Distributed Precoding for Satellite-Terrestrial Integrated Networks Without Sharing Channel State Information at the Transmitters

Conceitos essenciais

The authors propose a novel distributed precoding method that incorporates a rate-splitting strategy to efficiently manage the interference in satellite-terrestrial integrated networks without requiring the sharing of channel state information between the satellite and the terrestrial base station.

Resumo

The key highlights and insights of the content are:

The authors consider a satellite-terrestrial integrated network (STIN) scenario where a low-earth-orbit (LEO) satellite serves satellite users (SUs) outside the coverage of a terrestrial base station (BS), while the terrestrial BS serves terrestrial users (TUs). Due to full frequency reuse, the interference from the satellite to some TUs is a significant issue.

To address this interference problem, the authors propose a distributed precoding method that employs a rate-splitting (RS) strategy. The key features are:

The messages intended for the SUs are split into common and private parts, where the common parts are jointly encoded and decoded by the SUs and the interfered TUs.
The precoders at the satellite and the terrestrial BS are designed in a distributed manner without sharing channel state information at the transmitters (CSIT).

To enable the distributed precoding design, the authors present a spectral efficiency decoupling technique that disentangles the total spectral efficiency function into two distinct terms, each of which depends solely on the satellite's precoder and the terrestrial BS's precoder, respectively.

The authors then develop a generalized power iteration inspired optimization algorithm to solve the non-convex and non-smooth precoding optimization problem, leveraging the first-order optimality condition.

Simulation results demonstrate that the proposed distributed precoding method with the rate-splitting strategy outperforms the existing precoding methods in terms of spectral efficiency and computational complexity.

Estatísticas

The authors use the following key metrics and figures to support their proposed method:
"The SINR of the common stream 𝑠𝑐 for TU 𝑘 ∈ Kint
𝑡 and SU 𝑢 are respectively given as"
SINRbs
𝑐,𝑘∈Kint
𝑡 = (zH
𝑘f𝑐)2 / (𝑃𝑡/𝑃𝑠 ∑𝐾𝑡
𝑗=1 (hH
𝑘v𝑗)2 + ∑𝐾𝑠
𝑖=1 (zH
𝑘f𝑝,𝑖)2 + 𝜎2/𝑃𝑠)
SINRsat
𝑐,𝑢= (gH
𝑢f𝑐)2 / (∑𝐾𝑠
𝑖=1 (gH
𝑢f𝑝,𝑖)2 + 𝜎2/𝑃𝑠)
"The SINR of the private stream 𝑚𝑘 for the TUs in K𝑡 is given by"
SINRbs
𝑝,𝑘∈Kint
𝑡 = (hH
𝑘v𝑘)2 / (∑𝐾𝑡
𝑗=1,𝑗≠𝑘 (hH
𝑘v𝑗)2 + 𝑃𝑠/𝑃𝑡 ∑𝐾𝑠
𝑖=1 (zH
𝑘f𝑝,𝑖)2 + 𝜎2/𝑃𝑡)
SINRbs
𝑝,𝑘∉Kint
𝑡 = (hH
𝑘v𝑘)2 / (∑𝐾𝑡
𝑗=1,𝑗≠𝑘 (hH
𝑘v𝑗)2 + 𝜎2/𝑃𝑡)
"The SINR of the private stream 𝑠𝑝,𝑢 for SU 𝑢 is"
SINRsat
𝑝,𝑢= (gH
𝑢f𝑝,𝑢)2 / (∑𝐾𝑠
𝑖=1,𝑖≠𝑢 (gH
𝑢f𝑝,𝑖)2 + 𝜎2/𝑃𝑠)

Citações

"To address the interference management problem in STINs, this paper proposes a novel distributed precoding method."
"Key features of our method are: i) a rate-splitting (RS) strategy is incorporated for efficient interference management and ii) the precoders are designed in a distributed way without sharing channel state information between a satellite and a terrestrial BS."
"To resolve the non-smoothness raised by the RS strategy, we approximate the spectral efficiency expression as a smooth function by using the LogSumExp technique; thereafter we develop a generalized power iteration inspired optimization algorithm built based on the first-order optimality condition."

