The transmission of semantic information related to users’ interests can significantly reduce the network transmission burden. However, if a base station transmits semantic information to a group of users simultaneously, partially similar interests among them inevitably produce repetitive transmission and higher energy consumption. Additionally, the worst channel condition among users impedes enhancements to network capacity. To achieve green communication and enhance network capacity, this paper exploits shared and individualized channels of cooperative rate splitting multiple access (RSMA) for semantic information assignment. Meanwhile, the appropriate relay is selected for network capacity enhancement based on semantic information assignment, energy consumption, and channel status. Then, semantic information assignment, relay selection, time resource allocation, and rate splitting are jointly optimized to minimize energy consumption. The formulated problem is an intractable mixed-integer non-linear programming problem, which can be resolved with the Dinkelbach method and successive convex approximation technique. A customized algorithm, namely SRTR, is proposed for green cooperative RSMA networks. Simulation results reveal that the SRTR algorithm can increase network capacity and flexibly allocate semantic data, resulting in a 21% reduction in energy consumption compared with traditional cooperative RSMA.

Collaborative perception in Internet of Things (IoT) networks exchanges perceptual information to close blind regions caused by limited fields of view and occlusions. Transmitting raw or high-resolution streams is often impractical under bandwidth, latency, and energy constraints. We present a task-oriented semantic communication framework that integrates cooperative perception with semantic mode adaptation. It (i) assigns helpers to a receiver's blind cells; (ii) on each helper-receiver link, selects a semantic mode (image- to- image or image- to- text) and a symbol budget from offline similarity-SINR profiles to meet fidelity and deadline targets; and (iii) performs interference-aware FDMA and power allocation under per-node budgets. We define a value-weighted QoS that combines coverage value, semantic fidelity, and delay, and propose SCRA, a four-stage solver that decomposes the mixed-integer nonconvex problem while preserving feasibility. To date, no end-to-end integration of semantic communication with cooperative perception has been demonstrated. Simulations show consistent gains over tra-ditional communication and fixed-mode semantic baselines in network sum-QoS and value-weighted blind-area coverage across bandwidth regimes, with improved robustness under stronger interference.

Task-oriented semantic communication effectively facilitates the completion of specific tasks by conveying users' interests. Nevertheless, when a base station conveys task-oriented semantic information to multiple users, these users exhibit both shared and distinct interests, which leads to increased redundancy and communication costs. To reduce transmission redundancy and cost, in this paper, a flexible and efficient multicast and unicast transmission scheme based on task-oriented semantic information is investigated for users with overlapping interests. Specifically, a joint energy-delay cost function is adopted to measure the communication cost caused by transmission redundancy, which facilitates a trade-off between energy consumption and transmission delay. Then, to minimize the network cost, this paper leverages the inherent benefits of common and private streams in rate splitting multiple access (RSMA) and design a semantic information allocation strategy. The RSMA common stream is further divided into the super-common and user-common streams. A semantic information allocation mechanism is established according to the common and private streams to flexibly allocate multiple semantic information. Then, semantic information selection on super-common stream, the proportional strategy of user-common stream, and rate splitting are jointly designed to minimize the network cost. The problem is a mixed-integer problem. Block coordinate descent and successive convex approximation technique are adopted to address it. An algorithm, namely SURA, is proposed for semantic information allocation in RSMA networks. Simulation results demonstrate the proposed SURA algorithm can adaptively allocate semantic information and reduce the network cost.

Autonomous aerial vehicles (AAVs) can collect data from industrial Internet of Things (IoT) devices that experience poor channel conditions caused by the obstruction of large industrial equipment. However, due to the mobility of AAVs and stochastic industrial data generation, extreme events with significantly high latency may occur during data collection, resulting in unreliable communication. Besides, AAV speed variation brings challenges to achieving green communication and reliable data collection. In this article, we propose a dynamic AAV-assisted resource allocation scheme to collect data reliably for green industrial IoT. Specifically, the queue tail distribution is adopted to characterize the occurrence probability of extreme events, which indicates the reliability of the queue length. Then, given the impact of AAV speed on energy consumption and queue reliability, we aim to minimize energy consumption constrained by tail distribution and optimize AAV speed to ensure reliable data collection. Furthermore, the device access, bandwidth allocation, power control, and AAV speed are jointly optimized for minimizing the long-term energy consumption of AAVs and industrial IoT devices, constrained by the tail distribution of the queue length. The formulated problem is intractable due to intricately coupled variables and stochastic characteristics. To resolve it, we propose a novel algorithm, namely JDBPS, which can achieve reliable data collection and green communication. Simulation results demonstrate that the proposed JDBPS algorithm can constrain tail distribution while reducing transmit power of industrial IoT devices by 15.7% compared with the fixed AAV speed scheme.

The knowledge-enhanced BERT model solves the problem of lacking knowledge in downstream tasks by injecting external expertize, and achieves higher accuracy compared with BERT model. However, owning to large-scale external knowledge is utilized into knowledge-enhanced BERT, some shortcomings comes such as information noise, lower accuracy and weak generalization ability, and so on. To solve this problem, a knowledge enhancement method BERTBooster which combines incremental learning and gradient optimization is proposed. BERTBooster disassembles the input text corpus into entity noun sets through entity noun recognition, and uses the incremental learning task denoising entity auto-encoder to create an incremental task set of entity nouns and external knowledge triples. Furthermore, BERTBooster introduces a new gradient optimization algorithm ChildTuningF into BERT model to improve the generalization ability. BERTBooster can effectively improve the factual knowledge cognition ability of CAGBERT model and improve the accuracy of the model in downstream tasks. Experiments are carried out on six public data sets such as Book_Review, LCQMC, XNLI, Law_QA, Insureace_QA, and NLPCC-DBQA. The experimental results show that the accuracy rate in downstream tasks is increased by 0.65% on average after using BERTBooster on CAGBERT.

In Industrial Internet of Things (IIoT), it is essential to acquire timely device status information to ensure efficient operation. In this article, we consider a wireless monitoring system in IIoT and employ the concept of Age of Information (AoI) to characterize the timeliness of device status information in the system. Considering the impact of resource constraints on information acquisition, we apply a pull-based model to control the entire process of sampling, transmission, and processing associated with device status updates, which constitutes a system-wide AoI minimization problem. The formulated problem is a mixed-integer nonconvex problem, due to the temporal correlation of AoI and the intractability of the implicit AoI-associated objective function. We introduce the concept of average AoI earnings to equivalently substitute the optimization objective. The original problem in consecutive time slots is decomposed into the per-time slot average AoI earnings maximization problem to deal with the temporal correlation of AoI. Then, an online slot-by-slot optimization algorithm (SBSA) is proposed to control device status updates without long-term system state information. Simulation results show that SBSA can significantly improve the AoI performance of the system. However, the problem decomposition in SBSA inevitably brings approximation error. Hence, based on the actual transmission and processing in the system, we get the lower bound of the system total AoI by designing a multislot optimization algorithm (MSA) and analyze the approximate error caused by SBSA. Through simulation results, SBSA has a substantially lower computational complexity, while maintaining acceptable approximation error in comparison to MSA.