This paper proposes a novel dual-intelligent reflecting surface (IRS) aided interference-limited simultaneous wireless information and power transfer (SWIPT) system with independent power splitting (PS), where each receiving antenna applies different PS factors to offer an advantageous trade-off between the useful information and harvested energy.We separately establish the near- and hybrid-field channel models for IRS-reflected links to evaluate the performance gain more precisely and practically. Specifically, we formulate an optimization problem of maximizing the harvested power by jointly optimizing dual-IRS phase shifts, independent PS ratio, and receive beamforming vector in both near- and hybrid-field cases. In the near-field case, the alternating optimization algorithm is proposed to solve the non-convex problem by applying the Lagrange duality method and the difference-of-convex (DC) programming. In the hybrid-field case, we first present an interesting result that the AP-IRS-user channel gains are invariant to the phase shifts of dual-IRS, which allows the optimization problem to be transformed into a convex one. Then, we derive the asymptotic performance of the combined channel gains in closed-form and analyze the characteristics of the dual-IRS. Numerical results validate our analysis and indicate the performance gains of the proposed scheme that dual-IRS-aided SWIPT with independent PS over other benchmark schemes.

Fluid antenna multiple access (FAMA) has recently emerged as a simple, promising scheme for large-scale multiuser connectivity, offering strong scalability with low implementation complexity. Nevertheless, most existing FAMA studies focus on downlink transmission under perfect channel state information (CSI) at the receiver side, while the uplink counterpart remains largely unexplored. This paper proposes a novel codebook-based port selection and combining (CPSC) FAMA framework for the uplink communications without CSI at the base station (BS). In the proposed scheme, a predefined codebook is designed and broadcast by the BS. Each user equipment (UE) employs a fluid antenna, acquires its local CSI and independently chooses the most suitable codeword, activates the corresponding fluid antenna ports, and determines the combining weights to achieve a two-way match between the selected codeword and the instantaneous effective channel. The BS then separates the superimposed user signals through codebook-guided projection operations without requiring global CSI or multiuser joint optimization. To handle potential codeword collisions, three lightweight scheduling strategies are introduced, offering flexible trade-offs between signaling overhead and collision avoidance. Simulation results demonstrate that the proposed CPSC-FAMA approach achieves substantially higher rates than fixed-antenna systems while maintaining low complexity. Moreover, the results confirm that amortizing the optimization cost over the UEs effectively reduces the BS processing burden and enhances scalability, making the proposed scheme a strong candidate for future sixth-generation (6G) networks.

This paper proposes a novel pattern-reconfigurable fluid reconfigurable intelligent surface (FRIS) framework, where each fluid element can dynamically adjust its radiation pattern based on instantaneous channel conditions. To evaluate its potential, we first conduct a comparative analysis of the received signal power in point-to-point communication systems assisted by three types of surfaces: (1) the proposed pattern-reconfigurable FRIS, (2) a position-reconfigurable FRIS, and (3) a conventional RIS. Theoretical results demonstrate that the pattern-reconfigurable FRIS provides a significant advantage in modulating transmission signals compared to the other two configurations. To further study its capabilities, we extend the framework to a multiuser communication scenario. In this context, the spherical harmonics orthogonal decomposition (SHOD) method is employed to accurately model the radiation patterns of individual fluid elements, making the pattern design process more tractable. An optimization problem is then formulated with the objective of maximizing the weighted sum rate among users by jointly designing the active beamforming vectors and the spherical harmonics coefficients, subject to both transmit power and pattern energy constraints. To tackle the resulting non-convex optimization problem, we propose an iterative algorithm that alternates between a minimum mean-square error (MMSE) approach for active beamforming and a Riemannian conjugate gradient (RCG) method for updating the spherical harmonics coefficients. Simulation results show that the proposed pattern-reconfigurable FRIS significantly outperforms traditional RIS architectures based on the 3GPP 38.901 and isotropic radiation models, achieving average performance gains of 161.5% and 176.2%, respectively. Additionally, it reduces the required number of antennas and RIS elements by over 300%, offering substantial improvements in hardware efficiency.

