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Minimization of Bandwidth for Required End-to-End Latency Implementing IRS in a 6G Network

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01 July 2024

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03 July 2024

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Abstract
The goal of the investigation is to increase coverage, which is ensure better transmission around IoT services using IRS within a two-tier 6G system comprising of one micro cell tier running beneath a macro cellular tier. As a result, the study tested and compared the reliability of traditional connectivity with IRS-enhanced connectivity in terms of upstream end-to-end latency using computer-based measuring methods. The study concluded that the implementation of IRS greatly improves coverage, which means transmission effectiveness, for 6G services.
Keywords: 
Subject: Engineering  -   Electrical and Electronic Engineering

1. Introduction

With the widespread commercial use of 5th Gen (5G) mobile networks, the Internet of Things usage possibilities have expanded. From 2nd Gen (2G) to 5G, networking resource optimization is critical for mobile communication success [1,2,3,4]. Nevertheless, wireless communications are reaching Shannon limitations, and the current resources are insufficient [5,6,7,8,9]. Contrary to the 5G wireless communication system, the next level (6G) communication system offers a greater coverage, allowing for integrated interconnection of satellite, sky, the surface, and water. Some novel resource optimization measures must be used to guarantee Quality of Service (also called QoS) in 6G systems [10,11,12,13].
The 6G system is envisioned as expansive, with ambitious future goals. It is projected to deliver intelligently enabled seamless connection everywhere while consuming less energy, hence improving society and quality of life overall. The growing need for connecting gadgets and internet traffic is the primary driving reason behind the need for 6G advancement [14,15,16,17,18,19]. The primary driving and expected developments regarding 6G are exceptionally high data speeds of 1Tbps, incredibly low latency (a tenth of 5G), 50 times quicker than 5G, 2x greater energy efficiency, along with 2x higher spectrum efficiency [20,21,22,23,24]. The standard organizations, especially the International Telecommunications Union-Radio Communications Sector (ITU-R), demonstrated the cutting edge of 6G activity by 2030 on the basis of quantity as well as quality for the modern era, predicting that mobile data usage will surpass the limit of greater than 5ZB per monthly [25,26,27,28,29,30]. Other properties of a 6G wireless communication system include better dependability, new spectrum, excellent network availability, cognitive networking, sustainable communication, computation, location, control, green connectivity, and monitoring [31,32,33,34,35,36]. The 6G flagship-based initiatives (like 6Genesis) have already begun to target sophisticated technology. The primary characteristics consist of a delay of less than 0.1ms in the user’s device plane along with 1ms in the command plane, downlink spectral accuracy of 100bps/Hz or further, the operation in the sub-terahertz as well as terahertz groups, expected data rates of approximately 1Tbps, as well as battery-free IoT appliances [37,38,39,40,41,42,43]. The network’s reach has been increased by using various technology integrations including satellite and terrestrial networks [44,45,46,47,48,49]. The 6G network is projected to provide native intelligence, increased spectrum efficiency, and worldwide coverage. The notion of cognitive intelligence in 6G, manifested as decision-making, has cleared the path for the growth of communication networks. Multitude large-scale executions are predicted as a result of 6G network technology. In terms of programs, the 6G network may accommodate high-end data rate services such as virtual reality, augmented reality, unified reality, 3D visualization, implants, internet of medical things, autonomous driving, sensing, and the network of intelligent applications [50,51,52,53,54,55,56,57].
6G cellular networks must have THz interactions, intelligently reflecting surfaces (IRS), holographic multi-input multi-output (HMIMO), symbiotic radio, multi-tier heterogeneous networks, cell-free network connectivity, and so on [58,59,60,61,62,63]. Furthermore, it is anticipated that 6G will include an artificial intelligence-based cloud-edge-device collaborative ground-air-space interconnected ecosystem [64,65,66,67,68,69,70].
Global wireless networks, especially in metropolitan areas, have become increasingly diverse in recent years in order to handle exponentially expanding traffic data [71,72,73,74,75,76,77]. Wireless mobile networks that comprise base stations with diverse features are unbalanced, and such BSs function independently [78,79,80,81,82]. Cross-tier disturbance has a significant negative impact on system performance. As a result, collaboration across diverse BSs and the blending of heterogeneous networking is a pressing issue in next-generation networks [83,84,85,86,87,88,89].
An IRS constitutes a rectangular metasurface composed of a large number of reflecting elements that has lately received academic interest for its capacity to dramatically increase the energy and spectrum efficiency of communication systems by changing wireless transmission circumstances [90,91,92,93,94]. IRS components can reflect the arriving signal with the appropriate phase shift. IRS continually alters the propagation of the reflecting signal, resulting in constructive signals combination and destructive noise suppression by the receiving device. As a consequence, QoS can be enhanced at the receiving end [95,96].
As a result, the research compared the performance of traditional micro cellular connectivity to IRS-based micro cellular connectivity in terms of upstream end-to-end latency.

