How 6G Will Transform Augmented and Virtual Reality in

Healthcare, Education, and Entertainment: Opportunities and

Challenges

Muhammad Irawan Agung and Zulkifli Tahir

Department of Informatics, Hasanuddin University, Indonesia

Keywords: 6G, Augmented Reality (AR), Virtual Reality, Latency, High-Speed Connectivity.

Abstract: The Advent of 6G technology is poised to revolutionize several industries, particularly healthcare, education,

and entertainment, through the enhancement of Augmented Reality (AR) and Virtual Reality (VR)

applications. With its increased data transfer speeds, ultra-low latency, and expanded network capacity, 6G

promises to deliver more immersive and real-time AR/VR experiences. In healthcare, 6G can enable advance

applications like remote diagnosis and therapy via VR-based telemedicine and real-time patient monitoring

through AR. In education, it will create more engaging interactive virtual learning environments, allowing for

highly personalized experience through AR/VR simulations. Meanwhile, in entertainment, 6G will redefine

how users engage with digital content, offering more immersive and collaborative experience through the

metaverse and VR-based games. However, realizing this potential come with challenges, including the need

for robust infrastructure, ensuring data privacy and security, and addressing the hardware limitations required

to support such sophisticated AR/VR systems. This paper explores both the opportunities and the hurdles that

must be overcome to fully leverage 6G technology for AR/VR applications in these key sectors.

1 INTRODUCTION

The integration of Augmented Reality (AR) and

Virtual Reality (VR) has progressed significantly

across multiple domains, with a particular emphasis

on medical education. Studies suggest that

incorporating AR and VR in medical training can

greatly enhance procedural proficiency, student

involvement, and long-term knowledge retention

(Tene et al., 2024; Al-Ansi et al., 2023a). These

technologies enable learners to rehearse medical

procedures in a controlled, risk-free environment, a

benefit that traditional techniques often lack.

However, despite the evident advantages, existing

technologies encounter challenges, primarily related

to network latency and bandwidth, which impede the

large-scale adoption of AR and VR, particularly in

real-time applications like surgical training or

diagnostic exercises (Jilani Saudagar et al., 2024).

With the advent of 6G technology, these barriers

are expected to be significantly reduced. 6G offers

extremely low latency, high data transfer speeds, and

increased network capacity, making it a game-

changer for AR and VR applications. This technology

is set to open up vast new possibilities for enhancing

training in medical fields, education, and

entertainment, providing smoother and more

immersive real-time experiences (Zawish et al., 2024;

Yang et al., 2022).

Expanding upon prior studies, the incorporation

of AR and VR into medical education has

demonstrated considerable potential, especially in

offering practical experience with medical

simulations, surgical procedures, and healthcare

interventions (Sharma et al., 2024; Porambage et al.,

2023; Nguyen et al., 2022). Additionally, this

research emphasizes how AR and VR can play a

crucial role in remote diagnostics and treatment,

enabling patients with disabilities to access medical

care from the convenience of their own homes.

Data analysis of 28 studies shows that while VR

positively impacts medical training, it was used to

measure "performance" more frequently (16 times)

than AR (4 times). However, a chi-square test found

this relationship was not statistically significant (p =

0.052). This indicates that further research with larger

sample sizes and improved methodologies is needed

to establish the technology's full effectiveness.

A descriptive analysis shows a largely favorable

impact of AR/VR on learner performance, with a

164

Agung, M. I. and Tahir, Z.

How 6G Will Transform Augmented and Virtual Reality in Healthcare, Education, and Entertainment: Oppor tunities and Challenges.

DOI: 10.5220/0014276100004928

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Innovations in Information and Engineering Technology (RITECH 2025), pages 164-170

ISBN: 978-989-758-784-9

majority (57.14%) reporting a significant positive

effect and another portion (35.71%) also showing

positive results. However, with a small fraction

(3.57%) being neutral or unevaluated, the findings are

not entirely uniform. This inconsistency highlights a

critical need for future research using more robust

methodologies (Cui et al., 2023).

In healthcare, VR offers medical professionals

efficient, risk-free, and repeatable skills training for

surgical procedures. In education, AR and VR

provide personalized, interactive learning by

simulating complex scientific and historical concepts

that are impractical for traditional classrooms (Guo et

al., 2022).

In the entertainment sector, 6G is set to open new

opportunities for more immersive and realistic

gaming experiences. The integration of AR/VR into

entertainment, supported by 6G’s fast, low-latency

connectivity, will enable users to interact more deeply

with virtual worlds, offering a more engaging

experience than ever before.