This paper develops a comprehensive framework for the performance analysis of fluid antenna system (FAS)-enabled unmanned aerial vehicle (UAV) relaying networks operating in the finite blocklength regime. This work establishes a rigorous methodology for characterizing system reliability under diverse propagation environments. Closed-form expressions for the block error rate (BLER) are derived by employing a tractable eigenvalue-based approximation of the spatially correlated UAV-to-user link, whose underlying independent diversity components are modeled as Nakagami-mfading. This approach addresses both line-of-sight (LoS) dominant rural and probabilistic non-line-of-sight (NLoS) urban scenarios. Furthermore, a high signal-to-noise ratio (SNR) asymptotic analysis is developed, revealing the fundamental diversity order of the UAV-to-user link. Based on this, we further address the practical issue of energy efficiency. A realistic energy efficiency maximization problem is formulated, which explicitly accounts for the time and energy overhead in the FAS port selection process. An efficient hierarchical algorithm is then proposed to jointly optimize the key system parameters. Extensive numerical results validate the analysis and illustrate that while FASs can yield substantial power gains, the operational overhead introduces a non-trivial trade-off, leading to an optimal number of ports and fundamentally different UAV deployment strategies in rural versus urban environments. In summary, this work provides both foundational analysis and practical design guidelines for FAS-enabled UAV communications.

Fluid antenna systems (FAS) utilize position reconfigurability to improve spatial diversity in wireless communications. However, a rigorous framework for error probability analysis under spatially correlated channels remains absent. This paper fills this gap by deriving a closed-form asymptotic expression for the symbol error rate (SER). This mathematical expression establishes the fundamental scaling law between the error performance and the spatial correlation matrix. A key insight from our analysis is that the achievable diversity gain depends entirely on the effective rank of the spatial channel, rather than the total number of antenna ports. To quantify this effective rank, we propose a dual approach: a theoretical derivation and a geometry-based algorithm. Both methods rigorously prove that the effective rank converges to a fundamental limit of$2W+1$, where$W$denotes the normalized aperture width. Specifically, the geometry-based algorithm extracts distinct performance thresholds from the eigenvalue spectrum of the channel. These thresholds perfectly match the derived theoretical limit. Furthermore, the proposed effective rank model demonstrates higher accuracy than existing approaches in the literature. Based on this robust framework, we offer a complete characterization of diversity gains and coding gains. The analytical results reveal a definitive design principle: enlarging the physical aperture increases the effective rank and drives performance improvements, whereas simply increasing port density within a fixed aperture yields diminishing returns.

Integrated Sensing and Communications (ISAC) is emerged as one of the key technologies in next generation wireless systems. However, ISAC systems have been commonly explored neglecting mutual coupling. This paper investigates the mutual coupling exploitation to further improve the performance of ISAC systems. We aim to maximize the sensing beampatern gain by optimizing the tunable loads and the dual-functional beamforming while satisfying the minimum signal-to-interference-plus-noise (SINR) per user and the hardware constraint of the tunable loads. To solve the non-convex problem, we propose a penaltybased iterative algorithm to obtain a stationary point. Specifically, in each iteration we adopt the block coordinate descent (BCD) method where the dual-functional beamforming is obtained by using Lagrange duality, and the tunable loads are solved with closed forms. Numerical results demonstrate the notable gains and effectiveness of the proposed algorithms compared to the baseline schemes. To the best of our knowledge, this is the first study utilizing the MC effect to improve system performance in an ISAC system with tunable loads.

Spatial scattering modulation (SSM), an emerging millimeter-wave multiple-input multiple-output (MIMO) modu-lation technique, exploits spatial beam resources to enhance the modulation degree of freedom. However, to address the problem that the detection performance of existing scalar-based maximum likelihood (SML) detector is not optimal and the complexity is too high, this paper develops vector-based maximum likelihood (VML) and low-complexity (LC) detectors for the structural char-acteristics of SSM system receivers, respectively. The complexity of the proposed VML algorithm is slightly higher than that of the traditional SML detection algorithm, while the complexity of the proposed LC detection algorithm is reduced by 50% compared with the traditional SML detection algorithm. Numerical results show that at average bit error probability (ABEP) = 10–5, the signal-to-noise ratio (SNR) required for the VML-based ABEP values is 5.5 dB less than that obtained from SML detection. Moreover, the proposed LC algorithm detection performance also saves SNR of 1 dB over SML detector.

