11 research outputs found
Joint QoS-Aware Scheduling and Precoding for Massive MIMO Systems via Deep Reinforcement Learning
The rapid development of mobile networks proliferates the demands of high
data rate, low latency, and high-reliability applications for the
fifth-generation (5G) and beyond (B5G) mobile networks. Concurrently, the
massive multiple-input-multiple-output (MIMO) technology is essential to
realize the vision and requires coordination with resource management functions
for high user experiences. Though conventional cross-layer adaptation
algorithms have been developed to schedule and allocate network resources, the
complexity of resulting rules is high with diverse quality of service (QoS)
requirements and B5G features. In this work, we consider a joint user
scheduling, antenna allocation, and precoding problem in a massive MIMO system.
Instead of directly assigning resources, such as the number of antennas, the
allocation process is transformed into a deep reinforcement learning (DRL)
based dynamic algorithm selection problem for efficient Markov decision process
(MDP) modeling and policy training. Specifically, the proposed utility function
integrates QoS requirements and constraints toward a long-term system-wide
objective that matches the MDP return. The componentized action structure with
action embedding further incorporates the resource management process into the
model. Simulations show 7.2% and 12.5% more satisfied users against static
algorithm selection and related works under demanding scenarios
A comparison of the materials, methods and features that are used for the fabrication of scaffolds.
<p>A comparison of the materials, methods and features that are used for the fabrication of scaffolds.</p
Osteoblast-like MG-63 cells attach on the CS5 scaffold.
<p>The high affinity of human bone cells indicated that the fabricated biocomposites act as a biomimic of the human bone scaffold. Live color staining and fluorescence graphs of Nuclear/Actin contact analysis are shown in upper panel. (a) unattached scaffold, (b) live cell staining of formazan on scaffold, and (c) fluorescence on scaffold. The live cells on scaffolds were incubated in a tetrazolium dye bath at 37°C for 4 h. Green signals indicate actin, and blue signals indicate the nuclear site of cells. (d). CS5 scaffolds showing a long-term survival period of 1–6 days. Cell numbers corresponding to the OD<sub>570</sub> values represented approximately 836 ± 37 cells for each 0.1 OD value by MTT assay, for normalization with cell counting. ** P<0.01 compared to day 1 group.</p
XRD of CS0, CS5 and CS9 after heat treatment at 1300°C.
<p>XRD of CS0, CS5 and CS9 after heat treatment at 1300°C.</p
The compressive strength of specimens of CS0, CS5 and CS9 for different heat treatment temperatures:(a) compressive strength, (b) density, (c) volume expansion and (d) porosity.
<p>The compressive strength of specimens of CS0, CS5 and CS9 for different heat treatment temperatures:(a) compressive strength, (b) density, (c) volume expansion and (d) porosity.</p
The EDS of CS5 after heat treatment at 1300°C.
<p>The EDS of CS5 after heat treatment at 1300°C.</p
Schematics for the laser-aided gelling process.
<p>(1) a CO<sub>2</sub> laser, (2) a laser scanner, (3) a working platform, (4) a scraper and (5) a feeder.</p
The microstructure of CS0 after heat treatment at various temperatures:(a) 900°C, (b) 1100°C, (c) 1300°C and (d) 1500°C.
<p>The microstructure of CS0 after heat treatment at various temperatures:(a) 900°C, (b) 1100°C, (c) 1300°C and (d) 1500°C.</p