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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]

This survey paper provides a comprehensive overview of integrating Multiple-Input Multiple-Output (MIMO) with Intelligent Reflecting Surfaces (IRS) in wireless communication systems. IRS is known as reconfigurable metasurfaces, have emerged as a transformative technology to enhance wireless communication performance by manipulating the propagation environment. This work delves into the fundamental concepts of MIMO and IRS technologies, exploring their benefits and applications. It subsequently investigates the synergies of resource allocation and energy efficiency that emerge when these technologies are combined, elucidating the IRS improved in MIMO systems through signal manipulation and beamforming. Through an in-depth analysis of various techniques and cutting-edge algorithms in resource allocation and energy efficiency can explore the key research areas such as optimization techniques, beamforming strategies and practical implementation consideration. Furthermore, it provides open research directions, individually addressing topics such as limitations of resource allocation and energy efficiency in the MIMO IRS system. This paper offers insights into MIMO-enabled IRS systems challenges and future trends. Through presenting a consolidated view of the current state-of-the-art, this survey underscores their potential to revolutionize wireless communication paradigms, ushering in an era of enhanced connectivity, spectral efficiency and improved coverage.

The integration of intelligent reflecting surfaces (IRS) into cooperative non-orthogonal multiple access (NOMA) systems revolutionizes wireless networks by enhancing signal strength, mitigating interference, and optimizing spectral efficiency. The cooperative NOMA (CNOMA) framework, empowered by IRS technology, further promises enhanced performance, robustness, and scalability for next-generation wireless networks as compared to NOMA only systems. This paper explores the synergy between IRS and NOMA to leverage cooperative techniques for superior wireless system design. Fundamental principles, technological advancements, and potential applications of IRS-assisted CNOMA systems are discussed, highlighting existing works. Both underlay and overlay NOMA principles are examined in conjunction with IRS in the paper. Spatial modulation-aided CNOMA is explored for multiple-input multiple-output (MIMO) systems, along with its advantages and practical challenges. Additionally, the paper discusses fundamental principles and technological advancements of IRS-assisted CNOMA systems, emphasizing solutions to potential challenges and the role of machine learning (ML)/deep learning (DL) in resource optimization like transmit power and IRS phase settings. Simulation results are presented to highlight the benefits of IRS-aided CNOMA system design. Finally, the paper outlines future directions and potential research topics in IRS-aided CNOMA.

In this paper, re-configurable intelligent reflecting surfaces (IRS) based on cell-free communications to serve multi-user (MU) are considered. This is to enhance the transmission for the next generation of wireless communications. This technique has witnessed lots of interest recently due to its ability to increase diversity gain, especially in the presence of obstacles between the users and the service providers. The IRS contains low-cost and large-scale reflection elements that work passively to guide the electromagnetic waves toward the direction of interest. These re-configurable meta-surface cells have reflection coefficients that can be adjusted by changing their phase shift to enhance the desired signal of interest and apply interference mitigation. Moreover, the IRS can be exploited to improve the overall sum rate throughput and reduce outage probability. The proposed system considers a transmission between multiple base stations (BSs) that equipped with multiple antennas and several single antenna users through an IRS in the presence and absence of a traditional path between them. Optimization techniques are employed to select the optimum beamforming precoders and to control the IRS’s phase shifts by steering the incident signals toward the intended users. Furthermore, an off-the-shelf power allocation optimization approach, called Polyhedron, is exploited to enhance the overall spectral efficiency (SE) and energy efficiency (EE) of the proposed system and reduce the required transmitted power. The proposed system with the suggested optimization approaches demonstrates significant improvement in the SE with a considerable reduction of the entire transmitted power by all BSs especially when increasing the number of antennas at the BSs along with using a higher number of IRS’s reflected elements.

In this work, we examine an intelligent reflecting surface (IRS) assisted downlink non-orthogonal multiple access (NOMA) scenario intending to maximize the sum-rate of users. The optimization problem at the IRS is quite complicated, and non-convex since it requires the tuning of the phase shift reflection matrix. Driven by the rising deployment of deep reinforcement learning (DRL) techniques that are capable of coping with solving non-convex optimization problems, we employ DRL to predict and optimally tune the IRS phase shift matrices. Simulation results reveal that the IRS-assisted NOMA system based on our utilized DRL scheme achieves a high sum-rate compared to OMA-based one, and as the transmit power increases, the capability of serving more users increases. Furthermore, results show that imperfect successive interference cancellation (SIC) has a deleterious impact on the data rate of users performing SIC. As the imperfection increases by ten times, the rate decreases by more than 10%.

