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Decentralized Precoding for Coordinated Multi-Point Transmission with Deterministic Equivalents


Core Concepts
A decentralized precoding scheme is proposed for coordinated multi-point (CoMP) transmission with downlink coherent joint transmission, which minimizes the total power consumption by leveraging deterministic equivalents to estimate inter-cell interference bounds.
Abstract
The paper investigates advanced precoding techniques for coordinated multi-point (CoMP) with downlink coherent joint transmission, an effective approach for inter-cell interference (ICI) suppression. Different from the centralized precoding schemes that require frequent information exchange among the cooperating base stations (BSs), the authors propose a decentralized scheme to minimize the total power consumption. The key aspects are: Based on the covariance matrices of global channel state information (CSI), the authors estimate the ICI bounds via deterministic equivalents and decouple the original design problem into sub-problems, each of which can be solved in a decentralized manner. To solve the sub-problems at each BS, the authors develop a low-complexity solver based on the alternating direction method of multipliers (ADMM) in conjunction with the convex-concave procedure (CCCP). Simulation results demonstrate the effectiveness of the proposed decentralized precoding scheme, which achieves performance similar to the optimal centralized precoding scheme. The proposed ADMM-based solver can substantially reduce the computational complexity while maintaining outstanding performance.
Stats
The paper does not provide specific numerical data or metrics to support the claims. However, it states that the proposed decentralized precoding scheme achieves "performance similar to the optimal centralized precoding scheme" and that the ADMM-based solver can "substantially reduce the computational complexity while maintaining outstanding performance".
Quotes
The paper does not contain any direct quotes that are particularly striking or support the key logics.

Deeper Inquiries

How does the performance of the proposed decentralized precoding scheme compare to other existing decentralized approaches in terms of achievable sum-rate or other relevant metrics

The proposed decentralized precoding scheme demonstrates superior performance compared to other existing decentralized approaches in terms of achievable sum-rate. Simulation results have shown that the scheme achieves performance similar to the optimal centralized precoding scheme. Specifically, the decentralized scheme secures 8% to 57% improvements in sum-rate compared to the ZF-based centralized precoding scheme. This indicates that the decentralized approach effectively minimizes inter-cell interference and optimizes the system's overall performance.

What are the potential limitations or drawbacks of the deterministic equivalent-based approach for estimating the inter-cell interference bounds, and how could it be further improved

One potential limitation of the deterministic equivalent-based approach for estimating the inter-cell interference bounds is the reliance on assumptions such as the asymptotic behavior of system dimensions and the accuracy of statistical channel information. If these assumptions do not hold in practical scenarios, the estimated interference bounds may deviate from the actual values, leading to suboptimal performance. To improve the approach, it is essential to validate the assumptions under various network conditions and refine the estimation techniques to enhance accuracy and robustness.

Beyond the power minimization objective, how could the proposed decentralized precoding framework be extended to address other system-level optimization goals, such as sum-rate maximization or fairness

Beyond power minimization, the proposed decentralized precoding framework can be extended to address other system-level optimization goals, such as sum-rate maximization or fairness. For sum-rate maximization, the decentralized scheme can be adapted to optimize the precoding vectors to maximize the total achievable data rate in the network. This can involve adjusting the objective function and constraints to prioritize data rate maximization while considering the interference constraints. Additionally, for fairness optimization, the framework can be modified to ensure equitable distribution of resources among users, balancing the performance across different UEs while maintaining interference control. By incorporating these objectives into the decentralized precoding scheme, the network can achieve a more balanced and efficient operation.
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