Search

6G wireless networks have raised increasing attention with computation-sensitive services such as AI Internet of things (AIoT) and mobile augmented reality/virtual reality (AR/VR) applications. Mobile edge computing (MEC) provides rich computation resources for user equipments (UE) at the edge of networks. Aided by MEC servers, computation-intensive applications that are commonly modeled as Directed Acyclic Graphs (DAG) can be performed locally and offloaded to MEC servers to enhance execution efficiency. However, it is a key issue to efficiently provide low latency with limited energy. In this paper, we investigate a multiobjective task scheduling problem in MEC-aided 6G network. Then, an improved multiobjective cuckoo search (IMOCS) algorithm is proposed to deal with a DAG-based task scheduling problem, which aims to reduce the execution latency and energy consumption of UE. Particularly, the proposed IMOCS algorithm is based on the single-objective cuckoo search algorithm and Pareto dominance. An external archive is used to record nondominated solutions, whose update strategy improves the quality of solutions by the aid of fast nondominated sorting and crowding distance sorting. Simulation results demonstrate that IMOCS algorithm outperforms other four benchmark algorithms, which can provide optimal task scheduling policy for MEC severs in 6G networks. [ABSTRACT FROM AUTHOR]

Recently, mobile edge computing (MEC) has been recognized as an emerging paradigm to meet the ever-increasing computation demands. Besides, multiple radio access technology (multi-RAT) has been proposed to enhance the network throughput and service reliability. However, the two technologies have been evolving independently. In this paper, we will develop an integrated multi-RAT and MEC network framework, which enables smart devices (SDs) to offload computation tasks over multiple RAT links in parallel. Considering the resource-limited nature as well as the time-varying property of the proposed network, we then focus on investigating long-term fairness guarantee mechanisms to facilitate fair resource sharing/allocation between SDs. Specifically, we formulate a max-min stochastic optimization problem with the objective of maximizing the minimum long-term time-average offloading utility. An adaptive task splitting and resource allocation algorithm is proposed based on the Lyapunov optimization technology, which jointly optimizes the SD task splitting and uplink transmit power allocation, the RAT subcarrier allocation, and the MEC computation frequency allocation. The adaptive algorithm can accommodate to the time-varying network dynamics without requiring their distribution information. Moreover, the proposed algorithm is shown to have polynomial computation complexity and be asymptotically optimal by rigorous analysis. Simulation results verify the theoretical analysis and show that the Jain's fairness index of the proposed algorithm converges to 1 as the number of time-slots grows, which outperforms the benchmark algorithms. [ABSTRACT FROM AUTHOR]

To satisfy the high requirements on operation efficiency in the 5G network, self-organizing network (SON) is envisioned to reduce the network operating complexity and costs by providing SON functions, which can optimize the network autonomously. However, different SON functions have different time scales and inconsistent objectives, which leads to conflicting operations and network performance degradation, raising the needs for SON coordination solutions. In this paper, we devise a multiple time-scale coordination management scheme (MTCS) for densely deployed SONs, considering the specific time scales of different SON functions. Specifically, we propose a novel analytical model named ${M}$ time-scale Markov decision process, where SON decisions made in each time-scale consider the impacts of SON decisions in other ${M}-{1}$ time scales on the network. Furthermore, in order to manage the network more autonomously and efficiently, a Q-learning algorithm for SON functions in the proposed MTCS scheme is proposed to achieve a stable control policy by learning from history experience. To improve energy efficiency, we then evaluate the proposed MTCS scheme with two functions of mobility load balancing and energy saving management with designed network utility. The simulation results show that the proposed SON coordination scheme significantly improves the network utility with different quality of experience requirements while guaranteeing stable operations in wireless networks. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the cross-layer resource optimization for wireless relay networks with dynamic node-selfishness information. The degree of intrinsic selfishness (DeIS) and the degree of extrinsic selfishness (DeES) of the relay node (RN) are defined as the effects of its own residual energy resource and the incentive in an incentive mechanism, respectively. A model of two-timescale node-selfishness dynamics is constructed to describe the time-varying characteristics of the RN's DeIS and DeES. Based on the two-timescale node-selfishness dynamics of the RNs, a two-timescale resource-optimization dynamical algorithm (TRODA) is employed to control the transmit powers of both RNs and end users (EU), as well as the flow rates of the data packets injected by all EUs. Meanwhile, the stability and the tracking error of the TRODA under the two-timescale node-selfishness dynamics are analyzed by Lyapunov theory. Our simulation results demonstrate that the two-timescale optimization algorithm is stable and has trivial tracking error. [ABSTRACT FROM AUTHOR]