2. Related Literature

The feasibility and efficacy of employing IRSs in 6G-based recognizing has just been examined in research [97,98,99]. Several studies have looked into ways to use IRSs as anchoring for localization within the context of device-based sensing. The study [100] examined an angle-based localization mechanism made up of a BS, numerous IRSs, and user devices. To solve the maximum probability problem for determining angle details, a two-step technique was proposed. Initially an exhaustive search approach is implemented throughout a discrete structure to find a few favorable initial points, following which a gradient decent hunt method is used to find a more effective angle approach. The user may then be localized using its angles to various anchors, such as the BS along with IRSs.

3. Measurement Model

3.1. IRS-Based Model

The power for IRS-powered cell is measured by (Equation (1)) [101],
P r e c U L ( i r s ) = p t r U L ( i r s ) l x l y G s t λ 2 c o s θ t r c o s θ r e c A 2 g t r g r e c T t r 2 R r e c 2 m 1 m 2 2 ( 4 π ) 3
where G s t   = 4 π d x d y λ 2 represents the IRS dispersion gain. l x and l y express the IRS’s structures. θ t r and θ r e c describe the broadcast and reception angles. A denotes the reflection factor. g t r and g r e c represent the transmit and receive amplification gain. T t r and R r e c are the aggregate amount of the transmitter and receiver components. m 1 =   x U e − x i r s 2 + y U e − y i r s 2 + z U e − z i r s 2 indicates the space between the transmission gadget at ( x U e , y U e , z U e ) the IRS positioned at ( x i r s , y i r s , z i r s ) coordinates. m 2 =   x i r s − x m c 2 + y i r s − y m c 2 + z i r s − z m c 2 indicate the space across the IRS along with the base station situated at ( x m c , y m c , z m c ) coordinates.
The upstream throughput in the case of IRS model is determined by (Equation (2)),
T r U L ( i r s ) = B W   l o g 2 1 + P r e c U L ( i r s ) i n t + G w n
where int indicates the interference and G w n denotes the noise and.
The transmission delay in uplink is derived by the equation below (Equation (3)),
d U L ( i r s ) = U p l i n k   D a t a   S i z e T r U L ( i r s )

3.2. Conventional Model

The upward power can be measured by (Equation (4)) [102],
P r e c U L ( C ) = p t r U L ( C ) λ 2     z a ( 4 π ) 2
where z =   ( x U e − x m c ) 2 + ( y U e − y m c ) 2 + ( z U e − z m c ) 2 indicate the spacing between the transmitting equipment and the base stations. λ =   c / f c specifies the spectral length of the stream. f c specify the bandwidth in Hz. c demonstrates the apparent velocity of light in ms-1. α specifies the factor influencing the loss.
The upstream spectral efficiency equation is stated as follows (Equation (5)),
T r U L ( C ) = B W   l o g 2 1 + P r e c U L ( C ) i n t + G w n
The transmission delay is derived by (Equation (6)),
d U L ( C ) = U p l i n k   D a t a   S i z e T r U L ( C )

