This paper investigates a practical precoding scheme for rate-splitting multiple access (RSMA) in multi-user multiple-input single-output (MU-MISO) systems where the nonlinear characteristics of power amplifiers are explicitly taken into account. To enhance performance in the presence of distortion caused by nonlinear power amplifiers, we propose an iterative accelerated gradient-based precoding scheme. The proposed precoding scheme is further integrated with a novel power scaling function that dynamically adjusts transmit power to satisfy practical power consumption constraints while maximizing an achievable sum rate. The gradient of the achievable sum rate for the considered system is derived to support efficient gradient-based optimization. We also prove that the proposed iterative precoding scheme converges to a critical point. Simulation results demonstrate that the proposed precoding scheme consistently outperforms modified conventional schemes including distortion-aware and weighted minimum mean square error beamforming, across both overloaded and underloaded scenarios. In particular, the proposed scheme demonstrates consistent performance in high-power regimes, where conventional schemes exhibit severe degradation due to distortion. Furthermore, the proposed precoding scheme exhibits faster convergence compared to the conventional benchmark.

In this paper, we propose a reconfigurable intelligent surface (RIS) element allocation based low complexity algorithm for a multi-user multiple-input single-output orthogonal frequency division multiplexing system. First, we propose a concept called a metric that depends solely on the channel and represents the minimum rate. Through the metric, the RIS reflection coefficients can be updated without using alternating optimization. Next, we formulate and solve a metric maximization problem to verify that the metric successfully represents the desired objective, i.e., minimum rate maximization. Finally, by utilizing the concept of RIS element allocation, we propose a low complexity algorithm that maximizes the metric. Simulation results show that the proposed metric successfully represents the minimum rate and outperforms existing benchmarks. Moreover, the RIS element allocation algorithm displays remarkable performance, achieving similar performance with the existing benchmarks even with extremely low complexity.

In this paper, we propose a novel sum rate maximization algorithm with rate-splitting (RS) for a hybrid precoding downlink system. By utilizing the benefits of RS, we define a group RS (GRS) framework that serves multiple user equipments without being limited by the number of radio frequency chains. We then formulate and solve a sum rate maximization problem with a quality-of-service constraint to derive analog and digital precoders as well as rate-split. Through alternating optimization and a meticulous use of convex optimization techniques, we first derive the analog precoder utilizing the Riemannian conjugate gradient approach by defining a penalty factor and approximating the common rate. Next, the digital precoder and rate-split are derived by utilizing the successive convex approximation approach. To compensate for the excessive complexity of the proposed algorithm, we also derive a low complexity algorithm for the GRS framework that optimizes only the power of the digital beamformers and derives the directions of the analog and digital beamformers using linear operations. Simulation results verify the superiority of the GRS framework by comparing the performance with existing benchmarks. Furthermore, we corroborate via simulations that the low complexity algorithm achieves greater performance than the existing benchmarks even with low complexity.

Visible spectrum is an emerging frontier in wireless communications for enhancing connectivity and safety in vehicular environments. The vehicular visible light communication (VVLC) system is a key feature in leveraging existing infrastructures, but it still has several critical challenges. Especially, VVLC channels are highly correlated due to the small gap between light emitting diodes (LEDs) in each headlight, making it difficult to increase data rates by spatial multiplexing. In this paper, we exploit recently synthesized gold nanoparticles (GNPs) to reduce the correlation between LEDs, i.e., the chiroptical properties of GNPs for differential absorption depending on the azimuth angle of incident light are used to mitigate the LED correlation. In addition, we adopt a signal-to-leakage-plus-noise ratio (SLNR)-based precoder to support multiple users. The ratio of RGB light sources in each LED also needs to be optimized to maximize the sum SLNR satisfying a white light constraint for illumination since the GNPs can vary the color of transmitted light by the differential absorption across wavelength. The nonconvex optimization problems for precoders and RGB ratios can be solved by the generalized Rayleigh quotient with the approximated shot noise and successive convex approximation (SCA). The simulation results show that the SLNR-based precoder with the optimized RGB ratios significantly improves the sum rate in a multi-user vehicular environment and the secrecy rate in a wiretapping scenario. The proposed SLNR-based precoding verifies that the decorrelation between LEDs and the RGB ratio optimization are essential to enhance the VVLC performance.

