Enhancement of channel capacity of IRS-aided OWC system for 6G wireless communication

1 Introduction

The rapid evolution of communication technologies has set the stage for 6G networks to revolutionize data transmission by offering ultra-high speeds, low latency, and massive connectivity. In the 6G wireless mobile communication system, the carrier frequency is very high, so the signal loss is also very high. The communication range can be significantly extended by employing an IRS-aided Optical Wireless Communication (OWC) system. These networks represent the networks of future communication systems. Several factors that influence the channel capacity (CC) of the IRS-aided OWC system have been discussed. Among the technologies underpinning 6G networks, OWC has evolved as a substitute and complement to existing RF systems. OWC operates in the visible, infrared, and ultraviolet light spectrum, providing an unlicensed, interference-free communication medium [Di et al., 2023). This makes OWC particularly advantageous for indoor environments, where it can support high-capacity networks with minimal interference. Despite its potential, the OWC system faces critical challenges, particularly line-of-sight (LoS) blockages caused by physical obstructions that can severely disrupt connectivity. Such interruptions limit the practicality of the OWC system, especially in dynamic environments. To address this issue, researchers have proposed the integration of IRSs into OWC networks. IRSs are passive devices capable of dynamically reflecting signals to redirect light paths, thereby mitigating the effects of blockages and enhancing the overall system performance. They have demonstrated the importance of IRSs in enhancing robustness, coverage, and spectrum efficiency in wireless communication systems (Hamad et al., 2024). They explored the application of IRSs in laser-based OWC networks, showing significant improvements in data rates under practical deployment constraints (Hamad et al., 2024). Similarly, Sun et al. (2023) assessed capacity enhancement in MIMO-OWC systems assisted by optical intelligent reflecting surfaces (OIRSs), emphasizing the importance of optimizing IRS configurations for capacity gains (Zhao, 2019).

Visible light communication (VLC) represents a transformative approach within optical wireless communication, addressing the limitations of RF-based systems. Although significant advancements have been made in achieving high data rates and system reliability, ongoing research continues to address existing challenges. The integration of VLC into diverse applications, ranging from indoor communication to IoT and underwater communication, is the technology that can solve the future problems. Hamad et al. (2024) discussed the sum rate and the optimization of the sum rate of an IRS-aided OWC system. Galisteo et al. (2019) designed a transceiver with a higher modulation rate and sensitivity. Yu et al. (2021) revealed an improvement in the modulation bandwidth and channel capacity of the OWC system. (Abdellah and Merazka, 2024) discussed the enhancement in the throughput and bit error rate of the OWC system. Authors in Vieira et al. (2024) reported improved Bit Error Rate (BER) performance under different channel conditions in the OWC system. Urban traffic could be managed through VLC and reinforcement learning (Galvão et al., 2024). Lu et al. (2024) discussed the bandwidth and transmission quality of the OWC system. Iqbal et al. (2024) enhanced the security of the physical layer in the IRS-assisted VLC system.

Vela and Hacioglu (2024) worked on the security issues of the IRS-aided OWC system. VLCs with quantized channel information were discussed by Munoz et al. (2022) for multiple users. The performances of VLC and RF technology were compared in Vijayalakshmi et al. (2025). Wang et al. (2024) enhanced the data rate of the OWC system. Shi et al. (2024) formulated a problem to enhance the system’s confidentiality and maximize the secrecy rate for the UE. Cinemre et al. (2025) showed better bit error rates in low-to-moderate SNR regimes. Park and Kim (2025) confirmed communication between a vehicle headlamp and a traffic light. Rehman et al. (2025) developed a software-based Li-Fi system utilizing software-defined radio to enable underwater communication. Sherlin (2023) discussed the main application and research challenges of VLC. Its applications could be enhanced by the use of the IRS-aided OWC system with advanced techniques (Sikder et al., 2025). Vieira et al. (2025) discussed the applications of VLC in airports to increase safety and efficiency. Naser et al. (2022) worked on VLC with AI, which can be used to enhance energy efficiency (EE) and privacy. Singh et al. (2024) worked on the latency and reliability of VLC. Razzaque et al. (2024) worked on the capacity, spectral efficiency (SE), throughput, and digital modulation techniques of IRS-aided OWC networks.

