This is an open access article under the CC BY license : Al-Khwarizmi Engineering Journal Al-Khwarizmi Engineering Journal ISSN (printed): 1818 – 1171, ISSN (online): 2312 – 0789 Vol. 21, No. 2, June, (2025), pp. 106-116 Impact of the Number of Reflecting Elements and Positioning of the Reconfigurable STAR-Passive-ORIS in MIMO FSO Systems Batool Rafid Natiq1*, Lwaa Faisal Abdulameer2 and Mohammad Bagher Dowlatshahi3 1,2Department of Information and Communication Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad, Iraq 3Department of Computer Engineering, Faculty of Engineering, Lorestan University, Khoramabad, Iran *Corresponding Author’s Email: batoul.abd2203@kecbu.uobaghdad.edu.iq (Received 11 December 2024; Revised 9 January 2025; Accepted 27 February 2025; Published 1 March 2025) https://doi.org/10.22153/kej.2025.02.004 Abstract This study aims to improve the performance of reconfigurable STAR-passive-OIRS in multiple input multiple output- based free-space optical (FSO) communication systems. The study focuses on analysing the influence of two main factors: the number of reflective elements and the location of the smart surface. STAR-RIS technology has shown great potential in improving spectrum efficiency and signal-to-noise and interference ratio (SINR) and in increasing channel gain by providing 360° coverage and reducing interference in environments with line-of-sight constraints. Results reveal that increasing the number of reflective elements enhances signal strength and improves SINR, whilst placing the smart surface closer to the source or receiver achieves optimal performance. The study highlights STAR-RIS technology’s ability to overcome traditional FSO systems’ limitations and improve network performance in modern wireless communication environments. Keywords: Free space optics (FSO); Intelligent reflecting surface (IRS); Nonorthogonal multiple access (NOMA); Radio frequency (RF); and simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). 1. Introduction In recent years, the high speed and simplicity of wireless communication environments have become essential [1]. Free space optical (FSO) communications are considered a potential alternative to offer quick data transfer rates [2]. FSO is a promising candidate to meet the requirements of 5G and beyond (B5G), such as high capacity, high bandwidth, fast speed and low power consumption [3]. However, the trees and walls are regarded as obstacles and limitations; therefore, FSO technology has certain fundamental drawbacks, such as little to no support and limited mobility for nonline of sight (NLOS) [4]. In addition, the quality of FSO is affected by several factors, such as atmospheric turbulence (AT) (e.g. fog and rain) [5], [6]. FSO’s performance might be enhanced by many methods such as the hybrid models of dual-hop RF-FSO integrating the advantages of FSO and RF communication [7]. Research in this direction has many types which use hybrid RF-FSO technologies together [7], [8], [9], [10]. Although using FSO/RF together has the advantage of helping overcome the disadvantages of FSO, this solution increases the complexity and cost of the system [10]. The decode and forward (DF) relay strategy can improve quality to enhance performance and mitigate disturbances [11], but it has drawbacks such as latency and high power consumption [11]. An intelligent reflecting surface (IRS), which is a surface with many reflecting elements, can be used to reflect signals in the desired direction. Furthermore, it can overcome FSO’s NLOS limitations, increase signal coverage, enhance channel conditions and create a virtual line of sight