Utility-Aware Resource Allocation for Multigroup Collaborative Perception System

Joint routing and pricing control in congested mixed autonomy networks

The Perception–Decision–Control (PDC) pipeline forms the core architecture enabling environmental perception, path planning, and trajectory execution in Connected and Autonomous Vehicles (CAVs). Uncertainty, as a key factor limiting system reliability and safety, permeates all stages of this pipeline, continuously generation, evolution, propagation, and amplification along the pipeline. This paper focuses on Link-Level Uncertainty management and systematically reviews modeling and mitigation strategies for uncertainty in the PDC pipeline. First, this paper distinguishes between externally induced and internally modeled perception uncertainties. Then, this paper analyzes the evolution of upstream uncertainty within the decision-making module, highlighting modal conflicts and policy ambiguity in trajectory reasoning and intent inference. Subsequently, this paper investigates the downstream propagation of decision uncertainty into the control stage, leading to distorted execution signals and reduced stability. Finally, this paper summarizes mainstream mitigation strategies including perception confidence modeling, robust decision optimization, and fault-tolerant control execution. In contrast to traditional module-specific approaches, future research should emphasize system-level modeling of chain-wise uncertainty evolution, develop cross-layer collaborative representation and optimization mechanisms, and construct end-to-end risk-aware frameworks to ensure safe and robust CAV operation in complex, dynamic environments.

Empowering collaborative knowledge construction through the implementation of a collaborative argument map tool

A novel energy-aware utility maximization for efficient device-to-device communication in LTE-WiFi networks under mixed traffic scenarios

AI-based resource allocation techniques in D2D communication: Open issues and future directions

Perception systems in single-vehicle autonomous driving face inherent limitations under challenging conditions such as adverse weather, severe occlusions, and non-line-of-sight (NLOS) scenarios. These constraints significantly compromise the safety and reliability of autonomous vehicles. Collaborative perception, enabled by vehicle-to-vehicle (V2V) communication, offers a promising solution by integrating multi-view and multi-location sensor data to construct a global environmental awareness beyond the perception capability of any single vehicle. It is considered a critical pathway toward high-level autonomous driving. Although collaborative perception is key to enhancing the robustness and sensing range of autonomous systems, most existing studies rely on simulations or data collected in simplified scenarios. There remains a notable lack of validation in complex, dynamic real-world environments. To address this gap, this paper introduces CampusSyn, a large-scale collaborative perception dataset collected in a challenging university campus setting. CampusSyn is characterized by its authentic collaborative setup and rich environmental complexity. The dataset was constructed using two platforms equipped with heterogeneous sensors, including LiDAR and GPS. It offers over 1,000 meticulously annotated frames with more than 6,000 3D bounding boxes. The annotations cover multiple categories such as cars and vans, and include object tracking IDs as well as attribute labels.

Collaborative cloud-edge-end computing is a promising solution to support computation-intensive and latency-sensitive tasks by utilizing rich computing resources of cloud datacenters and low access delay of mobile edge computing (MEC) servers. Compared with traditional cloud computing and MEC, the cloud-edge environment has a stronger heterogeneity of servers and networks, resulting in significant differences between servers in the computation speed and access delay. However, few studies on cloud-edge-end task offloading focused on the characteristic of 5G heterogeneous networks in the cloud-edge environment. In this paper, we study the task offloading problem for collaborative cloud-edge-end computing in MEC-enabled small cell networks with low-cost distributed wireless backhaul. We aim to minimize the energy consumption of all user devices (UDs) via jointly optimizing the offloading decision, UDs’ transmission power, and the allocation of spectrum and computation resources. To solve the non-convex problem, we decouple the original problem into three subproblems, and design an efficient method with solving these three subproblems iteratively to obtain a high-quality solution. The simulation results indicate that our proposed method can lead to significant reduction in the energy consumption of all UDs compared with other conventional methods.

