Uplink Received Power Analysis of a 6G Network Deploying IRS

doi:10.21203/rs.3.rs-3751918/v1

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Uplink Received Power Analysis of a 6G Network Deploying IRS

https://doi.org/10.21203/rs.3.rs-3751918/v1

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The investigation's principal purpose is to expand coverage, i.e., enable superior transmission, by utilizing IRS inside micro cellular transmission across a two-tier 6G infrastructure constituted by one micro cellular tier running within a macro cell-based tier. As a consequence, the paper investigated and contrasted the performance of regular micro cellular connectivity to IRS-enhanced micro cellular networking in terms of upstream received power utilizing simulation-based measuring approaches. According to the research, implementing IRS enhances coverage, i.e., transmission effectiveness, for 6G services significantly.

A 5G network would be unable to provide a genuinely immersive experience in order to provide a fully integrated along with intelligent network [1-4]. Although 5G wireless networks are greatly improved over current systems, they are going to be insufficient to fulfill the demands of future intelligent, developing, and automated systems within 10 years [5-8]. In compared to 4G connection, the 5G Network provides additional capabilities and a greater level of reliability [9-14]. Many novel techniques are included in 5G technologies, such as, the utilization and regulation of numerous frequency ranges (such as, mmWave as well as optical spectrum) along with the convergence of approved or unlicensed bands [15-19]. However, the fast expansion of data-centric and autonomous technologies will outpace the capabilities of 5G wireless networks. 5G communications significantly underestimates the combination of networking, intelligence, detection, supervision, and computing resources. This integration, however, will be required for future applications [20-26]. Specific gadgets, such as virtual reality headsets, need to expand beyond 5G (B5G), since data rates of a minimum of 10 Gbps are necessary [27-32]. As 5G hits its endpoints in 2030, the design objectives for the following phase are still being considered in the literature [33-38].

New services that might be provided by a sixth generation system include (i) massive interfaces between human-machine infrastructure [39-43], (ii) ubiquitous computing between local devices and cloud computing [44-49], (iii) multi-data fusion to create multi-verse maps along with mixed realities [50-54], and (iv) sensing and control accuracy [55-60]. To meet the 6G aim and overcome the limitations of 5G, B5G wireless networks must be constructed with unique and appealing features [61-63]. The 6G communication networks will overcome the constraints of the 5G network by unveiling an entirely novel set of future services including environmental sensing cognitive ability and novel interactions between humans and machines, the understanding integration of AI, along with the integration of novel innovations such as ultrahigh frequency (THz), 3D communication, quantum connectivity, holographic beam creating, and backscatter interaction [64-69]. All prior attributes, such as network concentration, high output, considerable latency, decreased energy consumption, and mass networking, would be essential for 6G. In contrast, 6G infrastructure builds on previous generations by incorporating upcoming technology and revolutionary networks [70-76]. AI, intelligent wearable technology, implants, self-sustaining devices, monitoring, and 3D mapping are among the new offerings [77-83]. The ability to handle massive volumes of information alongside high-data accessibility per device is the most important need for wireless networks running on 6G [84-88].

In 6G, the 5G concept will be stretched and evolved considerably. In the meantime, the 6G device may enhance efficiency and optimize customer QoS by many orders of magnitude over 5G, among other intriguing aspects. It offers device safety, user data protection, and easy operations [89-92]. The 6G communications infrastructure is expected to be a worldwide facility. In some cases, the 6G utilization rate is estimated to be about 1 Tbps. This 6G gadget can have 1000 times’ greater simultaneous wireless connectivity than the 5G device. Furthermore, ultra-long contact requiring a latency of lower than 1 ms seems necessary [93-97]. The most exciting aspect of 6G lies in the fact it has full support for autonomous car technology. Video traffic on 6G networks is predicted to outnumber other types of data traffic. The most important technologies driving 6G include the THz group, AI, optical wireless connectivity (OWC), 3D connectivity, unmanned aerial vehicles (UAV), Intelligent Reflecting Surface (IRS), and wireless power transmission [98-100].

The purpose of this paper has been to contrast the viability of conventional micro cellular interacting with an IRS-enhanced the micro cell-based interactions in terms of upstream received power employing a two-tier 6G framework with a micro cell-based tier (or layer) operating within a macro cell-based tier.

Verma et al. [101] investigated green connectivity for 6G large connected objects using cluster-based dissemination of data. The investigation has also not taken into account the implementation of IRS under this situation. Guo et al. [102] conducted an investigation on the huge IoT ecosystem driven by 6G, concentrating on the drivers, fundamental technological needs, implementation scenarios, and trends. Nevertheless, the paper does not address the use of IRS being a driver of connected devices. Okogbaa et al. [103] examined the design, deployments, and limits of the IRS rollout in terms of the Internet of Everything paradigm, taking into account the upcoming 6G infrastructure.

Considering ${p}_{T}^{ul}$ as the uplink transmit power.

