English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Our main objective is to reduce power consumption by responding to the instantaneous bit rate demand by the user for 4th Generation (4G) and 5th Generation (5G) Massive MIMO network configurations. Moreover, we present and address the problem of designing green LTE networks with the Internet of Things (IoT) nodes. We consider the new NarrowBand-IoT (NB-IoT) wireless technology that will emerge in current and future access networks. In this context, we apply emerging evolutionary algorithms in the context of green network design. We investigate three different cases to show the performance of the new proposed algorithm, namely the 4G, 5G Massive MIMO, and the NB-IoT technologies. More specifically, we investigate the Teaching-Learning-Optimization (TLBO), the Jaya algorithm, the self-adaptive differential evolution jDE algorithm, and other hybrid algorithms. We introduce a new hybrid algorithm named Jaya-jDE that uses concepts from both Jaya and jDE algorithms in an effective way. The results show that 5G Massive MIMO networks require about 50% less power consumption than the 4G ones, and the NB-IoT in-band deployment requires about 10% less power than guard-band deployment. Moreover, Jaya-jDE emerges as the best algorithm based on the results.

A Novel Design Approach for 5G Massive MIMO and NB-IoT Green Networks Using a Hybrid Jaya-Differential Evolution Algorithm