Wenjun Xu’s research while affiliated with Beihang University and other places

In this paper, we propose a power control algorithm for a dual-functional radar-communication (DFRC) unmanned aerial vehicle (UAV) swarm in a multi-target tracking (MTT) scenario. During MTT, UAVs need to communicate with others to exchange information and detect targets simultaneously. To measure sensing accuracy, we first derive the Cramer-Rao bound (CRB) of sensing in 3-D space. Then, an interference minimization problem under the constraint of UAV’s flying ability, communication quality, and sensing accuracy is proposed to balance the performance between communication and sensing while controlling the inter-UAV interference. The formulated problem is solved by decomposing it into three sub-problems due to its intractability, and a signal-to-interference-plus-noise ratio (SINR) and CRB-aware power control algorithm based on mean field game (MFG) is proposed for the power control sub-problem. Simulation results demonstrate that our proposed power control algorithm reduces the interference and cost and improves communication performance simultaneously, compared to conventional schemes.

End-to-end image transmission has recently become a crucial trend in intelligent wireless communications, driven by the increasing demand for high bandwidth efficiency. However, existing methods primarily optimize the trade-off between bandwidth cost and objective distortion, often failing to deliver visually pleasing results aligned with human perception. In this paper, we propose a novel rate-distortion-perception (RDP) jointly optimized joint source-channel coding (JSCC) framework to enhance perception quality in human communications. Our RDP-JSCC framework integrates a flexible plug-in conditional Generative Adversarial Networks (GANs) to provide detailed and realistic image reconstructions at the receiver, overcoming the limitations of traditional rate-distortion optimized solutions that typically produce blurry or poorly textured images. Based on this framework, we introduce a distortion-perception controllable transmission (DPCT) model, which addresses the variation in the perception-distortion trade-off. DPCT uses a lightweight spatial realism embedding module (SREM) to condition the generator on a realism map, enabling the customization of appearance realism for each image region at the receiver from a single transmission. Furthermore, for scenarios with scarce bandwidth, we propose an interest-oriented content-controllable transmission (CCT) model. CCT prioritizes the transmission of regions that attract user attention and generates other regions from an instance label map, ensuring both content consistency and appearance realism for all regions while proportionally reducing channel bandwidth costs. Comprehensive experiments demonstrate the superiority of our RDP-optimized image transmission framework over state-of-the-art engineered image transmission systems and advanced perceptual methods.

The increasing demands for massive data transmission pose great challenges to communication systems. Compared to traditional communication systems that focus on the accurate reconstruction of bit sequences, semantic communications (SemComs), which aim to successfully deliver information connotation, have been regarded as the key technology for next-generation communication systems. Most current SemCom systems focus on an E2E trained neural network (NN) for semantic extraction and interpretation, regarding the parameters of the NN as the implicit synchronized background knowledge. However, the implicit knowledge base (KB)-based architectures lack interpretability and flexibility, which limits the performance of SemComs. In this article, we propose a SemCom architecture that employs explicit semantic bases (Sebs), which serve as the basic units to describe semantic information. In specific, the mathematical model of Sebs is first proposed to build explicit KB. Then, the Seb-based SemCom architecture is proposed, consisting of a communication mode and a KB update mode to enable the evolution of communication systems. Specifically, the modules of Sem-codec and channel codec are dedicatedly designed, with the assistance of explicit KB for efficient and robust transmission of semantics. Moreover, unequal error protection is strategically implemented, considering the intent of communications and the importance of Sebs, thereby ensuring reliability of critical semantics. To assess the effectiveness of the proposed Seb-based SemCom architecture, a case study focusing on an image transmission task is conducted. Simulations show that the proposed Seb-based SemComs outperforms state-of-art works in LPIPS by over 20% under varying communication intents, with more robust performance under fluctuating channel conditions, indicating the flexible and robust transmission of the proposed Seb-based SemComs.

In this paper, we propose a novel interference-suppressed semantic-aware non-orthogonal multiple access (IS-SNOMA) framework for downlink image transmission in the semantic-aware communication networks. The proposed IS-SNOMA is able to mitigate the inter-user interference in the non-orthogonal transmission for multiple semantic-oriented users (SU) or the coexistence of SUs and bit-oriented users (BU). 1) For the homogeneous transmission of semantic streams, we propose a pair of novel semantic difference (SeD) aware IS-SNOMA transceivers to accommodate multiple SUs over the same resource block. A novel SeD-aware superposition coding (SeDSC) technique and SeD-aware successive interference cancellation (SeDSIC) technique are specially designed to mitigate the semantic-level interference. 2) For the heterogeneous transmission of semantic-bit streams, we develop a pair of syntactic difference (SyD) aware IS-SNOMA transceivers to multiplex channels for SUs and BUs. The heterogeneous semantic symbols and bit sequences are superposed and separated with the proposed SyD-aware SC technique (SyDSC) and SyD-aware SIC technique (SyDSIC), respectively. Simulation results demonstrate the advantages of the proposed frameworks in improving the communication efficiency of both SUs and BUs, compared with OMA and NOMA-aided transmission benchmarks.