In this letter, we investigate the node’s dynamic selfishness and the rate allocation in time-varying selfish wireless networks (SeWNs). We model the dynamics of the node’s selfishness by designing a virtual forwarding selfishness queue (v-FSQ) and a virtual receiving selfishness queue (v-RSQ). Specifically, the v-FSQ is constructed with the consideration of the node’s residual energy and employed incentives, while the v-RSQ is constructed with the consideration of the node’s data queue state information. By virtual of nodes’ v-RSQs and v-FSQs, a stochastic optimization problem is formulated to maximize the time-average network utility, which is related to the SeWNs throughput, whilst bounding the nodes’ selfishness queues. The dynamic rate allocation is obtained with the aid of the Lyapunov optimization theory. Simulation results verify the theoretical analysis of our proposed scheme. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the multiservice delivery between the source–destination pairs in distributed selfish wireless networks (SeWN), where selfish relay nodes (RN) expose their selfish behaviors, i.e., forwarding or dropping multiservices. Owing to the effect of the RNs’ node-selfishness on the multiservices, a distributed framework of the node-selfishness management is constructed to manage the RN’s node-selfishness information (NSI) in terms of its available resources, the employed incentive mechanism and the quality-of-service (QoS) requirements, and the other RNs’ NSI in terms of their historical behaviors. In this framework, the RNs’ NSI includes the degree of node-selfishness (DeNS), the degree of intrinsic selfishness (DeIS) and the degree of extrinsic selfishness (DeES). Under the distributed node-selfishness management, a path selection criterion is designed to select the most reliable and shortest path in terms of RNs’ DeISs affected by their available resources, and the optimal incentives are determined by the source to stimulate forwarding multiservices of the RNs in the selected path. Our simulation results demonstrate that this framework effectively manages the RNs’ NSI, and the optimal strategies of both the path selection and the incentives are determined. [ABSTRACT FROM AUTHOR]

In this letter, we investigate the dynamic packet delivery through a specific path in selfish wireless networks (SeWNs) with cascaded selfish relay nodes (RN). A dynamic node-selfishness model is designed to formulate the variation of the RN's degree of node-selfishness (DeNS) with both its own resource and the incentive controlled by the source. When the node-selfishness dynamics are unknown, using the neural network (NN), we further identify the node-selfishness model and then approximate the optimal incentives for maximizing the infinite-horizon utility, namely the tradeoff between the path reliability and the incentive cost in the long term. Simulation results demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM PUBLISHER]

Perovskite ferroelectrics (PFs) have been the dominant piezoelectric materials for various electromechanical applications, such as ultrasonic transducers, sensors, and actuators, to name a few. In this review article, the development of PF crystals is introduced, focusing on the crystal growth and piezoelectric activity. The critical factors responsible for the high piezoelectric activity of PFs (i.e., phase transition, monoclinic phase, domain size, relaxor component, dopants, and piezoelectric anisotropy) are surveyed and discussed. A general picture of the present understanding on the high piezoelectricity of PFs is described. At the end of this review, potential approaches to further improve the piezoelectricity of PFs are proposed. [ABSTRACT FROM AUTHOR]