We propose machine learning (ML) based optimization methods for modulation and digital predistortion (DPD), that overcome the signal distortion due to power amplifier (PA) non-linearity and memory effects. The proposed methods generate and exploit an adjusted modulation constellation to compensate for a given PA non-linearity, whereas conventional and widely-employed methods mainly rely on DPD to that end. This potentially removes the need for DPD if memory effects are not detrimental and enables the transmitter to operate close to the power saturation region, increasing the PA power efficiency. The AI/ML framework to learn an adjusted constellation is trained to produce a target square quadrature amplitude modulation (QAM) signal at the PA output. We also present a DPD learning architecture for the adjusted constellations. The proposed methods outperform square 16-ary/64-ary QAMs with DPD by more than 1 dB and are within 0.2~10.3 dB of the theoretical performance at a symbol error rate of 0.01, when the PA operates in its saturation region.

Semantic segmentation confronts challenges with traditional networks tailored exclusively for RGB inputs, which may suffer from quality degradation under adverse conditions like low-level illumination or inclement weather. Recent advancements have shown promising outcomes by integrating RGB images with corresponding thermal infrared (TIR) images. However, effectively fusing features from both modalities remains a significant challenge. In this paper, we introduce a novel approach termed Multimodal Frequency Spectrum Fusion Schema (MFSFS) for semantic segmentation of RGB-T images. MFSFS leverages the advantages of the frequency spectrum to effectively extract and utilize multimodal feature information. To mitigate redundant information’s adverse effects during multimodal fusion in the frequency domain, we propose a diversity-oriented contrastive learning approach. Simulation results demonstrate that MFSFS achieves competitive performance while maintaining a relatively smaller model size.

Future wireless networks, in particular, 5G and beyond, are anticipated to deploy dense Low Earth Orbit (LEO) satellites to provide global coverage and broadband connectivity. However, the limited frequency band and the coexistence of multiple constellations bring new challenges for interference management. In this paper, we propose a robust multilayer interference management scheme for spectrum sharing in heterogeneous satellite networks with statistical Channel State Information (CSI) at the Transmitter (CSIT) and Receivers (CSIR). In the proposed scheme, Rate-Splitting Multiple Access (RSMA), as a general and powerful framework for interference management and multiple access strategies, is implemented distributedly at Geostationary Orbit (GEO) and LEO satellites, coined Distributed-RSMA (D-RSMA). By doing so, D-RSMA aims to mitigate the interference and boost the user fairness of the overall multilayer satellite system. Specifically, we study the problem of jointly optimizing the GEO/LEO precoders and message splits to maximize the minimum rate among User Terminals (UTs) subject to a transmit power constraint at all satellites. A robust algorithm is proposed to solve the original non-convex optimization problem. Numerical results demonstrate the effectiveness and robustness towards network load and CSI uncertainty of our proposed D-RSMA scheme. Benefiting from the interference management capability, D-RSMA provides significant max-min fairness performance gains compared to several benchmark schemes.

Low earth orbit (LEO) satellite systems with sensing functionality are envisioned to facilitate global-coverage service and emerging applications in 6G. Currently, two fundamental challenges, namely, inter-beam interference among users and power limitation at the LEO satellites, limit the full potential of the joint design of sensing and communication. To effectively control the interference, a rate-splitting multiple access (RSMA) scheme is employed as the interference management strategy in the system design. On the other hand, to address the limited power supply at the LEO satellites, we consider low-resolution quantization digital-to-analog converters (DACs) at the transmitter to reduce power consumption, which grows exponentially with the number of quantization bits. Additionally, optimizing the total energy efficiency (EE) of the system is a common practice to save the power. However, this metric lacks fairness among users. To ensure this fairness and further enhance EE, we investigate the max-min fairness EE of the RSMA-assisted integrated sensing and communications (ISAC)-LEO satellite system. In this system, the satellite transmits a quantized dual-functional signal serving downlink users while detecting a target. Specifically, we optimize the precoders for maximizing the minimal EE among all users, considering the power consumption of each radio frequency (RF) chain under communication and sensing constraints. To tackle this optimization problem, we proposed an iterative algorithm based on successive convex approximation (SCA) and Dinkelbach’s method. Numerical results illustrate that the proposed design and RSMA architecture outperforms strategies maximizing the total EE of the system, space-division multiple access (SDMA), and orthogonal multiple access (OMA) in terms of max-min fairness EE and the communication-sensing trade-off.

