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

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

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 OD₅₇₀ 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

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 CO₂ 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