Joint Power Allocation and Beamforming for In-band Full-duplex Multi-cell Multi-user Networks

Conceitos essenciais

Optimizing joint power allocation and beamforming for in-band full-duplex multi-cell multi-user networks.

Resumo

The paper explores a robust scheme for joint power allocation and beamforming in in-band full-duplex multi-cell multi-user (IBFD-MCMU) networks. It addresses the challenges of self-interference cancellation (SIC) and co-channel interference (CCI) in IBFD radios, proposing an iterative algorithm to enhance analog self-interference cancellation depth. The method aims to achieve significant spectral efficiency gains with less effect on downlink communication compared to existing null-space projection methods. By leveraging beamforming for SIC, the proposed algorithm reduces computational complexity while maintaining system throughput. The study also considers hardware impairments, channel uncertainty, and practical constraints in its design approach.

Estatísticas

Our method can achieve 42.9% of IBFD gain in terms of spectral efficiency with only antenna isolation.
The computation time is reduced by at least 20% compared to existing schemes due to faster convergence speed.
With single-antenna users, our algorithm saves at least 40% of the computation time at the cost of < 10% sum rate reduction.

Citações

Principais Insights Extraídos De

Joint Power Allocation and Beamforming for In-band Full-duplex Multi-cell Multi-user Networks

by Haifeng Luo,... às arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.10832.pdf

Perguntas Mais Profundas

How does the proposed joint power allocation and beamforming scheme compare to other existing methods

The proposed joint power allocation and beamforming scheme in the context of in-band full-duplex multi-cell multi-user networks offers several advantages compared to existing methods. Firstly, it addresses the challenge of self-interference cancellation by leveraging beamforming techniques for interference management. This approach enhances the analog self-interference cancellation depth provided by precoders, leading to a more efficient utilization of resources without compromising downlink communication quality. In contrast to traditional null-space projection methods that may distort transmitted beams and sacrifice downlink capacity, the proposed scheme minimizes residual self-interference power effectively.
Furthermore, the algorithm accounts for hardware impairments and channel uncertainty, making it robust in real-world scenarios where imperfect CSI or limited dynamic range of receivers can impact system performance. By formulating a non-convex optimization problem into two sub-problems - interference management through beamforming and power allocation - with closed-form solutions, the proposed method achieves significant gains in spectral efficiency while ensuring effective self-interference suppression.

What are the implications of reducing computational complexity while maintaining system throughput

Reducing computational complexity while maintaining system throughput has significant implications for practical implementation and operational efficiency. By optimizing power allocation and beamforming jointly with lower computational requirements, the proposed algorithm streamlines processing tasks without compromising network performance. This reduction in complexity translates to faster convergence speeds during iterative processes, ultimately saving computation time.
Maintaining system throughput ensures that data transmission rates remain high even as computational demands decrease. This balance between complexity reduction and throughput preservation is crucial for enhancing overall network efficiency and resource utilization. It allows for more agile adaptation to changing network conditions while minimizing operational costs associated with excessive computational overhead.

How can advancements in hardware technology impact the implementation of such algorithms

Advancements in hardware technology play a pivotal role in enabling the implementation of sophisticated algorithms like joint power allocation and beamforming schemes in wireless communication systems. Improved hardware capabilities such as higher-resolution DACs/ADCs contribute to enhanced signal processing accuracy, facilitating more precise control over transmit/receive operations.
For instance:

Higher Dynamic Range: Advanced transceiver components with extended dynamic ranges reduce distortion levels caused by transmitter/receiver saturation.
Increased Processing Power: More powerful processors enable faster computations required for complex algorithms like digital self-interference cancellation.
Enhanced RF Components: Upgraded RF chains enhance signal fidelity across multiple antennas, improving overall system performance.
These advancements not only support the efficient execution of algorithms but also pave the way for implementing cutting-edge technologies like full-duplex communication with improved reliability and scalability within multi-cell environments.