestrial’s communication, using multi-input multi-output (MIMO) technologies to overcome the bandwidth limitation problem is highly restricted in underwater acoustic communication by high inter-channel interference (ICI) and the channel multipath effect. Recently, the spatial modulation techniques (SMTs) have been presented as an alternative solution to overcome these issues by transmitting more data bits using the spatial index of antennas transmission. This paper proposes a new scheme of SMT called spread-spectrum fully generalized spatial modulation (SS-FGSM) carrying the information bits not only using the constellated data symbols and index of active antennas as in conventional SMTs, but also transmitting the information bits by using the index of predefined spreading codes. Consequently, most of the information bits are transmitted in the index of the transmitter antenna, and the index of spreading codes. In the proposed scheme, only a few information bits are transmitted physically. By this way, consumed power transmission can be reduced, and we can save the energy of underwater nodes, as well as enhancing the channel utilization. To relax the receiver computational complexity, a low complexity deep learning (DL) detector is proposed for the SS-FGSM scheme as the first attempt in the underwater SMTs-based communication. The simulation results show that the proposed deep learning detector-based SS-FGSM (DLSS-FGSM), compared to the conventional SMTs, can significantly improve the system data rate, average bit error rate, energy efficiency, and receiver’s computational complexity.

Optimized deep learning driven signal detection and adaptive channel estimation in underwater acoustic IoT networks

“…Numerous researchers have started integrating wireless communication with deep learning. [12][13][14] Neural networks have recently shown promise in applying UWAC-OFDM communication, where the channel structure is either unknown or too complicated to be analytically defined. These unknown and complicated channels are analyzed using the recently evolving DL techniques.…”

“…Recently, there has been a lot of interest in using deep learning to streamline the architecture of an end‐to‐end communication system. Numerous researchers have started integrating wireless communication with deep learning 12–14 . Neural networks have recently shown promise in applying UWAC‐OFDM communication, where the channel structure is either unknown or too complicated to be analytically defined.…”

Generating Noise-like Communication Codes with Deep Learning

“…3 Machine learning (ML) offers a new strategy for achieving LPD. 4 Specifically, with recent advances in deep learning (DL), including generative-adversarial networks (GANs), 2 we hypothesize that ML can be used to develop encoding schemes that will be difficult to distinguish from the natural noise-both residing in the RF environment and manifesting in the electronic circuitry of radio receivers.…”

Reinforcement Learning-Based Adaptive Switching Scheme for Hybrid Optical-Acoustic AUV Mobile Network

“…The deployment of underwater sensor networks (USNs) has enabled extensive marine activities of ocean monitoring and exploring [1,2], in which both underwater acoustic communication (UAC) and underwater wireless optical communication (UWOC) are utilized for underwater networking. Although UAC is the only reliable and dominated technology that currently enables medium and long-range underwater wireless communications, it also suffers from several shortcomings (e.g., limited bandwidth, low-speed, high propagation delay, and high energy consumption [3]), which may make it difficult to meet growing application demands, such as transferring underwater real-time ultra high-definition videos and conducting real-time remotely controlled operations [4,5].…”