Jinglin Shi

In this paper, we present a novel orthogonal Doppler frequency modulation (ODFM) to meet the challenging requirements of high-mobility communications. Firstly, we revisit the basic concepts of multicarrier modulations (MCMs) and discuss the capability to explore both time and frequency diversity in doubly selective channels for the most popular MCMs. We then illustrate how ODFM maps information symbols into Doppler-frequency domain and converts them into time-delay domain for transmission, where we show ODFM has the potential to exploit both delay space (frequency) and Doppler space (time) diversity. Specifically, we propose two types of ODFM schemes using the conventional time-frequency domain orthogonal pulses (TFOP-ODFM) and the novel delay-Doppler domain orthogonal pulses (DDOP-ODFM). We give exact channel input-output relations (IORs) in the Doppler-frequency domain of these two ODFM schemes and show their properties in efficient channels, implementation complexity, and others. Subsequently, we simulate the performance of these ODFM schemes with other MCMs, such as orthogonal frequency division multiplexing (OFDM), and orthogonal time frequency space (OTFS) modulation. In particular, we investigate the performance of the TFOP-ODFM in the physical downlink shared channel of the Five-Generation New Radio (5G NR) link, where TFOP-ODFM show better performance than OFDM and OTFS.

In MIMO-OTFS systems, accurate CSI estimation with minimal pilot overhead is crucial. While guard-interval (GI)-based pilot patterns suppress data interference, they substantially increase overhead. To overcome this, we propose an interference-free, low-overhead channel estimation framework that exploits channel sparsity. Specifically, we introduce the delay-Doppler-angle basis expansion model (DDA-BEM) to reformulate the estimation task as a compressed sensing problem. Leveraging the sparsity in the DDA domain, we design a pilot pattern that inherently avoids data interference without requiring GIs. Simulation results demonstrate a 54% reduction in pilot overhead compared to state-of-the-art methods, while maintaining comparable estimation accuracy.

To facilitate the popularization and application of semantic communication, it is crucial to ensure its efficient coexistence with traditional digital communication networks. In this paper, we investigate a non-orthogonal multiple access (NOMA)-enabled heterogeneous semantic communication (SemCom) and bit communication (BitCom) coexistence system under an orthogonal frequency division multiplexing (OFDM) framework. The deployment of fluid antennas at the user side is treated as a crucial strategy to enhance system efficiency and mitigate interference. Within the above OFDM framework, we construct a frequency domain channel with multicarriers, based on the multipath channel characteristics exhibited by different fluid antenna ports. Subsequently, a sum semantic rate maximization problem is formulated by optimizing the subcarrier allocation, power allocation, and the fluid antenna port selection of the users. Note that the derived problem is challenging to solve through conventional methods due to the presence of highly coupled continuous and discrete variables. To address this challenge, we propose a dual proximal policy optimization (DPPO) algorithm as a solution for this problem, which divides the problem into two parts and leverages separate subnetworks to solve each part. Simulation results demonstrate the efficiency of the proposed algorithms in enhancing the sum semantic rate.

Centralized open radio access networks (O-RANs) aggregate computing resources of multiple cells to improve resource utilization and achieve statistical multiplexing gain (SMG), i.e., the ratio of the number of deployed resources (DepR) for distributed radio access network (D-RAN) to that for O-RAN. SMG of O-RAN has been investigated with infinite number of cells with homogeneous bursty-temporal traffic features. However, in practice, the number of cells is limited, and in the era of Internet of Things (IoT), cells with heterogeneous bursty-temporal traffics (het-traffics), should be considered. This paper analyzes the SMG of O-RAN with limited het-traffic cells. First of all, given traffic models of het-traffic cells following different bursty lognormal distribution and periodic time-varying features, required resources (ReqR) for D-RAN can be derived. Then, to obtain the distribution of ReqR for O-RAN with limited het-traffic cells, a Gaussian mixture model (GMM) based resource distribution approximation (GMM-RDA) is proposed. Thus SMG can be obtained for any given service rate. In simulations, the effectiveness of GMM-RDA is verified by Kolmogorov-Smirnov (K-S) test, and the accuracy of achieved SMG can be up to 99.7%, which is about 33% higher than that obtained by the law of large numbers (LLN) for infinite cells. Moreover, het-traffic cells resulting from service diversification can enhance SMG in O-RAN by 34.7%.

Low-orbit satellite communication networks have gradually become the research focus of fifth-generation (5G) beyond and sixth generation (6G) networks due to their advantages of wide coverage, large communication capacity, and low terrain influence. However, the low earth orbit mega satellite network (LEO-MSN) also has difficulty in constructing stable traffic transmission paths, network load imbalance and congestion due to the large scale of network nodes, a highly complex topology, and uneven distribution of traffic flow in time and space. In the service-based architecture proposed by 3GPP, the introduction of service function chain (SFC) constraints exacerbates these challenges. Therefore, in this paper, we propose GDRL-SFCR, an end-to-end routing decision method based on graph neural network (GNN) and deep reinforcement learning (DRL) which jointly optimize the end-to-end transmission delay and network load balancing under SFC constraints. Specifically, this method constructs the system model based on the latest NTN low-orbit satellite network end-to-end transmission architecture, taking into account the SFC constraints, transmission delays, and network node loads in the end-to-end traffic transmission, uses a GNN to extract node attributes and dynamic topology features, and uses the DRL method to design specific reward functions to train the model to learn routing policies that satisfy the SFC constraints. The simulation results demonstrate that, compared with graph theory-based methods and reinforcement learning-based methods, GDRL-SFCR can reduce the end-to-end traffic transmission delay by more than 11.3%, reduce the average network load by more than 14.1%, and increase the traffic access success rate and network capacity by more than 19.1% and two times, respectively.

