Underwater optical wireless communication (UOWC) is the primary technology that provides high-speed and low-latency area-intensive data interaction for the Internet of Underwater Things. The turbulence effect seriously affects the optical signal transmission, and establishing a comprehensive turbulence channel model is essential to improve the antiturbulence capability of the UOWC system. In this paper, a hybrid phase screen model based on the oceanic turbulence optical power spectrum inversion and the extended Zernike polynomials is proposed, which has sufficient frequency components and can better characterize the turbulence effect caused by the underwater changes in temperature and salinity. By optimizing the number and position of the hybrid turbulence phase screen, the root mean square error of the probability density function of the received light intensity between the simulation and the experimental measurement is only 1.8468 at a turbulence intensity of 0.1538. Finally, we demonstrate a hybrid quadrature amplitude modulation multi-pulse pulse-position modulation (QAM-MPPM) technique to improve the antiturbulence performance of the UOWC system. The results show that under the same conditions, the UOWC system with a hybrid QAM-MPPM technique outperforms those with single QAM and MPPM modulation by 3 and 1 dB, respectively. The hybrid modulation technique also exhibits a better bit error rate and average outage probability performance under different turbulence intensities.

This paper introduces the rate-splitting multiple access (RSMA) to underwater wireless optical communications (UWOC) and investigates the ergodic rate metric for the RSMA-based UWOC system over turbulence-induced fading channels. Specifically, the model of the RSMA applied to UWOC is established, where an aggregated channel with the combined effects of absorption, scattering, and oceanic turbulence is considered. To quantity the ergodic rate of the RSMA-based UWOC system, an approximation of the instantaneous signal to interference plus noise ratio (SINR) is derived using the Fenton-Wilkinson moment matching method. With the approximated SINR, this paper further presents a high-accurate approximation of the ergodic rate in terms of the scaled complementary error function and Tayler series. Numerical results are demonstrated to evaluate the ergodic rate of the RSMA-based UWOC system and to validate the excellent match between the derived approximated analytical expression of ergodic rate and the results obtained from the original integral expression and Monte Carlo simulations.

With the explosive proliferation of connected devices and mobile users in the Internet-of-things, multiple access techniques are urged to be developed for the next generation wireless communications. Recently, rate-splitting multiple access (RSMA) has been a promising communication technology that holds advantages of strong robustness, low complexity, and high spectral efficiency, which can be integrated with the indoor visible light communication (VLC) broadcast system to compensate for the shortcomings of limited modulation bandwidth of LEDs. However, the research on the RSMA-based VLC systems is still in its infancy and there exist various problems to be explored. To benefit from the RSMA technique, this paper investigates the energy efficiency optimizations for both single-cell and multi-cell RSMA-based VLC broadcast systems. Specifically, these two systems are modeled, where the VLC broadcast channel follows Lambertian radiation model, and the splitting design and successive interference cancellation of RSMA are employed to mitigate the multi-user interference. Especially for multi-cell networks, the zero-forcing approach is adopted to eliminate the inter-cell interference. To maximize the energy efficiency, the precoding and power allocation problems are formulated for single-cell and multi-cell networks while accommodating multiple constraints including dynamic operation ranges of LEDs, QoS requirements, and interference elimination. For solving these non-convex fractional problems, two pieces of successive convex approximation (SCA)-based algorithms are proposed, in which the variable transformation and linear approximation are adopted. Simulation results indicate that the proposed schemes can achieve superior energy efficiency with fast convergence for various network loads and user deployments.

Multiuser multirelay network is a potential scenario to fulfill the transmission requirements of various sources and high-volume traffic for the Internet of Underwater Things. To efficiently complete concurrent transmissions for multiple users, this article investigates the joint relay assignment and power allocation problem for multiuser multirelay networks based on the underwater optical wireless communication (UOWC). Specifically, the multiuser multirelay network for UOWC based on decode-and-forward relaying is modeled, where the absorption, scattering, solar radiation noise, and oceanic turbulence of UOWC are all considered. The joint optimization problem of relay assignment and power allocation is formulated as a mixed-integer programming problem, where the average outage probability is minimized with the constraint of total transmitted power. To solve this joint problem, an alternating optimization method is employed, which alternately optimizes the relay assignment and power allocation subproblems. The relay assignment subproblem is modeled as a weighted bipartite matching problem and solved by an improved Kuhn-Munkres algorithm, whereas the power allocation subproblem is proved to be quasiconvex and solved by an iterative bisection algorithm. The simulation results indicate that the proposed schemes significantly reduce the average outage probability with fast convergence.