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 1a shows the measurement result of transmission delay in terms of bandwidth and data size in the context of cell-edge devices. Figure 1b illustrates the result of transmission delay in terms of bandwidth (reduced i.e., 60 MHz) and data size (100 Kb) when the transmitter-receiver separation distance is 50 m. Figure 1c visualizes the measurement of transmission delay in terms of bandwidth (maximum i.e., 200 MHz) and data size (increased i.e., 350 Kb) when the transmitter-receiver separation distance is 50 m. Figure 1d represents the uplink transmission delay considering the coverage region (200 MHz of bandwidth and 100 Kb of data size).
According to the specification or the requirement of 6G less than 1 ms end-to-end delay is required for the applications and services. At least 200 MHz bandwidth is required to offload data of size 100 Kb within 1 ms end-to-end delay when the IoT devices are 100 m away from the base station as per Figure 1a. When the devices are 50 m away from the base station at least 60 MHz bandwidth is required to offload data of size 100 Kb within 1 ms delay according to Figure 1b and with the maximum bandwidth (200 MHz) 350 Kb of data can be offloaded within 1 ms end-to-end delay as per Figure 1c. Figure 1d represents the 3D coverage region and delay for the maximum data size and bandwidth for better realization from which it can be realized that with a shorter transmitter-receiver separation the end-to-end delay requirement can be obtained with a lower amount of bandwidth.
Figure 2a,b visualize the uplink end-to-end transmission delay in terms of bandwidth and data with the varied number of Tx-Rx elements of IRS (for cell-edge devices). Figure 2c represents the uplink transmission delay considering the coverage region (85 MHz of bandwidth and 1000 Kb of data size).
Analyzing Figure 2a,b it is evident that with the increase in the number of transmit-receive elements the required end-to-end latency is achieved with a reduced amount of bandwidth. 85 MHz bandwidth is required to offload 1000 Kb of data when the number of transmit-receive elements in 128x128 and when the number of transmit-receive elements gets increased to 256x256 70 MHz bandwidth is enough to offload 1000 Kb of data within 1 ms end-to-end delay. The end-to-end transmission delay in the case of uplink for the 3D coverage region is better realized in Figure 2c.
With the adoption of IRS the required end-to-end transmission delay can be achieved with a lower amount of bandwidth even if the data size is high. In the case of the conventional communication model maximum allowable 200 MHz bandwidth can offload up to 100 Kb of data and in the case of the IRS-assisted model, only 85 MHz bandwidth is required to offload 1000 Kb (considered maximum data size) of data within the end-to-end delay of less than 1 ms (if the number of transmit-receive elements of the IRS raised the required end-to-end delay can be obtained even with a lower amount of bandwidth).

5. Conclusion

The project aimed to improve access to services within the context of upcoming 6G networks by combining IRS onto a micro cell underlying a two-tier system that functions at the macro cell-level. 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 micro-cell infrastructure, as well as formulas for calculating uplink throughput. According to the experiments, IRS-aided micro-cell networks outperform typical non-IRS microcell networking. Furthermore, an IRS-aided infrastructure conserves energy by reducing transmission power.

Declaration of Competing Interest

The authors declare no known competing interests.

