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Intelligent Reflecting Surfaces (IRS) and Multiple-Input Single-Output (MISO) technologies are essential in the fifth generation (5G) networks and beyond. IRS optimizes the signal propagation and the coverage and is a viable approach to address the issues caused by fading channels that limits the spectral efficiency, while MIMO enhances data rates, reliability, and spectral efficiency by using multiple antennas at both transmitter and receiver ends. This paper proposes an IRS-assisted MISO system using the Orthogonal Space-Time Block Code (OSTBC) scheme to enhance the channel reliability and reduce the Bit Error Rate (BER) in wireless communication systems. The proposed system exploits the benefits from the transmit diversity gain of the OSTBC scheme as well as from the bit energy to noise power spectral density (Eb/No) improvement of the IRS technology. The presented work explores these combined technologies across different modulation schemes. The obtained results outperform the similar previously published works by considering higher-order modulation schemes as well as the deployment of rate ¾ OSTBC-assisted IRS. Moreover, the obtained results demonstrate that the integration of OSTBC with IRS can yield significant performance improvements in terms of Eb/No by 7 dB and 13 dB when using 16 reflecting elements and 64 reflecting elements, respectively. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles (IoV) is revolutionizing transportation systems by enabling seamless communication and collaboration among vehicles, roadside units (RSUs), and cloud servers. However, the dynamic and diverse nature of IoV environments raises significant concerns regarding security vulnerabilities and operational efficiency. In response to these challenges, this study proposes an innovative approach that integrates collaborative cryptographic techniques with intelligent reflecting surfaces (IRS). Our approach leverages advanced encryption methods, such as the Advanced Encryption Standard (AES), to ensure secure data transmission, while intelligent reflecting surfaces dynamically adjust their reflective properties to enhance signal propagation and reception. We present a comprehensive network model and algorithmic framework for implementing our proposed strategy, with a specific emphasis on cryptographic protocols and the role of intelligent reflecting surfaces in enhancing both communication security and efficiency. Through theoretical analysis and discussion, we highlight the potential advantages of integrating intelligent reflecting surfaces into secure physical layer (PL), IoV networks, including expanded network coverage, reduced communication overhead, and enhanced energy efficiency. Moreover, we address security threats and vulnerabilities in IoV environments, including potential attacks such as eavesdropping, data tampering, and denial of service. We discuss strategies for mitigating these security risks through the combined use of cryptographic techniques and intelligent reflecting surfaces, thereby bolstering the resilience and robustness of IoV systems. [ABSTRACT FROM AUTHOR]

Symbiotic Radio (SR) is one of the techniques recognized by 6G for wireless communication networks performance enhancement. In this paper, SR is used to improve the performance of the Internet of Things (IoT) network by enabling IoT tags backscatter the neighbor smart phone primary signal rely on the None Orthogonal Multiple Access (NOMA) technique. Furthermore, Intelligent Reflecting Surfaces (IRS) are also proposed to enhance the channel Quality of Service (QoS); the service performance; between the IoT tags and the smartphones either using LTE or Wi-Fi network by smartly reconfiguring the signal propagation for performance improvement. We formulate an optimization problem to achieve the optimum location and phase shifts of the IRS, aiming to maximize the throughput of the IoT system. Proximal Policy Optimization (PPO) algorithm is introduced as a solution for this problem. The main idea of PPO is to minimize the divergence between the new and old policy while maximizing the expected reward. This is achieved by using a surrogate objective function that approximates the policy update. Simulation results demonstrate that the proposed algorithms can improve the total system data rate by an average of 40% above the system without using IRS and it also, improves the system capacity by 40% on average when compared to a system without the IRS scheme at smart phones p =4 which serve tags T =20. [ABSTRACT FROM AUTHOR]

Implementing intelligent reflecting surfaces (IRSs), in high frequency based beyond 5G networks, has become a necessity to overcome the harsh blockage issues that exist in these bands. IRSs can supply user equipment (UEs) with multi alternative virtual line of sight (LOS) links, hence enhancing the spectral efficiency (SE) of the system. As a result of deploying multi IRSs as communication assistants, the step of IRSs-UEs association is required to optimally assign each UE to its best IRS; consideration of the interference between different links is needed, to maximize the system performance. However, this process will be a time and power consuming problem, if conventional schemes, which exhaustively search all possible association patterns to find the optimum one for communication, is adapted. Although iterative search based schemes can reduce this complexity, they still need feedback signaling in real time. Hence, they will be inefficient in terms of power consumption and delay. Moreover, optimal placement of the multi-IRSs in the network, to enlarge the system performance, is still an open issue and needs to be studied. Consequently, in this paper, to handle the IRSs-UEs association problem, we propose a neural network (NN) based scheme using a multi-IRSs aided multi input multi output (MIMO) system. In this system, the estimated angles of arrival (AoAs) of UEs are used as input features for the NN, which is trained to associate each UE to its best IRS based on this information; then, within each IRS, passive beamforming is performed. Adapting this NN in online mode guarantees obtaining better performance while relaxing the complexity of association and increasing response time, giving a performance comparable to the exhaustive and iterative search based schemes. The proposed NN based scheme determines the association pattern without searching or feedback signals. Moreover, the proposed approach maintains the system SE nearly similar to the optimum performance obtained by the conventional scheme. Secondly, a criterion is suggested for optimal deployment of multi IRSs in the network, depending on maximizing the average summation UEs signal-to-interference-plus-noise ratio (SINR). Numerical results prove that this strategy outperforms a reference one, which aims to guarantee certain performance by maximizing minimum UE SINR. In contrast the proposed strategy achieves better system and per UE spectral efficiency. [ABSTRACT FROM AUTHOR]

