Techno-economic comparison of MIMO and DAS cost models in 5G networks

Bouras, Christos; Kokkalis, Stylianos; Κόλλια, Αναστασία; Papazois, Andreas

doi:10.1007/s11276-018-1780-6

Cited by 22 publications

(14 citation statements)

References 17 publications

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“…Then, based on market data sampling, the following typical values are shown in Table 1. The values of Table 1 are also confirmed in Table 2 in the study by Buras et al [21], who present cost parameters and system variables related to MIMO systems. The continuation of the current cost study will be based on Table 1.…”

Section: Cost Approach Of Mimo Systemssupporting

confidence: 74%

“…But both of those studies are rather technical comparisons and not real economic approaches. A real econometric study was published by Bouras et al [21] and elements from this study were used as values in the current approach.…”

Section: Previous Workmentioning

confidence: 99%

“…According to Bouras et al's[21] analysis, the overall Cost of using a MIMO system can be distinguished in two main types:-FCOST (Fixed Cost): the cost of acquiring and installing a MIMO system, which is considered as a stable cost, and -VCOST (Variable Cost): the cost of the daily operation and maintenance. In a running system, the cost of day-to-day management, power consumption, possible repayments of capital, bank interest, other bank charges, etc.…”

mentioning

confidence: 99%

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Evaluation Methodology of MIMO Networks Performance over Rayleigh Fading

Stergiou¹,

Angelis²,

Margariti³

2020

IJCNC

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Today, the explosive growth of Multiple Input Multiple Output (MIMO) systems has resulted in a high data rate and consequently permits the operation of a variety of applications. The MIMO networks are multiparameter systems, so the choice of a suitable MIMO network in wireless communications is a complex issue. In this paper, a multi-factoring evaluation and comparison framework was introduced and applied to MIMO systems. The proposed methodology is based on a general distance function, named the General Evaluation Factor. This method was applied to MIMO networks that operate over Rayleigh fading channels with different antenna nodes and spacing. The implementation of this method was based on different capacities and cost values. Nevertheless, while only two factors (capacity and cost) were studied in this paper, the proposed approach was able to incorporate additional performance metrics that might be essential for many wireless system designs. The presented framework and results aspire to be useful for network engineering, especially when finding a balance between contradictory factors (e.g. cost and performance metrics) on MIMO networks.

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Section: Cost Approach Of Mimo Systemssupporting

confidence: 74%

Section: Previous Workmentioning

confidence: 99%

mentioning

confidence: 99%

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Evaluation Methodology of MIMO Networks Performance over Rayleigh Fading

Stergiou¹,

Angelis²,

Margariti³

2020

IJCNC

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“…45 , Optimize X X (21) According to equation 19, industry needs to minimize energy usage, latency and energy waste/leakages to get optimum energy results. On the other hand, improved spectrum efficiency will help in timely management and decision making and will accelerate the industry processes.…”

Section: Ec Aec Pec mentioning

confidence: 99%

“…According to figure 4, energy cycle is composed of various steps which include: energy demands in various sectors of life; this demand leads towards energy generation using resourceefficient ways, the generated energy is stored and delivered to various sectors of the economy for consumption. Finally, energy optimization for CapEx which include long term expenses and energy optimization for OpEx which include day-to-day energy expenses [20,21].…”

Section: Introductionmentioning

confidence: 99%

Privacy Protection and Energy Optimization for 5G-Aided Industrial Internet of Things

et al. 2020

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The 5G is expected to revolutionize every sector of life by providing interconnectivity of everything everywhere at high speed. However, massively interconnected devices and fast data transmission will bring the challenge of privacy as well as energy deficiency. In today's fast-paced economy, almost every sector of the economy is dependent on energy resources. On the other hand, the energy sector is mainly dependent on fossil fuels and is constituting about 80% of energy globally. This massive extraction and combustion of fossil fuels lead to a lot of adverse impacts on health, environment, and economy. The newly emerging 5G technology has changed the existing phenomenon of life by connecting everything everywhere using IoT devices. 5G enabled IIoT devices has transformed everything from traditional to smart, e.g. smart city, smart healthcare, smart industry, smart manufacturing etc. However, massive I/O technologies for providing D2D connection has also created the issue of privacy that need to be addressed. Privacy is the fundamental right of every individual. 5G industries and organizations need to preserve it for their stability and competency. Therefore, privacy at all three levels (data, identity and location) need to be maintained. Further, energy optimization is a big challenge that needs to be addressed for leveraging the potential benefits of 5G and 5G aided IIoT. Billions of IIoT devices that are expected to communicate using the 5G network will consume a considerable amount of energy while energy resources are limited. Therefore, energy optimization is a future challenge faced by 5G industries that need to be addressed. To fill these gaps, we have provided a comprehensive framework that will help energy researchers and practitioners in better understanding of 5G aided industry 4.0 infrastructure and energy resource optimization by improving privacy. The proposed framework is evaluated using case studies and mathematical modelling.

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