In this article, a novel multistream downlink communication system based on the transmissive reconfigurable intelligent surface (RIS) transceiver is proposed. Specifically, a transmissive RIS transceiver architecture is first elaborated, where the downlink communication mechanism and the difference from the conventional multiantenna transceivers are introduced, respectively. More importantly, the generation of RIS element control signals based on time-modulated array (TMA) is illustrated in detail, which can jointly take into account multistream modulation signals and beamforming design. Correspondingly, the harmonic signal extraction scheme at the user is also given. Then, since the design of beamforming has an impact on the system performance, we propose a beamforming optimization algorithm based on matrix lifting, successive convex approximation (SCA) and difference-convex (DC) programming under the constraints of user signal-to-interference-plus-noise ratio (SINR) and available useful power of RIS elements. Furthermore, we analyze the bit error rate (BER) performance of the proposed architecture from two perspectives of transmit multiplexing and transmit diversity. Finally, the convergence behavior of the beamforming algorithm, the impact of different system parameter configurations on system performance and the BER performance of different schemes under the system are verified by numerical simulations.

Edge content caching of Internet of Vehicles (IoVs) is a key technology for alleviating backhaul strain and reducing access latency. To protect the privacy of vehicular users, Federated learning (FL) is employed by sharing vehicles’ local models instead of data. However, vehicles may leave the coverage range of serving node before completing the local model training. To enhance model aggregation efficiency, asynchronous Federated learning (AFL) is employed, which allows asynchronous aggregation without waiting for all vehicles to update their local models. In practice, the local models are susceptible to malicious tampering during the global aggregation process. To solve this problem, we propose a secure AFL (SAFL) framework by incorporating a Z-score-based weight detection method within AFL. Moreover, To improve caching efficiency and adapt to the highly dynamic IoV environments, we introduce an innovative proactive caching approach by combining a conditional variational autoencoder and generative adversarial network to predict popular contents, thereby improving the cache hit ratio. Additionally, based on the prediction results of popular content, we optimize intelligent decision-making using multiagent deep reinforcement learning (DRL) to reduce the content transmission delay. Extensive simulations are performed based on real-world datasets and experimental results demonstrate that the proposed SAFL and multiagent DRL hybrid technique outperforms other baseline approaches.

In this paper, we propose a novel transmissive reconfigurable intelligent surface (TRIS) transmitter-enabled spatial modulation (SM) multiple-input multiple-output (MIMO) system. In the transmission stage, a column-control activation strategy is implemented for the TRIS panel, where the specific column elements are activated per time slot. Concurrently, the receiver employs the maximum likelihood detection technique. Based on this, for the transmit signals, we derive the closed-form expressions for the upper bounds of the average bit error probability (ABEP) of the proposed scheme from different perspectives, employing both vector-based and element-based approaches. Furthermore, we provide the asymptotic closed-form expressions for the ABEP of the TRIS-SM scheme, as well as the diversity gain. To improve the performance of the proposed TRIS-SM system, we optimize ABEP with a fixed data rate. Additionally, we provide lower bounds to simplify the computational complexity of improved TRIS-SM scheme. The Monte Carlo simulation method is used to validate the theoretical derivations exhaustively. The results demonstrate that the proposed TRIS-SM scheme can achieve better ABEP performance compared to the conventional SM scheme. Furthermore, the improved TRIS-SM scheme outperforms the TRIS-SM scheme in terms of reliability.

