Abstract
Two main concerns for designing a wireless system are more network capacity and less energy consumption. Recently, distributed antenna system (DAS) has received considerable attention due to its potential to provide higher spectral efficiency (SE) and uniform coverage for cellular networks. In this regard, this paper compares the performance of DAS with centralized antenna system (CAS) in LTE-A system in terms of energy efficiency (EE), where practical restrictions such as out-of-cell interference, path loss, and small scale fading are taken into account. Furthermore, the EE and system power consumption are investigated under three different cell-load scenarios (high, moderate, and low load) where different numbers of antennas are activated and remaining of antennas are under sleep mode. Finally, based on the tradeoff between power-saving and EE, two optimal DAS antenna deployments are proposed for low and moderate cell-load scenarios. The results reveal that DAS considerably outperforms CAS in terms of EE and by optimal deploying antennas of DAS significant power-saving and EE are achievable. The proposed methodology achieved savings of up to 27.63% in terms of energy savings in a macrocell with guarantee of a high capacity of data.
Highlights
The sudden increase in subscribers and demand for highspeed data has prompted cellular operators to increase the number of base stations (BSs) to fulfil the needs of mobile subscribers
It can be seen that distributed antenna system (DAS) with 4 transmit antennas (1-3/1) performs close to centralized antenna system (CAS) with 8 transmit antennas (8-0/0), while the network considerably consumes less energy
The results show that, by applying the proposed scheme to the cellular system, the amount of energy consumption can be reduced by 8.41 K Watt per day, which means that 27.63% of the network power can be saved
Summary
The sudden increase in subscribers and demand for highspeed data has prompted cellular operators to increase the number of base stations (BSs) to fulfil the needs of mobile subscribers. This increase has subsequently increased the overall energy consumption, operational costs, and carbon footprint of cellular networks. A number of interesting works have been carried out to address the issue, by implementation of “greener” cellular networks that are less expensive to operate. Overall, these improvements can be achieved through two approaches. The second approach is to adopt the intelligent management of network elements based on traffic load variations [3], which is the focus of this work
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