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  69. Hadeel Elayan, Raed M. Shubair, and Josep M. Jornet. “Characterising THz propagation and intrabody thermal absorption in iWNSNs.” IET Microwaves, Antennas & Propagation 12, no. 4 (2018): 525-532. [CrossRef]
  70. Sandip Ghosal, Arijit De, Ajay Chakrabarty, and Raed M. Shubair. “Characteristic mode analysis of slot loading in microstrip patch antenna.” In 2018 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting, pp. 1523-1524. IEEE, 2018.
  71. Raed M. Shubair. “Improved smart antenna design using displaced sensor array configuration.” Applied Computational Electromagnetics Society Journal 22, no. 1 (2007): 83.
  72. Dana Bazazeh, Raed M. Shubair, and Wasim Q. Malik. “Biomarker discovery and validation for Parkinson’s Disease: A machine learning approach.” In 2016 International Conference on Bio-engineering for Smart Technologies (BioSMART), pp. 1-6. IEEE, 2016.
  73. Yazan Al-Alem, Ahmed A. Kishk, and Raed M. Shubair. “Enhanced wireless interchip communication performance using symmetrical layers and soft/hard surface concepts.” IEEE Transactions on Microwave Theory and Techniques 68, no. 1 (2019): 39-50. [CrossRef]
  74. R. M. Shubair, and A. Merri. “A convergence study of adaptive beamforming algorithms used in smart antenna systems.” In 11th International Symposium on Antenna Technology and Applied Electromagnetics [ANTEM 2005], pp. 1-5. IEEE, 2005.
  75. Mayar Lotfy, Raed M. Shubair, Nassir Navab, and Shadi Albarqouni. “Investigation of focal loss in deep learning models for femur fractures classification.” In 2019 International Conference on Electrical and Computing Technologies and Applications (ICECTA), pp. 1-4. IEEE, 2019.
  76. Yazan Al-Alem, Ahmed A. Kishk, and Raed M. Shubair. “One-to-two wireless interchip communication link.” IEEE Antennas and Wireless Propagation Letters 18, no. 11 (2019): 2375-2378. [CrossRef]
  77. E. M. Ardi, R. M. Shubair, and M. E. Mualla. “Adaptive beamforming arrays for smart antenna systems: A comprehensive performance study.” In IEEE Antennas and Propagation Society Symposium, 2004., vol. 3, pp. 2651-2654. IEEE, 2004.
  78. S. Elmeadawy, and R. M. Shubair. “Enabling technologies for 6G future wireless communications: Opportunities and challenges. arXiv 2020.” arXiv preprint. arXiv:2002.06068.
  79. Yazan Al-Alem, Raed M. Shubair, and Ahmed Kishk. “Efficient on-chip antenna design based on symmetrical layers for multipath interference cancellation.” In 2016 16th Mediterranean Microwave Symposium (MMS), pp. 1-3. IEEE, 2016.
  80. Mohamed Ibrahim Alhajri, N. T. Ali, and R. M. Shubair. “2.4 ghz indoor channel measurements.” IEEE Dataport (2018).
  81. Hadeel Elayan, Cesare Stefanini, Raed M. Shubair, and Josep M. Jornet. “Stochastic noise model for intra-body terahertz nanoscale communication.” In Proceedings of the 5th ACM International Conference on Nanoscale Computing and Communication, pp. 1-6. 2018.
  82. Nadeen R. Rishani, Raed M. Shubair, and GhadahAldabbagh. “On the design of wearable and epidermal antennas for emerging medical applications.” In 2017 Sensors Networks Smart and Emerging Technologies (SENSET), pp. 1-4. IEEE, 2017.
  83. Raed M. Shubair, and Hadeel Elayan. “Enhanced WSN localization of moving nodes using a robust hybrid TDOA-PF approach.” In 2015 11th International Conference on Innovations in Information Technology (IIT), pp. 122-127. IEEE, 2015.
  84. Hadeel Elayan, Hadeel, and Raed M. Shubair. “Towards an Intelligent Deployment of Wireless Sensor Networks.” In Information Innovation Technology in Smart Cities, pp. 235-250. Springer, Singapore, 2018.
  85. Asimina Kiourti, and Raed M. Shubair. “Implantable and ingestible sensors for wireless physiological monitoring: a review.” In 2017 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting, pp. 1677-1678. IEEE, 2017.
  86. M. I. AlHajri, N. T. Ali, and R. M. Shubair. “2.4 ghz indoor channel measurements data set.” UCI Machine Learning Repository (2018).
  87. Abdul Karim Gizzini, Marwa Chafii, Ahmad Nimr, Raed M. Shubair, and Gerhard Fettweis. “Cnn aided weighted interpolation for channel estimation in vehicular communications.” IEEE Transactions on Vehicular Technology 70, no. 12 (2021): 12796-12811. [CrossRef]
  88. Yazan Al-Alem, Raed M. Shubair, and Ahmed Kishk. “Clock jitter correction circuit for high speed clock signals using delay units a nd time selection window.” In 2016 16th Mediterranean Microwave Symposium (MMS), pp. 1-3. IEEE, 2016.
  89. M. I. AlHajri, N. T. Ali, and R. M. Shubair. “2.4 ghz indoor channel measurements data set.” UCI Machine Learning Repository, 2018.”.