https://doi.org/10.22153/kej.2025.02.004 Batool Rafid Natiq Al-Khwarizmi Engineering Journal, Vol. 21, No.2, pp. 106-116 (2025) 107 LOS [5], [4]. Hybrid FSO/RF communication systems that use a reconfigurable intelligent surface (RIS) can further improve FSO performance [12]. Optical intelligent reflecting surfaces (OIRS) have the potential to improve the capabilities of future generations of optical wireless communication systems by reconfiguring the propagation environment of optical waves [13]. IRS and OIRS can be passive or active and reconfigurable or nonreconfigurable [14][15][16][17]. RISs capable of simultaneous transmission and reflection, known as STAR-RIS, are increasingly prevalent. STAR- RIS can reflect and refract signals, providing complete wireless coverage and enhanced degrees of freedom (DOFs) and providing transmitters for users on both sides. This type is different from the traditional IRS, which only reflects signals to users on the same side, thus solving the limitation of traditional IRS, which cannot be used when the transmitter and receiver are on different sides [18]. In addition, the number of reflecting elements and IRS improve performance and increase gain, and this number depends on the type of application and the requirement of the design of the communication system [12]. Furthermore, integrating multiple access methods with the IRS is essential to address communication requirements efficiently for a wide range of services. These methods play a role in optimising the allocation of frequencies, ensuring speedy communication and promoting the development and expandability of the communication systems. One of these methods is orthogonal multiple access (OMA), which is employed by the IRS and offers advantages such as reducing interference [19]. OMA includes techniques such as frequency division multiple access, orthogonal frequency division multiple access, code division multiple access and time division multiple access, which are effective in reducing interference to users, thereby improving reliability [19]. However, OMA encounters impediments as an effect of altered channel conditions and data patterns, which can lead to the underutilisation of spectrum resources and a reduction in capacity [19]. On the one hand, nonorthogonal multiple access (NOMA) techniques offer an idea of shared resource utilisation, which substantially enhances efficiency through intelligent power allocation [20]. It effectively deals with signal weakening by strategic power allocation and using advanced coding methods that allow multiple data transmissions simultaneously [21][22]. By judicious use of resources, NOMA improves the resilience and efficiency of IRS. Successive interference cancellation (SIC) plays a role in this context. SIC helps untangle overlapping signals, which is important when multiple users are involved in NOMA scenarios [21]. Multiple-input multiple-output (MIMO) strategies have been extensively studied to enhance data rates and reliability without putting strain on time and spectrum resources [23]. This system consistently outperforms the single-input single- output system [23]. In the modern world of FSO, reconfigurable STAR-passive-OIRS is a potential technology that can enhance the efficacy of spectrum and network coverage. By manipulating the environment surrounding the radio waves, reconfigurable STAR- passive-OIRS technology offers the ability to enhance the overall system performance whilst reducing interference and increasing the SINR. In addition, strategically locating reconfigurable STAR-passive-OIRS plays a crucial role in increasing efficiency, especially in systems with LOS limitations. In this study, we investigate the effect of optimising IRS location and the number of reflective elements on the performance improvement of a STAR-RIS-based FSO communication system. The paper is organised as follows: Section 1 contains the introduction. Section 2 details the associated work. Section 3 presents the mathematical model, whilst Section 4 provides numerical results and discussions. Section 5 presents the study’s conclusion. 