Multiple-input Single-output Systems Research Articles

Robust Beamforming for Downlink Multi-Cell Systems: A Bilevel Optimization Perspective

Utilization of inter-base station cooperation for information processing has shown great potential in enhancing the overall quality of communication services (QoS) in wireless communication networks. Nevertheless, such cooperations require the knowledge of channel state information (CSI) at base stations (BSs), which is assumed to be perfectly known. However, CSI errors are inevitable in practice which necessitates beamforming technique that can achieve robust performance in the presence of channel estimation errors. Existing approaches relax the robust beamforming design problems into semidefinite programming (SDP), which can only achieve a solution that is far from being optimal. To this end, this paper views robust beamforming design problems from a bilevel optimization perspective. In particular, we focus on maximizing the worst-case weighted sum-rate (WSR) in the downlink multi-cell multi-user multiple-input single-output (MISO) system considering bounded CSI errors. We first reformulate this problem into a bilevel optimization problem and then develop an efficient algorithm based on the cutting plane method. A distributed optimization algorithm has also been developed to facilitate the parallel processing in practical settings. Numerical results are provided to confirm the effectiveness of the proposed algorithm in terms of performance and complexity, particularly in the presence of CSI uncertainties.

Performance analysis of IRS-aided MISO system under spatial correlation and interference

In this paper, we investigate the performance of an intelligent reflecting surface (IRS) aided multiple-input single-output (MISO) wireless system in the absence of direct link. We focus on the practical scenario where the N reflecting elements of the IRS have a spatial correlation between them, and both the destination and IRS are subject to interferences. Moreover, the IRS design with discrete phase shifting is considered. In order to quantify the impact of spatial correlation, phase error and interference on the system performance, we provide closed-form and asymptotic expressions for the system outage probability. Additionally, approximations for the average channel capacity are also derived. To gain further insight, the achievable diversity gain of both partial and full correlation cases is quantified. The analytical and simulation results reveal that spatial correlation has a dominant effect and reduces the system diversity order, but it is beneficial to the average channel capacity. Phase error results in performance degradation. Interference degrades the system performance only by reducing the coding gain. Compared to the direct path, interference from the reflected path plays a major role in reducing the system performance.

Closed-Form Global Optimization of Beyond Diagonal Reconfigurable Intelligent Surfaces

Reconfigurable intelligent surfaces (RISs) allow controlling the propagation environment in wireless networks by tuning multiple reflecting elements. RISs have been traditionally realized through single connected architectures, mathematically characterized by a diagonal scattering matrix. Recently, beyond diagonal RISs (BD-RISs) have been proposed as a novel branch of RISs whose scattering matrix is not limited to be diagonal, which creates new benefits and opportunities for RISs. Efficient BD-RIS architectures have been realized based on group and fully connected reconfigurable impedance networks. However, a closed-form solution for the global optimal scattering matrix of these architectures is not yet available. In this paper, we provide such a closed-form solution proving that the theoretical performance upper bounds can be exactly achieved for any channel realization. We first consider the received signal power maximization in single-user single-input single-output (SISO) systems aided by a BD-RIS working in reflective or transmissive mode. Then, we extend our solution to single-user multiple-input multiple-output (MIMO) and multi-user multiple-input single-output (MISO) systems. We show that our algorithm is less complex than the iterative optimization algorithms employed in the previous literature. The complexity of our algorithm grows linearly (resp. cubically) with the number of RIS elements in the case of group (resp. fully) connected architectures.

