Huici Wu

This paper investigates an explicit polar coding scheme for a two-user discrete memoryless multiple access wiretap channel with partial rate-limited feedback (MAC-WT-PLF). Feedback is exploited to increase channel-input correlation, inject dummy messages, and encrypt messages using a one-time pad. Existing MAC-WT polar coding schemes assume independent channel inputs, which cannot support the correlation requirement. Therefore, we propose an explicit polar mapping that leverages feedback to introduce correlation between the channel inputs. This mapping allows both the transmitter and the receiver to restructure the correlation in opposite decoding directions. The proposed scheme relies on source polarization, block Markov coding, superposition coding, lossy source coding, and ratesplitting, without making symmetry or degradation assumptions on the channel model. Rigorous information-theoretic asymptotic analysis establishes that the proposed scheme ensures both reliability and strong secrecy, and attains the entire achievable secrecy rate region given in prior work.

Unmanned aerial vehicle (UAV) swarms are pivotal in the applications such as disaster relief, aerial base station (BS) and logistics transportation. These scenarios require the capabilities in accurate sensing, efficient communication and flexible control for real-time and reliable task execution. However, sensing, communication and control are studied independently in traditional research, which limits the overall performance of UAV swarms. To overcome this disadvantage, we propose a deeply coupled scheme of integrated sensing, communication and control (ISCC) for UAV swarms, which is a systemic paradigm that transcends traditional isolated designs of sensing, communication and control by establishing a tightly-coupled closed-loop through the co-optimization of sensing, communication and control. In this article, we firstly analyze the requirements of scenarios and key performance metrics. Subsequently, the enabling technologies are proposed, including communication-and-control-enhanced sensing, sensing-and-control-enhanced communication, and sensing-and-communication-enhanced control. Simulation results validate the performance of the proposed ISCC framework, demonstrating its application potential in the future.

Uncrewed aerial vehicle (UAV) swarms are pivotal in the applications such as disaster relief, aerial base station (BS) and logistics transportation. These scenarios require the capabilities in accurate sensing, efficient communication and flexible control for real-time and reliable task execution. However, sensing, communication and control are studied independently in traditional research, which limits the overall performance of UAV swarms. To overcome this disadvantage, we propose a deeply coupled scheme of integrated sensing, communication and control (ISCC) for UAV swarms, which is a systemic paradigm that transcends traditional isolated designs of sensing, communication and control by establishing a tightly-coupled closed-loop through the co-optimization of sensing, communication and control. In this article, we firstly analyze the requirements of scenarios and key performance metrics. Subsequently, the enabling technologies are proposed, including communication-and-control-enhanced sensing, sensing-and-control-enhanced communication, and sensing-and-communication-enhanced control. Simulation results validate the performance of the proposed ISCC framework, demonstrating its application potential in the future.

While integrated sensing and communication (ISAC) beamforming has become a research hotspot recently, most proposed approaches remain scenario-specific and suffer from limited generalizability. In this paper, we propose a flexible beamforming design framework for multiple-input multiple-output ISAC (MIMO-ISAC) systems. To characterize the optimal performance tradeoff between the sensing and communication (S&C), the objective is to maximize the weighted sum of S&C mutual information (MI). An integrated fractional programming (IFP) framework is first proposed, from which a semi-closed-form solution for the optimal beamformer is derived. The proposed framework is further extended to several representative ISAC scenarios, including fairness-aware tradeoffs, imperfect channel state information (CSI), dynamic radar cross-section (RCS) variations, and massive MIMO systems. By incorporating advanced optimization techniques, the IFP framework effectively addresses the challenges arising in these scenarios with only minor modifications while still yielding semi-closed-form solutions. Moreover, we show that the IFP framework is closely related to the projected gradient descent (PGD) method, and its computational efficiency can be further enhanced through gradient acceleration techniques. Simulation results demonstrate that the IFP framework and its variants consistently outperform benchmark schemes across diverse system settings. The proposed low-complexity and accelerated schemes reduce computation time by approximately 65% and 89%, respectively, with only marginal performance degradation. These results highlight the flexibility and generality of the proposed IFP framework for ISAC beamforming design and are expected to provide valuable insights for the development of future 6G systems.