With the development of deep learning technologies, the accuracy of trajectory prediction has significantly improved. Leveraging predicted trajectories for motion planning can reduce the risk of collisions between vehicles more effectively. However, prior studies have shown that directly using predicted trajectories in planning often leads to suboptimal performance and may even cause collisions. To address this, we propose a novel framework that integrates trajectory prediction with artificial potential fields (APFs), enabling the effective use of prediction results in planning. In this paper, we introduce a trajectory planning framework that constructs a spatiotemporal risk (STR) field based on deep learning-based trajectory prediction and inter-vehicle interactions. The planning module then employs an optimization-based approach that considers multiple objectives, including safety, comfort, rule compliance, efficiency, and human likeness. Evaluated on a real-world dataset, our method achieves an 8.72% improvement in minimum Anticipated Collision Time ($\text{ACT}\_{\min }$) and a 36.0% gain in efficiency compared to the baseline Driving Safety Field (DSF) method. Compared with other competing approaches, our method improves $\text{ACT}\_{\min }$ by 10.67% while maintaining competitive performance in terms of comfort, efficiency, and human likeness. The results from the NuPlan closed-loop experiments indicate that our method exhibits superior planning capabilities in complex scenarios, surpassing the performance of all baseline methods. Furthermore, closed-loop experiments demonstrate that our approach enables continuous and collision-free planning in highly interactive traffic scenarios.

This paper studies an internet of things (IoT) network where a fusion center relies on multi-view and correlated information generated by multiple sources to monitor various regions. Each region possesses hard age of correlated information (AoCI) constraints for information update, and accordingly we propose a scheduling policy to satisfy such needs and minimize the required wireless resources. We first approximate the problem to a dual bin-packing problem. Secondly, efficient scheduling policies are identified when the age constraints possess special mathematical properties, where the number of channels at most required is analyzed. Optimality conditions of the proposed policies are presented. For general constraints, a two-step grouping algorithm for multi-view (TGAM) is proposed to establish scheduling policies. Under TGAM, the constraints are mapped into a combination of the special constraints. To quickly identify an optimized mapping from a vast solution space, TGAM heuristically groups the regions according to their constraints and then searches for the optimal mapping for each group. Numerical results demonstrate that, compared to a derived lower bound, the proposed TGAM requires only 1.07% more channels. Additionally, the number of regions that can be served by TGAM is significantly larger than the state-of-the art algorithm, given the number of channels.

As the rapid-spreading disease COVID-19 occupies the world, most governments adopt strict control policies to alleviate the impact of the virus. These policies successfully reduced the prevalence and delayed the epidemic peak, while they are also associated with high economic and social costs. To bridge the microscopic epidemic transmission patterns and control policies, simulation systems play an important role. In this work, we propose an agent-based disease simulator for indoor public spaces, which contribute to most of the transmission in cities. As an example, we study Guangzhou Baiyun International Airport, which is one of the most bustling aviation hubs in China. Specifically, we design a high-efficiency mobility generation module to reconstruct the individual trajectories considering both lingering behavior and crowd mobility, which greatly enhances the credibility of the simulated mobility and ensures real-time performance. Based on the individual trajectories, we propose a multi-path disease transmission module optimized for indoor public spaces, which includes three main transmission paths as close contact transmission, aerosol transmission, and object surface transmission. We design a novel convolution-based algorithm to mimic the diffusion process, which can leverage the high concurrent capability of the graphics processing unit to accelerate the simulation process. Leveraging our simulation paradigm, the effectiveness of common policy interventions can be quantitatively evaluated. For mobility interventions, we find that lingering control is the most effective mobility intervention with 32.35% fewer infections, while increasing social distance and increasing walking speed have a similar effect with 15.15% and 18.02% fewer infections. It demonstrates the importance of introducing crowd mobility into disease transmission simulation. For transmission processes, we find the aerosol transmission involves in 99.99% of transmission, which highlights the importance of ventilation in indoor public spaces. Our simulation also demonstrates that without strict entrance detection to identify the input infections, only performing frequent disinfection cannot achieve desirable epidemic outcomes. Based on our simulation paradigm, we can shed light on better policy designs that achieve a good balance between disease spreading control and social costs.

Graphics processing units (GPUs) can improve deep neural network inference throughput via batch processing, where multiple tasks are concurrently processed. We focus on novel scenarios that the energy-constrained mobile devices offload inference tasks to an edge server with GPU. The inference task is partitioned into sub-tasks for a finer granularity of offloading and scheduling, and the user energy consumption minimization problem under inference latency constraints is investigated. To deal with the coupled offloading and scheduling introduced by concurrent batch processing, we first consider an offline problem with a constant edge inference latency and the same latency constraint. It is proven that optimizing the offloading policy of each user independently and aggregating all the same sub-tasks in one batch is optimal, and thus the independent partitioning and same sub-task aggregating (IP-SSA) algorithm is inspired. Further, the optimal grouping (OG) algorithm is proposed to optimally group tasks when the latency constraints are different. Finally, when future task arrivals cannot be precisely predicted, a deep deterministic policy gradient (DDPG) agent is trained to call OG. Experiments show that IP-SSA reduces up to 94.9% user energy consumption in the offline setting, while DDPG-OG outperforms DDPG-IP-SSA by up to 8.92% in the online setting.