Disco Intelligent Reflecting Surfaces: Active Channel Aging for Fully-Passive Jamming Attack

Due to the open communications environment in wireless channels, wireless networks are vulnerable to jamming attacks. However, existing approaches for jamming rely on knowledge of the legitimate users’ (LUs’) channels, extra jamming power, or both. To raise concerns about the potential threats posed by illegitimate intelligent reflecting surfaces (IRSs), we propose an alternative method to launch jamming attacks on LUs without either LU channel state information (CSI) or jamming power. The proposed approach employs an adversarial IRS with random phase shifts, referred to as a “disco” IRS (DIRS), that acts like a “disco ball” to actively age the LUs’ channels. Such active channel aging (ACA) interference can be used to launch jamming attacks on multi-user multiple-input single-output (MU-MISO) systems. The proposed DIRS-based fully-passive jammer (FPJ) can jam LUs with no additional jamming power or knowledge of the LU CSI, and it can not be mitigated by classical anti-jamming approaches. A theoretical analysis of the proposed DIRS-based FPJ that provides an evaluation of the DIRS-based jamming attacks is derived. Based on this detailed theoretical analysis, some unique properties of the proposed DIRS-based FPJ can be obtained. Furthermore, a design example of the proposed DIRS-based FPJ based on one-bit quantization of the IRS phases is demonstrated to be sufficient for implementing the jamming attack. In addition, numerical results are provided to show the effectiveness of the derived theoretical analysis and the jamming impact of the proposed DIRS-based FPJ.

Multi-IRS-Aided Millimeter-Wave Multi-User MISO Systems for Power Minimization Using Generalized Benders Decomposition

Difficulties in controlling IRSs to form the optimized passive beamforming have rarely been considered in intelligent reflecting surface (IRS)-aided systems, which are summarized as follows: 1) sending the optimized passive precoding vectors to the IRS controller incurs significant control overheads; 2) implementing the optimized passive precoding needs to set massive modes in the IRS control circuit. To address these issues, we investigate codebook-based passive beamforming for multi-IRS-aided millimeter-wave (mmWave) multi-user multiple-input single-output (MU-MISO) systems, where the control overheads are reduced to several scalars and the number of modes set in the IRS control circuit is reduced to that of codewords. Moreover, we formulate a joint passive and active precoding problem in the multi-IRS-aided mmWave MU-MISO system as a mixed-integer nonlinear programming (MINLP) problem, and then develop a generalized Benders decomposition (GBD)-based joint passive and active precoding algorithm. The proposed algorithm offers near-optimal performance (≥ 99.9%) with significantly-reduced computational complexity. Simulation results show that the proposed algorithm achieves energy savings of up to 50% and 95%, compared to the benchmark by the maximum ratio transmission and that without IRSs, respectively. In addition, the energy savings increase with the number of reflecting elements packed on each IRS as well as that of codewords.

Theoretical Model and Experiments of Focused Phased Array for Cross-Medium Communication in Misaligned Transmitter/Receiver Scenarios

Direct communication across the water-air interface has attracted wide attention because of its potential applications in integrated air-sea networking. In this article, three different cross-medium communication systems have been proposed, including line-of-sight (LOS) optical links, laser-induced acoustic (LIA) systems, and translational acoustic radio frequency (TARF) systems. However, LOS optical links are sensitive to turbid water, misalignment of the transmitters/receivers, and the influence of surface waves. LIA systems are unstable with poor repeatability. TARF systems require the alignment of transmitters/receivers. Moreover, in situations where the location of detection points is required to be frequently moved for flexible communication, LOS optical links, and TARF systems both failed. This article proposes a cross-medium focused phased array (CMFPA) model, which is a multiple-input single-output system, to enlarge the detection area and increase the overall wave amplitude at the water surface. Theoretical analyses and experimental results indicate that the CMFPA can relax the requirement of precise alignment between communication nodes and improve performance in cross-medium communication when compared to a single-input single-output system. The study is expected to provide a foundation for cross-medium communication between underwater and airborne nodes in misaligned transmitters/receivers scenarios.

Online deep learning based energy efficient optimization for IRS-assisted eMBB and URLLC services

In this paper, we consider a downlink multiple-input single-output (MISO) system for enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communication (URLLC) services assisted by intelligent reflecting surface (IRS). We formulate an optimization problem which aims at maximizing energy efficiency (EE) by jointly optimizing the beamforming vectors at the BS, IRS reflection coefficients matrix and resource allocation while satisfying the requirement of quality of service (QoS). The EE maximization problem is non-convex and contains various constraints. In this paper, a primal–dual online deep learning (PDODL) algorithm is proposed to tackle this issue. Specifically, we transform the original problem into the primal–dual problem by integrating constraints into the objective function. Then, online deep learning (DL) algorithm is adopted where the optimization variables are viewed as the network parameters which are further expressed in the form of unconstrained counterparts. The PDODL algorithm takes the objective function of the primal–dual problem as loss function because its decrease through network training is equal to the solving process of the optimization problem. Simulation results verify the effectiveness of the proposed algorithm and IRS can significantly improve the EE performance of the system.

