Optimizing Spectral Efficiency in RIS-Assisted Cell-Free Massive MIMO NOMA Systems

核心概念

The core message of this article is to propose a joint optimization framework to maximize the sum spectral efficiency of a reconfigurable intelligent surface (RIS)-assisted cell-free massive multiple-input multiple-output (mMIMO) non-orthogonal multiple access (NOMA) system, considering imperfect channel state information and imperfect successive interference cancellation.

摘要

The article considers a RIS-assisted cell-free mMIMO NOMA system, where each access point (AP) serves all the users with the aid of the RIS. The authors practically model the system by considering imperfect instantaneous channel state information (CSI) and employing imperfect successive interference cancellation at the users' end.

The key highlights and insights from the article are:

1. The authors derive a closed-form downlink spectral efficiency (SE) expression using the statistical CSI, which depends only on large-scale fading coefficients and simplifies to the existing works as special cases.

2. The authors propose a novel successive Quadratic Transform (successive-QT) algorithm to optimize the transmit power coefficients using the concept of block optimization along with quadratic transform, and then use the particle swarm optimization technique to design the RIS phase shifts.

3. Numerical investigations reveal that NOMA outperforms orthogonal multiple access in terms of SE for the RIS-assisted cell-free system, and the RIS-assisted link is more advantageous in lower transmit power regions where the direct link between AP and user is weak.

4. The proposed joint optimization framework significantly improves the sum SE of the system compared to the non-optimized case. The impact of the intra-cluster interference on the SE is also significantly reduced, especially at the higher transmit power region.

統計資料

The authors use the following key metrics and figures to support their analysis:

Closed-form expression for the downlink spectral efficiency (SE) of the nth user in the kth cluster, given by:
Γkn = (Desired Signal Power) / (Beamforming Gain Uncertainty + Inherent Intra-cluster Interference + Residual Intra-cluster Interference + Inter-cluster Interference + 1)

The authors show that the derived closed-form SE expression simplifies to the existing works on cell-free mMIMO, RIS-assisted systems, and NOMA as special cases.

Numerical results demonstrate that NOMA outperforms orthogonal multiple access in terms of SE for the RIS-assisted cell-free system.

The authors show the effectiveness of the proposed joint optimization framework in improving the sum SE of the system compared to the non-optimized case.

引述

"The RIS-assisted link is more advantageous at lower transmit power regions where the direct link between AP and user is weak."
"NOMA outperforms orthogonal multiple access schemes in terms of SE."
"The proposed joint optimization framework significantly improves the sum SE of the system."

從以下內容提煉的關鍵洞見

Analysis and Optimization of RIS-Assisted Cell-Free Massive MIMO NOMA Systems

by Malay Chakra... 於 arxiv.org 09-18-2024

https://arxiv.org/pdf/2407.04006.pdf

深入探究

How can the proposed framework be extended to consider multiple RISs in the cell-free mMIMO NOMA system?

The proposed framework can be extended to incorporate multiple Reconfigurable Intelligent Surfaces (RISs) in the cell-free massive MIMO Non-Orthogonal Multiple Access (NOMA) system by adapting the channel modeling, optimization algorithms, and system architecture.

Channel Modeling: The aggregate channel from multiple RISs to users must be modeled. This involves defining the channels from each access point (AP) to each RIS and from each RIS to the users. The overall channel can be expressed as a sum of the contributions from each RIS, taking into account the spatial correlation and large-scale fading effects. The channel model can be represented as:
[
u_{mkn} = l_{mkn} + \sum_{r=1}^{R} h_{rkn}^H \Theta_r g_{mr}
]
where (R) is the number of RISs, and (h_{rkn}) and (g_{mr}) represent the channels from the RIS to the user and from the AP to the RIS, respectively.

Optimization Framework: The joint optimization problem for power allocation and RIS phase shifts can be reformulated to account for multiple RISs. The optimization variables will now include the phase shifts for each RIS and the power control coefficients for each AP. The optimization problem can be expressed as:
[
P1: \max_{\eta, \Theta_1, \Theta_2, \ldots, \Theta_R} \sum_{k=1}^{K} \sum_{n=1}^{N} SE_{kn}(\eta, \Theta_1, \Theta_2, \ldots, \Theta_R)
]
subject to the same constraints as before, but now incorporating the additional RIS phase constraints.

Algorithm Design: The optimization algorithms, such as the successive quadratic transform (QT) and particle swarm optimization (PSO), can be adapted to handle the increased complexity of multiple RISs. This may involve developing new heuristics or iterative methods that efficiently converge to a solution while managing the increased number of variables.

