In this paper, we study an uplink reconfigurable intelligent surface (RIS) aided orthogonal frequency division multiplexing (OFDM) system, where the RIS simultaneously acts as a passive reflector for a user’s quadrature phase-shift keying (QPSK) OFDM signal and an information source which sends information through spatial modulation of the reflected signal. We present a maximum-likelihood (ML) detector for the detection of RIS signals and analyze the error probability theoretically. We derive closed-form expressions of the error probability for the detection of RIS signals for the special case of flat-fading channels. Then we analyze the error probability of the detection of user’s signals. Analysis indicates that faulty detection of RIS signal results in channel estimation errors, consequently compromising the precision of OFDM signal detection. High correlation between code words limits the error propagation to the user but decreases accuracy for RIS signal’s detection. Addressing this, an index modulation with beamforming (IM-BF) code is proposed to enhance the transmission for the user, and a quadrature reflection modulation with beamforming (QRM-BF) code is proposed for a performance trade-off between the user and the RIS. Simulation results demonstrate the theoretical results, and show that the QRM-BF code has better performance for the RIS transmission, and both schemes outperform benchmark schemes from the literature for the user’s transmission. Simulation results also show that the IM-BF scheme significantly improves the user transmission performance compared to the state of the art.

The airborne mobile wireless networks (AMWNs) composed of unmanned aerial vehicles (UAVs) communication networks is one of the emerging application scenarios of the Space-Ground-Sea Integrated Networks in 6G. Due to its high mobility, UAV exhibits great advantages in assisted cellular network communication and emergency communication. The multi-antenna technology and quality of service (QoS) can help cellular networks reduce latency and improve network capacity. In this paper, we jointly optimize the transmit power and flight trajectory of the multiuser MISO UAV communication system to improve the total effective capacity of the system. Specifi-cally, zero-forcing (ZF) beamforming and block diagonalization are adopted to eliminate multiuser interference. In addition, the optimization problem with regard to maximizing the total effective capacity of the system under the condition of delay constraint is proposed. Furthermore, an iterative optimization algorithm jointly considering UAV trajectory and subchannel transmit power is considered, in which we use successive convex optimization techniques and the block coordinate descent method to solve the thorny non-convex problem. Simulation results verify the effectiveness and convergence of the proposed algorithm.

In this paper, we investigate the multiple-input- multiple-output (MIMO) two-way amplify-and-forward (AF) relaying system with simultaneous wireless information and power transfer (SWIPT), where two users harvest energy from the relay signal by power splitting (PS) scheme. Under the transmit power constraints at all nodes, the energy optimization problem is formulated which maximizes the total residual energy of two users by optimizing the splitting ratios and the precoding matrixes at the relay and two users, while still guaranteeing sufficient signal-to-noise ratios (SNR) at two user nodes. We divide the complex non-convex objective problem into three subproblems which are then solved through the proposed alternating optimization (AO) scheme. Numerical results are provided to verify the analysis and demonstrate the efficiency of the proposed scheme.

Image super-resolution (SR) plays a significant role in Internet of Vehicles (IoV), and is widely used in many important applications, such as object recognition and vehicle identification. However, in certain cases, the quality of acquired images is low and general SR algorithms are inapplicable. Aiming at the disadvantages of the traditional image super- resolution methods which are mainly based on interpolation and example learning, this paper utilizes compressed sensing (CS) and sequence information to present a new approach of image super-resolution. In order to keep high frequency information and reduce ringing and jagged artifacts, the proposed method takes advantage of the useful information between multiple image frames and jointly trains coupled dictionaries for the low- resolution (LR) and high-resolution (HR) image block pair. By fusing atoms of LR dictionary, the atom of HR dictionary is obtained and the HR image can be recovered in terms of the learned HR dictionary. The experimental results show that the proposed algorithm has better performance in both subjective assessment and objective standards, including Entropy and Average Gradient.