Energy Efficient Joint Resource Allocation and Power Control for D2D Communications (1703.07041v1)

Abstract: In this paper, joint resource allocation and power control for energy efficient device-to-device (D2D) communications underlaying cellular networks are investigated. The resource and power are optimized for maximization of the energy efficiency (EE) of D2D communications. Exploiting the properties of fractional programming, we transform the original nonconvex optimization problem in fractional form into an equivalent optimization problem in subtractive form. Then, an efficient iterative resource allocation and power control scheme is proposed. In each iteration, part of the constraints of the EE optimization problem is removed by exploiting the penalty function approach. We further propose a novel two-layer approach which allows to find the optimum at each iteration by decoupling the EE optimization problem of joint resource allocation and power control into two separate steps. In the first layer, the optimal power values are obtained by solving a series of maximization problems through root-finding with or without considering the loss of cellular users' rates. In the second layer, the formulated optimization problem belongs to a classical resource allocation problem with single allocation format which admits a network flow formulation so that it can be solved to optimality. Simulation results demonstrate the remarkable improvements in terms of EE by using the proposed iterative resource allocation and power control scheme.

• The paper presents a novel iterative scheme using fractional programming and a two-layer method to solve the nonconvex problem of maximizing energy efficiency in D2D communications.
• Simulation results show their proposed iterative scheme significantly improves energy efficiency compared to existing methods, demonstrating the trade-off with interference management.
• This work provides a foundation for energy-efficient D2D frameworks and suggests future research could explore decentralized implementations or machine learning for dynamic network adaptation.

Energy Efficient Joint Resource Allocation and Power Control for D2D Communications

The paper "Energy Efficient Joint Resource Allocation and Power Control for D2D Communications" by Jiang et al., presents an optimized framework for energy efficiency (EE) in device-to-device (D2D) communications that operate underlay within cellular networks. The authors address the challenging problem of resource allocation and power control to maximize EE in D2D networks, taking significant strides in transforming the nonconvex optimization problem through fractional programming techniques and proposing a novel iterative approach.

Problem Context and Significance

D2D communications, implemented as an underlay in cellular systems, hold promise in substantially enhancing both spectrum efficiency (SE) and EE. However, the shared spectrum usage between D2D users and cellular users (CUs) necessitates intricate resource allocation and power control mechanisms to alleviate interference and optimize performance metrics.

Previous studies have focused on improving system throughput and reducing interference via resource allocation strategies or power control techniques. These prior works, however, primarily revolve around enhancing SE and minimizing interference, often sidelining the maximization of EE—a growing imperative given the stagnation in battery technology versus the energy demands of modern applications.

Methodology Overview

The primary contribution of this work is the formulation of a joint resource allocation and power control problem aimed at maximizing the EE of D2D communications. The optimization problem is intrinsically nonconvex due to its fractional objective nature. To tackle this, the authors employ a transformation into an equivalent subtractive form using fractional programming, thereby enabling tractability.

An iterative optimization scheme is crafted based on the Dinkelbach method, which resolves the transformed optimization problem efficiently. Central to their approach is a two-layer method that segregates the problem into power control and resource allocation subproblems. The power values are deduced through root-finding methods ensuring the satisfaction of minimum rate and power constraints, while resource allocation is executed through an assignment problem framework that guarantees optimal RB usage without intra-RB interference.

Numerical Results and Analysis

Simulation results underscore the effectiveness of the proposed iterative joint resource allocation and power control scheme. The authors demonstrate significant EE improvements over existing schemes, particularly in scenarios with varying maximum D2D transmit powers and interference constraints.

A key insight from the simulations is the trade-off between EE and interference management, a balance critical to the deployment of efficient D2D systems in real-world scenarios. Notably, the impact of the distance between D2D users and the scaling parameters, such as the minimum rate constraint, is systematically analyzed, showcasing the flexibility and robustness of the proposed solution in adapting to network conditions.

Implications and Future Directions

The joint resource allocation and power control scheme presented in this paper marks a pivotal advancement in energy-efficient D2D communication frameworks. By framing EE maximization within a rigorously defined optimization problem and delivering a scalable solution, this work sets the stage for further exploration in dynamic network environments and broader applications across heterogeneous networks and IoT contexts.

Future research directions may entail exploring decentralized implementations of the proposed algorithm to mitigate signaling overhead issues inherently associated with centralized solutions. Furthermore, integrating machine learning techniques to predict network dynamics and adapt resource allocation strategies accordingly can further enhance both EE and SE in D2D communications.

In conclusion, this paper provides a comprehensive and effective solution to enhancing the EE of D2D communications while addressing interference challenges, underscoring its potential utility in next-generation wireless networks.