To meet the growing demand for data traffic, spectrum-rich optical wireless communication (OWC) has emerged as a key technological driver for the development of 6G. The resonant beam communication (RBC) system, which employs spatially separated laser cavities as the transmitter and receiver, is a high-speed OWC technology capable of self-alignment without tracking. However, its transmission through the air is susceptible to losses caused by obstructions. In this paper, we propose an intelligent reflecting surface (IRS) assisted RBC system with the optical frequency doubling method, where the resonant beam in frequency-fundamental and frequency-doubled is transmitted through both direct line-of-sight (LoS) and IRS-assisted channels to maintain steady-state oscillation and enable communication without echo-interference, respectively. Then, we establish the channel model based on Fresnel diffraction theory under the near-field optical propagation to analyze the transmission loss and frequency-doubled power analytically. Furthermore, communication power can be maximized in real-time by dynamically controlling the beam-splitting ratio between the two channels according to the varying loss levels encountered over air. Numerical results validate that the IRS-assisted channel can compensate for the losses in the obstructed LoS channel and misaligned receivers, ensuring that communication performance reaches an optimal value with dynamic ratio adjustments.

This paper presents a novel multi-stream downlink communication system that utilizes a transmissive reconfigurable intelligent surface (RIS) transceiver. Specifically, we elaborate the downlink communication scheme using time-modulated array (TMA) technology, which enables high order modulation and multi-stream beamforming. Then, an optimization problem is formulated to maximize the minimum signal-to-interference-plus-noise ratio (SINR) with user fairness, which takes into account the constraint of the maximum available power for each transmissive element. Due to the non-convex nature of the formulated problem, finding optimal solution is challenging. To mitigate the complexity, we propose a linear-complexity beamforming algorithm based on consensus alternating direction method of multipliers (ADMM). Specifically, by introducing a set of auxiliary variables, the problem can be decomposed into multiple sub-problems that are amenable to parallel computation, where the each sub-problem can yield closed-form expressions, bringing a significant reduction in the computational complexity. The overall problem achieves convergence by iteratively addressing these sub-problems in an alternating manner. Finally, the convergence of the proposed algorithm and the impact of various parameter configurations on the system performance are validated through numerical simulations.

In this paper, we propose a novel transmissive reconfigurable intelligent surface (TRIS) transceiver-enhanced robust and secure integrated sensing and communication (ISAC) network. A time-division sensing communication mechanism is designed for the scenario, which enables communication and sensing to share wireless resources. To address the interference management problem and hinder eavesdropping, we implement rate-splitting multiple access (RSMA), where the common stream is designed as a useful signal and an artificial noise (AN), while taking into account the imperfect channel state information and modeling the channel for the illegal users in a fine-grained manner as well as giving an upper bound on the error. We introduce the secrecy outage probability and construct an optimization problem with secrecy sum-rate as the objective functions to optimize the common stream beamforming matrix, the private stream beamforming matrix and the timeslot duration variable. Due to the coupling of the optimization variables and the infinity of the error set, the proposed problem is a nonconvex optimization problem that cannot be solved directly. In order to address the above challenges, the block coordinate descent (BCD)-based second-order cone programming (SOCP) algorithm is used to decouple the optimization variables and solving the problem. Specifically, the problem is decoupled into two subproblems concerning the common stream beamforming matrix, the private stream beamforming matrix, and the timeslot duration variable, which are solved by alternating optimization until convergence is reached. To solve the problem, S-procedure, Bernstein’s inequality and successive convex approximation (SCA) are employed to deal with the objective function and non-convex constraints. Numerical simulation results verify the superiority of the proposed scheme in improving the secrecy energy efficiency (SEE) and the Cramér-Rao boundary (CRB).

