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Analysis of the Uplink Spectral Characteristics Deploying IRS in 6G Multi-tier Network

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12 June 2024

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13 June 2024

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Abstract
The study employed simulation-based measurement approaches to evaluate and compare the performance of conventional networks to IRS-enhanced systems in terms of upstream spectral efficiency. The study discovered that applying IRS greatly improves coverage, or transmitting potency, for 6G services.
Keywords: 
6G
;  
IRS
;  
Networking
Subject: 
Engineering  -   Electrical and Electronic Engineering

1. Introduction

The commercial deployment of 5G began in 2019, with greater acceptance projected in 2021 as well as thereafter. There have been peripheral research interests concerning potential wireless networks with 6G technology [1,2,3,4,5,6]. The COVID-19 epidemic brought more firms online, ushering in a "New Normal" with a global workplace [7,8,9,10,11]. Ericsson predicts that 5G deployment will accelerate as more individuals embrace the transition to a globalized workplace [12,13,14,15,16,17]. The ensuing increase in Internet usage highlights the need for improved connection to fulfill the rising demand for increasingly severe network standards. This is essential to enable new technologies including extended reality, haptics, networked autonomous systems, e-medicine, and the Industry Internet of Things (IIoT) that are highly dependent upon latency and require extremely high transmission speeds [18,19,20,21,22,23,24,25]. For instance, ultra-low latency along with ultra-fast data speeds minimizes crash rates while improving autonomous vehicle security [26,27,28,29,30,31]. These applications are required to provide independent and smart life, multimodal virtual experiences, intelligent cities, intelligent farming, and other features [32,33,34,35]. Unfortunately, the anticipated 5G networks are unable to handle these increasing demands. Thus, there is an urgent need for the establishment of 6G communication systems. 6G wireless infrastructures are also planned to address social needs, helping the implementation of the Global Goals for Sustainable Development (SDGs) [36,37,38,39,40,41]. The estimated network requirements for 6G include ultra-fast transmission speeds of up to 1Tbps, exceptionally low latency of fewer than 1ms, enhanced mobility and coverage, adaptable and productive combination of trillion-level things, peak spectral effectiveness of 60 b/s/Hz, extremely high system stability, and enhanced network security [42,43,44,45,46].
6G is predicted to offer data throughput in the range within terabits per second together with latencies of less than 1 millisecond. It is projected to power the internet of all things with 107 linked devices per km2 [47,48,49,50,51,52]. To accomplish this, 6G will rely on sub-terahertz as well as terahertz bandwidth (300 GHz – 10 THz), that give a higher frequency range than the millimeter wave bandwidth (30-300 GHz) used in 5G [53,54,55,56,57,58,59]. Exploring the greater frequency band is required since the sub-6GHz area is already overcrowded. Aside from providing greater spectrum, Terahertz spectrum enables better data speeds, which are required in 6G networks. However, sending at higher frequencies results in considerable path loss, limiting the distance for broadcast [60,61,62,63,64,65,66]. This study addresses this issue as well as other THz transmission issues, such as hardware limits. Furthermore, optical wireless technologies including Visible Light Communication, or VLC as well as Free Space Optics interactions are thoroughly examined [67,68,69,70,71].
Furthermore, technologies like as cell-free massive MIMO [72,73,74,75,76,77], Intelligent Reconfigurable Surface (IRS) [78,79,80,81], and Artificial Intelligence (AI) [82,83,84,85,86], which will likely dictate the implementation of 6G, are discussed. We are considering using RIS in doors and windows to reflect incoming signals without interference. Additionally, we investigate why IRS technologies are better to conventional relays. Massive MIMO technique is implemented in 5G, resulting in a denser system of access points [87,88,89,90]. The aforementioned is expanded in 6G to accommodate an infrastructure with no actual cells (cell-free). The advantages are enormous, since it increases spectral efficiency for transmission networks. Nevertheless, there are difficulties in acquiring channel information, as well as worries about the health dangers connected with such a comprehensive network of access points. There is minimal literature concerning these problems, necessitating the necessity for this assessment. We also believe that Pervasive AI is crucial to achieving 6G [91]. Deep Neural Networks and Artificial Neural Networks have been offered as tools for creating intelligent networks [92,93,94].
In recent times, we have seen a rapid increase in research on mobile communications employing IRSs. IRSs have been used to create novel wireless transceiver topologies, which might result in a paradigm change in transceiver architecture and lower hardware costs for future transmission systems [95,96,97]. Furthermore, IRSs can manipulate the electromagnetic wave propagating environment. In today's wireless communication infrastructure, the wireless environment, or the physical items that impact the transmission of radio waves, is uncontrolled. During the investigation and development phases of wireless communication platforms, only transceiver devices, signal processing methods, and transmission protocols may be designed to quickly adjust to the radiation environment. IRSs, on the reverse side, have the potential to render the wireless environment configurable and programmable, opening up previously untapped prospects for improving the functionality of wireless networks [98,99,100].
The goal of the study appeared to compare the viability of regular communications to IRS-enhanced links in the context of uplink spectral efficiency using a two-tier 6G structure which comprises micro or tiny cell tiers (or layers) operating within a macro cell-based layer.