Task-oriented communications with semantic encoders are promising to enhance the communication efficiency, by selecting and transmitting valuable data according to task requirements/queries. However, existing semantic encoders lack the capability to track the changing in queries, leading to biased data selection. This paper proposes a query-aware semantic encoder, i.e., Query-Data Cross (QDC) encoder for task-oriented communications. By consistently focusing on data features that are most relevant to the current query at the transmitter, QDC can adapt to changing queries. Based on the dynamic semantic relevance obtained by QDC, a relevance-based data selection and bandwidth allocation optimization (RDSBA) problem is formulated, considering a multi-device task-oriented communication system, where devices should transmit valuable data with high relevance to the queries broadcasted by the base station (BS). RDSBA aims to maximize the data profit of all devices, which is defined as the difference between the relevance of data selected for the BS and the cost of obtaining the data. Then, a DRL-based data selection and bandwidth allocation (DRL-DB) algorithm is proposed to solve the NP-hard optimization problem. Simulation results demonstrate that QDC can smartly track the changing in queries and achieve an accuracy of at least 85% in relevance evaluation, more than 8% higher than existing schemes. Based on the relevance provided by QDC, the proposed RDSBA scheme with DRL-DB can increase the data profit by at least 18%, comparing to existing schemes.

Environment-dependent applications, such as HD maps, that rely on environmental information as input data deserve further research to reduce the amount of transmission data. The cooperation of crowd-sensing and local pre-processing is a potential solution to sense and pre-process the environmental data in real-time. However, the sensing and computing capabilities of different participants are heterogeneous, which may lead to significant differences in the total amount of transmission data (TATD). Therefore, a novel communication-efficient participant selection strategy is proposed, incorporating heterogeneous sensing, communication, and computing resources. The matching process between target sensing sub-regions and participants, along with pre-processing task allocation, is jointly optimized to minimize the TATD. A heuristic algorithm with low complexity is developed to solve the optimization problem. Performance evaluations show that the proposed mechanism can reduce the TATD by up to 62.8% compared with benchmark mechanisms.

Occlusion presents a major obstacle in the development of pedestrian detection technologies utilizing computer vision. This challenge includes both inter-class occlusion caused by environmental objects obscuring pedestrians, and intra-class occlusion resulting from interactions between pedestrians. In complex and variable urban settings, these compounded occlusion patterns critically limit the efficacy of both one-stage and two-stage pedestrian detectors, leading to suboptimal detection performance. To address this, we introduce a novel architecture termed the Attention-Guided Feature Enhancement Network (AGFEN), designed within the deep convolutional neural network framework. AGFEN improves the semantic information of high-level features by mapping it onto low-level feature details through sampling, creating an effect comparable to mask modulation. This technique enhances both channel-level and spatial-level features concurrently without incurring additional annotation costs. Furthermore, we transition from a traditional one-to-one correspondence between proposals and predictions to a one-to-multiple paradigm, facilitating non-maximum suppression using the prediction set as the fundamental unit. Additionally, we integrate these methodologies by aggregating local features between regions of interest (RoI) through the reuse of classification weights, effectively mitigating false positives. Our experimental evaluations on three widely used datasets demonstrate that AGFEN achieves a 2.38% improvement over the baseline detector on the CrowdHuman dataset, underscoring its effectiveness and potential for advancing pedestrian detection technologies.

Collaborative mobile edge computing enables resource-constrained edge facilities to work cooperatively for computation-intensive tasks. However, as the number of tasks demanded by various applications increases, resource competition is inevitable in edge facilities. Existing works tackle the resource competition problem with a first come first served (FCFS) scheme, which is blind to different delay requirements among tasks. This may result in tasks with higher delay requirements waiting a long time for service, thereby reducing overall service quality. This paper proposes a prioritized queuing scheme with task dependency (PQTD), which allows high-prioritized sub-tasks with higher delay requirements to jump into the queue ahead of low-prioritized sub-tasks with lower delay requirements. To describe the complicated delay change caused by queue-jumping, a joint DAG-queue delay (JDQD) model is proposed, which analyzes the chain reaction of delay changes caused by the processing queue on the server and the task dependency. With JDQD, a multi-task assignment optimization problem is formulated to maximize the average satisfaction degree (AvgSatD), which is defined according to the priorities of the tasks and their delay requirements. Then, a tree-based algorithm is proposed to solve the NP-hard optimization problem, i.e., Monte Carlo Tree Search (MCTS). Simulation results demonstrate the effectiveness of the PQTD queuing scheme and tree search mechanism of MCTS. Overall, PQTD + MCTS can increase AvgSatD by at least 45.8% with an acceptable complexity.