This paper proposes a millimeter-wave (mmWave) multiple-input multiple-output (MIMO) transmission scheme termed spatial scattering shift keying (SSSK), which exploits spatial scattering modulation (SSM) to encode information through the indices of channel scatterers rather than conventional symbol constellations. The proposed SSSK achieves superior reliability compared to amplitude-phase modulation (APM) schemes, while simultaneously reducing hardware complexity. Specifically, the scatterer-index-based signaling mechanism mitigates the detection complexity inherent in APM systems by avoiding explicit symbol-level demodulation. In addition, we illustrate the advantages of SSSK by investigating the interaction between SSSK and fading channels. We derive closed-form expressions for the average bit error probability (ABEP) tight upper bound of the proposed scheme using two different approaches based on the greedy detection algorithm. To gain more insights, we further derive the asymptotic ABEP expression and diversity gain. To characterize the performance, we rigorously derive tight upper bounds on the ABEP using two complementary approaches: union bound and pairwise error probability analysis under a greedy detection framework. Furthermore, asymptotic ABEP expressions are established to reveal the achievable diversity gain. Moreover, we design maximum likelihood (ML) detectors with serial and parallel architectures and corresponding ABEP upper bounds. Simulations validate the analytical derivations and demonstrate SSSK outperforms APM in ABEP at high signal-to-noise ratios. The proposed greedy detector reduces computational complexity compared to the serial ML detector while maintaining comparable ABEP performance.

This paper studies a multiple intelligent reflecting surfaces (IRSs) collaborative localization system where multiple semi-passive IRSs are deployed in the network to locate one or more targets based on time-of-arrival. It is assumed that each semi-passive IRS is equipped with reflective elements and sensors, which are used to establish the line-of-sight links from the base station (BS) to multiple targets and process echo signals, respectively. Based on the above model, we derive the Fisher information matrix of the echo signal with respect to the time delay. By employing the chain rule and exploiting the geometric relationship between time delay and position, the Cramér-Rao bound (CRB) for estimating the target’s Cartesian coordinate position is derived. Then, we propose a two-stage algorithmic framework to minimize CRB in single- and multi-target localization systems by joint optimizing active beamforming at BS, passive beamforming at multiple IRSs and IRS selection. For the single-target case, we derive the optimal closed-form solution for multiple IRSs coefficients design and propose a low-complexity algorithm based on alternating direction method of multipliers to obtain the optimal solution for active beaming design. For the multi-target case, alternating optimization is used to transform the original problem into two subproblems where semi-definite relaxation and successive convex approximation are applied to tackle the quadraticity and indefiniteness in the CRB expression, respectively. Finally, numerical simulation results validate the effectiveness of the proposed algorithm for multiple IRSs collaborative localization system compared to other benchmark schemes as well as the significant performance gains.

Spatial scattering modulation (SSM), an emerging millimeter-wave (mmWave) multiple-input multiple-output (MI-MO) modulation technique, exploits spatial beam resources to enhance the modulation degree of freedom. However, to address the problem that the detection performance of existing scalarbased maximum likelihood (SML) detector is not optimal and the complexity is too high, this paper develops vector-based maximum likelihood (VML) and low-complexity (LC) detectors for the structural characteristics of SSM system receivers, respectively. Then, we give corresponding analysis for the complexity of each of the three detectors. Based on the SML, VML, and LC detectors, we derive the union upper bound of average bit error probability (ABEP) for the SSM scheme, respectively. Monte Carlo simulations validate the correctness of the analytical derivation and show that when ABEP = 10–5, the signal-to-noise ratio (SNR) required for the VML-based ABEP values is 5.5 dB less than that obtained from SML detection. Moreover, compared with SML, the detection complexity of the proposed LC algorithm is reduced by about 50% and the transmit SNR also saves 1 dB SNR. Furthermore, when the number of scatterers is higher, the ABEP performance advantage of the SSM system is more fully unlocked.

In this article, we propose a novel uplink communication system enabled by a transmissive reconfigurable intelligent surface (RIS) transceiver, where orthogonal frequency division multiple access (OFDMA) is applied to multiple users. Specifically, we explore a novel receiver architecture that includes a transmissive RIS and a single horn antenna for reception. Additionally, a channel model based on both planar and spherical waves is developed, accounting for far-field and near-field effects. To achieve the maximum system sum-rate of uplink communications while adhering to Quality-of-Service (QoS) constraints, we propose a joint optimization problem that optimizes power allocation, subcarrier allocation, and transmissive RIS coefficient. However, this problem is nonconvex in view of the strong interdependence among the optimization variables, posing significant challenges for direct solution. Thus, the alternating optimization (AO) algorithm architecture is employed, which decouples optimization variables and divide the problem into two subproblems. The first subproblem focuses on jointly optimizing power allocation and subcarrier allocation, and it is addressed by utilizing the Lagrangian dual decomposition method. Meanwhile, concerning the design of the transmissive RIS coefficient, the second subproblem is tackled by means of the successive convex approximation (SCA) approach. Subsequently, these two subproblems are solved in an alternating manner until the convergence criterion is met. Finally, the numerical results indicate that the proposed algorithm exhibits excellent convergence performance and effectively enhances the system sum-rate compared to other benchmark algorithms.