2. Related Work A future technique for future 5G and B5G wireless communications to improve coverage and capacity is RIS, which has a low hardware cost and energy consumption [24]. In [25], the authors examined the potential of IRSs to alleviate the LOS constraint of FSO systems. In [12], the authors suggested a novel wireless transmission method that employs RIS for hybrid RF/FSO communications. In [26], the researchers investigated the mathematical model and design of IRS-assisted optic communication systems, which are employed to reduce the LOS demand for multilink FSO systems. In [27], the impact of defects in SIC technology when using IRS with NOMA systems was discussed. In [28], the researchers improved the use of reconfigurable RIS in radio frequency systems. The focus was on choosing the optimal number of adjustable reflective elements within the RIS to achieve improved data transfer rates and energy efficiency. In [29], the authors discussed how to optimise the Batool Rafid Natiq Al-Khwarizmi Engineering Journal, Vol. 21, No.2, pp. 106-116 (2025) 108 location of the IRS to achieve optimal performance in wireless communication systems. The paper presented an analytical study to determine the optimal location of IRS by analysing the effect of different settings, such as surface height and stability, phase signal distribution and the effect of antenna orientation. A new STAR system design, which is based on reconfigurable intelligent surfaces (STAR-RISs), was proposed [30]. The conventional RIS was contrasted and investigated in comparison with STAR-RISs [31]. IRS and STAR-RIS were systematically reviewed in [32] and [33], with a detailed explanation of them, clarifying their benefits and their general impact on the performance of networks for 5G, B5G and 6G. Furthermore, their uses and challenges were presented with possible future research directions. In [34], a mathematical model was proposed to describe the mechanism of simultaneous transmission and reflection in STAR-RIS, and new resource allocation schemes were designed to improve the efficiency of transmission and reflection, including power distribution between the two directions and development of algorithms to improve performance, such as optimising beamforming and maximising data transfer rate. In [35], the integration of active STAR-RIS with massive MIMO communication systems was proposed. In [36], STAR-RIS was proposed to enhance covert communications in NOMA networks. The researchers focused on designing a system that ensures the existence of the communication is hidden from eavesdroppers whilst improving the efficiency of data transmission. The table below shows some previous works using RIS or STAR-RIS with different technologies and channels and their most important contributions. Table 1, Comprehensive review of RIS and STAR-RIS with many techniques in RF and FSO communication systems Ref. Type of IRS Techniques Channel type Contribution [25] IRS No FSO Focused on improving signal quality and increasing communication distance [12] IRS no RF/FSO Improved the performance of hybrid communication systems that combine RF and FSO systems [26] IRS MIMO FSO Developed mathematical models to analyse