Energy efficiency (EE) problem has become an important and major issue in satellite communications. In this paper, we study the beamforming design strategy to maximize the EE of rate-splitting multiple access (RSMA) for the multibeam satellite communications by considering imperfect channel state information at the transmitter (CSIT). We propose an expectation-based robust beamforming algorithm against the imperfect CSIT scenario. By combining the successive convex approximation (SCA) with the penalty function transformation, the nonconvex EE maximization problem can be solved in an iterative manner. The simulation results demonstrate the effectiveness and superiority of RSMA over traditional space-division multiple access (SDMA). Moreover, our proposed beamforming algorithm can achieve better EE performance than the conventional beamforming algorithm.

This paper investigates the joint beamforming design problem to achieve max-min rate fairness in a satellite-terrestrial integrated network (STIN) where the satellite provides wide coverage to multibeam multicast satellite users (SUs), and the terrestrial base station (BS) serves multiple cellular users (CUs) in a densely populated area. Both the satellite and BS operate in the same frequency band. Since rate-splitting multiple access (RSMA) has recently emerged as a promising strategy for non-orthogonal transmission and robust interference management in multi-antenna wireless networks, we present two RSMA-based STIN schemes, namely the coordinated scheme relying on channel state information (CSI) sharing and the cooperative scheme relying on CSI and data sharing. Our objective is to maximize the minimum fairness rate amongst all SUs and CUs subject to transmit power constraints at the satellite and the BS. A joint beamforming algorithm is proposed to reformulate the original problem into an approximately equivalent convex one, which can be iteratively solved. Moreover, an expectation-based robust joint beamforming algorithm is proposed against the practical environment when the satellite channel phase uncertainties are considered. Simulation results demonstrate the effectiveness and robustness of our proposed RSMA schemes for STIN and exhibit significant performance gains compared with various baseline strategies.

In this paper, we consider a multi-antenna dual-functional radar-communication (DFRC) satellite system, where the satellite has a dual capability to simultaneously communicate with downlink satellite users (SUs) and probe detection signals to a moving target. To design an appropriate DFRC waveform, we investigate the rate-splitting multiple access (RSMA)-assisted DFRC beamfoming, and employ the Cramér-Rao bound (CRB) as a radar performance metric, which represents a lower bound on the variance of unbiased estimators. The beamforming is optimized to minimize the CRB subject to quality of service (QoS) constraints of SUs and a per-feed transmit power budget. Satellite communication and detecting ground/ sea objects in a bistatic mode are accomplished simultaneously using the DFRC waveform we designed. Simulation results demonstrate that the proposed RSMA-assisted DFRC beamforming outperforms the conventional space-division multiple access (SDMA) strategy in terms of the communication-sensing trade-off and target estimation performance in a multibeam satellite system.

This paper studies the precoder design problem of achieving max-min fairness (MMF) amongst users in multigateway multibeam satellite communication systems with feeder link interference. We propose a beamforming strategy based on a newly introduced transmission scheme known as rate-splitting multiple access (RSMA). RSMA relies on multi-antenna rate-splitting at the transmitter and successive interference cancellation (SIC) at the receivers, such that the intended message for a user is split into a common part and a private part and the interference is partially decoded and partially treated as noise. In this paper, we formulate the MMF problem subject to per-antenna power constraints at the satellite for the system with imperfect channel state information at the transmitter (CSIT). We also consider the case of two-stage precoding which is assisted by on- board processing (OBP) at the satellite. Numerical results obtained through simulations for RSMA and the conventional linear precoding method are compared. When RSMA is used, MMF rate gain is promised and this gain increases when OBP is used. RSMA is proven to be promising for multigateway multibeam satellite systems whereby there are various practical challenges such as feeder link interference, CSIT uncertainty, per-antenna power constraints, uneven user distribution per beam and frame-based processing.

This work focuses on the promising Rate-Splitting Multiple Access (RSMA) and its beamforming design problem to achieve max-min fairness (MMF) among multiple co-channel multicast groups with imperfect channel state information at the transmitter (CSIT). Contrary to the conventional linear precoding (NoRS) that relies on fully treating any residual interference as noise, we consider a novel multigroup multicast beamforming strategy based on RSMA. RSMA relies on linearly precoded Rate-Splitting (RS) at the transmitter and Successive Interference Cancellation (SIC) at the receivers, and has recently been shown to enable a flexible framework for non-orthogonal transmission and robust interference management in multi-antenna wireless networks. In this work, we characterize the MMF Degrees-of-Freedom (DoF) achieved by RS and NoRS in multigroup multicast with imperfect CSIT and demonstrate the benefits of RS strategies for both underloaded and overloaded scenarios. Motivated by the DoF analysis, we then formulate a generic transmit power constrained optimization problem to achieve MMF rate performance. The superiority of RS-based multigroup multicast beamforming compared with NoRS is demonstrated via simulations in both terrestrial and multibeam satellite systems. In particular, due to the characteristics and challenges of multibeam satellite communications, our proposed RS strategy is shown promising to manage its inter-beam interference.