Device-to-device (D2D) communications can allow different devices to communicate in short range, which greatly meets the demand of effective transmissions. In this paper, a spectral and energy-efficient carrier index keying-aided M -ary differential chaos cyclic shift keying (CIK-MDCCSK) system is proposed for D2D communications, where multiple cyclic-shifted M -ary information-bearing signals are superimposed and transmitted by a subcarrier, and extra keying bits are transmitted by indices of the selected subcarriers. Furthermore, a carrier keying detection and M -ary de-mapping algorithm is proposed to retrieve the information bits transmitted by M -ary symbols and indices of carrier keying. The bit error rate (BER) expressions of the proposed CIK-MDCCSK system are derived over the additive white Gaussian noise (AWGN) and D2D channels, and the optimal power coefficient is deduced for performance enhancement. Then, the spectral efficiency, energy efficiency, and complexity of CIK-MDCCSK are analyzed and compared to benchmark systems to show its superiority. Next, the proposed CIK-MDCCSK system is expanded into its enhanced version for more effective transmission without degrading the BER performance. It is demonstrated by performance comparisons that the proposed CIK-MDCCSK system can achieve more than twice energy efficiency and up to 3 dB gain in BER performance compared to state-of-the-art benchmarks.

Underwater acoustic channel (UAC) is featured as fast time-varying characteristic, and challenges the transmission designs. To countermine the time variation effect, we propose an adaptive design for orthogonal frequency division multiplexing (OFDM) transmission systems by utilizing the long-term stability of the second-order statistics of the channel state information (CSI). We derive the analytical expression of signal-to-interference-plus-noise-ratio (SINR) at each subcarrier to reach the target error performance based on the statistical information of the CSI. Thereafter, a new adaptive coding and bit-power loading algorithm with low computational complexity is proposed to pursuit the highest achievable bit rate with fixed error rate. The validity of SINR calculations and the effectiveness of the proposed adaptive algorithm are demonstrated under various conditions, wherein both simulated and measured channels have been tested.

In a multi-beam communication scenario where Infrastructure-to-Vehicle (I2V) and Vehicle-to-Vehicle (V2V) communications coexist, the limited spectrum of resources force V2V users to reuse the orthogonal frequency bands allocated to I2V, inevitably introducing cross-layer interference between I2V and V2V. Furthermore, the adoption of a multi-beam communication architecture exacerbates beam interference, significantly degrading the overall network’s communication and sensing performance. To address these challenges, this paper proposes an integrated sensing and communication (ISAC) beam allocation algorithm, termed IMSBA, which jointly optimizes beam direction, transmission power, and spectrum resource allocation to effectively mitigate the interference between I2V and V2V while maximizing the overall network performance. Specifically, IMSBA employs a joint optimization framework combining Multi-Agent Proximal Policy Optimization (MAPPO) with a Stackelberg game. Within this framework, MAPPO leverages vehicle perception data to dynamically optimize V2V beam steering and frequency selection, while the Stackelberg game reduces computational complexity through hierarchical decision-making and optimizes the joint power allocation among V2V users. Additionally, the proposed scheme incorporates a V2V cooperative sensing domain-sharing mechanism to enhance system robustness under adverse conditions. The experimental results demonstrated that, compared with existing baseline schemes, IMSBA achieved a 92.5% improvement in V2V energy efficiency while significantly enhancing both communication and sensing performance. This study provides an efficient and practical solution for spectrum-constrained scenarios in millimeter-wave Internet-of-Things (IoT), offering substantial theoretical insights and practical value for the efficient operation of intelligent transportation system (ITSs).

In a multiple-sequences-spread-spectrum (MSSS) communication system, the transmitted signal is made up of superimposed cyclic shift sequences, each sequence carrying a symbol, so the data rate is inceased. In this context, we propose an in-phase/quadrature index modulation-aided MSSS system (MSSS-I/Q-IM), which achieves higher data rate and better bit-error-rate (BER) performance. The new system maps the additional bits to the cyclic shift length of the superimposed sequences of I/Q branches. The sequences in two branches carry the real and imaginary parts of the M-ary phase-shift-keying (MPSK) symbols, respectively. We derive the theoretical BER of the MSSS-I/Q-IM system over an additive white Gaussian noise (AWGN) channel. Then, the simulation experiments of the proposed system and MSSS system over an underwater acoustic channel are analyzed and compared. Finally, field experiments are carried out in the pool environment. The experiment results show that the proposed scheme can achieve higher data rate or better BER performance via adjusting the IM parameter. This is important for the time-variant underwater acoustic transmission environments.

This paper explores an underwater localization system aimed at addressing the challenge of accurately locating a target node in an inhomogeneous underwater environment. In this system, multiple receivers with known positions collaborate to enhance the localization process. The presence of the stratification effect causes underwater acoustic signals to propagate along a curved path, introducing non-Gaussian noise and leading to localization errors. Traditional localization algorithms, such as the weighted least squares algorithm, are designed for ideal underwater environment with low-Gaussian noise, assuming the signal is transmitted in a straight line. However, these algorithms are limited in their applicability. To overcome this limitation, we put forth a deep learning-based underwater range difference correction network (DL-URDCN). This innovative approach effectively removes the non-Gaussian noise in range difference, converting it into low-Gaussian noise. Furthermore, an iterative weighted least squares method based on Taylor series expansion (TIWLS) algorithm for the time-difference-of-arrival (TDOA) is utilized to solve a localization problem. Simulation results demonstrate that the DL-URDCN-TIWLS algorithm outperforms existing benchmarks in terms of localization performance and satisfies the Cramér Rao Lower Bound (CRLB).

Uncoordinated random-access protocols are well-suited for underwater acoustic (UWA) networks due to their simplicity and low overhead. However, their performance is hindered by severe collisions and the challenging characteristics of UWA channels such as rich multipath and Doppler effect. Existing UWA physical layer waveforms struggle to resolve collisions while maintaining high data rates. This paper introduces ZCMod, a high-rate waveform allowing uncoordinated concurrent random access in UWA networks. ZCMod employs a Zadoff–Chu (ZC) sequence-based modulation that assigns unique ZC sequences to users to minimize inter-user interference and encodes multiple bits through cyclic shifts of the sequences to improve data rates. ZCMod further addresses the unique challenges of UWA channels via two new designs: 1) a shape-based demodulation approach that estimates the data-induced shift of channel response shape between the preamble and data symbols to handle rich multipath, and 2) an auxiliary modulation approach that modulates each data symbol with two ZC sequences, one for extracting current channel response shape and the other for data modulation, to handle the fast time-varying channel. Experimental results in a lake and a swimming pool and extensive simulation results show that a) ZCMod achieves around 100% higher throughput compared with the state-of-the-art (SOTA) approaches in quasi-static channels, and b) ZCMod maintains comparable throughput in fast time-varying channels as in quasi-static conditions, where the SOTA approaches experience significant degradation.