2026· IEEE Transactions on Cognitive Communications and Networking· Vol 12, pp. 10232-10248· 0 citations· 47 references
Abstract
The emergence of sixth-generation (6G) communication systems promotes an integrated sensing and communications (ISAC) framework to address growing demands for seamless connectivity. This paper proposes a cooperative resource allocation method (CRAM) that enables uninterrupted service for mobile terminals (MTs) moving across heterogeneous base stations (BSs) without resource handovers—inspired by a spotlight tracking a moving actor. Specifically, we tackle the challenge of dynamically managing resources in macro–micro heterogeneous ISAC networks by exploiting real-time MT speed and location information. CRAM achieves a spotlight effect for MTs through two core strategies: a resource pre-allocation mechanism driven by network topology sensing, and a resource sharing strategy that mitigates ping-pong effects in micro-BS overlapping regions. For high-velocity MTs, spectrum resources are assigned at macro-BSs to enhance service quality and minimize handovers. Furthermore, we incorporate the Cramer–Rao lower bound to derive optimized resource allocation policies adapted to diverse MT speeds and locations. Simulation results demonstrate that CRAM tailors resource distribution to MT characteristics, guaranteeing zero-interruption connectivity while maximizing system performance. In comparison with existing benchmarks, CRAM improves system throughput by 24.5% and 20.9%, reduces the total number of handovers by 29.1% and 16.5%, and increases the average signal-to-interference-plus-noise ratio (SINR) for all MTs by 41.3% and 30.3%. These outcomes highlight CRAM’s potential to redefine 6G network management by substantially boosting network efficiency and user experience.
Integrated sensing, communication, and computation (ISCC) provides a critical enabling platform in supporting the diverse services in the Internet of Vehicles (IoV). However, effective heterogeneous IoV service provisioning relies on both communication-centric and beyond-communication performance metrics, making unified resource allocation challenging. Moreover, competition from concurrent services for limited multi-dimensional resources is intensified in dynamic vehicular environments. In this paper, we investigate the resource allocation problem for concurrent communication and target classification services in an ISCC-enabled IoV system. To solve the problem, we first introduce the value of service (VoS) to unify communication rate and classification accuracy into a common measure that captures the degree of heterogeneous service fulfillment. To reduce the complexity of dynamic problem optimization, we propose a digital twin-assisted proximal policy optimization (DTPPO) algorithm, in which the digital twin exploits both current and historical information to generate predictive information, thereby enhancing policy learning in dynamic environments. Furthermore, we develop a large language model (LLM)-enhanced DTPPO (LLM-DTPPO) algorithm, which leverages the contextual understanding and domain knowledge of LLMs to reshape the reward function and improve resource allocation performance under multi-dimensional resource competition. Simulation results based on real-world vehicle mobility traces demonstrate that the proposed algorithms outperform existing benchmark schemes.
Bangzhen Huang, Zhang Liu, Lianfen Huang et al.· IEEE Transactions on Network...· 0 citations
The next generation of wireless systems extends ultra-reliable low-latency communications (URLLC) to the realm of massive connections, termed mURLLC. To address the inherent conflict between stringent quality of service (QoS) requirements in URLLC and the problem of severe and highly fluctuating interference behind demands of massive connectivity, effective fast fading (FF) mitigation and resource allocation strategies are crucial. Through in-depth analysis of FF characteristics, this paper derives optimized configurations for two FF mitigation approaches: protection margin reservation and $K$ -repetition. Furthermore, we integrate these FF mitigation strategies into a hierarchical-clustering (HC)-based resource allocation algorithm for configured-grant in mURLLC. This results in a highly practical and efficient algorithm for managing radio resources and interference in mURLLC scenarios. Simulation results demonstrate that our proposed algorithm achieves over 65% reduction in resource consumption without compromising reliability, significantly enhancing network capacity to support demanding mURLLC applications.
Yichen Guo, Lili Xu, Yihang Cheng et al.· IEEE Transactions on Wireles...· 0 citations
An adaptive Beta-policy and delayed-update multi-agent soft actor-critic method, abbreviated as ABDMASAC, which uses a Beta policy to model bounded actions and achieves a better overall trade-off than the selected MASAC-backbone and on-policy MARL baselines under the considered simulation settings.
Zheng Yao, Jie Liu, Changjun Deng et al.· Computers, Materials & C...· 0 citations
A new paradigm for satisfying the ever-growing demands of real-time Sixth Generation (6G) applications is Mobile Edge Computing (MEC). Additionally, base stations and Internet of Things devices that incorporate renewable energy harvesting capabilities have the potential to lower grid energy use. To maximize system potential and lower carbon emissions, it is crucial to make effective decisions about job offloading and resource allocation. A carbon-aware MEC architecture that uses both grid and renewable energy sources is proposed in this paper. Our goal is to jointly manage resource allocation and task offloading while monitoring carbon emissions and task queue delays to optimize system behavior under uncertainty, specifically for stochastic workloads and variable renewable generation. To balance these two cost components (emissions and queue length), we create a combined optimization problem. We develop a deep deterministic policy gradient (DDPG)-based joint optimization technique to address this issue in a constantly changing environment. In the optimization, we consider greedy policy (GP) and full offloading (FO), as well as time-average carbon emission (TACE) and time-average queue length (TAQL) as performance metrics, and time-average queue length (TAQL) and full execution (FE) as baseline strategies; we also evaluate normalized time-average cumulative reward (NTACR). This method uses continuous-action reinforcement learning to generate efficient, real-time control policies. For the proposed MEC network, numerical statistics show that our approach can lead to effective offloading and lower carbon emissions.
M. Saeed, Rashid A Saeed, M. A. Ahmed et al.· 2026 6th International Confe...· 0 citations
The results demonstrate that the proposed PP-SAPF is suitable for real-time deployment in intelligent transportation systems (ITS) and autonomous vehicles where low latency, reliable connectivity, and adaptive resource management is significant.
Irshad Khan, Neetha Papanna Umalakshmi, Somshekhar Durgaiah et al.· Bulletin of Electrical Engin...· 0 citations
Achieving deterministic latency for time-sensitive flows within integrated 5G and Time-Sensitive Networking (TSN) ecosystem requires the active mitigation of stochastic delays inherent in 5G New Radio (NR). While existing research typically relies on pessimistic guard bands or over-provisioned time-domain resources via wired TSN mechanisms, these approaches fail to adaptively reserve NR resources under dynamic channel conditions to suppress Packet Delay Variation (PDV). This work addresses this gap by proposing a joint NR MAC scheduling and Link Adaptation (LA) framework. We introduce Link Adaptive Semi-Persistent Scheduling (LA-SPS), a framework that ensures cycle-synchronous uplink opportunities by dynamically reconfiguring resource budgets and modulation parameters from real-time channel feedback. To manage the combinatorial complexity of joint resource allocation, we employ a Graph Neural Network (GNN) to encode scalable network states and Proximal Policy Optimization (PPO) for stable, real-time decision-making. This modular framework functions as a radio-side control loop designed for seamless coupling with end-to-end Time-Aware Shaper (TAS) scheduler, enabling a fully co-adaptive industrial network.
Syed Tasnimul Islam, José Fontalvo-Hernández· International Conference on...· 0 citations