the performance of systems and proposed strategies to improve communication efficiency [28] IRS no RF Determined the optimal number of reflective elements in RIS to achieve remarkable improvements in the performance of wireless communication systems [27] IRS NOMA RF Analysed the system’s performance under nonideal SIC conditions and proposed improvements to increase communication efficiency [29] IRS no RF Proposed algorithms to determine the optimal location of RIS with the aim of enhancing signal quality and extending coverage [34] STAR-RIS no RF Developed algorithms to optimise active beamforming in the base station and passive beamforming in STAR-RIS, with the aim of improving system performance and reducing power consumption [35] STAR-RIS MIMO RF Developed algorithms for power distribution and beamforming optimisation [36] STAR-RIS NOMA RF Analysed the probability of eavesdroppers discovering the communication and developing strategies for packet design and resource allocation to ensure confidentiality of communication This study aims to address the research gaps related to determining the optimal location and number of elements for STAR-passive reconfigurable optical systems (STAR-passive- OIRS) in the MIMO-FSO environment. Although previous research has provided mathematical models for the simultaneous transmission and reflection mechanism in STAR-RIS and designed algorithms to optimise beamforming and resource allocation, the detailed impact of STAR-RIS Batool Rafid Natiq Al-Khwarizmi Engineering Journal, Vol. 21, No.2, pp. 106-116 (2025) 109 location and number of reflective elements on system performance has not been sufficiently studied. This study presents an integrated theoretical and experimental analysis to determine the optimal number of reflective elements and the ideal location of STAR-RIS in MIMO-enabled FSO systems by developing accurate mathematical models and analysing the system performance under effects such as dispersion, environmental disturbances and optical interference, thereby improving data transmission efficiency, reducing signal loss and maximising network resource utilisation. 3. System Model Figure 1 shows a single base station (BS) with eight optical sources, i.e. laser diodes (LDs); hence, S = 8. An LED system is not as suitable in this situation as LDs, which could allow for communication over long distances. There are users at the receiver. Each user has eight photodetectors (PDs) per user, P = 8. The STAR- Passive-OIRS should not be located in the midpoint between BS and clusters, as this would result in a low channel gain. However, in the proposed network, STAR-Passive-OIRS is located closer to BS. Reconfigurable-STAR-Passive-OIRS is utilised to get beyond the restrictions of FSO communications provide 360° coverage for both sides, and reach the signal to far-users. Fig. 1. Proposed system model. With heights of BS (hB), IRS (hI), and UE (hU), all users—BS and OIRS – are considered in a static position and are distinct from one another. The number of reflecting elements is (NI = 256), and RIS has a rectangular form (NIV×NIH). On–off keying (OOK) is used as the modulation type. OOK is a binary-level modulation scheme in which the "1" is used to indicate the presence of light, whilst "0" is used to indicate the absence of light in OOK. Huygens Fresnel is employed due to its facilities for the middle and far fields; ray-tracing is better appropriate for the near field. Huygens' principle is essential to understanding how signals are designed and routed in STAR-RIS systems. It provides the theoretical basis for how waves propagate and interact with smart surfaces, allowing