Design of Multi-User Noncoherent Massive SIMO Systems for Scalable URLLC.

This paper develops and optimizes a non-orthogonal and noncoherent multi-user massive single-input multiple-output (SIMO) framework, with the objective of enabling scalable ultra-reliable low-latency communications (sURLLC) in Beyond-5G (B5G)/6G wireless communication systems. In this framework, the huge diversity gain associated with the large-scale antenna array in the massive SIMO system is leveraged to ensure ultra-high reliability. To reduce the overhead and latency induced by the channel estimation process, we advocate for the noncoherent communication technique, which does not need the knowledge of instantaneous channel state information (CSI) but only relies on large-scale fading coefficients for message decoding. To boost the scalability of noncoherent massive SIMO systems, we enable the non-orthogonal channel access of multiple users by devising a new differential modulation scheme to ensure that each transmitted signal matrix can be uniquely determined in the noise-free case and be reliably estimated in noisy cases when the antenna array size is scaled up. The key idea is to make the transmitted signals from multiple geographically separated users be superimposed properly over the air, such that when the sum signal is correctly detected, the signal sent by each individual user can be uniquely determined. To further enhance the average error performance when the array antenna number is large, we propose a max-min Kullback-Leibler (KL) divergence-based design by jointly optimizing the transmitted powers of all users and the sub-constellation assignments among them. The simulation results show that the proposed design significantly outperforms the existing max-min Euclidean distance-based counterpart in terms of error performance. Moreover, our proposed approach also has a better error performance compared to the conventional coherent zero-forcing (ZF) receiver with orthogonal channel training, particularly for cell-edge users.

Optimization for Reflection and Transmission Dual-Functional Active RIS-Assisted Systems

Reconfigurable intelligent surface (RIS) has been deemed as one of potential components of future wireless communication systems because it can adaptively manipulate the wireless propagation environment with low-cost passive devices. However, due to the severe double path loss, the traditional passive RIS can provide sufficient gain only when receivers are very close to the RIS. Moreover, RIS cannot provide signal coverage for the receivers at the back side of it. To address these drawbacks in practical implementation, we introduce a novel reflection and transmission dual-functional active RIS (DF-ARIS) architecture in this paper, which can simultaneously realize reflection and transmission functionalities with active signal amplification to significantly extend signal coverage and enhance the quality-of-service (QoS) of all users. The problem of joint transmit beamforming and dual-functional active RIS design is investigated in RIS-enhanced multiuser multiple-input single-output (MU-MISO) systems. Both sum-rate maximization and power minimization problems are considered. To address their non-convexity, we develop efficient iterative algorithms to decompose them into several separate design problems, which are efficiently solved by exploiting fractional programming (FP) and Riemannian-manifold optimization techniques. Simulation results demonstrate the superiority of the proposed dual-functional active RIS architecture and the effectiveness of our proposed algorithms over various benchmark schemes.

Two-Timescale-Based Beam Training for RIS-Aided Millimeter-Wave Multi-User MISO Systems