Individual trajectory generation plays an important role in simulation tasks, reconstructing fine-grained mobility behaviors that can be used to evaluate epidemic risks, congestion risks, or commercial profit. Previous research works adopt the Newton’s mechanic-based particle model as their core algorithm, such as the Social Force model. However, real-world human mobility behaviors hardly follow the particle models, especially in the interior scenes where interactions between pedestrians and environments matter. In this article, we propose a Social Force-based trajectory simulator for interior scenarios that improve both trajectory quality and generation speed for interior scenarios. First, we introduce prior scene knowledge to guide the generation process, where pedestrians are armed with exploration behaviors that follow the group-level distribution. It provides more flexibility to simulate complicated human behaviors rather than straight-line movements, generating high-quality individual trajectories. Experiments show that the correlation between the aggregated population distribution of generated trajectories and ground-truth distribution is improved by 11.84% by our method. Second, we optimize the algorithm procedure by introducing a caching mechanism for tenderized intermediate values, along with graph-processing-unit-based implementation. Compared with the baseline Social Force model, we reduced the time consumption by 95%. More importantly, based on our simulation paradigm, we quantitatively evaluate several common mobility interventions in our simulation scenario, which can shed light on better policy designs in public spaces.

Obtaining crowd flow distribution with recognized human intention is extremely valuable for a series of applications for metropolitan cities. Previous solutions look at spatial correlation and temporal periodicity based on historical crowd flow information to calculate future crowd flow distribution. However, these mechanisms cannot recognize the intention behind crowd flow. We address this problem by leveraging a key insight – people's intention behind their movement is highly correlated with the point-of-interest (POI) distribution of the corresponding regions and adjacent regions. Therefore, we propose DeepFlowGen to model the complicated relationship between crowd flow, POI, check-ins, and time to generate intention-aware crowd flow. Specifically, we solve the conflict between dynamic crowd flow and static POI distribution by fusing the information in both time and POI domains. Besides, we employ a sequence of residual blocks in DeepFlowGen to address the challenges of modeling the diverse temporal rhythms and heterogeneous influence of POI. Furthermore, we examine the generated intention-aware crowd flow from two aspects to substantiate the reasonability of DeepFlowGen . Extensive experiments demonstrate that our model outperforms the state-of-the-art solutions by at most 30 percent in terms of NRMSE of total crowd flow. Moreover, the correlation between the generated intention-aware crowd flow and the check-in distribution across different categories of POIs is as high as 0.90 and 0.80 in Beijing and Shanghai. Combined with extensive case studies, we demonstrate the strong ability of our model in generating intention-aware crowd flow.

In federated learning (FL), devices contribute to the global training by uploading their local model updates via wireless channels. Due to limited computation and communication resources, device scheduling is crucial to the convergence rate of FL. In this paper, we propose a joint device scheduling and resource allocation policy to maximize the model accuracy within a given total training time budget for latency constrained wireless FL. A lower bound on the reciprocal of the training performance loss, in terms of the number of training rounds and the number of scheduled devices per round, is derived. Based on the bound, the accuracy maximization problem is solved by decoupling it into two sub-problems. First, given the scheduled devices, the optimal bandwidth allocation suggests allocating more bandwidth to the devices with worse channel conditions or weaker computation capabilities. Then, a greedy device scheduling algorithm is introduced, which selects the device consuming the least updating time obtained by the optimal bandwidth allocation in each step, until the lower bound begins to increase, meaning that scheduling more devices will degrade the model accuracy. Experiments show that the proposed policy outperforms state-of-the-art scheduling policies under extensive settings of data distributions and cell radius.

Deep neural networks (DNNs) are state-of-the-art solutions for many machine learning applications, and have been widely used on mobile devices. Running DNNs on resourceconstrained mobile devices often requires the help from edge servers via computation offloading. However, offloading through a bandwidth-limited wireless link is non-trivial due to the tight interplay between the computation resources on mobile devices and wireless resources. Existing studies have focused on cooperative inference where DNN models are partitioned at different neural network layers, and the two parts are executed at the mobile device and the edge server, respectively. Since the output data size of a DNN layer can be larger than that of the raw data, offloading intermediate data between layers can suffer from high transmission latency under limited wireless bandwidth. In this paper, we propose an efficient and flexible 2-step pruning framework for DNN partition between mobile devices and edge servers. In our framework, the DNN model only needs to be pruned once in the training phase where unimportant convolutional filters are removed iteratively. By limiting the pruning region, our framework can greatly reduce either the wireless transmission workload of the device or the total computation workload. A series of pruned models are generated in the training phase, from which the framework can automatically select to satisfy varying latency and accuracy requirements. Furthermore, coding for the intermediate data is added to provide extra transmission workload reduction. Our experiments show that the proposed framework can achieve up to 25.6X reduction on transmission workload, 6.01X acceleration on total computation and 4.81X reduction on end-to-end latency as compared to partitioning the original DNN model without pruning.