System Architecture: The physical deployment of multiple RISs should be strategically planned to maximize coverage and minimize interference. The placement of RISs should consider the geographical distribution of users and APs to enhance the overall system performance.

By extending the framework in these ways, the benefits of multiple RISs can be harnessed to further improve the spectral efficiency and reliability of the cell-free mMIMO NOMA system.

What are the potential challenges and trade-offs in implementing the joint optimization of power allocation and RIS phase shifts in a practical system?

Implementing the joint optimization of power allocation and RIS phase shifts in a practical system presents several challenges and trade-offs:

Complexity of Optimization: The joint optimization problem is inherently non-convex and can be computationally intensive, especially as the number of users, APs, and RIS elements increases. This complexity can lead to longer computation times and may require advanced optimization techniques, which can be challenging to implement in real-time systems.

Channel State Information (CSI) Acquisition: Accurate CSI is crucial for effective optimization. However, in practical scenarios, obtaining perfect instantaneous CSI is often impractical due to factors such as mobility, environmental changes, and pilot contamination. The reliance on statistical CSI can lead to suboptimal performance, particularly in rapidly changing environments.

Interference Management: The presence of multiple users and RISs can lead to increased intra-cluster and inter-cluster interference. Effective management of this interference is essential to ensure that the desired signals are not degraded. The optimization must balance power allocation to mitigate interference while maximizing the desired signal strength.

Hardware Limitations: The physical limitations of RIS elements, such as phase shift resolution and power handling capabilities, can impact the effectiveness of the optimization. Additionally, the deployment of multiple RISs may introduce practical constraints related to cost, energy consumption, and maintenance.

Trade-offs Between Power and Phase Optimization: There is often a trade-off between optimizing power allocation and RIS phase shifts. For instance, optimizing power may lead to increased interference, while optimizing phase shifts may not fully utilize the available power. Finding the right balance is crucial for maximizing overall system performance.

Scalability: As the number of users and RISs increases, the optimization framework must remain scalable. This requires efficient algorithms that can handle large-scale systems without significant degradation in performance or excessive computational burden.

Addressing these challenges requires a careful design of the optimization framework, robust algorithms, and consideration of practical constraints to ensure that the system can operate effectively in real-world scenarios.

How can the insights from this work be leveraged to design energy-efficient RIS-assisted cell-free mMIMO NOMA systems?

The insights from this work can be leveraged to design energy-efficient RIS-assisted cell-free massive MIMO NOMA systems in several ways:

Optimized Power Allocation: The proposed successive quadratic transform (QT) algorithm for power allocation can be utilized to minimize the total transmit power while maximizing the spectral efficiency. By carefully allocating power based on the channel conditions and user requirements, the system can reduce energy consumption without sacrificing performance.

Efficient RIS Phase Design: The insights into the impact of RIS phase shifts on system performance can guide the design of phase matrices that enhance signal quality and reduce interference. By optimizing the phase shifts to create constructive interference at the users, the system can achieve better performance with lower transmit power.

Adaptive Resource Management: The framework can be extended to include adaptive resource management strategies that dynamically adjust power allocation and RIS phase shifts based on real-time channel conditions and user demands. This adaptability can lead to significant energy savings, especially in scenarios with varying user activity and mobility.

Utilization of Statistical CSI: The work emphasizes the use of statistical CSI for deriving closed-form expressions for spectral efficiency. This approach can be beneficial in energy-efficient designs, as it allows for less frequent updates of channel information, reducing the overhead associated with CSI acquisition and thus saving energy.

Deployment Strategies: The findings regarding the advantages of RIS in weak direct link scenarios can inform deployment strategies that prioritize the placement of RISs in areas with poor coverage. This targeted deployment can enhance overall system performance and energy efficiency by improving the quality of service for users in challenging environments.

Interference Mitigation Techniques: The insights into managing intra-cluster and inter-cluster interference can lead to the development of advanced interference mitigation techniques. By effectively managing interference, the system can operate at lower power levels while maintaining high data rates, contributing to energy efficiency.

Sustainability Considerations: The design of energy-efficient RIS-assisted systems aligns with sustainability goals in wireless communications. By reducing energy consumption, the system can contribute to lower operational costs and a reduced carbon footprint, making it more environmentally friendly.

By leveraging these insights, designers can create RIS-assisted cell-free mMIMO NOMA systems that are not only high-performing but also energy-efficient, addressing the growing demand for sustainable wireless communication solutions.