While the potential of 6G to improve AR and VR

applications is vast, challenges such as the need for

specialized hardware, data security issues, and

scalable infrastructure cannot be overlooked. Thus, it

is essential to explore how 6G can be leveraged to

enhance the use of AR and VR in these key sectors

and to identify the hurdles that need to be overcome

for large-scale adoption of this transformative

technology.

2 RELATED STUDIES

The progression of wireless communication

technologies, particularly 5G and the upcoming 6G,

has led to extensive studies aimed at enhancing the

performance and dependability of data-heavy

applications like AR and VR. These applications

require extremely low latency and high data

throughput to ensure smooth and immersive user

experiences.

Numerous studies have explored how different

modulation schemes influence network performance

(Gupta et al., 2020). Examined QPSK, 16-QAM, and

64-QAM in the context of 5G systems, focusing on

their impact on bit error rate (BER) and signal-to-

noise ratio (Lee et al., 2020). Investigated the BER

performance under Rician and Rayleigh fading

channels, which are essential for simulating real-

world wireless environments.

With 6G on the horizon, new wireless

architectures and signal processing methods are being

explored (Zhang et al., 2019). Outlined the

requirements for 6G networks to support AR/VR

applications, emphasizing enhanced reliability and

reduced latency (Wang and Mao., 2020). Surveyed

the future directions of wireless communications for

immersive technologies.

In addition to modulation and channel modeling,

noise reduction has been a focus for improving signal

quality (Zhao et al., 2021). Demonstrated how

advanced channel coding and denoising methods can

reduce BER in 5G networks, improving AR/VR

performance.

Deep learning approaches have also been

explored to optimize wireless communication (Chen

et al., 2020). Reviewed deep learning techniques for

adaptive modulation, channel estimation, and

denoising, which are essential for next-gen network

performance.

3 METHODOLOGY

3.1 Dataset Description and

Preprocessing

The dataset used in this study consists of wireless

communication signals captured under various

modulation schemes, specifically QPSK, 16-QAM,

and 64-QAM, and transmitted through different

channel models, including Rayleigh and Rician

fading. The dataset contains raw received signals and

denoised signals stored as complex number strings,

alongside key metrics such as Signal-to-Noise Ratio

(SNR) and Bit Error Rate (BER), measured both

before and after applying denoising techniques (Tariq

et al., 2021).

3.1.1 Data Preprocessing

To preprocess the data, complex-valued strings from

the signal columns were parsed, and their real and

imaginary components were extracted. These

component lists were then averaged to convert the

vector data into scalar features for each sample

simplifying further analysis. The data for this study

was simulated using controlled wireless

communication models. These simulations emulate

real-world conditions that a 5G/6G network might

encounter, with different SNR levels and various

fading channel models (Rayleigh and Rician). The

dataset was generated by applying noise at different

levels and then applying denoising techniques to

remove the noise and assess the improvements in

BER and SNR. vector data into scalar features for

each sample, simplifying further analysis.

How 6G Will Transform Augmented and Virtual Reality in Healthcare, Education, and Entertainment: Opportunities and Challenges

165

To ensure numerical stability, Min-Max

normalization was applied to the extracted real and

imaginary components, scaling all features to a range

of 0 to 1. This normalization ensures that the data is

standardized for further statistical analysis and

machine learning tasks. Missing values in the dataset

were imputed with zeros to maintain dataset integrity

(

Siriwardhana et al

., 2021).

3.1.2 Denoising Techniques

Wavelet-based denoising was the primary technique

used to reduce BER and improve signal quality. The

process involved using a Discrete Wavelet Transform

(DWT) to decompose the signal into frequency

bands, allowing for the removal of high-frequency

components representing noise. This method is

effective because it removes noise while preserving

the signal's original low-frequency components (Liu

et al., 2022).

In addition to wavelet-based denoising, a median

filter was applied to further clean the signal. This

filter is effective at removing impulsive noise by

replacing each signal point with the median of

neighboring points, enhancing signal clarity.

.1.3 Dataset Analys

After preprocessing and denoising, the mean,

standard deviation, minimum, and maximum values

of the normalized real and imaginary components

were calculated. This descriptive analysis of the

dataset helps in understanding the distribution of the

data and the effectiveness of the denoising process.

Figure 1: Distribution of Normalized Real and

Imaginary Components of Received Wireless Signals.

Figure 1 illustrates the distribution of the

normalized real and imaginary components of the

received signals. The histograms supplemented by

Kernel Density Estimation (KDE) curves reveal that

both the real and imaginary parts of the received

signals are symmetrically distributed, centered

around a value of approximately 0.5, with no

significant skewness or bias (Vameghestahbanati et

al., 2023).