In this paper, we present a user-complaint prediction system for mobile access networks based on network monitoring data. By applying machine-learning models, the proposed system can relate user complaints to network performance indicators, alarm reports in a data-driven fashion, and predict the complaint events in a fine-grained spatial area within a specific time window. The proposed system harnesses several special designs to deal with the specialty in complaint prediction; complaint bursts are extracted using linear filtering and threshold detection to reduce the noisy fluctuation in raw complaint events. A fuzzy gridding method is also proposed to resolve the inaccuracy in verbally described complaint locations. Furthermore, we combine up-sampling with down-sampling to combat the severe skewness towards negative samples. The proposed system is evaluated using a real dataset collected from a major Chinese mobile operator, in which, events due to complaint bursts account approximately for only 0.3% of all recorded events. Results show that our system can detect 30% of complaint bursts 3 h ahead with more than 80% precision. This will achieve a corresponding proportion of quality of experience improvement if all predicted complaint events can be handled in advance through proper network maintenance.

The invention provides a cellular network synchronization method under a separation architecture, and synchronization devices. A control base station receives a GNSS signal to obtain high-precision time information, and a clock of the control base station is adjusted; the control base station and a service station are in wired connection for transmitting and receiving information, the control base station generates data including network time information required by a mobile user, and transmits the data to a mobile user terminal through an antenna of the control base station; the service base station calculates time offset of the service base station through signaling interaction with the control base station, so as to adjust a clock of the service base station; and the mobile user terminal receives the data including the network time information transmitted by the control base station and data including network time adjustment information transmitted by the service base station, and adjusts a clock of the mobile user terminal based on the network time information and the network time adjustment information.

In this letter, we propose a novel precoding scheme for base station (BS) cooperation in downlink cellular networks that allow overlapped clusters. The proposed precoding scheme is designed to mitigate the overlapping-BS interference by maximizing the so-called clustered virtual signal-to-interference-plus-noise ratio (CVSINR). Simulations show that with the proposed scheme, overlapped clustering provides substantial throughput gain over the traditional non-overlapped clustering methods, and user fairness is also improved.

PROBLEM TO BE SOLVED: To provide a radio communication method, a radio base station, and a radio communication system, capable of reducing signaling and resource consumption for use for the calculation of base station clustering, by selecting optimal base station clustering according to a communication state between a terminal and each base station.SOLUTION: In the radio communication method of the present invention, a first base station communicating with the terminal transmits a request message for consulting cooperative transmission to a second base station that communicates with the terminal in cooperation with the first base station. On the basis of the received request message, the second base station transmits response signaling to the first base station.

The invention provides a communication system and a management device which can adjust and control the communication ranges of base stations in a mobile communication network. The communication system is provided with a plurality of base stations which communicate with terminals, and is characterized by being also provided with the management device for controlling the communication ranges of the base stations, wherein the management device controls the communication range of one base station and/or the communication ranges of other base stations according to the communication request amount of terminals contained in the communication range of the base station in the plurality of base stations and the communication amount of the base station, and/or the communication request amount of terminals contained in the communication ranges of the other base stations adjacent to the base station and the communication amount of the other base stations.

We consider multiple base station (BS) cooperative transmission in downlink cellular networks to improve the spectral efficiency and the system capacity. Grouping BSs into clusters is a practical solution to realize BSs cooperation and reduce system complexity. However, it still suffers from inter-cluster interference, especially for the cluster-edge users. In this paper, clustering and scheduling are jointly considered to deal with the problem. The clusters are formed dynamically from users' point of view to minimize the inter-cluster interference, and are allowed to be overlapped. Accordingly, coordinated precoding scheme is designed to manage the intra-cluster interference. A greedy scheduling algorithm is proposed jointly with dynamic clustering. Simulations show that the proposed joint algorithm provides impressive average throughput gain over the non-joint ones, and the user fairness is improved significantly.

Disclosed are a communication control device, a communication control method and the system thereof, which provide efficient multi-base station joint services in a mobile communication system using dynamic clustering. Optimal base station clustering is chosen according to the communication state between a terminal and each base station, and the signaling and resources cost for calculating a base station clustering is reduced. The communication control device connects with multiple base stations through a network, and clusters the multiple base stations dynamically so that the clustered multiple base stations can provide services for the mobile terminal in union. The communication control device comprises: an interface, connecting with multiple base stations, receiving channel state information (1512) related to the mobile terminal from each base station; a memory unit, storing the channel state information (1512) of the mobile terminal received from the interface; a control unit, clustering each base station dynamically according to the channel state information (1512) of the mobile terminal stored in the memory unit.