| 1. |
- He, Qinglong, et al.
(författare)
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Additive manufacturing of dense zirconia ceramics by fused deposition modeling via screw extrusion
- 2021
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Ingår i: Journal of the European Ceramic Society. - : Elsevier BV. - 0955-2219 .- 1873-619X. ; 41:1, s. 1033-1040
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Tidskriftsartikel (refereegranskat)abstract
- A fused deposition modeling(FDM)system via screw extrusion suitable for feeding granular feedstocks with high solid loading was developed. Key parameters included aspect ratio of the screw, the compression ratio and pitch etc. In order to get constant extruded mass flow and wire diameter a processing window was determined by optimizing the barrel temperature, the nozzle diameter and the screwing speed. Microstructural characterization coupled with flexural strength measurement revealed that a higher printing temperature was beneficial to the inter layer bonding. The sintered zirconia ceramic samples with 99% of theoretical density of the 3 mol% yttria stabilized tetragonal zirconia polycrystal (3Y-TZP) and flexural strength of 890 +/- 60 MPa was obtained. A set of zirconia ceramic parts with complex geometries and controllable dimensional accuracy was also successfully prepared for demonstrating the potential of the technique.
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| 2. |
- He, Qinglong, et al.
(författare)
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Dynamic AP Selection and Cluster Formation with Minimal Switching for Green Cell-Free Massive MIMO Networks
- 2023
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Ingår i: 2023 Joint European Conference on Networks and Communications and 6G Summit, EuCNC/6G Summit 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 234-239
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Konferensbidrag (refereegranskat)abstract
- Aiming at the implementation of energy-efficient cell-free networks, several approaches have been proposed in the literature, which consider different access point (AP) switch ON/OFF (ASO) strategies for power minimization. Different from prior works, this paper focuses on additional factors that have an adverse effect not only on total power consumption but also on implementation complexity and operation cost. For instance, too frequent ON/OFF switching in an AP can lead to tapering off the potential power saving of ASO by incurring extra power consumption due to excessive switching. Indeed, frequent switching of APs might also result in thermal fatigue and severe lifetime degeneration. Moreover, time variations in the AP-UE (user equipment) clusters in favor of energy saving in a dynamic network bring additional signaling and implementation complexity. Thus, we propose a multi-objective optimization problem that aims to minimize the total power consumption together with AP switching and AP- UE clustering variations in comparison to the previous state of the network. The proposed problem is cast in mixed integer quadratic programming form and solved optimally. Our simulation results show that by limiting AP switching (node switching) and AP- UE cluster reformation switching (link switching), the total power consumption at the radio site only slightly increases, but the number of average switching drops significantly regardless of node or link switching. It achieves a good balance on the trade-off between radio power consumption and the side effects excessive switching will bring.
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| 3. |
- Topal, Ozan Alp, et al.
(författare)
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DRL-Based Joint AP Deployment and Network-Centric Cluster Formation for Maximizing Long-Term Energy Efficiency in Cell-free Massive MIMO
- 2023
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Ingår i: Conference Record of the 57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 993-999
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Konferensbidrag (refereegranskat)abstract
- In cell-free massive MIMO networks, scalability is one of the fundamental problems since a significant number of access points (APs) are widely distributed throughout the network area to cater to the needs of multiple user equipments (UEs). One approach to addressing this issue is through network-centric clustering, which involves dividing the network area into isolated clusters of APs, each connected to its cloud unit (CU). To address these challenges, this paper proposes a deep reinforcement learning (DRL) algorithm that jointly optimizes the network-centric cluster boundaries and decides AP deployment in each cluster to improve long-term energy efficiency. The DRL agent also aims to minimize the average UE drop rate by considering the delay requirements of each UE's requested service. The results show that at least 16% improvement in energy efficiency is obtained compared to the heuristically developed benchmarks.
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