Principais Insights Extraídos De

Distributed Precoding for Satellite-Terrestrial Integrated Networks Without Sharing CSIT

by Doseon Kim,S... às arxiv.org 04-02-2024

https://arxiv.org/pdf/2309.06325.pdf

Perguntas Mais Profundas

How can the proposed distributed precoding method be extended to incorporate more advanced interference management techniques, such as multi-layer rate-splitting or coordinated beamforming, while maintaining the distributed design

The proposed distributed precoding method can be extended to incorporate more advanced interference management techniques by integrating multi-layer rate-splitting or coordinated beamforming strategies while still maintaining the distributed design.
For multi-layer rate-splitting, the system can be enhanced by introducing additional layers of rate-splitting for more efficient interference management. This can involve splitting the messages into multiple parts, each decoded by different groups of users, allowing for more sophisticated interference handling. The precoding matrices can be optimized to cater to the decoding requirements of each layer, ensuring that the overall spectral efficiency is maximized.
In the case of coordinated beamforming, the distributed precoding method can be adapted to include coordinated beamforming schemes where the satellite and terrestrial base stations collaborate to optimize the beamforming vectors. This collaboration can be achieved through exchanging information on channel state or through iterative optimization algorithms that converge to a coordinated solution. By jointly designing the beamforming vectors, the interference between the satellite and terrestrial networks can be mitigated more effectively, leading to improved spectral efficiency.
By incorporating these advanced interference management techniques into the distributed precoding framework, the system can achieve higher spectral efficiency and better interference mitigation capabilities while still maintaining the decentralized and distributed nature of the design.

What are the potential challenges and trade-offs in applying the proposed method to other satellite-terrestrial integrated network scenarios, such as those with multiple satellites or multiple terrestrial base stations

Applying the proposed method to other satellite-terrestrial integrated network scenarios, such as those with multiple satellites or multiple terrestrial base stations, may present some challenges and trade-offs.
One potential challenge is the increased complexity of managing interference in scenarios with multiple satellites or terrestrial base stations. The distributed precoding method may need to be adapted to handle the interactions and interference among the different nodes in the network effectively. This could involve developing more sophisticated algorithms for optimizing the precoding matrices and coordinating the transmission strategies across multiple nodes.
Another challenge is the scalability of the distributed design. As the network grows in size with more satellites or base stations, the overhead of information exchange and coordination between nodes may increase. Balancing the need for efficient interference management with the complexity of the distributed algorithms becomes crucial in such scenarios.
Trade-offs may arise in terms of computational complexity and signaling overhead. As the network complexity increases, the computational requirements for optimizing the precoding matrices and coordinating transmissions may also escalate. This could lead to trade-offs between spectral efficiency gains and the computational resources needed to implement the distributed precoding method effectively.
Overall, adapting the proposed method to more complex satellite-terrestrial integrated network scenarios requires careful consideration of the challenges and trade-offs involved to ensure optimal performance and scalability.

Given the high mobility of LEO satellites, how can the proposed precoding method be adapted to handle the time-varying nature of the satellite-terrestrial channels and the associated CSIT estimation errors

Adapting the proposed precoding method to handle the time-varying nature of the satellite-terrestrial channels and the associated CSIT estimation errors due to the high mobility of LEO satellites can be achieved through several strategies:

Dynamic Channel Estimation: Implementing adaptive channel estimation techniques that can track the fast-changing channel conditions of LEO satellites. This could involve using Kalman filtering or other predictive algorithms to update the channel estimates in real-time based on the observed channel responses.

Robust Precoding Design: Developing precoding algorithms that are robust to channel variations and estimation errors. By incorporating robust optimization techniques, the precoding matrices can be designed to mitigate the impact of imperfect CSIT and time-varying channels, ensuring reliable communication links.

Feedback Mechanisms: Establishing efficient feedback mechanisms between the satellites and terrestrial base stations to exchange updated channel state information. This feedback loop can help in adapting the precoding strategies based on the latest channel estimates, improving the overall system performance.

Adaptive Transmission Schemes: Implementing adaptive transmission schemes that can dynamically adjust the precoding vectors based on the changing channel conditions. Techniques like adaptive modulation and coding can be employed to optimize the transmission parameters in response to channel variations.

By incorporating these adaptive strategies into the precoding method, the system can effectively handle the time-varying nature of the satellite-terrestrial channels and the associated CSIT estimation errors, ensuring robust and reliable communication in dynamic satellite networks.