Intelligent perception among multiple agents enables them to extend their individual observation capabilities by sharing sensory information, thereby improving the completeness and accuracy of environmental understanding. However, real-world communication is often subject to non-negligible delays, which can degrade the effectiveness of perception. To mitigate this, delay alignment is commonly employed to synchronize delayed observations to a common timestamp. Yet, both alignment errors and inherent discrepancies between multi-view observations can lead to inconsistencies in the estimated position and orientation of shared targets. These inconsistencies can accumulate during feature fusion, ultimately reducing the accuracy and reliability of the perception results.To address this challenge, we propose IPDA, a delay-aware multi-agent intelligent perception method that performs joint calibration in both temporal and spatial domains. In the temporal dimension, we design a historical alignment attention mechanism to model dynamic delay correction across sequences, ensuring temporal coherence. In the spatial dimension, we introduce a discrepancy-quantized co-sensing network that captures and compensates for multi-view spatial deviations caused by viewpoint diversity and alignment inaccuracy. IPDA is evaluated on two large-scale intelligent perception benchmarks, DAIR-V2X and OPV2V. Experimental results demonstrate that our method effectively mitigates delay-induced inconsistencies and consistently outperforms state-of-the-art baselines under various delay conditions.

Motor imagery electroencephalography (MI-EEG) decoding demonstrates significant potential for paralysis rehabilitation, and its generalization capability is often compromised by intersubject variability and scarcity of labeled target domain data. Meta-learning has emerged as a promising approach for unsupervised domain adaptation problem. However, existing implementations suffer from two critical limitations: insufficient feature extraction and overlooking the guiding role of unlabeled target data. To overcome these challenges, we propose a dual-attention meta-learning framework (DA-META) with model-agnostic architecture in this paper. The framework comprises three stages: meta-task construction, guided meta-training, and fine-tuning-free meta-testing. In the guided meta-training stage, DA-META incorporates two key attention mechanisms: an enhanced temporal attention module for effective feature extraction, and a cosine similarity-based attention module to leverage the guidance of target domain. Using EEGNet as the backbone network, DA-META achieves mean classification accuracies of 68.04% and 76.61% on self-collected datasets from patients and healthy subjects, and 73.29% and 80.93% on the public BCI Competition IV 2a and 2b datasets, outperforming state-of-the-art methods. When employing EEGNet, DeepConvNet, and EEG Conformer as backbone networks respectively, the framework achieves accuracy improvements of 5.17%, 2.56%, and 0.85% on the 2a dataset, compared to the baseline. These results demonstrate the framework's superior ability to handle inter-subject variability and its significant potential to improve practical applicability.

In this paper, we study the problem of mobile user pairing in non-orthogonal multi-access (NOMA) backscatter communication networks, in which re-pairing users after a change in the communication link can bring significant computational overhead. Firstly, We propose a user pairing scheme that employs the Kuhn-Munkres (KM) algorithm for the uplink of the monostatic backscatter communication network, which can effectively avoid the problem of frequent sorting mobile users. Secondly, to address the NOMA principle violations problem (NPVP) caused by mobile users, we propose a modified role switching (RS) technique based on the difference of user channel gain in which paired users switch their roles based on the magnitude of their channel gain after moving. Finally, we propose a dynamic power allocation scheme for mobile users to maximize the system throughput after pairing. In backscatter communication, the difference in power levels between paired users is achieved by setting different reflection coefficients. At the same time, it is necessary to consider the balance between data transmission and energy harvesting, and the reflection coefficient must not exceed the predetermined upper limit. Simulation results indicate that our proposed user pairing scheme outperforms conventional schemes in terms of the number of decoded users and system throughput.

Millimeter wave radar-based human activity recognition (HAR) technology has received much attention as a research hot-spot in recent years. Previous researches have demonstrated the feasibility of using millimeter wave radar for HAR. While existing work has achieved excellent performance in ideal environments, its application in life is still limited due to the intensive data collection required, the additional training needed to adapt to new domains (i.e., environments, people, and locations), and the high computational complexity associated with voxelization. To solve the above problems, we propose the radar point cloud recognition system RPCRS, which is capable of accurately recognizing human activities from noisy environments, has promising recognition performance for new users, environments and locations, and significantly reduces the computational overhead during system training. Firstly, RPCRS use the velocity information of the clustered point cloud data to extract the human activity subjects from the noisy background. Then, the size of the extracted non-uniform point cloud data is unified by removing or adding the number of point clouds. Secondly, in order to enhance the robustness of the system and reduce the data collection effort, we designed a data enhancement framework based on correlation between point cloud data and human activity changes. Finally, a lightweight neural network based on a multilayer perceptron (MLP) is used to classify the raw point cloud data of human activities, which reduces the computational complexity and memory requirements associated with voxelization. We evaluate our system with 5 different activities, which attains average accuracy of 95.40%. In addition, we evaluate the performance of the system in a new environment and with new users, which obtains an average accuracy of 94.53% and 95.08%, respectively.