  90. Nishtha Chopra, Mike Phipott, Akram Alomainy, Qammer H. Abbasi, Khalid Qaraqe, and Raed M. Shubair. “THz time domain characterization of human skin tissue for nano-electromagnetic communication.” In 2016 16th Mediterranean Microwave Symposium (MMS), pp. 1-3. IEEE, 2016.
  91. Melissa Eugenia Diago-Mosquera, Alejandro Aragón-Zavala, Fidel Alejandro Rodríguez-Corbo, Mikel Celaya-Echarri, Raed M. Shubair, and Leyre Azpilicueta. “Tuning Selection Impact on Kriging-Aided In-Building Path Loss Modeling.” IEEE Antennas and Wireless Propagation Letters 21, no. 1 (2021): 84-88. [CrossRef]
  92. WafaNjima, Marwa Chafii, and Raed M. Shubair. “Gan based data augmentation for indoor localization using labeled and unlabeled data.” In 2021 International Balkan Conference on Communications and Networking (BalkanCom), pp. 36-39. IEEE, 2021.
  93. Hadeel Elayan, Raed M. Shubair, Josep M. Jornet, Asimina Kiourti, and Raj Mittra. “Graphene-Based Spiral Nanoantenna for Intrabody Communication at Terahertz.” In 2018 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting, pp. 799-800. IEEE, 2018.
  94. Mikel Celaya-Echarri, Leyre Azpilicueta, Fidel Alejandro Rodríguez-Corbo, Peio Lopez-Iturri, Victoria Ramos, Mohammad Alibakhshikenari, Raed M. Shubair, and Francisco Falcone. “Towards Environmental RF-EMF Assessment of mmWave High-Node Density Complex Heterogeneous Environments.” Sensors 21, no. 24 (2021): 8419. [CrossRef]
  95. Mohamed I. AlHajri, Nazar T. Ali, and Raed M. Shubair. “A cascaded machine learning approach for indoor classification and localization using adaptive feature selection.” AI for Emerging Verticals: Human-robot computing, sensing and networking (2020): 205.
  96. Abdul Karim Gizzini, Marwa Chafii, Shahab Ehsanfar, and Raed M. Shubair. “Temporal Averaging LSTM-based Channel Estimation Scheme for IEEE 802.11 p Standard.” arXiv preprint (2021). arXiv:2106.04829 (2021).
  97. Yazan Al-Alem, Ahmed A. Kishk, and Raed Shubair. “Wireless chip to chip communication link budget enhancement using hard/soft surfaces.” In 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 1013-1014. IEEE, 2018.
  98. Mohamed I. AlHajri, Raed M. Shubair, and Marwa Chafii. “Indoor Localization Under Limited Measurements: A Cross-Environment Joint Semi-Supervised and Transfer Learning Approach.” In 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pp. 266-270. IEEE, 2021.
  99. Abdul Karim Gizzini, Marwa Chafii, Shahab Ehsanfar, and Raed M. Shubair. “Temporal Averaging LSTM-based Channel Estimation Scheme for IEEE 802.11 p Standard.” arXiv preprint (2021). arXiv:2106.04829 (2021).
  100. Yazan Al-Alem, Yazan, Ahmed A. Kishk, and Raed M. Shubair. “Employing EBG in Wireless Inter-chip Communication Links: Design and Performance.” In 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, pp. 1303-1304. IEEE, 2020.
  101. W. Tang et al., “Wireless Communications With Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurement,” in IEEE Transactions on Wireless Communications, vol. 20, no. 1, pp. 421-439, Jan. 2021. [CrossRef]
  102. M. Mozaffari, W. Saad, M. Bennis and M. Debbah, “Optimal Transport Theory for Cell Association in UAV-Enabled Cellular Networks,” in IEEE Communications Letters, vol. 21, no. 9, pp. 2053-2056, Sept. 2017. [CrossRef]
Preprints 110919 g001aPreprints 110919 g001b
Figure 1. (a) Bandwidth vs. data size vs. transmission delay in uplink (IoTD at 100 m dist.), (b) Bandwidth vs. data size vs. transmission delay in uplink (IoTD at 50 m dist. and reduced bandwidth), (c) Bandwidth vs. data size vs. transmission delay in uplink (IoTD at 50 m dist. and maximum bandwidth), (d) Transmitter-receiver separation (3D) vs. transmission delay (non-IRS).
Figure 1. (a) Bandwidth vs. data size vs. transmission delay in uplink (IoTD at 100 m dist.), (b) Bandwidth vs. data size vs. transmission delay in uplink (IoTD at 50 m dist. and reduced bandwidth), (c) Bandwidth vs. data size vs. transmission delay in uplink (IoTD at 50 m dist. and maximum bandwidth), (d) Transmitter-receiver separation (3D) vs. transmission delay (non-IRS).
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Figure 2. (a) Bandwidth vs. data vs. transmission delay (128x128 Tx-Rx elements and IRS placed at 100 m away from IoTD), (b) Bandwidth vs. data vs. transmission delay (256x256 Tx-Rx elements and IRS placed at 100 m away from IoTD), (c) Transmitter-receiver separation vs. delay(128x128 Tx-Rx elements, data size 1000 Kb, 85 MHz bandwidth).
Figure 2. (a) Bandwidth vs. data vs. transmission delay (128x128 Tx-Rx elements and IRS placed at 100 m away from IoTD), (b) Bandwidth vs. data vs. transmission delay (256x256 Tx-Rx elements and IRS placed at 100 m away from IoTD), (c) Transmitter-receiver separation vs. delay(128x128 Tx-Rx elements, data size 1000 Kb, 85 MHz bandwidth).
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