Hybrid Detection Algorithms Enhance 5G Healthcare Latency

By Samudrapom DamApr 1 2024Reviewed by Susha Cheriyedath, M.Sc.

In an article recently published in the journal Scientific Reports, researchers proposed novel hybrid detection algorithms to reduce latency in 5G/B5G communication for smart healthcare in rural areas.

Background

Smart hospitals can significantly enhance the quality of life by consistently providing health monitoring capabilities. These hospitals can offer access to specialists, telemedicine, and remote consultations often unavailable in rural locations, thus substantially reducing travel costs and time for patients and enhancing overall access to care.

Additionally, smart technologies like robotic surgery and artificial intelligence (AI)--powered diagnostics can enhance the efficiency and accuracy of healthcare delivery, specifically in rural regions. Latency is a critical metric for effectively deploying smart hospitals.

Remote surgery and healthcare, which are highly reliant on low latency, have witnessed a transformative development with the emergence of 5G technology. However, no technology has been developed that can offer low latency in every condition until now.

In the physical layer, an advanced waveform design similar to multiple inputs and multiple outputs (MIMO) and offers low latency can play a crucial role in 6G-based smart hospital development. However, designing such advanced detection algorithms for a MIMO system is challenging as multiple antennas must be installed at the base station.

Although many detection methods, like minimum mean square error (MMSE) and zero-forcing equalization (ZFE), have been proposed and implemented for the 4G/5G framework, these detection algorithms compromise the complexity and throughput of the framework.

The proposed approach

In this study, researchers proposed novel hybrid detection algorithms, including QR decomposition with the M-algorithm for maximum likelihood detection-MMSE (QRM-MLD-MMSE) and QRM-MLD-zero forcing (QRM-MLD-ZF), for use in massive MIMO (M-MIMO) technology. These hybrid detection algorithms for the 6G framework can reduce latency in MIMO-based non-orthogonal multiple access (NOMA) waveforms and provide optimum throughput with minimal latency while maintaining optimal bit error rate (BER) performance for efficient transmission of information in real time.

The objective of the study was to develop a hybrid detection approach that combines QRM-MLD with beamforming (BF) to optimize spectrum efficiency, throughput, and latency in large-scale MIMO and NOMA systems to leverage the 5G networks' capabilities. Researchers performed simulations to determine several parameters, including power spectral density (PSD) and BER over Rician and Rayleigh channels using both standard and proposed algorithms, including C-ZFE, C-MMSE, C-QRM-MLD, QRM-MLD-ZF, and QRM-MLD-MMSE.

QRM-MLD plays a critical role in decreasing latency in 5G networks. QR decomposition can efficiently mitigate latency by improving the symbol detection accuracy. Channel matrix decomposition allows a more accurate estimation of the transmitted symbols even during the presence of noise and interference, which reduces the need for reprocessing and retransmission of data, reducing communication latency.

Thus, 5G networks can benefit from faster data transmission and improved reliability, ensuring the operation of crucial applications with minimal delay and meeting the strict latency requirements of emerging technologies. ZFE is a simple detection method used in the M-MIMO framework.

This method obtains optimal signal detection in the presence of noise and interference as ZFE can effectively address inter-symbol interference (ISI) in communication systems. ZFE is beneficial in 6G due to its low computational complexity, which makes it suitable for low-latency and high-throughput communications.

Thus, ZFE can offer efficient equalization to mitigate interference and improve spectral efficiency with 6G's emphasis on massive connectivity and ultra-reliable and low-latency communication (URLLC). The MMSE is a popular technique for detecting signals efficiently in the presence of interference and noise. The MMSE provides optimal estimates under Gaussian noise assumptions to effectively minimize mean square error.

Significance of the study

Results demonstrated that the proposed hybrid algorithms could significantly enhance PSD and BER with lower complexity, indicating a significant improvement in 5G communication for smart healthcare applications. For instance, the BER performance for 64-sub-carriers in the Rician channel showed that a BER of 10⁻³ was realized at an SNR of 3.8 dB using the QRM-MLD-MMSE algorithm, 4.1 dB using QRM-MLD-ZF, 4.7 dB using C-QRM-MLD, 5.6 dB using C-MMSE, and 6 dB using C-ZF.

Thus, the QRM-MLD-MMSE algorithm displayed an SNR gain in the range of 0.9 dB-2.2 dB over the conventional methods. Overall, the proposed hybrid algorithms, specifically QRM-MLD-ZF, displayed a superior performance by achieving an SNR improvement of 3.2 dB when compared to existing standard/conventional techniques.

By realizing a low BER, the hybrid detection algorithms ensured reliable data transmission and minimized the need for error correction or retransmissions, thus improving the responsiveness of the overall system and reducing latency.

Additionally, the out-of-band emission (OOBE) was reduced, leading to high spectral access to the framework, while the framework complexity increases with the throughput. However, the simulation results revealed that PSD performance and high throughput were obtained with trivial intricacy.

To summarize, the findings of this study demonstrated the feasibility of using the proposed novel hybrid detection algorithms to improve the latency for 5G/B5G-based smart healthcare connectivity, specifically in rural areas.