In the case of optical wireless communication, the optical SINR at the receiving end is very important. The entire performance of any system, such as CC, SE, and EE, depends on the strength of SINR. Primarily, we have studied different factors affecting the SINR, data rate, and sum rate including channel bandwidth, receiver sensitivity and their comparison in LoS and IRS-aided environment at a distance of 700 mm. We derived the optimal expression for h_LoS, computed the maximum value of SINR, and implemented it to obtain the maximum data rate and sum rate. We computed the maximum data rate and maximum sum rate under different operating conditions. The obtained sum rate is approximately three times higher than the values reported in the previous research work (Hamad et al., 2024), as illustrated in Table 10 and Figure 14, demonstrating a significant performance improvement. A comprehensive analysis was carried out to implement an advanced digital modulation technique, namely, M-pulse amplitude modulation (M-PAM). Based on the analytical and numerical evaluations, 2-PAM was found to be the most suitable modulation scheme for transmission power of 1 W in the considered IRS-aided OWC system. The use of 2-PAM enhances the channel capacity, which is directly linked to improvements in both spectral efficiency (SE) and energy efficiency (EE). Furthermore, the technical validity of the proposed analysis was established by comparing the analytical results with those obtained from Monte-Carlo simulations, and a close match between the two confirms the accuracy of the theoretical model. A comprehensive performance analysis of this system, aimed at improving channel capacity based on the SINR, has not been previously reported in the literature. Therefore, the presented investigation provides a novel and meaningful contribution in this direction.

2 System model

In the IRS-aided OWC system, the kth users are randomly distributed over the communication plane and receive both illumination and data transmission from L optical access points (APs) installed on the ceiling. The detailed system model follows the framework proposed by Hamad et al. (2024). Each optical AP consists of multiple LEDs to ensure wide indoor coverage. An angle diversity receiver (ADR) equipped with multiple photodiode (PD) branches is employed at the user side, with each PD oriented in a different direction. The orientations of the photodiodes are defined by their elevation (E_L) and azimuth (A_z) angles. The corresponding optical channel response and propagation characteristics are described in detail by Alsulami et al. (2020). To enhance the received signal power, an IRS composed of M identical, passive, and smooth reflecting mirrors is used. The IRS is implemented as a mirror array, where each rectangular mirror is oriented using a roll angle (around the y-axis) and a yaw angle (around the z-axis). A sufficient number of mirrors are strategically placed and oriented within the indoor environment to improve coverage and enhance the received signal strength for each user.

The subsequent analysis considers both the LoS path directly connecting an AP and a user and the specular non-LoS path formed via a reflection off an IRS mirror.

2.1 Gain of the line-of-sight channel

The gain by the kth user from access point l is expressed as follows i.e., Equation 1:where n is the order Lambertian emission, Ar is the area of the detector, α(k,l) is the angle between the normal of access point l and the direction of the irradiance toward the kth user, δ(k,l) is the angle between the photodetector’s normal and the incoming light ray. The distance between the lth access point and the kth user is given by D_k,l (Hamad et al., 2024).

h k , l L o S = = 0 δ k , l > φ c n + 1 A r ⁡ cos n α k , l cos δ k , l 2 π D k , l 2 , 0 ≤ δ k , l ≤ φ c , (1)

2.2 IRS-reflected channel gain

The gain by the kth user from mirror m reflecting the signal from access point l is expressed as follows i.e., Equation 2:

h k , m , l I R S = 0 β k , m > φ c n + 1 ρ m A r d A m ⁡ cos n α m , l cos β k , m 2 π D m , l + D k , m 2 , 0 ≤ β k , m ≤ φ c . (2)

Here, ρ_m is the reflection coefficient of the mirror m, dA_m is the surface area of mirror m, α_m,l is the irradiance angle from access point l to mirror m, β_k,m is the angle of incidence at which the reflected signal from mirror m reaches user k, D_m,l is the distance between access point l and mirror m, and D_k,m is the distance between mirror m and user k (Hamad et al., 2024).

2.3 User data rate

In the given system model, the signal received by user k can be expressed as follows i.e., Equation 3 (Hamad et al., 2024):where x_k and P_k denote the transmitted signal and its corresponding power intended for a specific user, respectively, and z_k represents the real-valued additive white Gaussian noise (AWGN).

