[Paper Review] Downlink Energy Efficiency of Power Allocation and Wireless Backhaul Bandwidth Allocation in Heterogeneous Small Cell Networks

Haijun Zhang, Hao Liu+2 more|arXiv (Cornell University)|Oct 9, 2017

Advanced MIMO Systems Optimization20 references16 citations

TL;DR

This paper proposes a joint energy-efficient power and wireless backhaul bandwidth allocation scheme in OFDMA-based heterogeneous small cell networks to maximize spectral and energy efficiency. By formulating a non-convex optimization problem and decomposing it into convex subproblems, the authors design an iterative algorithm and a low-complexity suboptimal approach, achieving significant gains in energy efficiency while maintaining QoS constraints.

ABSTRACT

The widespread application of wireless services and dense devices access have triggered huge energy consumption. Because of the environmental and financial considerations, energy-efficient design in wireless networks becomes an inevitable trend. To the best of the authors' knowledge, energy-efficient orthogonal frequency division multiple access heterogeneous small cell optimization comprehensively considering energy efficiency maximization, power allocation, wireless backhaul bandwidth allocation, and user Quality of Service is a novel approach and research direction, and it has not been investigated. In this paper, we study the energy-efficient power allocation and wireless backhaul bandwidth allocation in orthogonal frequency division multiple access heterogeneous small cell networks. Different from the existing resource allocation schemes that maximize the throughput, the studied scheme maximizes energy efficiency by allocating both transmit power of each small cell base station to users and bandwidth for backhauling, according to the channel state information and the circuit power consumption. The problem is first formulated as a non-convex nonlinear programming problem and then it is decomposed into two convex subproblems. A near optimal iterative resource allocation algorithm is designed to solve the resource allocation problem. A suboptimal low-complexity approach is also developed by exploring the inherent structure and property of the energy-efficient design. Simulation results demonstrate the effectiveness of the proposed algorithms by comparing with the existing schemes.

Motivation & Objective

• To address the growing energy consumption in dense wireless networks due to increasing data traffic and device density.
• To jointly optimize downlink power allocation and wireless backhaul bandwidth allocation in OFDMA-based heterogeneous small cell networks.
• To maximize energy efficiency while satisfying user quality of service (QoS) requirements and circuit power constraints.
• To develop both near-optimal and low-complexity algorithms for practical deployment.

Proposed method

• Formulates the energy efficiency maximization problem as a non-convex nonlinear programming problem considering transmit power, backhaul bandwidth, channel state information, and circuit power.
• Decomposes the original problem into two convex subproblems: one for power allocation and one for backhaul bandwidth allocation.
• Derives the optimal power allocation per user via solving a strictly quasiconcave utility function, proven to have a unique maximum.
• Uses the Karush-Kuhn-Tucker (KKT) conditions and L’Hôpital’s rule to analyze the existence and uniqueness of the optimal power solution.
• Designs an iterative resource allocation algorithm that alternately optimizes power and bandwidth with guaranteed convergence.
• Proposes a suboptimal low-complexity algorithm by exploiting structural properties of the energy efficiency function.

Experimental results

Research questions

• RQ1 How can energy efficiency be maximized in heterogeneous small cell networks through joint power and backhaul bandwidth allocation?
• RQ2 What is the optimal power allocation strategy per user that maximizes energy efficiency under circuit power and QoS constraints?
• RQ3 Can the non-convex optimization problem be effectively decomposed into convex subproblems for tractable solution?
• RQ4 What is the impact of wireless backhaul bandwidth allocation on overall system energy efficiency?
• RQ5 How does the proposed algorithm compare in performance and complexity to existing schemes?

Key findings

• The proposed iterative algorithm achieves near-optimal energy efficiency by jointly optimizing power and backhaul bandwidth allocation.
• The optimal power allocation for each user is unique and corresponds to the peak of a strictly quasiconcave energy efficiency function.
• The optimal power solution exists and is derived analytically, with the derivative of the utility function changing sign only once.
• The limit behavior of the utility function shows that energy efficiency first increases and then decreases with transmit power, confirming the existence of a maximum.
• The suboptimal low-complexity algorithm achieves comparable performance to the iterative method with significantly reduced computational cost.
• Simulation results demonstrate that the proposed schemes outperform existing resource allocation methods in terms of energy efficiency and spectral efficiency.