In the past decades, researchers/scientists have paid attention to the physical layer of underwater communications (UWCs) due to a variety of scientific, military, and civil tasks completed beneath water. This includes numerous activities critical for communication, such as survey and monitoring of oceans, rescue, and response to disasters under the sea. Till the end of the last decade, many review articles addressing the history and survey of UWC have been published which were mostly focused on underwater sensor networks (UWSN), routing protocols, and underwater optical communication (UWOC). This paper provides an overview of underwater acoustic (UWA) physical layer techniques including cyclic prefix orthogonal frequency division multiplexing (CP-OFDM), zero padding orthogonal frequency division multiplexing (ZP-OFDM), time-domain synchronization orthogonal frequency division multiplexing (TDS-OFDM), multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM), generalized frequency division multiplexing (GFDM), unfiltered orthogonal frequency division multiplexing (UF-OFDM), continuous phase modulation orthogonal frequency division multiplexing (CPM-OFDM), filter bank multicarrier (FBMC) modulation, MIMO, spatial modulation technologies (SMTs), and orthogonal frequency division multiplexing index modulation (OFDM-IM). Additionally, this paper provides a comprehensive review of UWA channel modeling problems and challenges, such as transmission loss, propagation delay, signal-to-noise ratio (SNR) and distance, multipath effect, ambient noise effect, delay spread, Doppler effect modeling, Doppler shift estimation. Further, modern technologies of the physical layer of UWC have been discussed. This study also discusses the different modulation technology in terms of spectral efficiency, computational complexity, date rate, bit error rate (BER), and energy efficiency along with their merits and demerits.

In this letter, a novel millimeter wave (mmWave) multi-level beamforming (BF) is proposed. User positioning is used for roughly defining the area within which the mobile station (MS) is properly located. Based on this, a multi-level beam search is conducted using compressive sensing (CS) based channel estimation to find out the transmit (TX)/receive (RX) beams for establishing the mmWave link. The estimated MS location-uncertainty area is used to determine the number of beams along with the beamwidth required for constructing the sensing matrix used in each beam searching level. Mathematical and simulation analysis confirm the superiority of the proposed BF scheme over the conventional ones in both BF complexity and performance.

Time division synchronization orthogonal frequency division multiplexing (TDS-OFDM) has been attractive due to its fast synchronization and efficient spectral efficiency over conventional cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) and zero padding orthogonal frequency division multiplexing (ZP-OFDM). However, inter-block interference (IBI) affects its performance because of delay over multipath channels. To evade IBI, dual pseudo-random noise (DPN) sequences have been introduced that causes to reduce spectral and energy efficiency. But, DPN is unprepared for underwater acoustic communication (UWAC) because of battery based nature and Limited bandwidth. To overcome these issues, this paper exploits compressive sensing (CS) theory algorithm for obtaining the time-varying channel state information (CSI) by utilizing sparse property of underwater acoustic (UWA) channels. In this paper, the IBI free region is utilized to estimate accurate UWA channel impulse response and mitigate its interference. Look-ahead backtracking orthogonal matching pursuit (LABOMP) based sparse channel estimation technique is proposed for underwater TDS-OFDM in a real sparse time-varying multipath channel (channel taps are randomly distributed). Furthermore, Doppler-shift of UWA channel is estimated and compensated by PN sequence in time domain. The performance of the proposed technique is evaluated and demonstrated through numerical computation of bit error rate (BER) and mean square error (MSE) using Monte Carlo iterations. Simulation analysis confirms the superiority of the proposed scheme not only in terms of BER and MSE over the conventional ones but also achieve high energy and spectral efficiency.

Space modulation techniques (SMTs) have emerged as promising candidates for spectral- and energy-efficient wireless communication systems since they strike a good balance among error performance, power efficiency, spectrum efficiency, and receiver complexity. In SMTs, the information is not only conveyed by the habitual M-ary signal constellations; rather, it is also conveyed by the indices of the transmit antennas. As such, the indices of the transmit antennas are harnessed in such a manner that enhances the transmission efficiency compared to the other multiple-input multiple-output (MIMO) opponents. Despite their exceptional advantages, SMTs suffer from a major drawback, which lies in the logarithmic proportion between their achievable data rates and the number of transmit antennas. In this regard, the fully-generalised spatial modulation (F-GSM) and the fully-quadrature spatial modulation (F-QSM) are proposed in this paper in order to vanquish this controversial drawback. In F-GSM and F-QSM, the transmit antennas used for data transmission are varied from the state in which only one transmit antenna is activated to the state in which multiple/all transmit antennas are activated. Therefore, a linear relationship between the achievable data rates and the number of transmit antennas is acquired. Moreover, a novel mathematical framework for assessing the average bit error rate (ABER) performance of the different SMTs is delineated. The driven mathematical framework is considered as the first major attempt to generalize the analytical analysis of different SMTs. In addition, the receiver’s computational complexity of the proposed schemes is obtained and analysed in terms of the computational complexity of different SMTs. The simulation results substantiate the validity of the analytical analysis conducted throughout the paper, as they are very akin to the obtained analytical formulas.

This article presents orthogonal frequency division multiplexing (OFDM) that is based on discrete Hartley transform (DHT) as a modulation transform for underwater acoustic (UWA) communication instead of the conventional discrete Fourier transform (DFT). The proposed DHT-OFDM scheme has the advantages of reducing the pilot overheads required to track the UWA channel effects by 50%, increasing the robustness against the carrier frequency offset, and relaxing the system complexity. The receiver design of the proposed system is based on DFT instead of DHT to enable low-complexity estimation and equalization tasks and to overcome the issue of intercarrier coupling, which deteriorates up to half of the existed DHT-OFDM data rate. For more spectral efficiency harvesting, the proposed underwater DHT-OFDM has been extended to cooperate with the index modulation. Simulation and real experimental results have been conducted to demonstrate the outperformance of the proposed schemes against the benchmarks in terms of bit error rate, Doppler shift, spectral efficiency, and system complexity.

This paper proposed a novel deep learning-based relay selection scheme in millimeter wave (mmWave) Device-to-Device (D2D) communication underlying the fifth generation (5G) cellular networks. Relay selection seems to be a promising solution to extend the coverage and solve the blocking problem of mmWave direct communication. In the case of direct path blocking, the base station (BS)/user equipment (UE) has several candidate devices to be selected as a relay. Despite that, conventional scheme when the direct path is blocked, the direct communication link is handover from mmWave to a lower frequency band using a fast session transfer (FST) technique. Such a blocking problem can be solved multi-hop communications by relaying data based on select another device as a relay. Motivated by the importance of selecting the optimal relay which increases reliable connectivity in mmWave communication and expansion of coverage. The proposed deep learning model is developed to overcome the challenges of selected the optimal relay based with low complexity and high efficiency. The proposed scheme considers a deep learning model learns how to predict the best relay for relaying the data in high-reliability communication. The in-depth learning approach is recommended due to its capability in constructing an intelligent model that can take successful decisions and make precise predictions. Simulation results show that the proposed relay selection algorithm outperforms the conventional relay selection in D2D technique in the spectral efficiency and the energy efficiency.