[Paper Review] Energy Efficiency Optimization for MIMO Distributed Antenna Systems

Hong Ren, Nan Liu+2 more|arXiv (Cornell University)|Nov 12, 2015

Advanced MIMO Systems Optimization25 references3 citations

TL;DR

This paper proposes a joint transmit covariance and RAU selection optimization method to maximize energy efficiency (EE) in MIMO distributed antenna systems with rate constraints and per-RAU power limits. By decomposing the EE problem into rate maximization, EE maximization without rate constraints, and power minimization subproblems, and introducing a low-complexity distance-based RAU selection, the method achieves near-optimal EE performance with significantly reduced computational cost compared to exhaustive search.

ABSTRACT

In this paper, we propose a transmit covariance optimization method to maximize the energy efficiency (EE) for a single-user distributed antenna system, where both the remote access units (RAUs) and the user are equipped with multiple antennas. Unlike previous related works, both the rate requirement and RAU selection are taken into consideration. Here, the total circuit power consumption is related to the number of active RAUs. Given this setup, we first propose an optimal transmit covariance optimization method to solve the EE optimization problem under a fixed set of active RAUs. More specifically, we split this problem into three subproblems, i.e., the rate maximization problem, the EE maximization problem without rate constraint, and the power minimization problem, and each subproblem can be efficiently solved. Then, a novel distance-based RAU selection method is proposed to determine the optimal set of active RAUs. Simulation results show that the performance of the proposed RAU selection is almost identical to the optimal exhaustive search method with significantly reduced computational complexity, and the performance of the proposed algorithm significantly outperforms the existing EE optimization methods.

Motivation & Objective

• To address the lack of joint consideration of rate requirements and RAU selection in existing EE optimization methods for MIMO DAS.
• To maximize energy efficiency in a single-user MIMO DAS where both RAUs and the user are equipped with multiple antennas.
• To incorporate per-RAU power constraints and circuit power that scales with the number of active RAUs.
• To develop a computationally efficient RAU selection method that maintains high EE performance.

Proposed method

• Solves the EE maximization problem under a fixed set of active RAUs by decomposing it into three subproblems: rate maximization, EE maximization without rate constraint, and power minimization.
• Uses the Dinkelbach method to iteratively solve the EE maximization subproblem via a sequence of weighted sum-rate maximization problems.
• Proposes a novel distance-based RAU selection algorithm that selects RAUs based on their geometric proximity to the user, reducing computational complexity.
• Integrates RAU selection with transmit covariance optimization to jointly maximize EE while satisfying minimum rate requirements.
• Employs a bisection search over the energy efficiency parameter to find the optimal solution under rate and power constraints.
• Validates the optimality of the solution through theoretical analysis and convergence proofs based on properties of the EE function.

Experimental results

Research questions

• RQ1 How can energy efficiency be maximized in a MIMO DAS when both rate requirements and RAU selection are considered?
• RQ2 What is the optimal transmit covariance design for EE maximization under per-RAU power and total power constraints?
• RQ3 How does the inclusion of circuit power that scales with the number of active RAUs affect EE optimization?
• RQ4 Can a low-complexity RAU selection method achieve performance close to exhaustive search while maintaining high EE?
• RQ5 What is the impact of user rate requirements on the optimal number of active RAUs and overall EE?

Key findings

• The proposed distance-based RAU selection method achieves EE performance nearly identical to the optimal exhaustive search method, but with drastically reduced computational complexity.
• The algorithm significantly outperforms existing EE optimization methods that do not consider RAU selection or antenna selection in centralized systems.
• For a high rate requirement of 800 Mbps/Hz, EE degrades only at 6 RAUs under the proposed method, while it drops sharply with more RAUs in conventional approaches.
• The rate achieved by the proposed algorithm is comparable to rate maximization and EE-optimized methods without RAU selection, but with substantially better EE.
• The number of active RAUs increases with the total number of RAUs, but the proposed method maintains high EE by leveraging RAU selection diversity to offset rising circuit power.
• Theoretical analysis confirms that the optimal solution to the EE problem under rate constraints can be found via a bisection search over the energy efficiency parameter, with convergence guaranteed by the monotonicity of the EE function.