In this paper, we propose algorithms to minimize the energy consumption in millimeter wave/terahertz multi-user downlink communication systems. To ensure coverage in blockage-vulnerable high frequency systems, we consider cooperative rate-splitting (CRS) and transmission over multiple time blocks, where via CRS, multiple users cooperate to assist a blocked user. Moreover, we show that transmission over multiple time blocks provides benefits through smart resource allocation. We first propose a communication framework named improved distinct extraction-based CRS (iDeCRS) that utilizes the benefits of rate-splitting. With our transmission framework, we derive a performance benchmark assuming genie channel state information (CSI), i.e., the channels of the present and future time blocks are known, denoted as GENIE. Using the results from GENIE, we derive a novel efficiency constrained optimization (ECO) algorithm assuming instantaneous CSI. In addition, a simple but effective even data transmission (EDT) algorithm that promotes steady transmission along the time blocks is proposed. Simulation results show that ECO and EDT have satisfactory performances compared to GENIE. The results also show that ECO outperforms EDT when many users are cooperating, and vise versa.

In this paper, we propose reconfigurable intelligent surface (RIS) element allocation algorithms that maximize the minimum rate or sum rate in a multi-user (MU)-multiple-input single-output (MISO)-orthogonal frequency division multiplexing (OFDM) system aided with an RIS. Specifically, we first propose multiple metrics, which represent the minimum rate and sum rate, depending solely on the channel. We then propose multiple convex optimization algorithms to maximize the proposed metrics by updating the RIS matrix, validating that the metrics indeed represent the desired objectives successfully. Through the metrics, only a single RIS matrix update is required, contrast to existing works using alternating optimization. Finally, since the aforementioned optimization algorithms still suffer from high complexity, we propose RIS element allocation algorithms that update the RIS matrix to maximize the metrics with minimal complexities. Simulation results show that the RIS element allocation algorithms achieve remarkable performances close to existing benchmarks and exhaustive search with low complexities.

In this paper, we consider a cooperative rate-splitting (CRS) scenario in a terahertz (THz) downlink system. Specifically, we introduce a new CRS framework called extraction-based CRS (eCRS) that exploits the benefits of rate-splitting, grouping, and data extraction. Furthermore, by considering characteristics of the THz communication systems, we construct a novel cooperative channel model. We examine an extreme case of eCRS, where an optimization problem is formulated and solved based on the cooperative channel model. In simulation results, we corroborate the effectiveness of our proposed framework and the impact of the cooperative channel model.

Numerous studies claim that terahertz (THz) communication will be an essential piece of sixth-generation wireless communication systems. Its promising potential also comes with major challenges, in particular the reduced coverage due to harsh propagation loss, hardware constraints, and blockage vulnerability. To increase the coverage of THz communication, we revisit cooperative communication. We propose a new type of cooperative rate-splitting (CRS) called extraction-based CRS (eCRS). Furthermore, we explore two extreme cases of eCRS, namely, identical eCRS and distinct eCRS. To enable the proposed eCRS framework, we design a novel THz cooperative channel model by considering unique characteristics of THz communication. Through mathematical derivations and convex optimization techniques considering the THz cooperative channel model, we derive local optimal solutions for the two cases of eCRS and a global optimal closed form solution for a specific scenario. Finally, we propose a novel channel estimation technique that not only specifies the channel value, but also the time delay of the channel from each cooperating user equipment to fully utilize the THz cooperative channel. In simulation results, we verify the validity of the two cases of our proposed framework and channel estimation technique.

Due to its unique advantages, unmanned aerial vehicle (UAV) communication systems are promising candidates for next generation communication. Although, even with its potential, UAVs suffer from practical limitations such as en-ergy consumption. To mitigate this aspect, we propose a UAV energy consumption minimization technique through the assist of multiple terrestrial intelligent reflecting surfaces (IRSs). By generalizing the probabilistic line-of-sight model, we introduce a channel model capturing the characteristics of the IRSs and user equipments (UEs). Then, by deriving the closed form expression of the UE achievable rate, we formulate an optimization problem to minimize the UAV energy consumption for specific UE data requirements. With problem transformations for tractability and through the successive convex approximation technique, we achieve a local optimum solution. Through the results, we confirm that exploiting the IRSs is indeed extremely beneficial to minimize the UAV energy consumption.