Network-assisted full-duplex (NAFD) cell-free massive MIMO (CF-mMIMO) systems constitute a promising enabler for supporting dynamic downlink (DL) and uplink (UL) traffic demands in forthcoming sixth-generation (6G) wireless networks. In this paper, we analyze channel aging effects on NAFD CF-mMIMO systems. First, we propose an extended beamforming training scheme to relieve heavy pilot overhead for high-mobility channel estimation, significantly enhancing system performance through cross-link interference (CLI) cancellation. Then, we derive novel closed-form expressions for the UL/DL achievable SEs, enabling a comprehensive analysis of channel aging impacts on spectral efficiency (SE) and energy efficiency (EE) across diverse normalized Doppler shiftfDTsscenarios. Furthermore, we develop a joint optimization framework of transmission interval and power control to balance SE-EE tradeoffs while alleviating channel aging effects. We formulate a mixed-integer multi-objective optimization problem (MOOP), which is subsequently transformed into a tractable single-objective formulation via a weighted ℓpscalarizing method. Based on this analytical foundation, we propose a constrained deep reinforcement learning (DRL) algorithm that integrates a primal-dual optimization strategy with multi-agent deep deterministic policy gradient (MADDPG) for safe policy exploitation. Simulation results validate the accuracy of analytical expressions and demonstrate the superiority of the proposed algorithm over the conventional non-dominated sorting genetic algorithm-II (NSGA-II) in achieving Pareto-optimal SE-EE tradeoffs under channel aging.

Research on the health diagnosis of mechanical equipment has developed unprecedentedly in recent years, and a large number of diagnostic solutions have considerably improved the stability of mechanical equipment in industrial production. However, such satisfactory diagnostic performance relies on a large number of data samples, which are frequently difficult to obtain in real industrial scenarios. The traditional strategy of data sharing is no longer advisable due to the potential conflict of interest among users. A federated transfer learning scheme is proposed to alleviate the data island problem in industrial production while protecting data privacy. This solution adopts a distributed structure, which includes local model training and global model update. A differential training scheme is proposed to enhance the domain adaptability of the local model. The central server evaluates the contribution ability of each local model to the target task. It also weights and aggregates each client model on the basis of parameter importance ranking in the form of model fusion. The target task of the experiment is performed on two sets of bearing datasets. By comparing with other diagnostic methods, a conclusion can be drawn that the proposed scheme provides a promising federated learning method while protecting client data privacy.

High energy costs and carbon-neutral targets emphasize the economics and sustainability of mobile communications. This paper studies a long-term average energy transaction expenditure minimization problem for smart-grid powered heterogeneous cellular networks (SG-HCNs) where renewable energy is introduced. Renewable energy and wireless channel dynamics evolve over different timescales. Thus, we seek a two-timescale dynamic resource management solution in SG-HCNs, where the real-time joint issue of flow control, power allocation, and energy sharing of renewable energy, and the ahead-of-time two-way energy trading are considered. Based on a two-layer Lyapunov framework, a two-timescale dynamic optimization (TTDO) algorithm is developed for the proposed problem. Specifically, the real-time joint issue is decoupled into two subproblems, addressed by linear programming and successive convex approximation methods. An approximate solution for ahead-of-time two-way energy trading is achieved via the stochastic subgradient approach, where past data of related random events is referred to as prior knowledge that is required but difficult to acquire. Theoretically, the proposed TTDO algorithm can attain an asymptotic optimum and ensure queue stability. Simulation results verify the theoretical analysis and reveal that the proposed TTDO algorithm can obtain lower energy transaction expenditure than benchmarks. Besides, the proposed TTDO algorithm presents an energy-saving property.

The digital economy is a new economic form taking data as an important production factor and digital and intelligent technology as a driving force for transformation. The core idea is to extract and fuse the knowledge implicit in data and transform it into intelligence to drive the transformation of traditional manufacturing industries, and one of its key technologies is multi-modal data fusion. In this paper, an improved MPGA-ACO-BP algorithm is proposed, and combined with an improved entropy-weighted TOPSIS method comprehensive evaluation system, which effectively solves the problem of “data scale inconsistency” between modal data leading to difficult model fusion and fusion accuracy. Finally, the validity of the theory and methods of this paper are verified using the example of multi-modal data fusion tool wear prediction in an intelligence workshop. By distilling the corresponding evaluation metrics inductively, the improved comprehensive evaluation system in this paper can also be extended to different production control scenarios to provide them with the corresponding integration information, which has a certain practical value.

This paper proposes a two-timescale online resource management solution for smart-grid supplied heterogeneous cel-lular networks, where grid-energy pre-ordering in advance and bidirectional energy trading in real time are performed. We for-mulate a long-term average energy transaction cost minimization problem considering grid-energy pre-ordering, power allocation, and energy sharing. Leveraging the Lyapunov technique, succes-sive convex approximation method, and stochastic subgradient approach, we develop a two-timescale dynamic optimization (TTDO) algorithm to make online decisions on two time scales. It is theoretically proved that the proposed TTDO algorithm can asymptotically achieve optimality via tuning a control parameter. Numerical tests verify the theoretical results.