In this article, we investigate the performance of reconfigurable intelligent surface (RIS)-aided spatial shift keying (SSK) wireless communication systems with imperfect channel state information (CSI). Specifically, we study the average bit error probability (ABEP) of two RIS-SSK systems based on intelligent reflection and blind reflection modes. For the intelligent RIS-SSK scheme, we first derive the conditional pairwise error probability of the composite channel through maximum-likelihood (ML) detection. Subsequently, we derive the probability density function of the combined channel. Due to the intricacies of the composite channel formulation, an exact closed-form ABEP expression is unattainable through direct derivation. To this end, we resort to employing the Gaussian–Chebyshev quadrature method to estimate the results. Additionally, we employ$Q$-function approximation to derive the nonexact closed-form expression in the presence of channel estimation errors. For the blind RIS-SSK scheme, we derive both closed-form ABEP expression and asymptotic ABEP expression with imperfect CSI by adopting the ML detector. To offer deeper insights, we explore the impact of discrete reflection phase shifts on the performance of the RIS-SSK system. Finally, we extensively validate all the analytical derivations via Monte Carlo simulations.

Drawing inspiration from the advantages of intelligent reflecting surfaces (IRS) in wireless networks, this paper presents a novel design for intelligent omni surface (IOS) enabled integrated sensing and communications (ISAC). By harnessing the power of multi-antennas and a multitude of elements, the dual-function base station (BS) and IOS collaborate to realize joint active and passive beamforming, enabling seamless 360-degree ISAC coverage. The objective is to maximize the minimum signal-to-interference-plus-noise ratio (SINR) of multi-target sensing while ensuring the multi-user multi-stream communications. To achieve this, a comprehensive optimization approach is employed, encompassing the design of radar receive vector, transmit beamforming matrix, and IOS transmissive and reflective coefficients. Due to the non-convex nature of the formulated problem, an auxiliary variable is introduced to transform it into a more tractable form. Consequently, the problem is decomposed into three sub-problems based on the block coordinate descent algorithm. Semidefinite relaxation and successive convex approximation methods are leveraged to convert the sub-problem into a convex problem, while the iterative rank minimization algorithm and penalty function method ensure the equivalence. Furthermore, the scenario is extended to mode switching and time switching protocols. Simulation results validate the convergence and superior performance of the proposed algorithm compared to other benchmark algorithms.

Reconfigurable intelligent surface (RIS)-assisted index modulation system schemes are considered to be a promising technology for sixth-generation (6G) wireless communication systems, which can enhance various system capabilities such as coverage and reliability. However, obtaining perfect channel state information (CSI) is challenging due to the lack of a radio frequency chain in RIS. In this paper, we investigate the RIS-assisted full-duplex (FD) two-way space shift keying (SSK) system under imperfect CSI, where the signal emissions are augmented by deploying RISs in the vicinity of two FD users. The maximum likelihood detector is utilized to recover the transmit antenna index. With this in mind, we derive closed-form average bit error probability (ABEP) expression based on the Gaussian-Chebyshev quadrature (GCQ) method, and provide the upper bound and asymptotic ABEP expressions in the presence of channel estimation errors. To gain more insights, we also derive the outage probability and provide the throughput of the proposed scheme with imperfect CSI. The correctness of the analytical derivation results is confirmed via Monte Carlo simulations. It is demonstrated that increasing the number of elements of RIS can significantly improve the ABEP performance of the FD system over the half-duplex (HD) system. Furthermore, in the high SNR region, the ABEP performance of the FD system is better than that of the HD system.