γ k = h k , l L o S + h k , m , l I R S P k x k + h k , l L o S + h k , m , l I R S P k ′ x k ′ + z k , (3)

To eliminate multi-user interference, users can be served on separate frequency bands or time slots. Additionally, based on Equation 3, the SINR for user k can be expressed as follows i.e., Equation 4:where R₀ is the receiver’s responsivity and I_k is the interference experienced by user k from other optical AP. The term σ² _t represents the total noise.

sin R k = R 0 2 P k 2 h k , l L o S + h k , m , l I R S 2 I k 2 + σ t 2 , (4)

The data rate for user k is expressed as follows i.e., Equation 5 (Wang et al. (2024)):where B is the modulation bandwidth, K_in denotes the users that interfere with each other (i.e., those served by the same optical AP, and e/2π is the expression used for direct detection in intensity modulation (IM/DD).

R k = B K i n log 2 1 + e 2 π sin R k , (5)

2.4 Sum rate

For the sum rate, some equations are provided as follows i.e., Equations 6,7 (Razzaque et al., 2024): where m is the number of mirrors. So, the SINR of this system can be obtained as follows i.e., Equation 8: The data rate is calculated as follows i.e., Equation 9:

h k , l L o S = h b a s e L o S × e − 0.3 × b l o c k a g e s , (6)

h k , m , l I R S = ∑ m h k , m , l I R S , (7)

SIN R k = h k , l L o S + h k , m , l I R S 2 × R o 2 p t 2 I k 2 + σ t 2 . (8)

R k = B k i n log 2 1 + e 2 Π × sin R k . (9)

Mathematically, the sum rate is calculated as follows i.e., Equation 10:where R_k is the user’s achievable data rate and K is the number of connected users.

R s u m = ∑ k = 1 K R k , (10)

2.5 SINR of the system

The channel capacity of a wireless system must be enhanced to efficient management of exponentially growing traffic in 6G communication. The term SINR shows the ratio of received signal power to noise power at destination. To maximize the SINR, an optimal h L o S O p t is determined, which is obtained by differentiating Equation 8 with respect to h_LoS as follows i.e., Equations 11-15:

d S I N R d h L o S = 2 R 0 2 × p t 2 × h L o S + h I R S I k 2 + σ t 2 . (11)

The optimal value of h_LoS must satisfy the following:

d S I N R d h L o S = 0 , (12)

2 R o 2 × P t 2 × h L o S + h I R S I k 2 + σ t 2 = 0 . (13)

From the above equations, we obtain

h L o S O p t = h I R S . (14)

The maximum SINR is

sin R max = 4 R o 2 × P t 2 × h L o S 2 I k 2 + σ t 2 . (15)

The value of h_LoS ^opt depends solely on h_IRS, and the maximum SINR is obtained using Equation 15. This study first optimizes and determines the maximum achievable SINR. Using this optimized SINR value (flowchart is shown in Figure 1), the M-PAM digital modulation technique is applied to select an appropriate higher-order modulation scheme for the IRS-OWC system (Shrivastava et al., 2022), allowing a greater number of bits to be transmitted simultaneously and thereby enhancing the CC, SE, and EE of the system.

2.6 Bit error rate for the IRS-reflected channel

The SINR ratio for the kth user is expressed as follows:

sin R k = h k , l L o S + h k , m , l I R S 2 × R o 2 p t 2 I k 2 + σ t 2 . (16)

The values of SINR_k are calculated as follows: a) 14.52 dB for P_t = 1 W and b) 34.52 dB for P_t = 10 W.

The BER of M-PAM with Gray code is expressed as follows i.e., Equation 17:

B E R P A M ( M , S I N R k = 2 M − 1 M ⁡ log 2 ⁡ M Q S I N R k M − 1 , (17)

where M = 2ⁱ, i = 1,2,3 .

If the BER target is BER_t, the modulation thresholds are calculated as shown in Table 1 as follows i.e., Equation 18 (Wu et al., 2014):where Q − 1 (.) is the inverse Q function.

T h i = sin R K M = M − 1 Q − 1 B E R t M ⁡ log 2 M 2 M − 1 , (18)

TABLE 1. The calculated different modulation thresholds with BERs.