This paper investigates a long-term average total energy cost minimization problem via resource management, including admission control, power allocation, and Energy Sharing (ES) of renewable energy in Heterogeneous Cellular Networks powered by Grid-connected Smart Micro Grids (GSMG-HCNs). In GSMG-HCNs, both renewable and grid energy power the base stations. Unlike existing works, we consider the cost of both renewable and grid energy and formulate the power line loss process caused by ES into our model. To solve the proposed problem, we transform it into a real-time issue by the Lyapunov technique. The proposed Cost-Aware Online Resource Management (CAORM) algorithm decouples the real-time issue into two sub-problems, one of which is linear and the other is addressed based on the successive convex approximation approach. We theoretically prove the asymptotic optimality of the CAORM algorithm and a tradeoff between the average total energy cost and the average queue length. Simulation results reveal that the CAORM algorithm outperforms benchmarks in reducing total energy cost and can make appropriate decisions according to different unit costs of renewable energy. Besides, the designed distance-related ES loss rate can help obtain better solutions with lower ES losses.

Heterogeneous cellular networks with hybrid energy supplies can relieve traffic pressure and reduce grid energy consumption. In heterogeneous cellular networks, rational resource management can help improve system performances. In general, more than one performance is expected to do well, but there can exist a trade-off among different performance metrics, thus making resource management a multi-objective problem. The existing solution usually transforms a multi-objective problem into another single-objective problem by assigning weights for various objectives. However, it is difficult to know the exact weights in advance, and different systems call for different requirements for objectives. Hence, a multi-objective optimization approach based on the gravitational search algorithm (GSA) is proposed to find a series of Pareto optimal solutions. The decision-makers can select an appropriate solution according to the system requirement. In this work, three different multi-objective GSA-based algorithms are proposed to determine user association and power control, with the goal to optimize the traffic load balancing among small base stations and grid energy consumption per unit throughput simultaneously. The complexity of the proposed algorithms is analyzed, and simulations compare the performances of the proposed algorithms and the benchmark algorithm. Experimental results reveal the feasibility and effectiveness of this approach.

Service-based systems are a new software mode for distributed business processes integration. It is difficult for traditional testing methods to verify the functional and nonfunctional requirements of software. To address this problem, this paper proposes a visual verification platform to quantitatively compute the reliability and cost for evaluating the performance of service-based systems in the design phase. First, an extended automata model namely Probabilistic Reward Labeled Transition System (PRLTS) is proposed to formalize both the functional behaviors and nonfunctional features. Then, the formal language of probabilistic model checker PRISM is introduced to show the grammar of the target verification codes that we want to transform. Second, XML description tags of Business Process Execution Language (BPEL) is parsed to generate the functional behaviors using different kinds of transformation rules, based on which the probability matrix and reward concept are employed to denote the service’s reliability and cost, respectively. Third, the PRLTS model is turned into the input language of PRISM, where the graphic description language DOT of Graphviz is used as an intermediary to display system behaviors in a visual way. The model layout allows the designer to manually adjust the behaviors of the PRLTS model, where verification codes can be dynamically updated according to the changes in modified information. Fourth, to perform quantitative verification, the verification property in the form of the Probabilistic Computation Tree Logic (PCTL) formula can be automatically generated when the requirement model of the service-based system is inputted, during which the threshold value of qualitative property will be initially computed and returned as a recommended value. This allows the user to modify the qualitative property in an interactive way. Furthermore, experimental analysis of the real-world case study demonstrates the feasibility of the proposed method. Thus, our platform provides guidance for quantitative verification and graphical visualization for effectively generating formal models and checking the quantitative properties for service-based systems.

SLA (Service Level Agreement) negotiation plays a very important role in Manufacturing Grid. In order to solve its problem of practical application, firstly, we try the Nash negotiation method. Because the method depends on the negotiators' utility functions, and it is impracticable to construct the different players' utility function, we abandon it. Then we put forward the interactive multi-object negotiation method, because it can solve these negotiation problems without the negotiators' utility functions. The method utilizes the different negotiators' cross concessions of the different objects and seeks for the conflict bottleneck of the conflicts, and then solves the negotiation problems.

Manufacturing resource sharing and collaboration is the core point of manufacturing grid (MG). In order to realize the resource sharing and collaboration among the heterogeneous and distributed manufacturing resources, a WSRF (Web service resource framework)-based resource management system framework of manufacturing grid, and manufacturing resource encapsulation method are presented in this research. Manufacturing resources are encapsulated into WS-Resource structural entity entitled MG-Resources by stateful manufacturing resource encapsulation, Web service encapsulation and interfaces construction, which explains the description, encapsulation, publication and invocation of MG-Resources. The encapsulation process shields complexity and diversities of manufacturing resources, thus implements the heterogeneous manufacturing resource sharing and collaboration in MG effectively.

The core of Manufacturing Grid (MG) is Manufacturing Resource (MR) sharing and collaboration, so the efficient discovery of sharable MRs is the precondition and basis that the resource sharing, collaborative design and manufacturing can be realized successfully in MG. Aiming at the problem that semantic information is represented insufficiently when MRs are searched in MG, we propose an ontology-based MR discovery architecture which is based on semantic web technology. Then from the point of view based on semantics and QoS (Quality of Service), the main framework of resource matchmaking is constructed, in which three processes are included: task modeling, resource modeling, semantic modeling and resource matchmaking. On the basis of resource representation model and manufacturing featuresoriented task representation model, combining with the research results of semantic web, some pivotal concepts are defined about the matchmaking resources. Finally, by means of a functional semantic extending method based on the ontology and QoS, the MR matchmaking algorithm is constructed.