2. Literature Review

Chu et al. [101] investigated the functionality of IRS-assisted mobile IoT networks. The study aimed to maximize the system's total throughput while keeping transmission time planning IRS element phase changes, and distribution of bandwidth in mind. Li et al. [102] used the IRS to increase the effectiveness of spectrum identification in IoT devices and reduce energy usage. Verma et al. [103] explored the challenge of green connectivity for 6G large IoT networks using cluster-based dissemination of data. The research also did not take into account the use of IRS in such a scenario. Okogbaa et al. [104] examined the design, execution, and limits of the IRS's implementation in the context of the IoT perspective, taking into account the upcoming 6G infrastructure.

3. System Model

3.1. Conventional Micro Cell Model

The upward power can be measured by (Eq. 1) [105],
R r U = λ 2 p t U r a ( 4 π ) 2
where r = ( x U d − x m i ) 2 + ( y U d − y m i ) 2 + ( z U d − z m i ) 2 specify the separation of the transmission unit at ( x U d , y U d , z U d ) and the micro cell base stations is located at ( x m i , y m i , z m i ) coordinates. λ = c / f c indicates the wavelength of the signal. c illustrates the speed of light in ms-1. f c specifies the carrier's bandwidth in Hz. α identifies a factor of the loss.
The uplink SINR is derived by (Eq. 2),
S U = R R U G + i
where G denotes the noise and i indicates the interference.
The upstream spectral efficiency equation is stated as follows (Eq. 3),
S E U = l o g 2 1 + R R U G + i

3.2. IRS-Assisted Micro Cell Model

The power in upstream for IRS-powered micro cell is calculated by (Eq. 4) [106],
R r U ( i ) = p t U ( i ) d x d y m t 2 n r 2 λ 2 c o s θ r c o s θ t A 2 g t g r G s 64 π 3 j 1 j 2 2
where d x and d y present the IRS element's aspects. m t and n r are the total number of both transmitting and receiving elements. θ t denotes the transmission angle and θ r defines the reception angle. G s   = 4 π d x d y λ 2 indicates the IRS's distribution gain. g t denotes the transmission gain and g r indicates the receiver's amplification gain. A indicates the reflection component. j 1 = x U d − x I 2 + y U d − y I 2 + z U d − z I 2 reflects the distance that exists between the transmitting device along with the IRS at ( x I , y I , z I ) coordinates. j 2 = x I − x m i 2 + y I − y m i 2 + z I − z m i 2 represent the distance between the IRS and the micro cell base stations.
The SINR in uplink is measured by (Eq. 5),
S U ( i ) = R r U ( i ) G + i
The upstream spectral efficiency is calculated by (Eq. 6),
S E U ( i ) = l o g 2 1 + R r U ( i ) G + i

4. Results and Discussions

The study includes the measurement results obtained by using MATLAB-based calculations to implement the assessment approach, as well as evaluations of the result findings.
Figure 1 illustrates the measurement of uplink spectral efficiency for the conventional non-IRS micro cellular communication.
Examining Figure 9 it is evident that in the case of uplink the maximum and minimum spectral efficiencies are 11 b/s/Hz/m2 and 0.5 b/s/Hz/m2.
Figure 2 illustrates the uplink spectral efficiency over the coverage region in terms of the varied number of Tx-Rx elements of IRS.
The realization from Figure 2a,b is that, with the increase of the Tx-Rx elements of IRS the spectral efficiency increases.
The deployments of IRS increase the spectral efficiency twice that of the conventional communication model.

5. Conclusion

The project aimed to improve access to services within the context of upcoming 6G networks by combining IRS onto a two-tier system. A literature evaluation of relevant present investigations was conducted to provide perspective and identify research limitations or gaps. It developed a measuring model for conventional and IRS-aided infrastructure, as well as formulas for calculating uplink throughput. According to the experiments, IRS-aided networks outperform typical non-IRS networking.

Declaration of Competing Interest

The authors declare no known competing interests.

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Figure 1. Transmitter-receiver separation (3D) vs spectral efficiency in uplink (non-IRS).
Figure 1. Transmitter-receiver separation (3D) vs spectral efficiency in uplink (non-IRS).
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Figure 2. (a) Transmitter-receiver separation vs. spectral efficiency in uplink ( θ t = 60°, θ r = 60° and 128x128 Tx-Rx elements), (b) Transmitter-receiver separation vs. spectral efficiency in uplink ( θ t = 60°, θ r = 60° and 256x256 Tx-Rx elements).
Figure 2. (a) Transmitter-receiver separation vs. spectral efficiency in uplink ( θ t = 60°, θ r = 60° and 128x128 Tx-Rx elements), (b) Transmitter-receiver separation vs. spectral efficiency in uplink ( θ t = 60°, θ r = 60° and 256x256 Tx-Rx elements).
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