The suitable M-PAM method is determined as follows:

a) When SINR_k = 14.52 dB, the suitable method is 4-PAM as Th₂< SINR_k < Th₃ for B E R t = 10 − 6.

b) When SINR_k = 34.52 dB, the suitable method is 8-PAM

as Th3< SINR_k < Th4 for B E R t = 10 − 6.

3 Results and analysis

The simulation results are presented in this section, illustrating how transmitted power, the distance between the access point and the mirror, and the distance between the access point and the user in the LoS channel and IRS-reflected channel, respectively, affect the SINR, data rate, and spectral efficiency. Spectral efficiency is a critical metric that measures how efficiently a wireless network utilizes its available bandwidth to transmit data. The simulations have carried out in MATLAB based on the methodologies reported in previous studies on IRS-aided OWC systems (Hamad et al., 2024; Razzaque et al., 2024). The simulation parameters are shown in Table 2.

TABLE 2. Simulation parameters (Hamad et al., 2024).

In Figure 2, we have plotted SINR as a function of the distance between the AP and the user in the LoS channel. It demonstrates that as the distance increases, SINR consistently decreases, indicating signal degradation over longer ranges. Although higher transmission power improves SINR, the gain decreases beyond a certain point; this highlights the benefit of moderate power optimization to maintain link quality in LoS scenarios. The comparison of SINR for different values of transmitted power under LoS conditions is presented in Table 3. It can be observed that the maximum gain in SINR occurs when the transmitted power increases from 2 W to 5 W.

TABLE 3. SINR at a distance of 700 mm between the AP and the user for different values of transmitted power under LoS conditions.

In Figure 3, SINR is plotted as a function of the distance between the access point and the mirror in the IRS-reflected channel. It shows that compared to the LoS scenario, the SINR values are significantly lower and decrease more rapidly with distance. Despite using the same range of transmission power levels, the reflected path exhibits greater signal attenuation due to longer propagation and reflection losses. Although IRS can support communication in blocked environments, the comparison of SINR for different transmitted power levels under IRS conditions is presented in Table 4. It can be observed that as the transmission power increases, both the SINR intensity and the communication range also increase. An SINR comparison between the LoS and IRS channels at a distance of 700 mm between the AP and the user for different values of transmitted power is shown in Figure 4.

TABLE 4. SINR at a distance of 700 mm between the AP and the mirror for different values of transmitted power in the IRS channel.

Figure 5 shows the plotted data rate as a function of the distance between the AP and the user in the LoS channel. There is a noticeable decrease in the data rate as the distance increases. It shows that although higher transmission power enhances the data rate, the benefit decreases with increasing range. A comparison of the data rate for different values of transmitted power under LoS conditions is shown in Table 5 at a distance of 700 mm, and it can be verified that the data rate increases with transmission power.

TABLE 5. Data rate comparison between the AP and the user for different values of transmitted power under LoS conditions.

Figure 6 shows the plotted data rate vs. the distance between the AP and the mirror in the IRS channel. The data rate decreases more steeply with distance in this case compared to that in the LoS scenario due to reflection-induced attenuation. This shows that the IRS can extend coverage to blocked zones; the system’s data performance is highly sensitive to the length of the reflective path. A comparison of the data rate for different values of transmitted power under IRS conditions is shown in Table 6, and it can be observed that an increase in transmission power leads to higher data rates and extended range.

TABLE 6. Data rate at a distance of 700 mm between the AP and the mirror for different values of transmitted power in the IRS channel.

Figure 7 shows the compared data rates as a function of transmitted power in the LoS and IRS channels for a fixed distance of 700 mm between the AP and the user and the AP and the mirror, respectively. The LoS path consistently outperforms the IRS-reflected path across all power levels, confirming that a direct optical path provides a greater data rate. However, it is clear from the figure that IRS-reflected communication still offers connectivity when LoS is unavailable, and moderate transmission power supports both. The data indicate that improvements in IRS design and positioning can help narrow this performance gap, and combining LoS and IRS-reflected paths in hybrid OWC systems can enhance the overall coverage and reliability.

Figure 8 shows the plotted SINR vs. optical transmitted power for different LoS channel gains h_LoS. It is evident that SINR has the maximum value when h_LoS is optimum. This highlights the critical importance of achieving the optimal LoS alignment to maximize SINR, which directly enhances channel capacity and improves data throughput in OWC systems. A comparison of the SINR for different values of optical transmitted power under LoS conditions is shown in Table 7 and Figure 9. It can be observed that we have obtained the maximum SINR for optimal h_LoS.

