toplogo
Sign In

Multi-agent Reinforcement Learning-based Joint Precoding and Phase Shift Optimization for Enhancing Spectral Efficiency in RIS-aided Cell-Free Massive MIMO Systems


Core Concepts
A multi-agent reinforcement learning (MARL) algorithm incorporating fuzzy logic is proposed to jointly optimize the precoding matrix at the access points and the reflection coefficients at the reconfigurable intelligent surfaces (RISs) in order to maximize the sum spectral efficiency of the RIS-aided cell-free massive MIMO system.
Abstract
The paper investigates an RIS-aided cell-free massive MIMO (mMIMO) network and formulates an optimization problem to jointly optimize the precoding matrix at the access points (APs) and the reflection coefficients at the RISs in order to maximize the sum spectral efficiency (SE) of the system. To tackle this non-convex optimization problem, the authors propose a fully distributed multi-agent reinforcement learning (MARL) algorithm that incorporates fuzzy logic (FL). Unlike conventional approaches that rely on alternating optimization techniques, the proposed FL-MARL algorithm only requires local channel state information (CSI), reducing the need for high backhaul capacity. The key highlights of the proposed approach are: The MARL-CTDE (centralized training, decentralized execution) framework is adopted, where each AP is considered an agent that independently calculates the policy gradient for its local actor network using the collective abstract state and action as a basis. Fuzzy logic is integrated into the MARL algorithm to enhance the convergence speed and further reduce computational complexity. The FL-MARL algorithm exhibits a linear computational complexity increase with the number of APs, in contrast to the exponential increase of the conventional MARL approach. Simulation results demonstrate that the proposed FL-MARL algorithm effectively reduces computational complexity while achieving similar performance as conventional MARL methods in terms of sum SE.
Stats
The sum spectral efficiency (SE) of the RIS-aided cell-free massive MIMO system can be improved by up to 42% compared to the zero-forcing (ZF) precoding and 18% compared to the alternating optimization (AO)-based precoding.
Quotes
"The integration of FL into MADDPG yields substantial savings in computing resources." "FL-MADDPG is more suitable for a broader continuous scenario."

Deeper Inquiries

How can the proposed FL-MARL algorithm be extended to handle imperfect or partial CSI in the RIS-aided cell-free massive MIMO system

To extend the proposed FL-MARL algorithm to handle imperfect or partial CSI in the RIS-aided cell-free massive MIMO system, several modifications and enhancements can be implemented. Firstly, incorporating techniques like channel estimation and prediction can help in dealing with imperfect CSI. By utilizing historical channel information and predictive models, the system can adapt to variations in the channel state. Additionally, introducing robust optimization frameworks that account for uncertainties in CSI can enhance the system's resilience to imperfect information. Moreover, integrating advanced machine learning algorithms, such as transfer learning or meta-learning, can enable the system to adapt and learn from limited or noisy CSI data. By leveraging these strategies, the FL-MARL algorithm can effectively handle imperfect or partial CSI in the RIS-aided cell-free massive MIMO system.

What are the potential challenges and trade-offs in implementing the RIS-aided cell-free massive MIMO system in a real-world deployment, beyond the optimization of precoding and phase shift

Implementing the RIS-aided cell-free massive MIMO system in a real-world deployment poses several challenges and trade-offs beyond the optimization of precoding and phase shift. One significant challenge is the practical implementation of RIS elements, including the hardware constraints, power consumption, and scalability issues. Ensuring seamless integration of RISs with existing infrastructure and overcoming deployment complexities are crucial considerations. Trade-offs may arise in terms of cost-effectiveness versus performance gains, as deploying a large number of RISs can be expensive but may lead to substantial improvements in network capacity. Furthermore, addressing interference management, synchronization, and coordination among distributed APs and RISs presents additional challenges in real-world deployments. Balancing these trade-offs while ensuring efficient operation and maintenance of the system is essential for successful implementation.

Can the MARL-based approach be further generalized to jointly optimize other system parameters, such as user scheduling and power allocation, to enhance the overall performance of the RIS-aided cell-free massive MIMO network

The MARL-based approach can be generalized to jointly optimize other system parameters, such as user scheduling and power allocation, to enhance the overall performance of the RIS-aided cell-free massive MIMO network. By incorporating user scheduling algorithms based on reinforcement learning, the system can dynamically allocate resources to users based on their channel conditions and quality of service requirements. Additionally, optimizing power allocation strategies using MARL can improve energy efficiency and spectral efficiency in the network. By jointly optimizing user scheduling, power allocation, precoding, and phase shift design, the system can achieve better resource utilization, increased network capacity, and improved quality of service for users. This holistic approach to system optimization can lead to significant performance enhancements in RIS-aided cell-free massive MIMO networks.
0