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Register a new userAoI Minimization in Energy Harvesting and Spectrum Sharing Enabled 6G Networks
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Spectrum sharing is a method to solve the problem of frequency spectrum deficiency. This paper studies a novel AI based spectrum sharing and energy harvesting system in which the freshness of information (AoI) is guaranteed. The system includes a primary user with access rights to the spectrum and a secondary user. The secondary user is an energy harvesting sensor that intends to use the primary user spectrum opportunistically. The problem is formulated as partially observable Markov decision processes (POMDPs) and solved using two methods: a deep Q-network (DQN) and dueling double deep Q-Network (D3QN) to achieve the optimal policy. The purpose is to choose the best action adaptively in every time slot based on its situation in both overlay and underlay modes to minimize the average AoI of the secondary user. Finally, simulation experiments are performed to evaluate the effectiveness of the proposed scheme compared to the overlay mode. According to the results, the average AoI in the proposed system is less than that of the existing models, including only overlay mode. The average user access improved from 30% in the overlay mode to 45% in the DQN and 48% in the D3QN.
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This paper presents a novel unmanned aerial vehicle (UAV) aided mobile edge computing (MEC) architecture for vehicular networks. It is considered that the vehicles should complete latency critical computation intensive tasks either locally with on-board computation units or by offloading part of their tasks to road side units (RSUs) with collocated MEC servers. In this direction, a hovering UAV can serve as an aerial RSU (ARSU) for task processing or act as an aerial relay and further offload the computation tasks to a ground RSU (GRSU). In order to significantly reduce the delay during data offloading and downloading, this architecture relies on the benefits of massive multiple input multiple output (MIMO). Therefore, it is considered that the vehicles, the ARSU, and the GRSU employ large scale antennas. A three dimensional (3D) geometrical representation of the MEC enabled network is introduced and an optimization method is proposed that minimizes the weighted total energy consumption (WTEC) of the vehicles and ARSU subject to transmit power allocation, task allocation, and timeslot scheduling. The numerical results verify the theoretical derivations, emphasize on the effectiveness of the massive MIMO transmission, and provide useful engineering insights.
Mobile edge computing (MEC)-enabled Internet of Things (IoT) networks have been deemed a promising paradigm to support massive energy-constrained and computation-limited IoT devices. IoT with mobility has found tremendous new services in the 5G era and the forthcoming 6G eras such as autonomous driving and vehicular communications. However, mobility of IoT devices has not been studied in the sufficient level in the existing works. In this paper, the offloading decision and resource allocation problem is studied with mobility consideration. The long-term average sum service cost of all the mobile IoT devices (MIDs) is minimized by jointly optimizing the CPU-cycle frequencies, the transmit power, and the user association vector of MIDs. An online mobility-aware offloading and resource allocation (OMORA) algorithm is proposed based on Lyapunov optimization and Semi-Definite Programming (SDP). Simulation results demonstrate that our proposed scheme can balance the system service cost and the delay performance, and outperforms other offloading benchmark methods in terms of the system service cost.
The sixth generation (6G) network must provide performance superior to previous generations in order to meet the requirements of emerging services and applications, such as multi-gigabit transmission rate, even higher reliability, sub 1 millisecond latency and ubiquitous connection for Internet of Everything. However, with the scarcity of spectrum resources, efficient resource management and sharing is crucial to achieve all these ambitious requirements. One possible technology to enable all of this is blockchain, which has recently gained significance and will be of paramount importance to 6G networks and beyond due to its inherent properties. In particular, the integration of blockchain in 6G will enable the network to monitor and manage resource utilization and sharing efficiently. Hence, in this article, we discuss the potentials of blockchain for resource management and sharing in 6G using multiple application scenarios namely, Internet of things, device-to-device communications, network slicing, and inter-domain blockchain ecosystems.
Caching has been regarded as a promising technique to alleviate energy consumption of sensors in Internet of Things (IoT) networks by responding to users requests with the data packets stored in the edge caching node (ECN). For real-time applications in caching enabled IoT networks, it is essential to develop dynamic status update strategies to strike a balance between the information freshness experienced by users and energy consumed by the sensor, which, however, is not well addressed. In this paper, we first depict the evolution of information freshness, in terms of age of information (AoI), at each user. Then, we formulate a dynamic status update optimization problem to minimize the expectation of a long term accumulative cost, which jointly considers the users AoI and sensors energy consumption. To solve this problem, a Markov Decision Process (MDP) is formulated to cast the status updating procedure, and a model-free reinforcement learning algorithm is proposed, with which the challenge brought by the unknown of the formulated MDPs dynamics can be addressed. Finally, simulations are conducted to validate the convergence of our proposed algorithm and its effectiveness compared with the zero-wait baseline policy.
The theory of wireless information and power transfer in energy constrained wireless networks has caught the interest of researchers due to its potential in increasing the lifetime of sensor nodes and mitigate the environment hazards caused by conventional cell batteries. Similarly, the advancements in areas of cooperative spectrum sharing protocols has enabled efficient use of frequency spectrum between a licensed primary user and a secondary user. In this paper, we consider an energy constrained secondary user which harvests energy from the primary signal and relays the primary signal in exchange for the spectrum access. We consider Nakagami-m fading model and propose two key protocols, namely time-splitting cooperative spectrum sharing (TS-CSS) and power-sharing cooperative spectrum sharing (PS-CSS), and derive expressions for the outage probabilities of the primary and secondary user in decode-forward and amplify-forward relaying modes. From the obtained results, it has been shown that the secondary user can carry its own transmission without adversely affecting the performance of the primary user and that PS-CSS protocol outperforms the TS-PSS protocol in terms of outage probability over a wide range of Signal to noise ratio(SNRs). The effect of various system parameters on the outage performance of these protocols have also been studied.
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