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Beams with Unsymmetric Loadings

Beams with Unsymmetric Loadings

Analyzing a supported beam under unsymmetrical loadings is essential in structural engineering to understand how beams respond to varied force distributions. This analysis involves calculating the deflection and identifying points where the slope of the beam is zero, which are crucial for ensuring structural stability and functionality.
The first moment-area theorem determines the slope at any point on the beam. This theorem indicates that the change in slope between two points on a beam...

Beams with Symmetric Loadings

Beams with Symmetric Loadings

The moment-area method is an analytical tool used in structural engineering to determine the slope and deflection of beams under various loads. Consider a cantilever with a concentrated load and moment at the free end. The first step is constructing a free-body diagram to calculate the reactions at the fixed end. Next, the bending moment diagram is plotted to visualize how the bending moment varies along the beam's length, focusing on points where the bending moment equals zero.
The M/EI...

Maximum Power Transfer

Maximum Power Transfer

Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
By substituting the entire circuit with...

Design of Prismatic Beams for Bending

Design of Prismatic Beams for Bending

The design of prismatic beams, structural elements with a uniform cross-section, focuses on ensuring safety and structural integrity under load. The design process begins by determining the allowable stress, either from material properties tables, or by dividing the material's ultimate strength by a safety factor. This safety factor is essential for accommodating uncertainties, and varies depending on the material—timber, steel, or concrete—with each having unique strength and...

Prismatic Beams: Problem Solving

Prismatic Beams: Problem Solving

In the design of a supported timber beam subjected to a distributed load, both the beam's physical dimensions and the timber's characteristics, such as its grade and species, are critical. These factors determine the allowable stress values, which are crucial for calculating the necessary beam depth to ensure structural integrity and safety.
The design begins with analyzing the beam as a free body to identify moments and force balances, thereby determining support reactions. Next, the...

Sequence Networks of Rotating Machines

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...

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Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

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Deep Reinforcement Learning-Based Coordinated Beamforming for mmWave Massive MIMO Vehicular Networks.

Pulok Tarafder¹, Wooyeol Choi¹

¹Department of Computer Engineering, Chosun University, Gwangju 61452, Republic of Korea.

Sensors (Basel, Switzerland)

|March 11, 2023

Summary

This study introduces a deep reinforcement learning (DRL) approach for coordinated beamforming in millimeter wave (mmWave) systems. The novel scheme enhances mobile communication by reducing training overhead and latency, boosting data rates.

Keywords:

beamforming deep reinforcement learning massive MIMO mmWave vehicular network

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Area of Science:

Wireless communication systems engineering
Signal processing for telecommunications
Machine learning applications in networking

Background:

Millimeter wave (mmWave) beamforming is crucial for beyond fifth-generation (B5G) technology, utilizing Multi-input Multi-output (MIMO) systems.
High-speed mmWave applications face significant challenges including signal blockage, latency, and high training overhead for beamforming vector discovery in massive antenna arrays.

Purpose of the Study:

To propose a novel deep reinforcement learning (DRL) based coordinated beamforming scheme for mmWave systems.
To address challenges of blockage, latency, and training overhead in highly mobile mmWave applications.
To enhance the efficiency and performance of mmWave massive MIMO systems.

Main Methods:

A coordinated beamforming scheme is developed where multiple base stations (BSs) jointly serve a single mobile station (MS).
A deep reinforcement learning (DRL) model is employed to predict optimal beamforming vectors from a codebook of candidates.
The scheme focuses on mitigating training overhead and latency in dynamic mobile environments.

Main Results:

The proposed DRL-based coordinated beamforming scheme significantly increases achievable sum rate capacity.
The system demonstrates dependable coverage and low latency for highly mobile mmWave applications.
A remarkable reduction in training and latency overhead was observed compared to conventional methods.

Conclusions:

The novel DRL-based coordinated beamforming scheme effectively enhances mmWave massive MIMO system performance.
The solution provides a robust framework for dependable, low-latency, and efficient mmWave communication for mobile users.
This approach offers a promising direction for future B5G wireless networks.