Shaoshuai Gao

Golay Complementary Pairs (GCPs) are widely adopted in modern wireless standards, yet their ideal complementarity is confined to static or low-mobility scenarios, suffering from severe sidelobe leakage under high Doppler shifts. To address this challenge, this work proposes a lowcomplexity and Doppler-resilient complementary sequence design for high-mobility Integrated Sensing and Communication (ISAC) scenarios, which combines a robust waveform construction with a computationally efficient processing architecture. We first introduce a generalized Doppler-Resilient Complementary Sequence (DRCS) construction that synthesizes a global “thumbtacklike” ambiguity function, fundamentally resolving the Doppler sensitivity. Then, to mitigate the high Area-Time complexity (O(M²N³)) associated with large sequence sets, we derive a fast correlation algorithm exploiting the polyphase structure, reducing the complexity to O(MN² logN). Numerical results validate that the proposed design maintains robust sidelobe suppression in high-mobility scenarios and achieves superior Area-Time hardware efficiency compared to both conventional time-domain and FFT-based matched filter banks.

This work investigates the capacity-approaching input distribution (CAID) of the multiple-input single-output (MISO) optical intensity channel (OIC) in the presence of both per-antenna peak- and average-power constraints. Although OICs have been extensively studied in the literature, prior studies predominantly focus on capacity bounds and asymptotic behaviors, with limited attention directed towards CAIDs of OICs, particularly in the context of multi-input systems. For the first time, the exact CAID of the MISO OIC is determined by a successive minimum-power mapping (SMPM) strategy, in which the CAID problem is decomposed as an upper-level power allocation subproblem and a series of lower-level optimal input probability distribution (OIPD) subproblems for single-input single-output (SISO) cases. Adopting a hierarchical iteration structure, the binary search and successive linear least square algorithms are designed to address the power allocation subproblem in the outer iteration, while the layered primal-dual interior-point algorithm is employed to solve the SISO-OIPD subproblem in the inner iteration. The derived CAIDs for MISO OICs are presented and analyzed, and the corresponding mutual information results are compared with several theoretical bounds to validate the asymptotic tightness. It has been shown that the proposed CAID is structurally suboptimal but numerically approaches the theoretical capacity, especially in both low and high SNR regimes.

interuser interference (IUI) and intercell interference (ICI) are critical challenges faced by dense deployments of cellular visible light communication (VLC) systems, especially in scenarios involving Internet of Things (IoT) devices with Quality of Service (QoS) requirements. Thus, advanced techniques, such as rate splitting multiple access (RSMA), have been explored to deal with these challenges. However, the existing solutions based on RSMA primarily focus on spectral efficiency while neglecting the issue of fairness, i.e., the minimum achievable rate among users. Consequently, we propose a novel coordinated multipoint (CoMP) assisted cooperative RSMA (C-RSMA) scheme for aggregated VLC/radio frequency (RF) cellular systems to simultaneously address these issues. To maximize spectral efficiency under QoS constraints and enhance fairness, we formulate optimization problems to jointly optimize precoding and rate allocation. Given the inherent nonconvexity of these problems, we apply successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques to iteratively and effectively obtain near-optimal solutions. Experimental results demonstrate that, compared to the baseline schemes, the proposed scheme exhibits superior performance across varying performance metrics, user deployments, and network loads.

The aggregated visible light communication (VLC) and radio frequency (RF) system, which can be regarded as a heterogeneous multiple-input multiple-output system, has been studied to operate with novel multiple access technologies for effectively expanding spectrum resources. In this paper, we propose a novel cooperative rate splitting multiple access (RSMA)-based aggregated VLC/RF system that forwards part of common stream via device-to-device (D2D) RF sidelinks to tackle the problem of fairness among users. Meanwhile, with the objective of maximizing the minimum achievable rate, we jointly optimize the precoder, rate allocation of common stream, and power allocation of RF sidelinks under the constraint of limited total system power. Since the formulated optimization problem is non-convex, semidefinite relaxation (SDR) and successive convex approximation (SCA) methods are applied to solve it. Numerical results show that the proposed scheme significantly outperforms other baseline schemes in terms of user fairness, i.e., the minimum achievable rate in different scenarios and demonstrates robustness against blockages.

Visible light-based simultaneous position and orientation estimation (SPO) has drawn intensive attention in recent years due to its high accuracy and generality. A cone geometry-based SPO system has been proposed in our previous work, which shows superior performance in terms of estimation accuracy when compared with other existing schemes. However, there are still two issues need to be addressed, i.e., the excessive required number of light-emitting diodes (LEDs), and susceptibility to various radiation sources, high levels of noise, and strongly reflected lights. In this letter, we try to overcome the above challenges and achieve a robust and low-complexity SPO scheme. Firstly, more appropriate arrangements of LEDs are exploited to reduce the number of LEDs and the estimation uncertainty. Then, a hierarchical edge detection strategy is designed to enhance the estimation robustness by suppressing outliers. Numerical results indicate that the proposed system achieves superior accuracy and robustness with low complexity.

