The integrated satellite-terrestrial network (ISTN) with inter-satellite free space optical (FSO) links and satellite-to-ground (S2G) radio frequency (RF) links is becoming an important enabler for the Internet of Things (IoT). However, investigating the performance of the ISTN remains several challenges, i.e., the high mobility and long propagation delays of S2G links, and the highly correlated line-of-sight S2G channels. To address these challenges, we propose a hybrid RF/FSO cooperative satellite-terrestrial communication system that integrates the space time block code with cooperative transmission to enhance the coverage probability and communication reliability of satellite downlink transmission. We model the inter-satellite FSO channels by considering pointing and tracking errors, and the S2G RF channels using the shadowed-Rician fading model. Subsequently, we derive the probability density function and cumulative distribution function for both RF/FSO signal-to-noise ratio (SNR) under channel estimation errors and the sum of two RF SNRs from the same distribution family. Finally, for the proposed system, closed-form expressions of the outage probability (OP) and the upper bound for the average bit error probability (BEP) are derived. The proposed system outperforms SISO and MISO systems by reducing average BEP, outage probability, and robustness to channel estimation errors.

The rapid advancement in large foundation models is propelling the paradigm shifts across various industries. One significant change is that agents, instead of traditional machines or humans, will be the primary participants in the future production process, which consequently requires a novel AI-native communication system tailored for agent communications. Integrating the ability of large language models (LLMs) with task-oriented semantic communication is a potential approach. However, the output of existing LLM is human language, which is highly constrained and sub-optimal for agent-type communication. In this paper, we innovatively propose a task-oriented agent communication system. Specifically, we leverage the original LLM to learn a specialized machine language represented by token embeddings. Simultaneously, a multi-modal LLM is trained to comprehend the application task and to extract essential implicit information from multi-modal inputs, subsequently expressing it using machine language tokens. This representation is significantly more efficient for transmission over the air interface. Furthermore, to reduce transmission overhead, we introduce a joint token and channel coding (JTCC) scheme that compresses the token sequence by exploiting its sparsity while enhancing robustness against channel noise. Extensive experiments demonstrate that our approach reduces transmission overhead for downstream tasks while enhancing accuracy relative to the SOTA methods.

Low earth orbit (LEO) satellite communication systems have become the indispensable part of sixth generation (6 G) communications. However, since the LEO and geostationary earth orbit (GEO) satellite systems will inevitably share limited frequency resources, the communication signal from LEO satellites have the possibility to cause harmful interference to the GEO systems. To address this issue, this paper proposes an adaptive beamforming strategy to mitigate the interference while improving the system spectral efficiency (SE). In specific, we formulate the problem as the nonlinear mixed integer programming (NMIP) optimization problem, and apply the weighted minimum mean square error (WMMSE) and alternative optimization algorithm to obtain the closed-form solutions. Furthermore, to reduce the high complexity of beamforming when the LEO system serves a massive number of ground users (GU), we propose a channelaware user clustering scheme utilizing the channel correlation between GUs so that all GUs within the same cluster share the same precoding vector. Extensive simulations show that the proposed scheme effectively mitigates the interference.

Hybrid beamforming has been widely considered in millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) system, which can greatly reduce power consumption and hardware cost of data paths. Compared to the fully-connected hybrid beamforming architecture, the sub-connected architecture is more practical for its reduced complexity. However, optimal precoding design for the sub-connected architecture is not straightforward due to the specific block-diagonal structure of analog phase shifter network. Algorithms on fully-digital or fully-connected hybrid beamforming architecture cannot be directly applied to sub-connected architecture. Meanwhile, most existing precoding algorithms in such case can only solve the approximate problem, which results in significant performance loss. In this paper, we study the sum rate maximization problem in the sub-connected architecture. We first relax the objective function and derive a relaxed upper bound of the original problem. Then we propose an algorithm to solve the original problem with a local-optimal solution. Simulation results show that the proposed local-optimal algorithm outperforms the baseline algorithms with better sum rate and energy efficiency performance. Besides, the proposed algorithm also converges quickly and is robust.

In this paper, we propose an imaging scheme for unmanned aerial vehicle (UAV)-Enabled integrated sensing and communication (ISAC), where the UAV serves as a flexible communication auxiliary and a versatile sensing platform with the cooperation of a ground base station (GBS). To guarantee the performance of both sensing and communication, the proposed imaging scheme is based on the orthogonal frequency division modulation (OFDM) ISAC waveform and bistatic communication-assisted sensing strategy, which can be divided into three steps. Firstly, the UAV transmits OFDM ISAC signal, which contains the UAV position information to enable the communication-assisted sensing strategy. Secondly, the GBS receives the line-of-sight (LoS) ISAC signal from the UAV and the reflected ISAC signal from targets, in which the bistatic sensing architecture is designed to process data frequently and bypass the self-interference problem. Thirdly, the GBS preprocesses the received signal and reconstructs the image based on polar format algorithm (PFA) with the knowledge of UAV positions to relax the constraint of UAV trajectory. Numerical simulations are carried out to validate and evaluate the proposed UAV-enabled ISAC imaging scheme.

Massive multiple-input multiple-output (MIMO) is a key technology in next generation wireless communication. However, the increasing number of radio frequency (RF) chains results in higher cost and power consumption. Given that hundreds or even thousands of transmit antennas are equipped at the base station (BS), low resolution digital-to-analog converters (DACs) are preferred to reduce the power consumption on both DACs and power amplifiers (PAs). Currently, there have been some studies about the application of low-resolution DACs for single-carrier systems. For multi-carrier systems, this problem hasn’t been fully investigated. This paper aims to design a 1-bit downlink precoding algorithm for massive multi-user MIMO orthogonal frequency division multiplexing (OFDM) systems. A nonlinear precoding algorithm is proposed, which can address the non-convex optimization problem with discrete output constraint and guarantee convergence. Meanwhile, the proposed algorithm factors in the different path-losses experienced by different users. Furthermore, some approximation schemes can be applied to bring down the computational complexity of the proposed algorithm further. Simulation results illustrate that our algorithm performs the best among other nonlinear precoding methods in OFDM systems.