[Paper Review] Adaptive Multi-objective Optimization for Energy Efficient Interference Coordination in Multi-Cell Networks

Zesong Fei, Chengwen Xing+2 more|arXiv (Cornell University)|Aug 22, 2013

Advanced MIMO Systems Optimization20 references4 citations

TL;DR

This paper proposes an adaptive multi-objective power allocation scheme for energy-efficient interference coordination in multi-cell OFDMA networks. By formulating a bi-objective optimization problem that jointly maximizes throughput contribution and minimizes power consumption using Pascoletti-Serafini scalarization, the method enables distributed base station operation with tunable trade-offs between performance and energy efficiency, achieving near-optimal solutions across the Pareto frontier via parameter adaptation.

ABSTRACT

In this paper, we investigate the distributed power allocation for multi-cell OFDMA networks taking both energy efficiency and inter-cell interference (ICI) mitigation into account. A performance metric termed as throughput contribution is exploited to measure how ICI is effectively coordinated. To achieve a distributed power allocation scheme for each base station (BS), the throughput contribution of each BS to the network is first given based on a pricing mechanism. Different from existing works, a biobjective problem is formulated based on multi-objective optimization theory, which aims at maximizing the throughput contribution of the BS to the network and minimizing its total power consumption at the same time. Using the method of Pascoletti and Serafini scalarization, the relationship between the varying parameters and minimal solutions is revealed. Furthermore, to exploit the relationship an algorithm is proposed based on which all the solutions on the boundary of the efficient set can be achieved by adaptively adjusting the involved parameters. With the obtained solution set, the decision maker has more choices on power allocation schemes in terms of both energy consumption and throughput. Finally, the performance of the algorithm is assessed by the simulation results.

Motivation & Objective

• Address the challenge of balancing energy efficiency and inter-cell interference (ICI) mitigation in multi-cell OFDMA networks.
• Overcome limitations of non-cooperative games (performance loss) and cooperative games (high overhead) by introducing a pricing mechanism to enable distributed coordination.
• Formulate a bi-objective optimization problem per base station (BS) to simultaneously maximize throughput contribution and minimize power consumption.
• Enable flexible trade-offs between energy efficiency and spectral efficiency through adaptive parameter tuning in the scalarization framework.
• Provide a distributed solution that allows each BS to independently optimize its power allocation while maintaining network-wide performance goals.

Proposed method

• Introduce a new performance metric called 'throughput contribution' to quantify each BS's contribution to network spectral efficiency while accounting for interference costs.
• Formulate a bi-objective optimization problem (MOP) for each BS using multi-objective optimization theory, targeting both throughput contribution maximization and power consumption minimization.
• Apply the Pascoletti-Serafini scalarization method to transform the multi-objective problem into a scalar parametric problem (SP), enabling systematic exploration of the efficient set.
• Derive analytical relationships between scalarization parameters and minimal solutions, allowing the generation of the entire boundary of the efficient set by adjusting a single parameter.
• Design a distributed algorithm where each BS independently solves the scalarized problem using local channel state information and exchanged interference prices.
• Leverage Lagrange multipliers and Karush-Kuhn-Tucker (KKT) conditions to ensure optimality and convergence of the distributed solution process.

Experimental results

Research questions

• RQ1 How can energy efficiency and inter-cell interference coordination be jointly optimized in a distributed multi-cell OFDMA network?
• RQ2 What is the impact of varying the trade-off parameter in scalarized multi-objective optimization on the resulting power allocation and network performance?
• RQ3 Can a pricing mechanism effectively coordinate base stations toward a Pareto-optimal solution without centralized control?
• RQ4 To what extent does the proposed adaptive scalarization method achieve the full boundary of the efficient set in the multi-objective optimization problem?
• RQ5 How does the proposed scheme compare to existing non-cooperative or cooperative approaches in terms of spectral efficiency and energy consumption?

Key findings

• The proposed adaptive scalarization method successfully generates all solutions on the boundary of the efficient set by varying a single scalarization parameter.
• The method enables each base station to compute its optimal power allocation in a distributed manner using only local channel state information and exchanged interference prices.
• Simulation results demonstrate that the algorithm achieves performance close to centralized optimal solutions, particularly in high-SNR regimes where interference becomes the dominant performance limiter.
• The trade-off between throughput contribution and power consumption is effectively tunable, giving network operators flexible options based on energy or spectral efficiency priorities.
• The use of Pascoletti-Serafini scalarization ensures that every K-minimal solution of the multi-objective problem corresponds to a minimal solution of the scalarized problem, validating the theoretical foundation.
• The Lagrange multiplier analysis confirms that the same multiplier applies to both the original and transformed scalar problems, ensuring consistency and optimality of the solution path.