To fully utilize the existing optical network resource, the dynamic traffic grooming technology, which could efficiently multiplex many low-speed services arriving dynamically onto high-capacity optical channels, has been studied extensively and used widely. In this article, the problem of dynamic traffic grooming in joint optimization of IP over elastic optical network (EON) is studied. This problem consists of three sub-problems at two layers: the routing problem at the IP-layer (IPR), the routing, modulation level (RML), and the spectrum allocation (SA) problems at the optical layers. We proposed a novel topology-integration and adaptive-modulation based in cross-layer routing spectrum allocation (CL-RSA) algorithm, which follows a multi-cost approach that solves the IPR, the RML, and the SA problems jointly. The most important features of the proposed algorithm are merging physical topology with virtual topology and using the minimum-weight preferred method for route selection. Meanwhile, the effect of physical-layer impairments (PLI) on signal transmission is considered. In addition, the adaptive modulation scheme is used to allocate the spectrum resource. The simulation results show that the algorithm can effectively improve the spectrum resource utilization and reduce the network blocking probability compared to other algorithms in various network conditions, especially in the high load condition effectively.

In this paper, the problem of joint task, power, and spectrum allocation is studied for a mobile edge computing (MEC) based small cell network (SCN). In the considered network, each small base station (SBS) that is connected with an MEC server provides commutation service to users while each MEC server provides computation service to users, who will request various types of computational tasks including: collaboration computational task performed at both the user and the MEC sever, edge computational task processed at the MEC server, and local computational task implemented by user itself. As the data size of each computational task requested by each user varies, one must reconsider resource (transmit power and subcarrier) allocation and task partition to effectively service the users. We formulate this problem as an optimization problem aiming to minimize the transmission and computational delay. To solve this problem, a multiple stack Q-learning method is proposed. The proposed method can use multiple stacks to record the wireless environment and users' information so as to avoid repeatedly learning the same information and, thus improving the learning efficiency and speeding up the convergence. Simulation results show that the proposed resource allocation scheme provides 79\% and 63\% gains in terms of sum delay compared to cases that fully computed at user and fully computed at the MEC server, respectively.

In this paper, we propose a dynamic spectrum allocation (DSA) scheme DeepBlocks at the backdrop of sixth-generation (6G) communication networks that address the challenges of fixed spectrum allocations (FSA). The scheme exploits the advantages of deep-Q-network (DQN) and minimizes the search state explosion through a reward-penalty framework. A dynamic allocation of unallocated resource blocks (RBs) to mobile units (MUs) is carried out and once the allocation of RBs is complete, we integrate blockchain (BC) to record the transactional ledgers. The resource usage of MUs is recorded through smart contracts (SCs). We model the proposed scheme as a convex optimization problem, and subproblems are decomposed into a Pareto-optimal solution via Techebyecheff decomposition. In the simulation, we compare our scheme against FSA, and fifth-generation (5G) based DSA schemes like reinforcement learning (RL), deep neural networks (DNN)-based, and duelling DQN based schemes. The comparative analysis of 6G-DQN is modeled in terms of reward formulation, scalability of 6G-DQN-assisted DSA, and profit scenarios of BC-based allocation through intelligent channel control. The scheme proposes significant findings, with the best fit learning rate of 0.0001, and takes 500 episodes to converge to 60 total resource blocks. The servicing latency of the scheme is 272.4 ms, compared to 2010 ms in the duelling DQN approach. In spectrum allocation, an improvement of 26.32% is observed against non-DQN approaches, and 13.57% in the fairness parameter for spectrum allocation due to BC inclusion. The findings present the scheme efficacy for DSA over the aforementioned conventional approaches.

