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Top1. Introduction
The data rates required by mobile network subscribers have skyrocketed the last few years either via excessive social media usage or video-streams. Thus, most traffic is exchanged in video form online. Within the next decade almost 7-10 devices per person will be connected via the Internet. The Internet of Things (IoT) is a technology contributing in this direction, as simplistic domestic devices such as cookers, fridges etc. are connected to the Internet and offer the chance of remote managing of these appliances via applications in mobile phones.
Nowadays, mobile subscribers request incessant and excellent mobile service experience, contributing to binding large network resources. Users are not extremely content by network’s efficiency and ask for higher- quality services at more competent prices. They also require coverage on the most remote places, such as high altitudes, beaches etc. as social media are a huge trend nowadays and thus, users want to stay connected unceasingly.
On the other hand, providers’ investments need to reciprocate, as it is a well-known fact that large amounts of money were spent for the deployment of Long Term Evolution-Advanced (LTE-A) networks as well as the development of 5G and the expected or desired profit is yet to be received. As a result, companies may not show willingness to invest money in novel deployments, technologies or equipment.
Moreover, the traffic augmentation contributes to the depletion of the network resources for the users as well as the network. Therefore, operators should explore ways to augment Bandwidth (BW) and as a consequence coverage without highly augmenting the network’s cost. A possible answer to this issue could be to virtualize BW using the Network Function Virtualization (NFV) technique and seek inexpensive and easy ways to use more. NFVs are useful for creating virtual resources and thus equipping facilities in the whole network without inducing the high costs (capital, maintenance, operational) of hardware. It solely consists of software and it could replace hardware. Studies in the field of telecommunication technologies have proved that NFVs could reduce the network costs from 20% to 80% depending on the specific part that is substituted.
What is more, technologies with the ability to reallocate the network resources or use it more efficiently or introduce novel evolved concepts, such as small cells, Software Defined Networks (SDN), IoT, Massive Multiple Input Multiple Output (Massive MIMO), Cloud computing, Cognitive Radio (CR) will be highly exploited as they appear to be (Akyildiz et al., 2016) the 5G key enablers.
An ideal solution for succeeding better network performance could be to use the Milimeter Wave (mmWave) frequency bands. mmWave functions in the frequencies between 30-300 GHz that are not highly used, nowadays, as a result, they could be used for mobile networks. In these frequencies, currently except for astronomy equipment, the band is rather underused. Current mobile network technologies emit in 3.7-24 GHz (Wang et al., 2015). Thus, using higher spectra could contribute in offering the desired efficiency and drastically enhance the network performance.
What is more, it becomes obvious that mmWave stars in 5G networks and it is of extreme significance that the mmWave is analyzed in a techno-economic way, so that the most expensive and financially advantageous factors are pinpointed. On the one hand, all strong points should be fully exploited so that they enhance the networks’ conditions and the weaknesses should be reduced, so that the highest profits are offered to both end users, providers and operators. Thus, it is of paramount significance that mmWave technology is analyzed from a techno-economic point of view and the most cost bearing parameters are highlighted.
In this paper, mmWave is analyzed from a technical and financial point of view. Models are developed and several scenarios are analyzed. Several experiments are conducted using a Sensitivity Analysis (SA), namely checking several parameter prices and it is indicated whether or not a specific network parameter is not cost-effective for the network and therefore, several actions should be taken, such as specific research etc. that should reduce these network parameters. The specific models that are developed in (Bouras et al, 2014) are updated in this paper, several parameters are opted and checked. Conclusions concerning the mmWave are deduced and future research activity is proposed.