Reconfigurable intelligent surfaces (RISs), as a promising technology for 6 G communications, have received considerable attention. Herein, we investigate the downlink precoding in the RIS-aided millimeter-wave (mmWave) multi-user multiple-input single-output (MU-MISO) system, where the access point (AP) uses hybrid digital and analog precoders. Due to the constraints of practical hardware and the difficulty of acquiring channel state information, we consider codebook-based passive beamforming and analog beamforming, and then formulate the downlink precoding problem as a mixed-integer nonlinear programming (MINLP) problem. To solve this NP-hard MINLP problem with low channel estimation overheads, we investigate beam training for the codebook-based passive beamforming and analog beamforming, and the MINLP problem is then reduced to a traditional MU-MISO precoding problem. To further reduce the beam training overheads, we propose a two-timescale-based beam training (TT-BT) algorithm. We prove that the TT-BT algorithm achieves the maximum codebook-based passive and analog beamforming gain. To avoid the limitations imposed the use of the TT property, we design the analog precoder by aligning the analog beamforming to RISs. Moreover, we characterize the rate loss due to codebook-based quantization. Compared with the empirical BT (EBT) algorithm, the proposed TT-BT algorithm achieves better performance with significantly lower training and feedback overheads.

Sum throughput optimization of wireless powered IRS-assisted multi-user MISO system

Intelligent reflecting surface (IRS) is a promising technology for beyond-5G wireless communication systems. However, the energy demand of IRS is often overlooked in existing works, leading to performance issues in practical scenarios. To address this issue, this paper proposes an operating model based on time switching (TS) protocol for an IRS-assisted multi-user multiple-input single-output (MISO) system, which can provide energy for IRS through wireless power transfer (WPT) technology. The system throughput maximization problem is addressed to improve performance. Specifically, a two-stage algorithm combined with alternating optimization, denoted as TAO, is proposed. To further improve the optimization process in large-size IRS scenarios, an improved deep deterministic policy gradient (DDPG) method combined with TAO, denoted as TAO-DDPG, is also proposed. Numerical results demonstrate that the proposed TAO-DDPG algorithm achieves similar performance to TAO while greatly reducing the optimization time.

Multi-Level Design for Multiple-Symbol Non-Coherent Unitary Constellations for Massive SIMO Systems

This paper investigates non-coherent detection of single-input multiple-output (SIMO) systems over block Rayleigh fading channels. Using the Kullback-Leibler divergence as the design criterion, we formulate a multiple-symbol constellation optimization problem, which turns out to have high computational complexity to construct and detect. We exploit the structure of the formulated problem and decouple it into a unitary constellation design and a multi-level design. The proposed multi-level design has low complexity in both construction and detection. Simulation results show that our multi-level design has better performance than traditional pilot-based schemes and other existing low-complexity multi-level designs.

K-Means Based Constellation Optimization for Index Modulated Reconfigurable Intelligent Surfaces

Reconfigurable intelligent surface (RIS) has recently emerged as a promising technology enabling next-generation wireless networks. In this letter, we develop an improved index modulation (IM) scheme by utilizing RIS to convey information. Specifically, we study an RIS-aided multiple-input single-output (MISO) system, in which the information bits are conveyed by reflection patterns of RIS rather than the conventional amplitude-phase constellation. Furthermore, the K-means algorithm is employed to optimize the reflection constellation to improve the error performance. Also, we propose a generalized Gray coding method for mapping information bits to an appropriate reflection constellation and analytically evaluate the error performance of the proposed scheme by deriving a tight upper bound of the average bit error rate (BER). Finally, numerical results verify the accuracy of our theoretical analysis as well as the substantially improved BER performance of the proposed RIS-based IM transmission scheme.

Illegal Intelligent Reflecting Surface Based Active Channel Aging: When Jammer Can Attack Without Power and CSI

Illegal intelligent reflecting surfaces (I-IRSs), i.e., the illegal deployment and utilization of IRSs, impose serious harmful impacts on wireless networks. The existing I-IRS-based illegal jammer (IJ) requires channel state information (CSI) or extra power or both, and therefore, the I-IRS-based IJ seems to be difficult to implement in practical wireless networks. To raise concerns about significant potential threats posed by I-IRSs, we propose an alternative method to jam legitimate users (LUs) without relying on the CSI. By using an I-IRS to actively change wireless channels, the orthogonality of multi-user beamforming vectors and the co-user channels is destroyed, and significant inter-user interference is then caused, which is referred to as active channel aging. Such a fully-passive jammer (FPJ) can launch jamming attacks on multi-user multiple-input single-output (MU-MISO) systems via inter-user interference caused by active channel aging, where the IJ requires no additional transmit power and instantaneous CSI. The simulation results show the effectiveness of the proposed FPJ scheme. Moreover, we also investigate how the transmit power and the number of quantization phase shift bits influence the jamming performance.