Recently, the 5G as the next-generation network is a popular research and discussed widely. The architecture of 5G is a heterogeneous network, and it can support more networked types, like the ultra-dense network, traditional cellular network, and Machine to Machine communication. Although the high frequency and larger bandwidth have been using in 5G, resource allocation is still a critical issue that needs to be discussed and solved. Consider the spectrum resource is limited, but almost all users hope that equipment can get a better quality of services. Therefore, how to manage the spectrum resource and allocation is a big problem. Consider the fast-growing devices and traffic in the future; hence, task scheduling for UEs to reduce energy consumption will be focused on. To solve resource allocation and minimise energy consumption, the Markov decision chain is proposed to be used to predict the channel state. The modified particle swarm optimization (MPSO) is also used in this paper to find the best task scheduling. The simulation will be used to verify the performance of the mechanism that is used and compare it with PSO and first-in-first-service (FIFS). The result shows the method used can be scheduled for the task efficiently.

Efficient spectrum and resource allocation remains a fundamental challenge in fifth generation (5G) wireless networks due to heterogeneous traffic requirements, spectrum scarcity, and dynamic interference conditions. Conventional scheduling and optimization techniques struggle to meet the stringent quality of service (QoS) demands of enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), and massive machine-type communication (mMTC). This paper presents an improved deep reinforcement learning (DRL)-based optimization framework for adaptive spectrum and resource allocation in 5G networks. The proposed model integrates Deep Q-Network (DQN), Double DQN (DDQN), and DDQN with Prioritized Experience Replay (PER) to enhance convergence speed, stability, and scalability. A realistic simulation environment combining ray-traced channel data and packet-level traffic modeling was developed to evaluate system performance. Results demonstrate that the proposed DRL framework achieves up to 7% throughput improvement over proportional fair scheduling, reduces average latency by 10–12%, and lowers URLLC violation rates by approximately 15% under high-interference and heterogeneous traffic scenarios. These findings confirm the feasibility of DRL as a practical and scalable solution for real-time 5G resource management.

Due to the scarcity of spectrum resources in Non-orthogonal Multiple Access (NOMA) systems and insufficient satellite-ground integration in satellite Internet of Things (IoT), this paper investigates its issue in spectrum resource management. We propose a resource allocation method based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG) for NOMA enabled satellite IoT. We formulate the spectrum allocation problem of the satellite-ground integrated network as a distributed optimization problem. Then we decouple the problem into two sub-problems. Firstly, a user grouping method based on matching coefficients is defined, and a Linear Programming (LP) method is utilized for obtaining solution. Secondly, the power allocation problem is transformed into a multi-agent problem, where MADDPG is employed to allocate the power. Through this approach, the system is capable of real-time user association and spectrum resource allocation optimization, achieving optimal user grouping while maximizing system transmission rate. Based on the simulation results, the MADDPG-based method demonstrates fast convergence within 100 training iterations. The proposed MADDPG-based resource management method also achieves increased system transmission rate with more effective matching outcomes over Deep Deterministic Policy Gradient (DDPG), Orthogonal Multiple Access (OMA), and random allocation baselines.

Energy harvesting (EH) endows device-to-device (D2D) communication and cellular equipment with the ability of continuous communication to provide internet-of-things (IoT) services in natural areas. While the available energy, which relies on EH, becomes an extra nonnegligible factor in resource allocation. Besides, we integrate uplink non-orthogonal multiple access (NOMA) with D2D communication to provide multiple access for D2D transmitters for more efficient IoT service and more efficient utilization of limited spectrum. In this scenario, ingenious resource allocation approach is a key focus for utilizing the advantages in energy and spectral efficiency. Aiming to investigate the inherent resource allocation issue, we set our goal as maximizing the energy efficiency for both NOMA-based D2D groups and cellular users (CUs), where the power and spectrum allocation are both considered. Then we propose a two-stage game approach, which is theoretically proved to be capable of obtaining the equilibrium and a stable result, to solve the unilateral energy efficiency maximization problems. Besides, an energy-aware screening method is proposed to reduce the computations based on the available energy of user equipment. Finally, the effectiveness of our proposed method is verified through elaborated simulation results.

A sensing and etiquette reputation-based trust management for centralized cognitive radio networks