Sum-Rate Maximization Using Reconfigurable Holographic Surfaces and Reconfigurable Intelligent Surfaces in Multi-User Multiple-Input Single-Output Systems

แนวคิดหลัก

This research paper proposes a novel hybrid beamforming design using Reconfigurable Holographic Surfaces (RHS) and Reconfigurable Intelligent Surfaces (RIS) to maximize the sum-rate in millimeter-wave (mmWave) multi-user multiple-input single-output (MU-MISO) communication systems.

บทคัดย่อ

• Bibliographic Information: Gadamsetty, P. K., Hari, K. V. S., & Hanzo, L. (2024). Sum-Rate Maximization of RIS-Aided Digital and Holographic Beamformers in MU-MISO Systems. arXiv preprint arXiv:2410.15995.
• Research Objective: This paper investigates the joint optimization of digital, holographic, and passive beamformers in an RHS-RIS-aided MU-MISO system to maximize the sum-rate of all user equipment (UE).
• Methodology: The researchers develop an alternating maximization (AM) algorithm to decouple the non-convex optimization problem into three subproblems: digital beamforming, holographic beamforming, and RIS phase shift matrix optimization. They employ zero-forcing beamforming for digital beamforming, fractional programming for holographic beamforming, and the Riemannian conjugate gradient algorithm for optimizing the RIS phase shift matrix.
• Key Findings: Simulation results demonstrate that the proposed RHS-RIS-based hybrid beamformer significantly outperforms conventional counterparts operating without an RIS in multi-UE scenarios. The achieved sum-rate improvement ranges from 8 bps/Hz to 13 bps/Hz for various transmit powers at the base station (BS) and UEs.
• Main Conclusions: The integration of RHS and RIS technologies in mmWave MU-MISO systems offers substantial performance enhancements in terms of sum-rate, particularly in environments with blocked line-of-sight paths. The proposed AM algorithm effectively optimizes the active and passive beamformers to achieve these gains.
• Significance: This research contributes to the advancement of mmWave communication systems by proposing a novel and efficient hybrid beamforming design that leverages the advantages of both RHS and RIS. The findings have practical implications for enhancing data rates and coverage in future wireless networks.
• Limitations and Future Research: The study assumes perfect channel state information (CSI), which may not be realistic in practical scenarios. Future research could explore the impact of imperfect CSI and develop robust beamforming techniques. Additionally, investigating the energy efficiency and hardware complexity of the proposed system would be beneficial.

สถิติ

The sum-rate improvement attained ranges from 8 bps/Hz to 13 bps/Hz for various transmit powers at the base station (BS) and at the UEs.
RHSs typically have about 2.5 times as many elements as a phased array system having the same antenna directivity.
The radiation power to total power consumption for phased array and RHS systems is 4% and 25%, respectively.

คำพูด

"This is the first piece of work maximizing the sum-rate through the joint optimization of digital, holographic, and passive beamformers."
"Simulation results demonstrate the effectiveness of the proposed algorithm, exhibiting improvements over existing RHS-based methods operating without an RIS."

ข้อมูลเชิงลึกที่สำคัญจาก

Sum-Rate Maximization of RIS-Aided Digital and Holographic Beamformers in MU-MISO Systems

by Pavan Kumar ... ที่ arxiv.org 10-22-2024

https://arxiv.org/pdf/2410.15995.pdf

สอบถามเพิ่มเติม

How will the proposed RHS-RIS-based hybrid beamforming system perform in a mobile environment with dynamic channel conditions?

In a mobile environment with dynamic channel conditions, the performance of the proposed RHS-RIS-based hybrid beamforming system will be impacted by the channel coherence time. This refers to the time duration over which the channel can be considered static.
Here's a breakdown of the challenges and potential solutions:
Challenges:

Fast Fading: mmWave channels, especially in mobile scenarios, exhibit fast fading due to high mobility and small wavelengths. This means the channel changes rapidly, rendering the CSI outdated quickly.
Doppler Shift: Movement of the UE or scattering objects introduces Doppler shift, further distorting the received signal and impacting beam alignment.
Overhead and Latency: Frequent channel estimation and beamformer optimization are necessary to track the dynamic channel, leading to increased overhead and latency.
Potential Solutions:

Robust Beamforming: Design beamformers that are robust to small channel variations, such as using wider beams or employing techniques like robust optimization.
Channel Prediction: Implement channel prediction algorithms to estimate future channel states based on past observations, allowing for proactive beamformer adjustments.
Low-Overhead CSI Acquisition: Explore techniques like compressive sensing or beam alignment methods that require less frequent or lower-overhead channel estimation.
Exploiting Channel Sparsity: mmWave channels are often sparse, meaning only a few paths contribute significantly to the signal. This sparsity can be exploited for efficient channel estimation and beamforming.
Performance in Mobile Environments:
The performance in a mobile environment will depend on the specific channel conditions (speed of movement, scattering environment) and the effectiveness of the employed solutions. It's expected that the achievable sum-rate will be lower compared to static scenarios due to the aforementioned challenges. However, with appropriate techniques, the RHS-RIS system can still provide significant performance gains compared to conventional systems without RIS.

Could the computational complexity of the proposed AM algorithm be a limiting factor in practical implementations, and are there alternative optimization techniques that could offer a better trade-off between performance and complexity?

Yes, the computational complexity of the proposed AM algorithm, particularly the O(K²N²RIS) complexity for RIS phase optimization, could be a limiting factor in practical implementations, especially for large-scale systems with many RIS elements and users.
Alternative Optimization Techniques:
Several alternative optimization techniques could offer a better trade-off between performance and complexity:

Deep Learning: Train deep neural networks to learn the mapping between channel conditions and optimal beamforming matrices. This can significantly reduce runtime complexity once the network is trained.
Hierarchical Optimization: Decompose the optimization problem into smaller subproblems that can be solved independently and then combined. For example, cluster users or RIS elements into groups to reduce the problem size.
Sparse Optimization: Exploit the sparsity of mmWave channels and design beamformers with fewer active elements, reducing the number of optimization variables.
Stochastic Gradient Descent (SGD): Instead of computing the full gradient, SGD updates the variables based on a small batch of data, reducing computational cost per iteration.
Trade-off Considerations:
The choice of optimization technique depends on factors like:

Hardware Resources: Available processing power and memory constraints.
Latency Requirements: Maximum allowable delay for beamforming optimization.
Performance Target: Desired sum-rate performance and acceptable degradation compared to the AM algorithm.
Practical Implementations:
In practical implementations, a hybrid approach combining different techniques might be necessary. For instance, deep learning can be used for initial beamformer design, followed by a low-complexity algorithm for fine-tuning based on real-time channel feedback.

What are the broader implications of using metamaterials like RHS and RIS for shaping and controlling electromagnetic waves in various fields beyond wireless communication, such as imaging, sensing, and energy harvesting?

Metamaterials like RHS and RIS offer unprecedented control over electromagnetic waves, opening up exciting possibilities beyond wireless communication. Here are some broader implications:
1. Imaging:

Super-resolution Imaging: Overcome the diffraction limit of conventional lenses by creating sub-wavelength structures that manipulate light waves, enabling imaging with finer detail.
Perfect Lens: Theoretically achieve perfect imaging by using metamaterials with negative refractive index to amplify evanescent waves, which carry high-resolution information.
Medical Imaging: Develop non-invasive imaging techniques with enhanced resolution and depth penetration for improved diagnosis and treatment monitoring.
2. Sensing:

Hypersensitive Sensors: Design sensors with enhanced sensitivity to detect minute changes in the electromagnetic field, enabling applications in environmental monitoring, medical diagnostics, and security screening.
Spectroscopy: Create compact and tunable spectrometers using metamaterials to analyze the composition of materials by controlling the interaction of light with matter.
Wireless Power Transfer: Develop highly efficient and directional wireless power transfer systems by focusing electromagnetic energy to specific locations, enabling applications in charging electric vehicles and powering medical implants.
3. Energy Harvesting:

Metamaterial Absorbers: Design metamaterial structures that efficiently absorb electromagnetic energy from ambient sources like sunlight or radio waves and convert it into usable electrical energy.
Enhanced Solar Cells: Improve the efficiency of solar cells by using metamaterials to trap and concentrate light within the active layer, increasing light absorption and energy conversion.
4. Other Applications:

Cloaking and Camouflage: Create metamaterial cloaks that redirect electromagnetic waves around an object, rendering it invisible to certain frequencies.
Antennas and Waveguides: Design compact and high-performance antennas and waveguides with tailored radiation patterns and impedance matching using metamaterials.
Overall Impact:
The use of metamaterials like RHS and RIS has the potential to revolutionize various fields by enabling unprecedented control over electromagnetic waves. This can lead to the development of novel technologies with improved performance, reduced size, and enhanced functionality, impacting healthcare, energy, communication, and beyond.