The existing sub-6 GHz band is insufficient to support the bandwidth requirement of emerging data-rate-hungry applications and Internet of Things devices, requiring ultrareliable low latency communication (URLLC), thus making the migration to millimeter-wave (mmWave) bands inevitable. A notable disadvantage of a mmWave band is the significant losses suffered at higher frequencies that may not be overcome by novel optimization algorithms at the transmitter and receiver and thus result in a performance degradation. To address this, Intelligent Reflecting Surface (IRS) is a new technology capable of transforming the wireless channel from a highly probabilistic to a highly deterministic channel and as a result, overcome the significant losses experienced in the mmWave band. This paper aims to survey the design and applications of an IRS, a 2-dimensional (2D) passive metasurface with the ability to control the wireless propagation channel and thus achieve better spectral efficiency (SE) and energy efficiency (EE) to aid the fifth and beyond generation to deliver the required data rate to support current and emerging technologies. It is imperative that the future wireless technology evolves toward an intelligent software paradigm, and the IRS is expected to be a key enabler in achieving this task. This work provides a detailed survey of the IRS technology, limitations in the current research, and the related research opportunities and possible solutions. [ABSTRACT FROM AUTHOR]

In this paper, we derive a tight lower bound of the detection probability of the energy detector when intelligent reflecting surface (IRS) are used. The secondary source uses the energy detector to detect primary source activity. There is IRS between primary source and secondary source. The secondary sources compute the energy of the received signal from primary source and reflected on IRS. The proposed spectrum sensing algorithm using IRS offers 15, 21, 27, 33 dB gain with respect to conventional sensing without IRS for a number of reflectors K = 8 , 16 , 32 , 64 . We also used IRS for data communication between primary source and destination as well as the communication between secondary nodes. The proposed primary and secondary networks of cognitive radio network (CRN) using IRS offer 23, 29, 36, 43, 49 and 56 dB gain with respect to conventional CRN without IRS for a number of reflectors K = 8 , 16 , 32 , 64 , 128 , 256 . We show that the use of N = 20 , 10 , 5 symbols in energy detection offers up to 8.5, 7.7 and 4.7 dB gain with respect to a single symbol. We plot the miss detection probability P md versus the false alarm probability P f . For K = 16 reflectors, average SNR per bit E b / N 0 = - 10 dB and P f = 0.01 , P md = 210 - 3 , 710 - 3 , 2. 510 - 2 when N = 20 , 10 , 5 symbols are used in energy detection, whereas P md = 0.45 when a single symbol is used. [ABSTRACT FROM AUTHOR]

This work presents the symbol error rate (SER) and outage probability analysis of multi-layer unmanned aerial vehicles (UAVs) wireless communications assisted by intelligent reflecting surfaces (IRS). In such systems, the UAVs may experience high jitter, making the estimation and compensation of the end-to-end phase for each propagation path prone to errors. Consequently, the imperfect phase knowledge at the IRS should be considered. The phase error is modeled using the von Mises distribution and the analysis is performed using the Sinusoidal Addition Theorem (SAT) to provide accurate results when the number of reflectors $L\leq 3$ , and the Central Limit Theorem (CLT) when $L\geq 4$. The achieved results show that accurate phase estimation is critical for IRS based systems, particularly for a small number of reflecting elements. For example, the SER at 10−3 degrades by about 5 dB when the von Mises concentration parameter $\kappa =2$ and $L=30$ , but the degradation for the same $\kappa $ surges to 25 dB when $L=2$. The air-to-air (A2A) channel for each propagation path is modeled as a single dominant line-of-sight (LoS) component, and the results are compared to the Rician channel. The obtained results reveal that the considered A2A model can be used to accurately represent the A2A channel with Rician fading. [ABSTRACT FROM AUTHOR]