Energy and spectrum efficiency trade-off for Green Small Cell Networks

Abstract

Green Small Cell Networks aim at achieving high rates and low powers by offloading users with low signal-to-noiseratios from macrocell to the pico base station. In this work, we propose to jointly optimise energy efficiency (EE) and spectrum efficiency (SE) such that the network providers can dynamically tune the trade-off parameter for different design requirements. This paper formulates the EE-SE trade-off as a multi-objective optimisation problem (MOP) in the uplink of multi-user twotier Orthogonal Frequency Division Multiplexing Heterogeneous Networks. Using the weighted sum method, the MOP can be transformed into a single-objective optimisation problem (SOP). The proposed EE and SE trade-off optimisation problem is strictly quasi-concave. Hence, using Dual Decomposition approach, we derive the unique optimal solution. Numerical results demonstrate the effectiveness of the proposed approach and illustrate the fundamental tradeoff between EE and SE for different tradeoff parameters such as maximum transmission power and circuit power.

Electromagnetic Emission-Aware Schedulers for the Uplink of OFDM Wireless Communication Systems

“…However, contrary to [10], we develop generic algorithms for the multi-carrier instead of single carrier scenario. It can also be noted that resource allocation/scheduling in the uplink of OFDM systems have been very well investigated, however, mainly from an SE [12], [13], EE [14], [15] and more recently, joint SE and EE maximization [16], [17] perspective, where the authors jointly maximized both the SE and EE in green heterogeneous networks. Margin adaptive resource allocation has been investigated in [18]- [20], where the authors minimized transmit powers in OFDM systems.…”

Dynamic wireless resource allocation in ultra-dense network based on cooperative computing

“…When only considering single-resource optimization, the genetic algorithm solves the function division problem in the network and reduces operating costs through proper planning [7]. At the same time, spectrum and energy are usually formulated as multiresource optimization problems [8][9][10][11][12][13] . In contrast to single-resource optimization, multi-resource optimization produces a set of optimal solutions, primarily referred to as a Pareto optimal solution rather than a single optimal solution.…”

Maximizing Energy Efficiency in Heterogeneous Cellular Network with Massive MIMO and Small Cells

“…Numerical and analytical results show that the maximal EE was achieved wherein the number of antennas were deployed to serve the same magnitude of users. In [13], the authors considered a two-tier Orthogonal Frequency Division Multiple Access (OFDMA) based heterogeneous network and transformed the multi objective optimization problem of simultaneously maximizing EE and Spectrum Efficiency (SE) into an EE-SE tradeoff single objective optimization problem. The problem was solved by power allocation and user association scheme in which each user can only be associated with one BS.…”

“…The problem was solved by power allocation and user association scheme in which each user can only be associated with one BS. Different from [13], authors in [14] proposed a low-complexity joint subcarrier and power allocation algorithm to maximize EE with QoS constrained in multi-user multi-carrier OFDMA systems by Maclaurin series expansions technique with the tractable upper bound of truncation error. Different from [13] and [14], this paper maximized EE in heterogeneous cellular network with massive MIMO and small cells from the perspective of antenna deployment.…”