Satellite signals of opportunity positioning (SSOOP) attempts to work out a navigation solution with many non-global navigation satellite systems (GNSS) satellite signals, when GNSS signals are not available. It is promising in addressing GNSS vulnerability by using an overwhelming quantity of satellites with diverse signal formats, multiple radio bands, and global availability. How to figure out an applicable receiver position from the signals transmitted by anonymous satellites with unknown emission time? Toward massive SSOOP, this work contributes (a) a summary of the fundamental challenges by reviewing the mathematical formulations of SSOOP problem; (b) a set of proposed methods for SSOOP, including orbit predication, signal processing, and different modes of user positioning; (c) an analysis on the orbit prediction precision of two-line elements (TLEs) at different geographic locations on Earth and its impact on positioning performance, based on the orbit data obtained from our experimental (international ID: 40136) satellite that has a similar orbit height as the IRIDIUM satellites; and (d) the design and test result of an IRIDIUM SSOOP receiver prototype for verifying the proposed methods and corroborating the analysis, which showed a CEP ≈892 m (circular error probable) in standalone mode tests and a CEP ≈40 m in differential mode tests.

In this letter, we investigate the channel estimation problem in the reconfigurable intelligent surface (RIS)-aided wireless communication system. To recover channels accurately, we propose a novel diffusion model-based channel estimation method for combating the noise at the receiver effectively. Specifically, the channel recovery is accomplished via a continuous prior sampling process, where the prior information is derived from a U-Net that undergoes likelihood-based training. Additionally, in order to reduce the adverse effect of the phase noise at the RIS, we incorporate the gradient descent value of RIS phase into the sampling process. Simulation results demonstrate that the proposed method surpasses baselines in estimation accuracy, achieving a superior performance of more than 3.2 dB. Furthermore, the proposed method exhibits remarkable robustness, working effectively under different noise levels.

Semantic communication has shown great potential in efficiently accomplishing intelligent tasks. However, existing systems usually make a single-view decision in a deterministic manner while ignoring the intrinsic semantic noise caused by ambiguity. This may result in the misunderstanding during the exchange of semantic information. In this letter, we propose a novel probabilistic uncertainty coding (PUC) architecture to achieve versatile multi-view semantic communications with multiple outputs. Specifically, each feature region is characterized as a Gaussian distribution whose variance represents semantic uncertainty. Then, we leverage the Gaussian information bottleneck theory to jointly optimize the rate-distortion tradeoff between the semantic informativeness and inference performance. Moreover, we provide a semantic similarity metric to evaluate the accuracy of multi-view semantic communication. Considering the cross-modal task of semantic knowledge transfer, simulation results show that PUC outperforms conventional deterministic method and traditional separate transmissions. Due to its semantic-aware capability, the proposed PUC can make diverse yet plausible predictions in a highly reliable and low-latency manner.

Air–ground-integrated networks play a vital role in seamless coverage. The irregular topologies and dynamic loads require a comprehensive cooperation between terrestrial base stations (BSs) and unmanned aerial vehicles (UAVs) in air networks. In this letter, a joint UAV movement control and load balancing (LB) method is proposed to improve the load completion ratio (LCR). Specifically, a reinforcement learning (RL) method soft actor-critic (SAC) is utilized to control the UAV movements, while the LB is performed by tuning the cell individual offsets (CIOs) of BSs and UAVs. Moreover, to make the solution compatible to heterogeneous BSs and UAVs, an indirect step-and-slipping-window-based CIO tuning method is proposed to convert the actions of SAC into CIOs. Simulation results show that the proposed method outperforms the state-of-art baselines up to 29% in terms of the LCR.

This paper addresses the problem of delay-Doppler (DD) domain power allocation for orthogonal time frequency space (OTFS)-based cognitive radio (CR) systems with the high-mobility secondary user (SU) reusing the spectrum of the low-mobility primary user (PU). A fundamental challenge for such an OTFS-CR network is to maximize the SU's achievable rate while ensuring the quality of service (QoS) of the PU. For this goal, we first derive the explicit expressions of the signal-to-interference-plus-noise ratio (SINR) of the SU of the DD domain. Two cases of the interference introduced by the PU received at the SU are considered: the cases without and with successive interference cancellation (SIC). For the first case, we have proven that the maximal SU's achievable rate can be obtained through equal power allocation. For the second case, the optimization problem is more intractable. Utilizing the Karush-Kuhn-Tucker (KKT) conditions of the optimization problem, an asymptotic-optimal DD domain power allocation scheme has been proposed. Numerical simulations show the priority of the proposed algorithms, where the performance gain is significant compared with other classical allocation algorithms.