for improved network performance and ensuring that signals reach the user with high efficiency. A zero mean and variance σ2 additive noise is used to model AWGN, the system also accounts for the effects of path loss, shadowing, and scattering, which further impact the received signal quality. The Successive Interference Cancellation (SIC) technology is used. It enables increased spectrum utilisation efficiency, improved signal quality, reduced interference between users, support for a larger number of users in the same frequency resource, and reduced decoding error, making it essential for improving the performance of modern communications systems such as NOMA and 5G networks. The RIS is always utilised in a Rician channel distribution. It is suitable for AT and high altitudes. Furthermore, Let's say the working area is (WAx, WAy), and the transmit power is 50 mW. The reception noise power is −60 dBm. The transmitted signal between BS’s LDs and STAR IRS is denoted by: 𝑋𝑠(𝑡) = 𝛾(√𝑃𝑠𝑥𝑠(𝑡)𝑒 𝑗ωt + 𝐼𝐷𝐶) …(1) Each LD's current conversion is represented as 𝛾 [W/A], where γ ε [0,1] and IDC are constant DC currents that guarantee the transmitted power's positivity. 𝑃𝑠 is the power transmitted from the laser source s (s from 1 to 8 because we have 8 LDs), 𝑥𝑠(𝑡) is the signal sent from the laser source 𝑠 , which is a time function containing the data. ω is an angular frequency of the carrier signal. 𝑋𝑠(𝑡) denotes the ook symbol with zero mean and unit variance from the LD 𝑠, and 𝑋𝑠(𝑡) ∈ RNB ×1. The STAR RIS components receive the signal that is transmitted from the laser sources, the following is a representation of the signal that reaches the STAR RIS surface: 𝑦𝑅𝐼𝑆(𝑡) = ∑ ℎ𝑠𝑋𝑠(𝑡) 8 𝑠=1 …(2) Where ℎ𝑠 is the channel between laser source s and the STAR RIS as follows: ℎ𝑠 = (ℎ1, ℎ2, ……… . , ℎ8) 𝑇 …(3) Each element on the STAR RIS can either reflect or transmit the signal. The reflected or transmitted signal from element 𝑛 can be represented as: 𝑠𝑅𝑛(𝑡) = 𝑅𝑛 𝑦𝑅𝐼𝑆(𝑡) …(4) Batool Rafid Natiq Al-Khwarizmi Engineering Journal, Vol. 21, No.2, pp. 106-116 (2025) 110 𝑠𝑇𝑛(𝑡) = 𝑇𝑛 𝑦𝑅𝐼𝑆(𝑡) …(5) Where 𝑅𝑛 is the reflection coefficient of element n, 𝑇𝑛 is the transmission coefficient of element n, and the number of elements is 𝑁 = 1 to 256 n elements. The reflection and transmission coefficients of element n are: 𝑅𝑛 = √𝛽𝑛 𝑅 𝑒𝑗𝜙𝑛 𝑅 ...(6) 𝑇𝑛 = √𝛽𝑛 𝑇 𝑒𝑗𝜙𝑛 𝑇 ...(7) Where 𝛽𝑛 𝑅 , 𝛽𝑛 𝑇 ∈ [0, 1] are real-valued coefficients satisfying 𝛽𝑛 𝑅 + 𝛽𝑛 𝑇 ≤ 1 , and 𝜙𝑛 𝑅 , 𝜙𝑛 𝑇 ∈[0, 2𝜋) are the phase shifts introduced by element n for the transmitted and reflected signals, ∀𝑛 𝜖{1, 2,· · · , 𝑁}. For passive STAR-RIS elements, the local transmission and reflection coefficients must satisfy the following constraint by the law of energy conservation: |𝑅𝑛 | 2 + |𝑇𝑛 | 2 ≤ 1 ...(8) The near users reach the signal directly from LDs in BS, so we null the reflecting signal from STAR- RIS to near users, the STAR-RIS’s elements only transmit the signal to far users. After being transmitted by the elements of the STAR RIS, the signal reaches distant users through the channel between the STAR RIS and the PDs. The signal reaching user 𝑘 with eight photodetectors can be represented as 𝑌(𝑡) = ℛ(∑ ∑ ∑ ∑ 𝑇𝑛 𝑔𝑝 𝑘 256 𝑛=1 8 𝑠=1 8 𝑝=1 ℎ𝑠𝑋𝑠(𝑡)𝑒 𝑗(θs+ϕn+ϕp,k,n) 𝐾 𝑘=1 +�̅�(𝑡)). …(9) Sub. (1) in (9): 𝑌(𝑡) = 𝛾ℛ(∑ ∑ ∑ ∑ 𝑇𝑛 𝑔𝑝 𝑘256 𝑛=1 8 𝑠=1 8 𝑝=1 ℎ𝑠(√𝑃𝑠𝑥𝑠(𝑡)𝑒 𝑗(ωt) +𝐾 𝑘=1 𝐼𝐷𝐶)𝑒𝑗(θs+ϕn+ϕp,k,n) + 𝑁(𝑡)), …(10) where 𝑝 refers to the photodetector p of user 𝑘, and 𝑔𝑥,𝑝 𝑘 is the channel between element 𝑛 and photodetector 𝑝 of user 𝑘 . θs is the phase shift added by the channel between laser source s and STAR RIS and ϕs ∈[0, 2𝜋). ϕnis the phase shift added by the transmission