In this paper, we investigate a practical structure of reconfigurable intelligent surface (RIS)-based double spatial scattering modulation (DSSM) for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. A suboptimal detector is proposed, in which the beam direction is first demodulated according to the received beam strength, and then the remaining information is demodulated by adopting the maximum likelihood algorithm. Based on the proposed suboptimal detector, we derive the conditional pairwise error probability expression. Further, the exact numerical integral and closed-form expressions of unconditional pairwise error probability (UPEP) are derived via two different approaches. To provide more insights, we derive the upper bound and asymptotic expressions of UPEP. In addition, the diversity gain of the RIS-DSSM scheme was also given. Furthermore, the union upper bound of average bit error probability (ABEP) is obtained by combining the UPEP and the number of error bits. Simulation results are provided to validate the derived upper bound and asymptotic expressions of ABEP. We found an interesting phenomenon that the ABEP performance of the proposed system-based phase shift keying is better than that of the quadrature amplitude modulation. Additionally, the performance advantage of ABEP is more significant with the increase in the number of RIS elements.

In this study, we explore the performance of a reconfigurable reflecting surface (RIS)-assisted transmit spatial modulation (SM) system for downlink transmission, wherein the deployment of RIS serves the purpose of blind area coverage within the channel. At the receiving end, we present three detectors, i.e., maximum likelihood (ML) detector, two-stage ML detection, and greedy detector to recover the transmitted signal. By utilizing the ML detector, we initially derive the conditional pair error probability expression for the proposed scheme. Subsequently, we leverage the central limit theorem (CLT) to obtain the probability density function of the combined channel. Following this, the Gaussian-Chebyshev quadrature method is applied to derive a closed-form expression for the unconditional pair error probability and establish the union tight upper bound for the average bit error probability (ABEP). Furthermore, we derive a closed-form expression for the ergodic capacity of the proposed RIS-SM scheme. Monte Carlo simulations are conducted not only to assess the complexity and reliability of the three detection algorithms but also to validate the results obtained through theoretical derivation results.

In this paper, we investigate a state-of-the-art reconfigurable intelligent surface (RIS)-assisted spatial scattering modulation (SSM) scheme for millimeter-wave (mmWave) systems, where a more practical scenario that the RIS is near the transmitter while the receiver is far from RIS is considered. To this end, the line-of-sight (LoS) and non-LoS links are utilized in the transmitter-RIS and RIS-receiver channels, respectively. By employing the maximum likelihood detector at the receiver, the conditional pairwise error probability (CPEP) expression for the RIS-SSM scheme is derived under the two scenarios that the received beam demodulation is correct or not. Furthermore, the union upper bound of average bit error probability (ABEP) is obtained based on the CPEP expression. Finally, the derivation results are exhaustively validated by the Monte Carlo simulations.

This paper investigates the reconfigurable intelligent surface (RIS) assisted spatial scattering modulation (SSM) scheme for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems, in which line-of-sight (LoS) and non-line-of-sight (NLoS) paths are respectively considered in the transmitter-RIS and RIS-receiver channels. Based on the maximum likelihood detector, the expression for the conditional pairwise error probability (CPEP) of the RIS-SSM scheme is derived for both cases of correct demodulation of the received beam or not. Furthermore, we derive the closed-form expressions of the unconditional pairwise error probability (UPEP) by employing two different methods: the probability density function and the moment-generating function expressions with a descending order of scatterer gains. To provide more useful insights, we derive the asymptotic UPEP and the diversity gain of the RIS-SSM scheme in the high SNR region. Depending on UPEP and the corresponding Euclidean distance, we further give the union upper bound of the average bit error probability (ABEP). To acquire the effective capacity of the proposed system, a new framework for ergodic capacity analysis is also provided. Finally, all derivation results are validated via extensive Monte Carlo simulations and reveal that the proposed RIS-SSM scheme outperforms the benchmarks in terms of reliability.