In recent years, in-vehicle applications based on vehicular ad hoc networks (VANETs) have been continuously expanded. Many applications not only focus on delay and effective forwarding rate but also pay more attention to routing path multiplexing and throughput. However, in VANETs, it is challenging to establish real-time and robust multihop forwarding paths due to volatile topological information, disconnected network, churn rate, etc. In order to adapt to the new development trend of VANETs, a self-healing routing strategy (SR) with the ant colony optimization (ACO) is proposed in this article. SR introduces the ACO algorithm to establish routing paths to ensure connectivity and immediacy. The routing-build-ability (RBA) is defined to measure the forwarding capability of a vehicle. The RBA is derived from the delay and packet delivery ratio (PDR) using the fuzzy logic system, which can reduce the computational complexity. To reduce the overhead of path reconstruction performed due to path disconnection, in-road-repairing and intersection-repairing methods are proposed in this article, which prolong the duration of the optimal path and improve throughput. The simulation results and mathematical analyses demonstrate that the feasible SR can reduce the delay by 30%, shorten the time overhead to one sixth, promote the routing duration by three times, and enhance the throughput by three times.

In this paper, we investigate the user pairing schemes for NOMA-enabled backscatter communication network. Since the successful decoding of high channel gain users can significantly improve the system performance, to make more high channel gain users be decoded successfully, we propose a pairing scheme, M-UPS, which is based on the user channel gain difference. Firstly, in the case of two-user pairing, we divide users into the high channel gain group and the low channel gain group based on channel gain, then we provide a formula of the pairing distance threshold for the high channel gain user to select low channel gain users for pairing. Secondly, we extend the proposed paring scheme to the multi-user case, a dynamic user pairing scheme, which can adaptively form the different pairs with different numbers of users based on the channel gain differences. Simulation results show that M-UPS can outperform the Conventional-NOMA (C-NOMA), Uniform Channel Gain Difference-NOMA (UCGD-NOMA), and OMA in terms of the number of paired users and total throughput.

Much recent effort has been devoted to employing Unmanned Aerial Vehicles (UAVs) to implement airport-related tasks. However, a critical issue, collision avoidance, must be fully considered in this scenario. Herein, we study the efficient UAV navigation problem considering the safety issue near an airport. In detail, we first define the safe separation between the UAV and airplanes according to related aviation regulations. Thereafter, an effective tree-based scheme for navigating the UAV has been proposed to cope with the extra uncertainties induced by keeping the safe separation. An analytical derivation of the UAV's flying time is conducted to determine the optimal battery life. Extensive simulation is conducted to verify our proposed navigation scheme and the analytical derivation.

Interference alignment (IA) is a promising technique to effectively manage the interference. The realization of IA requires a proliferation of feedback bits. In a general centralized feedback model, the feedback rate of the precoder and the decoder affects the performance of the IA directly. In this paper, to improve the feedback efficiency of the precoder and the decoder, a strategy that can adaptively allocate the feedback bits of the precoder and the decoder is proposed. We consider the effect of link loss on the throughput loss caused by the quantization error of the precoder and decoder. An upper bound of leaked interference as a function of the link loss and the feedback bits of the precoders and the decoders is derived. The feasibility conditions for dynamically allocating the feedback bits of the precoder and the decoder are analyzed. It is proven that the properties of general asymmetric interference network can satisfy the feasibility conditions for dynamic feedback scheme. According to the simulation results, our proposed scheme achieves higher throughput compared with the conventional schemes in the asymmetric interference network.

Unmanned Aerial Vehicles (UAVs) have wide applications in many fields, e.g. multiple Unmanned Aerial Vehicles (UAVs) cooperatively tracking multiple targets. This paper studies the multiple UAVs cooperatively tracking multiple targets by vision surveillance system, where the images/videos of targets have direction requirements. One target is covered by a UAV if and only if its position is within the Field Of View (UAV) as well as the UAV is within a requested angle of the target's face direction. The objective is to maximize the total covered targets number by the UAVs, which can not be solved by existing models. A simple effective distributed, online cooperation algorithm for this problem is designed in this paper. The theoretical analysis shows our algorithm achieves constant factor to the optimal.

People's growing dependency on the Internet makes them want to access the network anytime and anywhere, even while driving cars. Although 3G or 4G networks can be used to achieve this goal, roadside APs have the advantages of low cost and high bandwidth. At present, however, APs cannot cover all the areas, which cause intermittent connectivity. In this paper, we propose the DSRelay, a scheme of cooperative downloading. Unlike some studies that multiple cars download files from the Internet and different cars deliver different blocks of files like a P2P network, our work concentrates on the request of downloading respectively. Making use of the characters of the limited communication range of WIFI network, DSRelay schedules the Dark Areas (DA) out of AP coverage areas. If a mobile client leaves an AP coverage area without finishing its downloading, using the proposed scheme, APs calculate meeting time and communication duration between client car and other registered cars, and choose a series of cars to carry data client needed so that the data can be transmitted to it at different slot in DA. Also, we analyze the influence of change of car speed and a corresponding solution is presented. This scheme utilizes DAs for extending the downloading areas of the client as much as possible. Simulation results indicate the benefits of the proposed scheme in terms of increasing throughput and reducing delay.