process of element n on the STAR RIS and ϕn ∈[0, 2𝜋) , ϕp,k,n is the phase shift added by the channel between element n and photodetector 𝑝 of user 𝑘 and ϕp,k,n ∈[0, 2𝜋) , and np,k(t)is the noise added to user 𝑘. Additionally, ℛ is the responsibility of the photodetector of photodetectors 𝑝. The channel between element 𝑛 and photodetector 𝑝 of user 𝑘 is denoted by 𝑔𝑇,𝑝 𝑘 = (𝑟𝑇,𝑘) 𝐻 𝑑𝑖𝑎𝑔(√𝛽𝑇,1𝑒 𝑗𝜙𝑇,1 , …… . , √𝛽𝑇,𝑛𝑒𝑗𝜙𝑇,𝑛)ℎ𝑠, …(11) where ℎ𝑠 is the channel vector between the transmitter and the STAR-RIS elements (Eq. [3]), 𝑟𝑇,𝑘= (𝑟𝑇,𝑘,1, 𝑟𝑇,𝑘,2, ……… . , 𝑟𝑇,𝑘,8)𝑇 is the channel vector between the STAR-RIS elements and the receiver 𝑘 in the transmission group, 𝛽𝑇,𝑛 , 𝜙𝑇,1are the amplitude and phase shift for the n-th element of the STAR-RIS. We can write 𝑔𝑇,𝑝 𝑘 as 𝑔𝑝 𝑘. To calculate the SINR, we calculate the signal power as 𝑃𝑘 = 𝐸[𝛾2ℛ2 ∑ ∑ ∑ 𝑃𝑠| ∑ 𝑇𝑛 𝑔𝑝 𝑘256 𝑛=1 8 𝑠=1 8 𝑝=1 𝐾 𝑘=1 ℎ𝑠| 2] = 𝛾2ℛ2 ∑ ∑ ∑ 𝑃𝑠𝐸[|∑ 𝑇𝑛 𝑔𝑝 𝑘256 𝑛=1 8 𝑠=1 8 𝑝=1 𝐾 𝑘=1 ℎ𝑠| 2] = 𝛾2ℛ2 ∑ ∑ ∑ 𝑃𝑠 ∑ |𝑇𝑛 | 2𝐸[|𝑔𝑝 𝑘 |2] 256 𝑛=1 8 𝑠=1 𝐸[8 𝑝=1 𝐾 𝑘=1 |ℎ𝑠| 2], … (12) where |𝑒𝑗(ωt+θs+ϕn+ϕp,k,n)|2 = 1, and 𝑃𝜅 is the signal power for user 𝑘. The interference for each user can denoted as 𝐼𝜅 = 𝛾2ℛ2 ∑ ∑ ∑ 𝑃𝑠 ∑ |𝑇𝑛 | 2𝐸[|𝑔𝑥,𝑝 𝑖 |2] 256 𝑛=1 8 𝑠=1 𝐸[8 𝑝=1 |ℎ𝑠| 2]𝑖≠𝑘 , …(13) where 𝐼𝜅 is the interference power for user 𝑘 from other users, 𝑝 refers to the photodetector p of user 𝑘, 𝑠 refers to the laser source 𝑠, 𝑖 refers to the other users 𝑖 ≠ 𝑘, 𝑇𝑛 𝑡ℎ𝑒 𝑡ℎ𝑒 transmission coefficient of element n on the STAR RIS, 𝑔𝑥,𝑝 𝑖 is the far-field channel between element n and the photodetector 𝑝 of user 𝑖, and 𝑃𝑠is Power transmitted from the laser source 𝑠. We use the perfect SIC to cancel the interference, if 𝑇𝑛 represents the transmission coefficient for each element on the RIS, and 𝑔𝑛,𝑘 represents the channel gain from element n to user 𝑘, the equations for total channel gain are as follows: For user 1 (U1): Total gain for U1=∑ 𝑇𝑛 𝑔𝑛,1 256 𝑛=1 For user 2 (U2): Total gain for U2=∑ 𝑇𝑛 𝑔𝑛,2 256 𝑛=1 For user 3 (U3): Total gain for U3=∑ 𝑇𝑛 𝑔𝑛,3 256 𝑛=1 On the basis of the calculated total channel gain for each user, we can order the users by their channel strength. For example, if the ordering is Total gain for U1 > Total gain for U2 > Total gain for U3, then user U1 receives the strongest signal from the RIS. The decoding order is determined as 𝑄1 < 𝑄2 < 𝑄3. Thus, interference cancellation begins with user 𝑄1, followed by user 𝑄2, and finally, user 𝑄3. The noise power is 𝑛𝑜𝑖𝑠𝑒 𝑝𝑜𝑤𝑒𝑟 = 𝐸[(�̅�)2] = 𝐸[�̅��̅�𝐻] = 𝐸 {[ 𝑁1 𝑁2 ⋮ 𝑁𝐾 ] [𝑁1 ∗ 𝑁2 ∗ ⋯ 𝑁𝐾 ∗ ]}= [ 𝜎𝑁1 2 0 0 0 0 𝜎𝑁2 2 0 0 0 0 ⋱ 0 0 0 0 𝜎𝑁𝐾 2 ] =𝜎𝑁 2, …(14) where N is an AWGN. Batool Rafid Natiq Al-Khwarizmi Engineering Journal, Vol. 21, No.2, pp. 106-116 (2025) 111 Signal-to-interference-plus-noise ratio (SINR): 𝑆𝐼𝑁𝑅 = 𝑠𝑖𝑔𝑛𝑎𝑙 𝑝𝑜𝑤𝑒𝑟 𝑖𝑛𝑡𝑒𝑟𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑝𝑜𝑤𝑒𝑟+𝑛𝑜𝑖𝑠𝑒 𝑝𝑜𝑤𝑒𝑟 , …(15) 𝑆𝐼𝑁𝑅 = 𝑃𝑘 𝜎𝑁 2 , …(16) 𝑆𝐼𝑁𝑅 = 𝛾2ℛ2 ∑ ∑ ∑ 𝑃𝑠 ∑ |𝑇𝑛 | 2𝐸[|𝑔𝑝 𝑘 |2] 256 𝑛=1 8 𝑠=1 𝐸[8 𝑝=1 𝐾 𝑘=1 |ℎ𝑠| 2] 𝜎𝑁 2 … (17) The average channel capacity determines how well an FSO communication system performs. It is the maximum amount of information that can be transferred in a specific time. The capacity of the i- th parallel channel for MIMO channels is represented by Ci = log2(1 + 𝑆𝐼𝑁𝑅) …(18) Then, the total MIMO capacity (C) is C= ∑ log2(1 + 𝑆𝐼𝑁𝑅) q i=1 , …(19) which represents the sum of individual capacities of each q information stream. The flowchart of the proposed system setup is shown below: Fig. 2. Flowchart of the proposed system 4. Numerical Results and Discussions In this section, we present the results of analysing the effect of reconfigurable STAR-RIS location and number of reflective elements on