3.1 Conventional Micro Cell Model

The uplink received power is measured by (Eq. 1) [104, 105],

$${p}_{R}^{ul}= \frac{{p}_{T}^{ul}{\lambda }^{2}}{{\left(4\pi \right)}^{2}{l}^{a}}$$

(1)

where $l= \sqrt{{({x}^{u}-{x}^{m})}^{2}+{({y}^{u}-{y}^{m})}^{2}+{({\mathcal{z}}^{u}-{\mathcal{z}}^{m})}^{2}}$ represents the distance between the sending device (base station) at $({x}^{u},{y}^{u},{\mathcal{z}}^{u})$ coordinates and micro base station at $({x}^{m},{y}^{m},{\mathcal{z}}^{m})$. $\alpha$ indicates the factor for the loss. $\lambda = c/{f}_{c}$ is the carrier wavelength. $c$ depicts light's speed in ms^− 1. ${f}_{c}$ indicates the radio frequency of the carrier in Hz.

3.2 IRS-Assisted Micro Cell Model

In the circumstance of IRS-assisted micro cellular communication power received in the uplink is computed as follows (Eq. 2) [106],

$${p}_{R}^{ul\left(IRS\right)}=\frac{{p}_{T}^{ul\left(IRS\right)}{d}_{x}{d}_{y}{{m}_{T}}^{2}{{n}_{R}}^{2}{G}_{T}{G}_{R}G{\lambda }^{2}\mathtt{c}\mathtt{o}\mathtt{s}\left({\theta }_{T}\right)\mathtt{c}\mathtt{o}\mathtt{s}\left({\theta }_{R}\right){\mathtt{A}}^{2}}{64{\pi }^{3}{\left({R}_{1}{R}_{2}\right)}^{2}}$$

(2)

where ${d}_{x}$ and ${d}_{y}$ specify the length and breadth of the component. ${m}_{T}$ and ${n}_{T}$ are the total numbers of transmission and reception components. ${G}_{T}$ represents transmitting gain and ${G}_{R}$ signifies the recipient's gain. ${G}_{ }=\frac{4\pi {d}_{x}{d}_{y}}{{\lambda }^{2}}$ represents the IRS's dispersal gain. ${\theta }_{t}$ denotes the transmitting angle and ${\theta }_{r}$ determines the receiving angle. ${R}_{1}= \sqrt{{\left({x}^{u}-{x}^{i}\right)}^{2}+{\left({y}^{u}-{y}^{i}\right)}^{2}+{\left({\mathcal{z}}^{u}-{\mathcal{z}}^{i}\right)}^{2}}$ represents the distance between the sending device and IRS situated at $({x}^{i},{y}^{i},{\mathcal{z}}^{i})$coordinates. ${R}_{2}= \sqrt{{\left({x}^{i}-{x}^{m}\right)}^{2}+{\left({y}^{i}-{y}^{m}\right)}^{2}+{\left({\mathcal{z}}^{i}-{\mathcal{z}}^{m}\right)}^{2}}$ reflects the distance over the IRS alongside the micro base station. $\mathtt{A}$ indicates a reflection factor.

The measurement findings produced using MATLAB-based modeling deploying the measurement concept is included in this portion of the article, as are remarks on the generated results.

4.1 Results for Conventional Micro Cell Model

Figure 1. Transmitter-receiver separation distance vs. received power in uplink (non-IRS)

Inspecting Fig. 6 it is realizable that, around − 63 dBm of received power in the uplink is obtained when the devices are located at 15 m away, around − 80 dBm of received power in the uplink is obtained when the devices are located at 50 m away, and in the case of cell-edge devices the received power in uplink is up to -91 dBm.

4.2 Results for IRS-Assisted Micro Cell Model

Figure 2. (a) Uplink received power vs transmit-receive angle (IRS placed at 100 m away from the IoTD), (b) Uplink received power vs transmit-receive angle (IRS placed at 25 m away from the IoTD)

According to Fig. 2 (a) and (b) the placement of IRS near the devices ensures better-received power. However, it is only viable for a cluster of devices located in a specific coverage region.

In the case of uplink, the maximum and minimum downlink received powers are − 54 and − 91 dBm (conventional) and − 20 and − 52 dBm (IRS-assisted). The IRS-assisted model performs significantly better even with the reduced transmit power.

The study aimed to improve service accessibility in the setting of upcoming 6G networks by putting IRS inside a micro cell within a two-tier network functioning beneath a macro cellular tier. A literature assessment of related current research was conducted to give insight and identify research constraints or gaps. It developed a measuring model for both traditional along with IRS-assisted micro cell networks, which includes numerous equations for calculating uplink spectral efficiency. According to the findings, IRS-assisted micro cell networks operate much better than typical non-IRS micro cell networks. Furthermore, an IRS-assisted network conserves energy by reducing the transmission power.

Declaration of Competing Interest

The authors declare no known competing interests.

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The authors declare no competing interests.

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Uplink Received Power Analysis of a 6G Network Deploying IRS

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Abstract

Figures

1. Introduction

2. Literature Review

3. Measurement Model

3.1 Conventional Micro Cell Model

3.2 IRS-Assisted Micro Cell Model

4. Results and Discussions

4.1 Results for Conventional Micro Cell Model

4.2 Results for IRS-Assisted Micro Cell Model

5. Conclusion

Declarations

Declaration of Competing Interest

References

Additional Declarations

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