Environment-Aware Codebook for Reconfigurable Intelligent Surface-Aided MISO Communications

Reconfigurable intelligent surface (RIS) is a revolutionary technology that can customize the wireless channel and improve the energy efficiency of next-generation cellular networks. This letter proposes an environment-aware codebook design by employing the statistical channel state information (CSI) for RIS-assisted multiple-input single-output (MISO) systems. Specifically, first of all, we generate multiple virtual channels offline by utilizing the location information and design an environment-aware reflection coefficient codebook. Thus, we only need to estimate the composite channel and optimize the active transmit beamforming for each reflection coefficient in the pre-designed codebook, while simplifying the reflection optimization substantially. Moreover, we analyze the theoretical performance of the proposed scheme. Finally, numerical results verify the performance benefits of the proposed scheme over the cascaded channel estimation and passive beamforming as well as the existing codebook scheme in the face of channel estimation errors, albeit its significantly reduced overhead and complexity.

Fourth-order cumulants based-least squares methods for fractional Multiple-Input-Single-Output Errors-In-Variables system identification

This paper presents new consistent methods for continuous-time Multiple-Input-Single-Output (MISO) Errors-In-Variables (EIV) systems by fractional models. The proposed idea is to use Higher-Order Statistics (HOS), such as fourth-order cumulants (foc), instead of noisy input and output measurements to obtain unbiased estimates. Firstly, all differentiation orders are assumed to be known a priori and linear coefficients are estimated. The developed estimator is based on minimizing the equation error and it is called fractional fourth-order based-least squares estimator ( $$frac-foc-ls$$ ). Secondly, the global commensurability of the fractional MISO system is considered. The $$frac-foc-ls$$ is combined with a non linear technique to estimate the global commensurate order along with linear coefficients. The developed algorithm is based on minimizing the output error and called fractional fourth-order cumulants based-least squares combined with global commensurate order optimization ( $$frac-foc-gcools$$ ). The consistency of the developed estimators, in presence of high levels of noise corrupting both the input and output measurements, is assessed through a numerical example with the help of Monte Carlo simulations.

Energy-Efficient Optimization of Multi-User NOMA-Assisted Cooperative THz-SIMO MEC Systems

The various requirements in terms of data rates and latency in beyond 5G and 6G networks have motivated the integration of a variety of communications schemes and technologies to meet these requirements in such networks. Among these schemes are Terahertz (THz) communications, cooperative non-orthogonal multiple-access (NOMA)-enabled schemes, and mobile edge computing (MEC). THz communications offer abundant bandwidth for high-data-rate short-distance applications and NOMA-enabled schemes are promising schemes to realize the target spectral efficiencies and low latency requirements in future networks, while MEC would allow distributed processing and data offloading for the emerging applications in these networks. In this paper, an energy-efficient scheme of multi-user NOMA-assisted cooperative THz single-input multiple-output (SIMO) MEC systems is proposed to allow the uplink transmission of offloaded data from the far cell-edge users to the more computing resources in the base station (BS) through the cell-center users. To reinforce the performance of the proposed scheme, two optimization problems are formulated and solved, namely, the first problem minimizes the total users’ energy consumption while the second problem maximizes the total users’ computation energy efficiency (CEE) for the proposed scheme. In both problems, the NOMA user pairing, the BS receive beamforming, the transmission time allocation, and the NOMA transmission power allocation coefficients are optimized, while taking into account the full-offloading requirements of each user as well as the predefined latency constraint of the system. The obtained results reveal new insights into the performance and design of multi-user NOMA-assisted cooperative THz-SIMO MEC systems. Particularly, with relatively high offloading rate demands (several Gbits/user), we show that (i) the proposed scheme can handle such demands while satisfying the predefined latency constraint, and (ii) the full-offloading model can be considered the most effective solution in conserving mobile devices’ resources as compared to the system with the partial-offloading model or the system without offloading.