Cooperative transmission is a promising technology to expand communication range and improve system performance for underwater optical wireless communications (UOWCs). To adequately exploit the benefits of cooperative UOWCs, the relay selection and power allocation problems require to be explored. This paper investigates the relay selection and power allocation issues for the cooperative UOWC in the presence of solar radiation noise. Specifically, the cooperative UOWC based on amplify-and-forward (AF) relaying is modeled, where the effects of absorption, scattering, and solar radiation noise are all considered. We design two optimization schemes that minimize the energy consumption and maximize the overall signal-to-noise ratio (SNR), respectively. Both schemes are proved to be strictly quasi-convex and solved by Levenberg-Marquardt (LM) algorithm. The effectiveness of the proposed schemes is evaluated in line-of-sight (LOS) links and non-line-of-sight (NLOS) links, and the main factor affecting the relay selection and power allocation are derived for both LOS links and NLOS links.

Both caching and interference alignment (IA) are promising techniques for future wireless networks. Nevertheless, most of existing works on cache-enabled IA wireless networks assume that the channel is invariant, which is unrealistic considering the time-varying nature of practical wireless environments. In this paper, we consider realistic time-varying channels. Specifically, the channel is formulated as a finite-state Markov channel (FSMC). The complexity of the system is very high when we consider realistic FSMC models. Therefore, we propose a novel big data reinforcement learning approach in this paper. Deep reinforcement learning is an advanced reinforcement learning algorithm that uses deep Q network to approximate the Q value-action function. Deep reinforcement learning is used in this paper to obtain the optimal lA user selection policy in cache-enabled opportunistic lA wireless networks. Simulation results are presented to show the effectiveness of the proposed scheme.

Recent advances in networking, caching and computing have significant impacts on the developments of vehicular networks. Nevertheless, these important enabling technologies have traditionally been studied separately in the existing works on vehicular networks. In this paper, we propose an integrated framework that can enable dynamic orchestration of networking, caching and computing resources to improve the performance of next generation vehicular networks. We formulate the resource allocation strategy in this framework as a joint optimization problem. The complexity of the system is very high when we jointly consider these three technologies. Therefore, we propose a novel deep reinforcement learning approach in this paper. Simulation results are presented to show the effectiveness of the proposed scheme.

As a promising interference management technique, interference alignment (IA) has many applications, such as in cognitive radio (CR) networks. In CR networks, due to the coexistence of the secondary users (SUs) and the primary users (PUs), the signal-to-interference-plus-noise-ratio (SINR) at the PUs may decrease dramatically, leading to degraded performance of the PUs. In this paper, a novel IA algorithm based on frequency scheduling is proposed to guarantee the performance of PUs while sharing the spectrum with the SUs. In the algorithm, we divide SUs into multiple clusters, each of which forms an individual IA-CR network while guaranteeing the performance of the PUs. Thus a double-win game is established such that the PUs achieve performance gain with the aid of SUs while the SUs obtain more spectral opportunities. Simulation results are presented to verify the effectiveness of the proposed IA algorithm and its suitability for spectrum sharing in CR networks.

Interference alignment (IA) is a promising technique that can effectively eliminate the interference in wireless networks. However, in traditional IA schemes, the signal to interference plus noise ratio (SINR) may significantly degrade, and the quality of service (QoS) may be unacceptable. In this paper, a novel IA scheme based on antenna switching (AS-IA) is proposed to improve the SINR of the received signal while guaranteeing the QoS in IA wireless networks. In the proposed scheme, some of the antennas are replaced by reconfigurable ones that can switch among preset modes, and the best channel coefficients are selected. Furthermore, to reduce the computational complexity, a sequential antenna switching IA (SAS-IA) scheme is proposed with only one antenna switching in each time slot, and the communication proceeds during the process of searching for the optimal solution. To further improve the performance of the SAS-IA scheme under imperfect channel state information (CSI), a filtering SAS-IA scheme is proposed through averaging the estimated CSI during the iterations of the distributed IA algorithm. Simulation results are presented to show the effectiveness and efficiency of the proposed schemes in improving the QoS of IA wireless networks.

Interference alignment (IA) is a promising technique that can eliminate the interference in multi-user communication networks effectively. However, it requires highly accurate and real-time channel state information (CSI) at both transmitters and receivers. In practical systems, it is difficult to obtain the perfect knowledge of a dynamic channel due to channel estimation errors, communication latency and capacity constraints. Particularly, transmitters in IA systems usually get imperfect CSI fed back from receivers with a delay, which will greatly affect the performance of IA. In this paper, the performance of IA with delayed CSI is studied, and the decrease of the total network capacity due to the delayed CSI is analyzed. To mitigate the influence of the delayed CSI, an IA scheme based on channel prediction is proposed using two easy-to-implement and practical channel predictors, minimum mean square estimate (MMSE) and weighted least squares error (WLSE) predictors. The CSI of the next time instant is predicted using the present and past CSI. Simulation results are presented to show the effectiveness of the channel prediction IA schemes with the delayed channel knowledge.