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]

In 6G-oriented vehicular Internet of things (IoT) services, the integration of a low altitude platform (LAP) and intelligent reflecting surfaces (IRS) provides a promising solution to achieve seamless coverage and massive connections at low cost. However, due to the open nature of wireless channels, how to protect the transmission of privacy information in LAP-based IRS symbiotic vehicular networks remains a challenge. Motivated by the above, this paper investigates the LAP and IRS enhanced secure transmission problem in the presence of an eavesdropper. Specifically, we first deploy a fixed LAP equipped with IRS to overcome the blockages and introduce artificial noise against the eavesdropper. Next, we formulate a total secure channel capacity maximization problem by optimizing the phase shift, power distribution coefficient, and channel allocation. To effectively solve the formulated problem, we design an iterative algorithm with polynomial complexity, where the optimization variables are solved in turn. In addition, the complexity and convergence of the proposed iterative algorithm are analyzed theoretically. Finally, numerical results show that our proposed secure transmission scheme outperforms the comparison schemes in terms of the total secure channel capacity.

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]

5G and beyond 5G (B5G) wireless systems promise to support services with different requirements in the same network, as enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine type communication (mMTC). One alternative is to consider the network slicing paradigm, where the wireless network resources are shared (or sliced) among active services with different requirements. In addition, another emerging technology, that is considered as a key enabler for B5G wireless systems, is the intelligent reflecting surfaces (IRS). From the deployment of an IRS, it is possible to improve the received signal quality and consequently increase the overall network capacity. Therefore, in this paper, we investigate the use of IRS to support simultaneous eMBB and URLLC services. We evaluate the achievable rate of an IRS-aided radio access network, where the uplink resources are shared between eMBB and URLLC users either under heterogeneous orthogonal multiple access (H-OMA) or heterogeneous non-orthogonal multiple access (H-NOMA) techniques. Results show that exploiting an IRS can considerably increase the eMBB rate and the URLLC reliability simultaneously, regardless of whether operating under H-OMA or H-NOMA. Moreover, we also provide some insights on the best user pairing strategy, showing that higher rates are achieved by matching many eMBB users near to the IRS with a URLLC user close to the base station.

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]

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.

In this paper, intelligent reflecting surface (IRS)-assisted orbital angular momentum (OAM) with non-orthogonal multiple access (NOMA) under imperfect successive interference cancellation (SIC) and imperfect channel state information (CSI) is proposed. Importantly, the communication links of both cell-center user (CCU) and cell-edge user (CEU) are developed through IRS when they are in the non-line-of-sight (NLoS) of the transmitter. The aim of developing the proposed system is the accommodation of massive connectivity and enhancement of sum capacity as well as user capacities with the help of NOMA. The performance of the proposed IRS-OAM-NOMA system is evaluated and compared with the existing techniques such as IRS-assisted OAM with orthogonal multiple access (OMA) OMA-OAM-IRS and NOMA-IRS. The simulation results show that the proposed IRS-OAM-NOMA system outperforms the existing techniques in terms of user capacity and sum capacity under ideal conditions and with imperfect successive interference cancellation (SIC) and imperfect channel state information (CSI). [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.

In this paper, the outage performance of uplink intelligent reflecting surface (IRS)-aided non-orthogonal and orthogonal multiple-access (NOMA/OMA) networks is investigated. Specifically, we consider a two-user equipment (UE) NOMA/OMA network, in which both UEs have both direct (UE → base station (BS)) and reflected (UE → IRS → BS) links. All the links between the UEs and the IRS/BS are modeled either as a Rayleigh or a Nakagami- m variate. To characterize these networks' outage performance, new statistics for the effective channel gains of the IRS-NOMA/OMA's UEs are derived. Based on that, closed-form expressions for the outage probability (OP) for each UE are derived. Monte Carlo simulations' results are provided to verify the accuracy of the analytical results. [ABSTRACT FROM AUTHOR]