3.1.4 Statistical Summary of Normalized

Signal Features

Table 1 provides a comprehensive summary of the

descriptive statistics for the normalized signal

features, including the mean, standard deviation,

minimum, and maximum values for both the received

and denoised signals. The relatively low standard

deviations (approximately 0.07 to 0.09) reflect a

moderate spread in the data, consistent with the

smooth distributions observed in Figure 1 (Zhang et

al., 2022).

Table 1: Descriptive Statistics of Normalized Real and

Imaginary Signal

Features for Received and Denoised

Signals.

Feature Mean Std. Dev Minimum Maximum

Received

Signal Real

0.5412 0.0883 0.0 1.0

Received

nal Ima

0.5264 0.0701 0.0 1.0

Denoised

nal Real

0.5243 0.0965 0.0 1.0

Denoised

Signal Imag

0.5262 0.0701 0.0 1.0

3.2 Performance Analysis

Performance evaluation in this study is focused on Bit

Error Rate (BER), a key metric for assessing the

integrity of signal transmission. BER quantifies the

ratio of erroneous bits received to the total bits

transmitted and serves as a direct indicator of signal

quality and network reliability.

The effectiveness of denoising techniques was

evaluated by measuring BER both before and after

denoising across three modulation schemes: QPSK,

16-QAM, and 64-QAM. These modulation schemes

represent common configurations in modern wireless

networks, including 5G and the emerging 6G

systems.

The mean BER values were calculated for each

modulation scheme using Equation (1):

𝑀𝑒𝑎𝑛𝐵𝐸𝑅 





∑

𝐵𝐸𝑅







(1)

Where:

• MeanBER represents the mean Bit Error Rate.

• 𝐵𝐸𝑅



denotes the Bit Error Rate of the i-

sample.

RITECH 2025 - The International Conference on Research and Innovations in Information and Engineering Technology

166

• n represents the total number of samples tested

for each type of modulation.

The average BER values for each modulation

scheme were computed as follows:

Table 2: Average Bit Error Rate (BER) Before and After

Denoising for Different Modulation Schemes.

Modulation

Mean BER

Before

Mean BER

Afte

QPS

0.1991 0.1196

16-QAM 0.1993 0.1200

64-QAM 0.1989 0.1192

The results indicate a significant reduction in BER

after applying denoising, confirming the

effectiveness of the denoising algorithms in

mitigating noise and enhancing signal fidelity. This

reduction is critical for applications like Augmented

Reality (AR) and Virtual Reality (VR), which rely on

high-quality, low-latency data transmission.

Enhanced signal quality ensures a more reliable and

immersive AR/VR experience by reducing

transmission errors and latency.

This improvement in BER is crucial for latency-

sensitive and high-data-rate applications such as

Augmented Reality (AR) and Virtual Reality (VR),

where data integrity directly influences the user

experience. Enhanced signal quality through effective

noise reduction contributes to lower latency, fewer

transmission errors, and ultimately more reliable and

immersive AR/VR interactions.

Figure X illustrates the comparative mean BER

values before and after denoising for each modulation

type, visually affirming the quantitative findings. In

conclusion, the performance analysis confirms that

advanced signal processing techniques, particularly

denoising, play a vital role in optimizing wireless

communication systems for next-generation

applications.

3.3 Correlation Analysis Between SNR

and BER

To understand how variations in network conditions

influence signal quality, a correlation analysis

between Signal-to-Noise Ratio (SNR) and Bit Error

Rate (BER) was performed. SNR, which is a key

metric for measuring signal quality, reflects the ratio

of signal power to noise power. BER, on the other

hand, quantifies the number of incorrect bits received

compared to the total bits transmitted.

Pearson’s correlation coefficient was used to

quantify the linear relationship between SNR and

BER. The formula for Pearson's correlation is as

follows (2):

∑



SNR

-SNR



BER

-BER





∑



SNR

-SNR



∑



BER

-BER



Where 𝑆𝑁𝑅



and 𝐵𝐸𝑅



represent

individual observations for 𝑆𝑁𝑅

and 𝐵𝐸𝑅, and

𝑆𝑁𝑅

and 𝐵𝐸𝑅 represent the mean values of SNR

and BER.

The correlation analysis showed a strong negative

correlation between SNR and BER, both before and

after applying the denoising techniques. This

indicates that as SNR increases, BER decreases,

meaning that higher signal quality (higher SNR) leads

to fewer transmission errors (lower BER).

Results of the Correlation Analysis:

The Pearson correlation coefficients between SNR

and BER are summarized below:

Table 3: Pearson Correlation Coefficients Between Signal-

to-Noise Ratio (SNR) and BER.

Condition Pearson Correlation Coefficient

Before

Denoisin

0.953

After

Denoising

0.890

These negative correlation values confirm that

improving SNR directly improves BER, which is

crucial for AR/VR applications that require high-

quality data transmission with low error rates. The

negative correlation between SNR and BER also

supports the effectiveness of denoising in reducing

transmission errors and enhancing signal fidelity.