the performance of FSO systems. The results aim to clarify the relationship between these key variables and spectrum efficiency, SINR and channel gain. Through graphs and quantitative analysis, we demonstrate how performance can be improved using STAR-RIS techniques in wireless communication environments with different challenges. Fig. 3. Spectral efficiency vs. surface area of STAR- RIS The graph in Figure 3 displays the relationship between surface area and spectral efficiency. When using the STAR-RIS system, spectral efficiency increases remarkably as the reflective surface area increases because increasing the surface area allows more control over the reflected or directed signal, which enhances the signal at the receiver and reduces interference. Fig. 4. SINR vs. number of reflecting elements of STAR-RIS In Figure 4, we notice that SINR increases continuously as the number of reflecting elements increases. The reason for this is that increasing the Batool Rafid Natiq Al-Khwarizmi Engineering Journal, Vol. 21, No.2, pp. 106-116 (2025) 112 number of elements allows signals to be directed more accurately to receivers, which increases the received signal strength. STAR-RIS works to reduce noise caused by dispersion and improve the received signal by controlling the phases of the reflected signals. As the elements increase, more constructive interference is achieved between the reflected signals, thus increasing the signal strength and improving the SINR. STAR-RIS proves its efficiency in enhancing signal quality as the number of elements increases, making it an ideal choice for improving the performance of wireless networks. We can use 16 elements for small, experimental, or space-constrained systems and 32 to improve performance in medium environments. The use of 64 elements is common in medium to large deployments to improve beamforming power. For advanced systems that provide good coverage in business environments, 128 elements are used. For high-performance systems such as 5G/6G systems or areas with high interference, 256 elements are used. For systems centred around large areas with great interference and beamforming advantages, 512 elements are used. In advanced model applications such as future 6G, especially in urban areas with a population orientation, 1024 elements are used. In large-scale environments or systems that require excellent chips and high-precision control, 4096 elements are used. For large applications at an advanced level such as stadium coverage, smart cities or space applications, where the available layouts are large and overall performance can be improved, 10,000 elements are used. The choice of number depends on the operating environment (indoor or outdoor), the availability of sufficient space and the performance requirements (such as improving SINR). Therefore, the choice depends on the balance between performance and complexity. Fig. 5. SINR vs. position of STAR-RIS. Figure 5 shows the relationship between SINR and the position of STAR-RIS. For an increase in SINR at the edges, the highest SINR values appear when the STAR-RIS position is close to one of the edges (at values close to 0 or 200 meters). The reason is that placing the STAR-RIS close to the source or receiver increases the routing efficiency and reduces dispersion. For a decrease in SINR in the middle, in the middle of the X-axis (at approximately 100 meters), the SINR values decrease considerably. This decrease occurs because of the increased effect of dispersion and noise when no strong direct path exists between the source and the receiver. The STAR-RIS should be placed close to the source or receiver to