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

TL;DR

This paper develops energy-efficient resource allocation algorithms for OFDMA systems with hybrid energy harvesting base stations, balancing optimality and computational complexity through offline and online approaches.

Contribution

It introduces a novel offline convex optimization framework and a low-complexity online algorithm for energy-efficient resource allocation in hybrid energy harvesting OFDMA networks.

Findings

The offline algorithm maximizes weighted energy efficiency.

The online algorithm achieves near-optimal performance with lower complexity.

Hybrid energy sources improve system sustainability and efficiency.

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.