Hybrid Relay and Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems

Reconfigurable intelligent surfaces (RISs) are viewed as key enablers for next-generation wireless communications. This paper investigates a multiuser downlink multiple-input single-output (MISO) system in which a multiantenna base station (BS) transmits information to multiple single-antenna users with the aid of both a half-duplex decode-and-forward (DF) relay and a full-duplex RIS. Active beamforming at the BS and the DF relay, as well as passive beamforming at the RIS, are jointly designed for system sum-rate maximization. The design problem is challenging to solve due to coupled beamforming variables. An alternating optimization (AO) based algorithm is proposed to tackle this complex co-design problem. Numerical results demonstrate the superior performance of the proposed hybrid relay–RIS system with a judicious joint beamforming design. Convergence and complexity analysis shows that the convergence rate of the proposed algorithm is dominated by the numbers of users and RIS elements, and the proposed scheme can converge in a few iterations even in the configuration of large numbers of users and RIS elements. Interesting tradeoffs posed in the joint design are discussed. An extension of the proposed design method to a related energy efficiency (EE) optimization problem is also outlined and implemented.

MISO System with Intelligent Reflecting Surface-Assisted Cellular Networks

This paper proposes an architecture based on Intelligent Reflecting Surfaces (IRSs) to improve the performance of future cellular networks. Specifically, we investigate the use of IRSs in combination with statistical Channel State Information (CSI) to enhance the coverage of Base Stations (BSs) in Multiple-Input Single-Output (MISO) systems. Furthermore, we exploit IRSs to reduce the complexity of the proposed architecture, and therefore the total cost, by reducing the number of required antennas at the transmitters (i.e., BSs). At first, we consider a Rayleigh fading channel between the transmitter and the receiver, and we assume the existence of a Line of Sight (LoS) between the BS and the IRS, as well as between the IRS and the destination. In the second part, we investigate the case of a Single-User Multiple-Input Single-Output (SU-MISO) system, where we study the benefits of IRSs in terms of coverage of the BS; then, we formulate a problem for a Multi-User Multiple-Input Single-Output (MU-MISO) system where the IRS is considered as a block of resources that can assist a certain number of users. The problem of managing the IRS resources is formulated as a nonlinear integer problem. We solve the optimization problem using an exhaustive search and propose two low-complexity heuristic algorithms. The performance of the system is evaluated with respect to a variable number of users, the position of the IRS, the required SNR, and the size of the cell. Simulation results corroborate the proposed approach and show that the introduction of the IRS in the network architecture enhances the overall performance of the network, extends the coverage area, enhances users’ satisfaction, and improves the SNR value, while optimizing the required number of antennas at the BS.

Diversity-Based Non-Coherent Signal Detector for Molecular Communication via Reaction-Diffusion

Molecular communication is attractive to the emerging nano-scale communication systems. Traditionally, a detector recovers the information from only the concentration of single messenger molecule, while ignoring the variation of multiple participants in biochemical reaction. In this paper, we propose a non-coherent signal detector, by fully exploiting this ubiquitous biochemical diversity property of multiple reacting molecules. After extracting the channel state information (CSI) independent non-coherent features of received signals, the dynamical transient characteristics of messenger, reactant and product molecules are all utilized to implement the diversity detection, thus formulating a functional single-input multiple-output (SIMO) system via reaction-diffusion communication that has been barely considered before. We design both hard and soft combination strategies to attain the potential diversity gain arise from the dynamical co-variation of participants. Theoretical analysis and numerical simulations are provided to demonstrate the advantages of our detector. Compared with conventional detectors that use only single messenger molecule, the bit error rate (BER) of is substantially reduced. Moreover, the BER performances of our non-coherent detector are even better than coherent maximum a posteriori (MAP) detector that requires accurate CSI estimation, which confirms the dramatic diversity gain provided by our detector. It would have great potentials in reliable nano-scale communications.