Rate splitting multiple access (RSMA), as a flexible multiple access and interference management strategy, bridges the nonorthogonal multiple access (NOMA) and the space division multiple acces (SDMA). In RSMA, users share a layer of common stream whose achievable rate is limited by the user with lowest channel quality. In this paper, a framework based on RSMA with user separation scheme is proposed, wherein the users are clustered into groups based on channel gain and the inter-group common streams are superimposed in power domain. Taking precoding and rate allocation into account, an optimization problem is formulated to maximize the sum rate, which is solved based on the successive convex approximation (SCA) method. To further enhance the performance, a user regrouping strategy is proposed by considering inter-group interferences, which is tackled with a simulated annealing (SA) method. Numerical results demonstrate that the proposed schemes flexibly allocate the rates to mitigate the effect of the above issue and achieve a significant sum rate gain in overloaded systems.

With the rapid development of wireless communication technology and the emergence of intelligent applications, higher requirements have been put forward for data communication and computing capacity. Multi-access edge computing (MEC) can handle highly demanding applications by users by sinking the services and computing capabilities of the cloud to the edge of the cell. Meanwhile, the multiple input multiple output (MIMO) technology based on large-scale antenna arrays can achieve an order-of-magnitude improvement in system capacity. The introduction of MIMO into MEC takes full advantage of the energy and spectral efficiency of MIMO technology, providing a new computing paradigm for time-sensitive applications. In parallel, it can accommodate more users and cope with the inevitable trend of continuous data traffic explosion. In this paper, the state-of-the-art research status in this field is investigated, summarized and analyzed. Specifically, we first summarize a multi-base station cooperative mMIMO-MEC model that can easily be expanded to adapt to different MIMO-MEC application scenarios. Subsequently, we comprehensively analyze the current works, compare them to each other and summarize them, mainly from four aspects: research scenarios, application scenarios, evaluation indicators and research issues, and research algorithms. Finally, some open research challenges are identified and discussed, and these indicate the direction for future research on MIMO-MEC.

Background: In recent years, the number of people with anxiety disorders has increased worldwide. Methods for identifying anxiety through objective clues are not yet mature, and the reliability and validity of existing modeling methods have not been tested. The objective of this paper is to propose an automatic anxiety assessment model with good reliability and validity.Methods: This study collected 2D gait videos and Generalized Anxiety Disorder (GAD-7) scale data from 150 participants. We extracted static and dynamic time-domain features and frequency-domain features from the gait videos and used various machine learning approaches to build anxiety assessment models. We evaluated the reliability and validity of the models by comparing the influence of factors such as the frequency-domain feature construction method, training data size, time-frequency features, gender, and odd and even frame data on the model.Results: The results show that the number of wavelet decomposition layers has a significant impact on the frequency-domain feature modeling, while the size of the gait training data has little impact on the modeling effect. In this study, the time-frequency features contributed to the modeling, with the dynamic features contributing more than the static features. Our model predicts anxiety significantly better in women than in men (r_Male = 0.666, r_Female = 0.763, p < 0.001). The best correlation coefficient between the model prediction scores and scale scores for all participants is 0.725 (p < 0.001). The correlation coefficient between the model prediction scores for odd and even frame data is 0.801~0.883 (p < 0.001).Conclusion: This study shows that anxiety assessment based on 2D gait video modeling is reliable and effective. Moreover, we provide a basis for the development of a real-time, convenient and non-invasive automatic anxiety assessment method.

With the widespread application of light emitting diodes (LEDs) and the rapid evolution of visible light communication (VLC), visible light-based positioning (VLP) is envisioned as a promising solution for indoor positioning due to its high accuracy and environmental friendliness. However, the performance of VLP is significantly affected by the unknown orientation of the user equipment (UE) and system parameters uncertainty. Besides, high computation complexity and vulnerability to hardware degradation of the VLP algorithms are also the challenges to meet. To handle the above issues, a novel cone geometry-based simultaneous position and orientation estimation (SPO) scheme with low complexity is proposed. By modeling the field of view (FOV) of the photodiode (PD) as a cone, the geometry relationship between the sensed LEDs and the UE position and orientation is established. The binary values of whether the LEDs signals are received or not are exploited, and an alternating flashing strategy for the LED arrays is designed to guarantee normal illumination and reduce bandwidth consumption. Moreover, the theoretical lower bound derived from Kanatani-Cramer-Rao bound (KCRB) is provided to analyze optimality of the estimator. Simulation results demonstrate the proposed scheme achieve an excellent performance gain with low complexity over two classical SPO baseline, and the critical factors that affect the performance are investigated.