4 RESULT AND DISCUSSION

4.1 Impact of 6G on AR/VR

Applications

The incorporation of 6G technology is expected to

greatly enhance AR and VR applications, primarily

by offering extremely low latency, faster data transfer

rates, and increased network capacity. These

improvements facilitate real-time, immersive

experiences, which are crucial for applications in

sectors such as medical education, training, and

entertainment.

The performance analysis demonstrated a

significant reduction in BER and improvement in

(2)

How 6G Will Transform Augmented and Virtual Reality in Healthcare, Education, and Entertainment: Opportunities and Challenges

167

signal quality after the application of denoising

techniques. This improvement is crucial for AR/VR

applications that rely on low latency and high data

throughput to provide seamless, immersive

experiences.

Like any major technological advancement, 6G

introduces several non-technical obstacles that need

to be overcome for its successful deployment. These

include issues related to data security, privacy, and

the constraints of current hardware infrastructure

4.2 Non-Technical Challenges in 6G-

Driven AR/VR Applications

While 6G holds significant promise for enhancing

AR/VR applications, several non-technical issues

pose challenges that could impede wide-scale

adoption. The integration of 6G with AR/VR

applications significantly increases the generation of

sensitive personal data, such as biometrics, medical

records, and real-time location information. While

beneficial, 6G's high-speed data transfer also elevates

the risk of exposing this data to cyberattacks and leaks

if not properly secured. Therefore, implementing

robust security measures like encryption, secure data

transfer, and user authentication is essential to protect

user privacy and build trust in these advanced

technologies. (Al-Ansi et al., 2023; Nguyen et al.,

2021).

Privacy in 6G-powered AR/VR is a major

concern, as the real-time capture of sensitive data in

fields like healthcare and education raises ethical and

legal questions. This necessitates clear privacy

regulations to protect users from data exploitation

without consent. Ultimately, balancing user

experience with robust privacy protection remains a

critical challenge for the expansion of 6G networks

(

Chowdhury et al., 2022; He et al., 2023).

Hardware limitations present a significant non-

technical challenge to leveraging 6G for AR/VR.

Despite 6G's high-speed capabilities, necessary

devices like AR glasses and VR headsets are still

constrained by issues of affordability, accessibility,

and processing power. Furthermore, a major hurdle in

hardware design is the need for these devices to be

both lightweight and energy-efficient to fully utilize

6G's potential (

Khan et al., 2022; Porambage et al.,

2022).

The infrastructure for widespread 6G network

deployment remains underdeveloped, especially in

rural and technologically limited regions. Achieving

universal adoption with equitable access will require

significant infrastructure investment and global

cooperation (

Saad et al., 2021; Dogra et al., 2021).

4.3 Addressing Non-Technical

Challenges for Future Research

To overcome these non-technical barriers, several

actions are required:

• Data privacy and security protocols need to be

further developed to protect users in AR/VR

environments. This includes end-to-end

encryption, anonymization techniques, and

secure cloud storage solutions.

• Regulations governing the use of personal data

in AR/VR applications must be updated to

reflect the advanced capabilities of 6G and its

ability to handle large volumes of sensitive data

• Hardware development must focus on creating

affordable, energy-efficient, and high-

performance devices that are capable of fully

utilizing 6G networks without compromising

the user experience.

• Governments, industries, and researchers

should collaborate on developing scalable 6G

infrastructure to ensure that the benefits of

AR/VR technologies are accessible to all,

including in underserved regions.

4.4 Conclusion of Discussion

While 6G presents significant opportunities for

enhancing AR/VR applications, the non-technical

challenges—such as data security, privacy concerns,

and hardware limitations—must not be overlooked.

Addressing these

challenges is crucial to ensure

that AR/VR experiences in sectors like healthcare,

education, and entertainment are not only

transformative but also secure, private, and accessible

to a global audience. Future research should focus on

overcoming these hurdles through policy

development, technological innovation, and

collaborative efforts from all stakeholders.

Key findings include:

• QPSK performs well in low SNR conditions,

while 64-QAM offers higher data throughput in

ideal conditions.

• Denoising consistently reduced BER, enhancing

signal quality across all modulation schemes,

which is crucial for AR/VR applications that rely

on high-quality, low-latency data transmission.

• There is a strong negative correlation between

SNR and BER, highlighting the importance of

improving SNR for better AR/VR performance.

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ACKNOWLEDGEMENTS

The authors would like to thank the members of the

Distributed Intelligence Research Group at

Hasanuddin University for their insightful

discussions and collaborative environment which

greatly supported this research

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