查看更多>>摘要：This article introduces a novel concept of near-field integrated sensing and communication (ISAC) within a cognitive radio (CR) framework. The secondary ISAC transmitter, equipped with an extremely large-scale array, aims to minimize transmit power while meeting the requirements for both communication and sensing, as well as satisfying an interference constraint for the primary receiver. We initially solve the formulated nonconvex problem using a semidefinite relaxation approach to achieve a globally optimal solution. To reduce computational complexity, we propose two suboptimal beamforming strategies: 1) zero-forcing (ZF)-based and 2) maximum ratio transmission (MRT)-based beamforming designs. These approaches provide a practical tradeoff between performance and complexity by fixing beam directions according to the principles of ZF and MRT. Additionally, we develop a robust beamforming design under imperfect channel state information in the communication and interference channels, ensuring reliable performance across all possible channel realizations within the uncertainty bounds. Simulation results confirm the effectiveness of the proposed methods, demonstrating power-efficient joint communication and sensing capabilities within the CR scenario.
查看更多>>摘要：Integrated sensing and communication (ISAC) has emerged as a cornerstone technology for achieving seamless coverage in next-generation networks. Moreover, the advent of extremely large-scale multiple-input-multiple-output significantly enhances ISAC’s potential, facilitating innovative applications in near-field (NF) ISAC. Nonetheless, ISAC networks face numerous challenges, with power consumption being one of the most critical concerns. To address this issue, we propose a novel low-power beamforming approach within the coordinated multipoint (CoMP) ISAC framework. Specifically, our approach involves orchestrating base station (BS) cooperation for seamless coverage and synergistically augmenting the sensing beam with the communication beam to reduce power consumption. By utilizing the NF communication theory, we accurately model signal propagation dynamics and formulate a beamforming optimization problem aimed at minimizing transmit power while adhering to transmission rate and object detection constraints, which is a nonconvex second-order cone programming (SOCP) problem. To overcome the nonconvexity of this problem, we propose a successive convex approximation (SCA)-based beamforming optimization algorithm that ensures convergence. Moreover, we propose a fast-converging algorithm that leverages the unique characteristics of both communication channel and sensing array response vector. Simulation results validate the effectiveness of the proposed scheme for the power minimization problem and yield essential design insights.
查看更多>>摘要：Due to the diversity of edge devices (EDs) and applications, edge systems are heterogeneous and have been applied in artificial intelligence fields, such as smart factories and intelligent transportation, which is called heterogeneous edge intelligence. Many studies employ computation offloading to transfer processing data from resource-scarce EDs to resource-rich edge servers. These studies primarily focus on the overall resource consumption of homogeneous edge systems, neglecting the system heterogeneity and the details of resource consumption. In this article, we construct a system model from a parallel perspective for the heterogeneous edge system with different processors, memory, and applications, which perceives the cost of energy and delay from three levels: system, application, and component. A hybrid metaheuristic algorithm combined with a greedy rule, hybrid mutation, and whale optimization algorithm (GHMWOA) is proposed to realize partial computation offloading. A partial offloading architecture of heterogeneous edge intelligence is proposed to validate our model and algorithm with real-world hardware and software. Experiment results not only show GHMWOA outperforms multiple classical optimization algorithms in minimizing energy consumption, but also discover on which system component energy consumption depends, and how properties of application and system influence the cost of energy.
查看更多>>摘要：The emergence of 5G and beyond 5G (5G/B5G) networks represents a pivotal advancement in mobile wireless communication, offering peak transmission rates up to 20 Gb/s. This unprecedented capacity enables a diverse range of broadband multimedia services, including IPTV and Voice/Video-over-IP applications. With the exponential growth in multimedia services, the likelihood of multiple user equipments (UEs) within the same geographic region subscribing to identical multimedia services has surged, leading to significant inefficiencies in bandwidth utilization and radio resource consumption. To address these challenges, the multicast broadcast single frequency network (MBSFN) mechanism has been introduced, which clusters multiple cells into a unified serving area. By employing coordinated multipoint transmission (CoMP), MBSFN facilitates the synchronous delivery of multicast data across multiple UEs, effectively mitigating intercell interference and enhancing overall system efficiency. This approach not only reduces the data reception time for UEs but also significantly conserves their energy consumption. Nevertheless, the optimal clustering of cells and scheduling of multicast transmissions remain open research problems. In this study, we tackle the dual challenges of cell clustering and multicast scheduling with the objective of minimizing UEs’ energy consumption, measured in terms of data reception time, while ensuring the stringent Quality of Service (QoS) requirements of multicast services. To this end, we propose a two-phase framework. The first phase identifies the effective cell clusters to serve as multicast areas, balancing resource efficiency with QoS constraints. The second phase leverages dynamic programming techniques to further optimize multicast scheduling within these areas, thereby minimizing UEs’ energy. Comprehensive simulation results demonstrate that the proposed scheme significantly enhances resource utilization and reduces UEs’ energy consumption, outperforming state-of-the-art approaches.
查看更多>>摘要：Through experimental endeavors, we explore the intersection of semantic communication (SemCom) and natural language processing (NLP) to address gaps in SemCom models, focusing on reducing ambiguity and enhancing 6G communication. Our approach involves two phases: 1) Phase 1: Designing an NLP-based system for fair classification, leveraging techniques, such as unsupervised style transfer and zero-shot learning to align human intuition with semantic specifications. 2) Phase 2: Developing modules to minimize and evaluate SemCom performance under channel impairments, integrating language models like DistilBERT and RoBERTa. Results are evaluated using area under the curve receiver operating characteristic (AUC-ROC) metrics across diverse classifiers. Implementation details are publicly available on GitHub. We provide invaluable insights toward learning to harness SemCom with NLP.
NETL
NSTL
IEEE