TABLE 7. SINR vs. optical transmission power for different line-of-sight channel gains (h_LoS).

Figure 10 shows the plotted SINR (dB) as a function of receiver responsibility (A/W) in the LoS channel. It shows that the maximum SINR is achieved for the optimal h_LoS, indicating that this is the most efficient operating point for balancing system design and performance in IRS-aided OWC networks. A comparison of the SINR for different values of transmitted power under LoS conditions is shown in Table 8 and Figure 11.

TABLE 8. Comparison of the SINR for different receiver responsivity for different line-of-sight gain (h_LoS) conditions.

Figure 12 shows the data rate of the OWC system vs. bandwidth. It shows that the data rate increases almost linearly with bandwidth for different SINR values, including the maximum. At 40 MHz bandwidth, the data rate reaches 385.8 Mbps for the maximum SINR, compared to only 63.8 Mbps for SINR = 3 dB. This shows that optimization not only improves individual user capacity but also significantly enhances system scalability with respect to the available bandwidth. A comparison of the data rate for different values of bandwidth and SINR values presented in Table 9. It can be observed that under optimum conditions, the data rate (i.e., channel capacity) reaches its maximum value at lower bandwidth, which reduces system cost and enables efficient utilization of the available bandwidth.

TABLE 9. Data rate vs. bandwidth for different SINR values.

Figure 13 shows the plotted optimized version of the sum rate analysis, where the optimal h_LoS value is used to obtain the maximum SINR. For comparison, Table 10 is prepared using data from Hamad et al. (2024) alongside our results. The comparison graph shown in Figure 14 demonstrates a significant improvement in performance for all mirror array and blockage scenarios. The achieved results are nearly three times higher than those reported in Hamad et al. (2024).

TABLE 10. Sum rate (maximum) vs. optical transmission power for different mirror array and blockage scenarios in the optimal h_LoS case and as per literature Hamad et al. (2024) for comparison.

Figure 15 shows the validation of the 4-PAM method. Using Equations 17, 18, BER versus optical SINR was evaluated through analytical analysis and Monte Carlo simulations. The results demonstrate a close match between the analytical and simulation outcomes.

At SINR = 14.5 dB,

a) Analytical BER = 5.15 × 10⁻⁷.

b) By Monto-Carlo simulation,

BER = 3.38 X 10 − 6.

4 Conclusion

This study has analyzed the enhancement of channel capacity in an IRS-aided OWC system. The analysis demonstrates that the LoS channel gain h_LoS decreases significantly with increasing distance between the access point and the user, leading to a decrease in SINR and the achievable data rate. This finding emphasizes the critical role of optimal access point placement in maintaining reliable and high-capacity LoS communication in IRS-assisted OWC systems. The integration of intelligent reflecting surfaces has been shown to effectively improve signal quality by enhancing the channel gain of reflected paths, particularly in scenarios where the LoS link is blocked or unavailable. While higher transmitted optical power improves the SINR and data rates for both LoS and IRS-reflected links, practical limitations such as power constraints and optical safety regulations restrict indefinite power scaling. Consequently, system performance strongly depends on efficient IRS design and precise alignment. Numerical results reveal that the optimal configuration achieves a maximum SINR of 22 dB at a receiver responsivity of 0.2 A/W under LoS conditions. At a bandwidth of 20 MHz, the maximum data rate of 200 Mbps is obtained. Furthermore, in the scenario with two mirror arrays and two blockages, the maximum sum rate reaches 32 Mbps, representing approximately a threefold improvement compared with that reported in the previous work. Based on the system model and performance evaluation, 4-PAM is identified as the most suitable digital modulation scheme for 1 W transmitted power of the proposed IRS-aided OWC system. Overall, the results confirm the effectiveness of IRS technology in significantly enhancing the capacity and reliability of OWC systems.

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Footnotes

Edited by: Raad Farhood Chisab, Middle Technical University, Iraq

Reviewed by: Rajandeep Singh, Guru Nanak Dev University, India

Hashim Elshafie, King Khalid University, Saudi Arabia