The radio environment map (REM), which can visualize the information of invisible electromagnetic spectrum, is vital for monitoring, management, and security of spectrum resources in cognitive radio (CR) networks. In view of a limited number of spectrum sensors and constrained sampling time, this paper presents a new three-dimensional (3D) REM construction scheme based on sparse Bayesian learning (SBL). Firstly, we construct a scenario-dependent channel dictionary matrix by considering the propagation characteristic of the interested scenario. To improve sampling efficiency, a maximum mutual information (MMI)-based optimization algorithm is developed for the layout of sampling sensors. Then, a maximum and minimum distance (MMD) clustering-based SBL algorithm is proposed to recover the spectrum data at the unsampled positions and construct the whole 3D REM. We finally use the simulation data of the campus scenario to construct the 3D REMs and compare the proposed method with the state-of-the-art. The recovery performance and the impact of different parameters on the constructed REMs are also analyzed. Numerical results show that the proposed scheme can reduce the required spectrum data and has higher accuracy under the low sampling rate.

Reconfigurable intelligent surface (RIS) can significantly enhance the service coverage of Tera-Hertz massive multiple-input multiple-output (MIMO) communication systems. However, obtaining accurate high-dimensional channel state information (CSI) with limited pilot and feedback signaling overhead is challenging, severely degrading the performance of conventional spatial division multiple access. To improve the robustness against CSI imperfection, this paper proposes a deep learning (DL)-based rate-splitting multiple access (RSMA) scheme for RIS-aided Tera-Hertz multi-user MIMO systems. Specifically, we first propose a hybrid data-model driven DL-based RSMA precoding scheme, including the passive precoding at the RIS as well as the analog active precoding and the RSMA digital active precoding at the base station (BS). To realize the passive precoding at the RIS, we propose a Transformer-based data-driven RIS reflecting network (RRN). As for the analog active precoding at the BS, we propose a match-filter based analog precoding scheme considering that the BS and RIS adopt the LoS-MIMO antenna array architecture. As for the RSMA digital active precoding at the BS, we propose a low-complexity approximate weighted minimum mean square error (AWMMSE) digital precoding scheme, and further design a model-driven deep unfolding active precoding network (DFAPN) by combining the proposed AWMMSE scheme with DL. Then, to acquire accurate CSI at the BS for the investigated RSMA precoding scheme to achieve higher spectral efficiency, we propose a CSI acquisition network (CAN) with low pilot and feedback signaling overhead. The proposed DL-based RSMA scheme for RIS-aided Tera-Hertz multi-user MIMO systems can exploit the advantages of RSMA and DL to improve the robustness against CSI imperfection, thus achieving higher spectral efficiency with lower signaling overhead.

Wireless Power Transfer (WPT) is expected to be a technology reshaping the landscape of low-power applications such as the Internet of Things. Multi-antenna multi-sine waveforms adaptive to the channel state information have been shown to be a promising building block of WPT. However, the current design is computationally too complex to be applied to large-scale WPT, where signals are sent across a large number (tens) of antennas and frequencies. This paper proposes efficient single/multi-user algorithms based on a generalizable optimization framework, in order to design transmit waveforms that maximize the weighted-sum/minimum rectenna output DC voltage. The study highlights the significant effect of the nonlinearity of the rectenna on the design of waveforms in single/multi-user systems. Interestingly, in the single-user case, the optimal spatial beamforming, obtained before optimizing power allocation across frequencies, turns out to be maximum ratio transmission. Nevertheless, in the general multi-user weighted-sum criterion maximization problem, optimizations in the frequency and the spatial domains are coupled. Assuming channel hardening, we also propose low-complexity algorithms based on asymptotic analysis, where the structure of the asymptotically optimal spatial beamforming can be found before optimizations. Simulation results confirm the inefficiency of the linear model-based design in single/multi-user systems. Moreover, simulation results show that the proposed algorithms (based on the nonlinear model) can benefit from an increasing number of sinewaves at a computational cost much lower than the existing method. Simulation results also indicate that the large-scale WPT architecture can boost the end-to-end power transfer efficiency and the transmission range.

Three-dimensional (3D) radio map, which characterizes the spatial distribution of the received signal strength (RSS) across a 3D space, can be a significant tool for wireless resource management and spectrum surveillance. However, incomplete 3D radio map is usually obtained in practice due to the infinite number of positions where aircraft perform RSS measurement suffering from constrained trajectories. In order to tackle this problem, we propose a 3D radio map reconstruction scheme based on generative adversarial networks (GANs), where a novel GAN variant with unsupervised learning is proposed. Specifically, the cost function of the variant integrates a reconstruction loss and an adversarial loss, while the network structure exploits ResNet and dilated convolution under the idea of patchGAN. Numerical results reveal that the network structure and the cost function can benefit the reconstruction scheme, yielding low-volatility and lower average mean squared error (MSE). Moreover, it is shown that the proposed scheme can outperform baselines in terms of the average MSE, even if the number of measured samples for inference significantly decreases.

This paper investigates relaying schemes in an amplify-and-forward multiple-input multiple-output relay network, where an energy-constrained relay harvests wireless power from the source information flow and can be further aided by an energy flow (EF) in the form of a wireless power transfer at the destination. However, the joint optimization of the relay matrix and the source precoder for the EF-assisted (EFA) and the non-EFA (NEFA) schemes is intractable. The original rate maximization problem is transformed into an equivalent weighted mean square error minimization problem and optimized iteratively, where the global optimum of the nonconvex source precoder subproblem is achieved by semidefinite relaxation and rank reduction. The iterative algorithm finally converges. Then, the simplified EFA and NEFA schemes are proposed based on channel diagonalization, such that the matrices optimizations can be simplified to power optimizations. Closed-form solutions can be achieved. Simulation results reveal that the EFA schemes can outperform the NEFA schemes. In addition, deploying more antennas at the relay increases the dimension of the signal space at the relay. Exploiting the additional dimension, the EF leakage in the information detecting block can be nearly separated from the information signal, such that the EF leakage can be amplified with a small coefficient.

In this paper, we initiate the study of rate-splitting multiple access (RSMA) for a mono-static integrated sensing and communication (ISAC) system, where the dual-functional base station (BS) simultaneously communicates with multiple users and detects multiple moving targets. We aim at optimizing the ISAC waveform to jointly maximize the max-min fairness (MMF) rate of the communication users and minimize the largest eigenvalue of the Cramér-Rao bound (CRB) matrix for unbiased estimation. The CRB matrix considered in this work is general as it involves the estimation of the angular direction, the complex reflection coefficient, and the Doppler frequency for multiple moving targets. Simulation results demonstrate that RSMA maintains a larger communication and sensing trade-off than conventional space-division multiple access (SDMA) and it is capable of detecting multiple targets with a high detection accuracy. The finding highlights the potential of RSMA as an effective and powerful strategy for interference management in the general multi-user multi-target ISAC systems.