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Downlink Energy Efficiency of Power Allocation and Wireless Backhaul Bandwidth Allocation in Heterogeneous Small Cell Networks (1710.02942v1)

Published 9 Oct 2017 in cs.IT and math.IT

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.

Citations (163)

Summary

  • The paper proposes a novel joint power and wireless backhaul bandwidth allocation approach to maximize energy efficiency in heterogeneous small cell networks, formulating it as a non-convex problem decomposed into convex subproblems.
  • Simulation results demonstrate that the proposed iterative algorithms, including a low-complexity approach, significantly improve energy efficiency compared to existing methods.
  • The study suggests the algorithms are scalable for larger networks and identifies future research avenues like non-unified backhaul and dynamic spectrum management.

The paper authored by Haijun Zhang et al., investigates critical aspects of energy-efficient design within the domain of heterogeneous small cell networks, particularly focusing on the optimization of power allocation and wireless backhaul bandwidth allocation using orthogonal frequency division multiple access (OFDMA). Despite the significant expansion and demand in wireless communication networks leading to higher energy consumption, the necessity of enhancing energy efficiency in such networks is underscored due to pressing environmental and economic reasons.

This research contributes a novel approach to optimizing energy efficiency in small cell networks, diverging from traditional methods centered on maximizing throughput. Instead, the paper emphasizes a comprehensive maximization of energy efficiency through intelligent resource allocation, influenced by channel state information and circuit power consumption. The authors have conceptualized and formulated this as a non-convex nonlinear programming problem that is then decomposed into two more manageable convex subproblems.

Fundamentally, the paper distinguishes itself by addressing both power allocation and wireless backhaul bandwidth allocation in a unified framework. This is pivotal given that the wireless backhaul—connection between the macro base station (BS) and small cell BSs—plays a crucial role in system performance. The authors propose algorithms for iterative resource allocation and a suboptimal low-complexity approach, demonstrating their efficacy through simulations.

Numerical Results and Implications

The algorithmic strategies articulated in the paper, notably the Gradient Assisted Binary Search (GABS) algorithm, align with the aim of achieving optimal power allocation and bandwidth assignment in small cell networks. Simulation results validate the proposed models through comparisons with existing schemes, showcasing significant improvements in energy efficiency. These results implicate the effectiveness of deploying such optimization algorithms, highlighting their viability in real-world applications where energy efficiency is paramount.

Moreover, the paper makes assertions on the scalability of the proposed algorithms, thus providing a blueprint for extending the model to accommodate larger networks with increased numbers of users and small cells. This has direct practical implications for the deployment and management of heterogeneous small cell networks within macrocell environments.

Theoretical and Future Considerations

The theoretical formulations within the paper open avenues for further exploration into non-unified backhaul bandwidth allocation and addressing inter-small-cell interference issues. The assurances of a globally optimal energy-efficient solution and the proposed iterative algorithm contribute to the foundation for future research inquiries into complex systems where heterogeneity and energy constraints intersect.

Future directions might also encompass advancing the algorithms to cater for dynamic spectrum management and adaptive resource configurations responsive to fluctuating network conditions. Additionally, refining the balance between energy efficiency and economic constraints remains a pertinent area where further investigation could yield beneficial insights.

In conclusion, the paper provides substantial contributions to the field of wireless communications, specifically in the optimization of small cell networks for enhanced energy efficiency. The integration of power and bandwidth allocations within a unified framework offers a promising trajectory for future developments in this domain, and sets a precedent for addressing the intricate challenges posed by growing multimedia service demands and limited power resources.