RIS-Based 6G Beamforming with Reduced Complexity | #sciencefather #scientistaward #database #HybridBeamforming #Beamforming #WirelessCommunications #SignalProcessing

Efficient Hybrid Beamforming for RIS-Assisted 6G Using Branchwise Analog Architectures and Wideband Modeling

Introduction to 6G and RIS Technologies

The sixth generation (6G) of wireless networks is expected to support ultra-high-speed, low-latency, and intelligent communication environments. Reconfigurable Intelligent Surfaces (RIS) are a key enabler in 6G, allowing the dynamic control of electromagnetic waves via programmable reflecting elements. RIS can be deployed on walls, ceilings, and other infrastructure surfaces to manipulate signal propagation, improving coverage and reliability in complex environments like urban cities.


Challenges in RIS-Assisted Hybrid Beamforming

While RIS offers significant benefits in energy and spectral efficiency, integrating it with traditional hybrid beamforming presents several challenges:

  • High hardware complexity due to dense phase shifter (PS) connections.

  • Increased computational burden during optimization of beamforming matrices.

  • Quantization constraints from low-resolution phase shifters.

  • Channel estimation difficulties because of passive RIS elements.

  • Power consumption and cost issues with dense analog front-ends.

The Need for Low-Complexity Architectures

Traditional hybrid beamforming architectures such as:

  • Single Phase Shifter (SPS)

  • Double Phase Shifter (DPS)

  • Combined Phase Shifter (CPS)

to suffer from hardware inefficiency and scalability issues as the number of antennas or RIS elements increases. This motivates the development of simplified yet effective beamforming solutions.

Proposed Branchwise Phase Shifter (BPS) Architecture

The BPS architecture introduces a new analog beamforming design where phase shifters are assigned per branch of antennas rather than per element, reducing complexity significantly.

Key Features:

  • Divides antennas and RIS elements into branches.

  • Employs a power splitter to distribute RF signals to each branch.

  • Utilizes finite-resolution PSs per branch instead of per antenna.

  • Incorporates a zero-forcing iterative algorithm for efficient beamforming.

B-MW Channel Modeling

A Branchwise Millimeter-Wave (B-MW) channel model is developed for RIS-aided wideband communication, capturing the frequency-selective behavior and spatial distribution of the channel:

  • Accounts for frequency-domain channel characteristics.

  • Models beam coverage and interference at each subcarrier.

  • Utilizes the steering vectors of Uniform Planar Arrays (UPA) and Uniform Linear Arrays (ULA).

Algorithm Design and Optimization

An iterative beamforming algorithm is proposed using the BPS architecture to:

  • Minimize transmit power while satisfying rate constraints.

  • Optimize the phase of PSs within quantized levels.

  • Achieve a computational complexity reduction of up to 50%.

Performance Evaluation

Metrics Used:

  • Beam Coverage (via polar histogram plots)

  • Achievable Rate (bps/Hz)

  • Computational Complexity (comparative)

Results Summary:

  • BPS outperforms SPS and CPS in both achievable rate and coverage.

  • Slightly lower performance than DPS at low antenna counts, but with far lower complexity.

  • Superior rate gain in multi-user wideband environments.

Practical Implications for 6G

The proposed BPS-based hybrid beamforming design:

  • Supports dense deployments with lower power and cost.

  • Enhances beamforming flexibility in blockage-prone urban and indoor environments.

  • Aligns well with metasurface-based systems and terahertz communications envisioned for future 6G networks.

Future Work

  • Extending the BPS model to dynamic RIS configurations.

  • Integration with machine learning for adaptive beamforming.

  • Testing in real-world 6G testbeds and urban deployments.

  • Exploration in terahertz and visible light communication (VLC) regimes.

#HybridBeamforming #mmWave #Beamforming #WirelessCommunications #SignalProcessing #PhasedArray

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