achieve the highest SINR and best system performance. Fig. 6. Channel gain vs. position of STAR-RIS. Figure 6 shows the relationship between the channel gain and the position of STAR-RIS. For the highest gain at the ends, the highest channel gain is shown when the STAR-RIS is positioned close to one of the ends (at 0 or 200 meters). The reason for this is that placing the STAR-RIS close to the source or receiver reduces signal loss and increases signal routing efficiency. The lower gain is in the middle because at the middle of the X-axis (approximately 100 meters), the channel gain drops considerably. This decrease indicates increased signal loss due to dispersion and poor signal routing when the STAR- RIS is placed far from the source and receiver. To achieve the best channel gain, the STAR-RIS should be placed close to the source or receiver rather than in the middle locations. Batool Rafid Natiq Al-Khwarizmi Engineering Journal, Vol. 21, No.2, pp. 106-116 (2025) 113 5. Conclusions This study demonstrated the effectiveness of STAR-passive-OIRS reconfigurable smart surfaces in enhancing the performance of MIMO-based FSO communication systems. The analysis revealed that increasing the number of reflective elements considerably boosts SINR and spectral efficiency, facilitating improved signal routing and reduced interference. Additionally, strategically positioning the smart surface closer to the source or receiver optimises channel gain and minimises signal loss and dispersion. These findings underscore the transformative potential of STAR-RIS technology in overcoming LOS dependency and mitigating atmospheric disturbances, which are critical challenges in optical communication networks. The results offer valuable insights into designing advanced optical networks, particularly in urban and challenging weather environments. Integrating STAR-RIS technology with hybrid (RF/FSO) systems presents a promising direction for enhancing the efficiency and reliability of next- generation communication networks. Future work can integrate STAR-RIS with adaptive control algorithms and AI for real-time optimisation. Additionally, reinforcement learning can be applied to determine optimal STAR-RIS placement in multiuser systems whilst optimising resource allocation. AI-driven intelligent elements could further enhance system adaptability by automatically adjusting reflection characteristics based on environmental variations. Finally, large- scale STAR-RIS implementations with thousands of reflective elements (e.g. 4,096 or 10,000 elements) could enable advanced applications such as smart cities, stadium coverage and satellite communications, thus balancing performance improvements with technical complexity. Abbreviations FSO RF B5G DF OWC NOMA OMA SIC IRS OIRS Free Space Optical Radiofrequency Beyond 5G Decode and Forward Optical Wireless Communication Nonorthogonal Multiple Access Orthogonal Multiple Access Successive Interference Cancellation Intelligent Reflecting Surface Optical Intelligent Reflecting Surface STAR- IRS MIMO AT DOFs LOS NLOS BSs Simultaneously Transmitting and Reflecting Intelligent Reflecting Surfaces Multiple Input And Multiple Output Atmospheric Turbulence Degrees-Of-Freedom Line Of Sight Nonline of Sight Base Stations References [1] M. A. Atiyah, L. F. Abdulameer, and G. 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LOSالبيئات ذات قيود خط البصر ) على التغلب على قيود أنظمة STAR-RISإلى المصدر أو المستقبل األداء األمثل. تسلط الدراسة الضوء على قدرة تقنية يحقق وضع السطح الذكي أقرب FSO ئات االتصاالت الالسلكية الحديثة. التقليدية وتحسين أداء الشبكة في بي mailto:batoul.abd2203@kecbu.uobaghdad.edu.iq