Energy-Efficient Resource Allocation in OFDMA Systems with Hybrid Energy Harvesting Base Station (1302.4721v1)

Abstract: We study resource allocation algorithm design for energy-efficient communication in an OFDMA downlink network with hybrid energy harvesting base station. Specifically, an energy harvester and a constant energy source driven by a non-renewable resource are used for supplying the energy required for system operation. We first consider a deterministic offline system setting. In particular, assuming availability of non-causal knowledge about energy arrivals and channel gains, an offline resource allocation problem is formulated as a non-convex optimization problem taking into account the circuit energy consumption, a finite energy storage capacity, and a minimum required data rate. We transform this non-convex optimization problem into a convex optimization problem by applying time-sharing and fractional programming which results in an efficient asymptotically optimal offline iterative resource allocation algorithm. In each iteration, the transformed problem is solved by using Lagrange dual decomposition. The obtained resource allocation policy maximizes the weighted energy efficiency of data transmission. Subsequently, we focus on online algorithm design. A stochastic dynamic programming approach is employed to obtain the optimal online resource allocation algorithm which requires a prohibitively high complexity. To strike a balance between system performance and computational complexity, we propose a low complexity suboptimal online iterative algorithm which is motivated by the offline optimization.

• The paper proposes efficient resource allocation algorithms for OFDMA systems with hybrid energy harvesting to maximize energy efficiency using both offline and online approaches.
• It transforms the non-convex optimization problem into a convex one, yielding an asymptotically optimal iterative algorithm for the offline scenario by prioritizing renewable energy.
• A low-complexity suboptimal online algorithm, based on stochastic dynamic programming insights, demonstrates rapid convergence and close-to-optimal energy efficiency using only causal information.

Energy-Efficient Resource Allocation in OFDMA Systems with Hybrid Energy Harvesting Base Station

The paper at hand proposes a novel paper on resource allocation for orthogonal frequency division multiple access (OFDMA) systems featuring a hybrid energy harvesting base station (BS). The objective is to enhance the energy efficiency of wireless communication networks harnessing both renewable and non-renewable energy sources. The authors approach this by designing efficient resource allocation algorithms that cater to both deterministic offline scenarios and practical online environments.

Problem Formulation and Methodology

The research focuses on maximizing the weighted energy efficiency of downlink data transmission. The problem is formulated as a non-convex optimization challenge over a finite horizon. It considers a hybrid energy model combining a constant non-renewable energy source and an energy harvester with finite storage capacity. The discrete optimization problem accounts for circuit energy consumption and a minimum data rate requirement. The authors employ nonlinear fractional programming and Lagrangian dual decomposition to transform the complex non-convex problem into a convex optimization, yielding an asymptotically optimal iterative resource allocation algorithm feasible for large OFDMA systems.

Offline Resource Allocation

For the offline scenario, the paper assumes non-causal knowledge of energy arrivals and channel gains, allowing an optimal solution through an iterative algorithm. The derived solution achieves maximal weighted energy efficiency by balancing the energy drawn from renewable and non-renewable sources, clearly prioritizing the former when feasible. Key numerical results demonstrate that the proposed iterative algorithm converges efficiently, requiring few iterations to stabilize.

Online Resource Allocation

Transitioning to a practical setting, the paper presents an optimal online algorithm using a stochastic dynamic programming approach sensitive to the prohibitive complexity. Therefore, a low-complexity suboptimal online iterative algorithm is proposed, leveraging the deterministic offline algorithm's insights. This suboptimal approach, tested via rigorous simulations, showcases rapid convergence and realizes close-to-optimal energy efficiency using only causal information.

Implications and Future Directions

The implications of this work are multifaceted. Practically, it offers a pathway toward sustainable wireless networks by minimizing carbon emissions and energy costs through efficient resource allocation strategies. Theoretically, it contributes by reinforcing the utility of hybrid energy systems in communication networks and providing frameworks for energy-efficient algorithm design. Future research directions may include investigating the effects of imperfect channel state information (CSI) and energy prediction inaccuracies on system performance, extending the framework to different network architectures, and exploring advanced machine learning techniques to enhance the adaptability and robustness of the proposed algorithms.

In summary, this research contributes significantly to the field of energy-efficient communications by addressing the challenges of leveraging hybrid energy harvesting in OFDMA systems. The proposed solutions exhibit promising efficacy in enhancing system energy efficiency, as substantiated by extensive theoretical analyses and simulation results.