To support passive Internet-of-Things (IoT) for future 6G networks, backscatter communication (BC) has emerged as a promising solution due to its ultra-low-power consumption nature. In this paper, we propose a novel multistatic passive IoT architecture which allows the reader to recover the information from multiple backscatter devices (BDs), excited by multiple remote continuous-waveform (CW) transmitters. At each BD, multiple backscatter antennas, in conjunction with cyclic delay transmission (CDT) and interleaved frequency division multiple access (IFDMA) framework, are deployed to achieve antenna gain, diversity advantage, as well as transmission orthogonality among the BDs. Furthermore, transmit beamforming at the CW transmitters and cyclic delays across the BDs are optimized to enhance the performance and ensure fairness among the BDs. Extensive simulation results have validated the effectiveness of the proposed framework under various scenarios, demonstrating substantial improvements in both reliability and fairness compared with conventional schemes.

Ambient IoT, which integrates backscatter communication and energy harvesting techniques, has emerged as a promising technology for massive connectivity in 6G. Symbiotic radio (SR), a novel backscatter communication paradigm, has shown great potential in advancing ambient IoT. Nevertheless, existing multiple access schemes for SR are limited by low transmission rates when enabling massive ambient IoT transmissions, primarily due to time-division access or inter-device interference. Additionally, the impact of energy harvesting on the multiple access system is rarely investigated. In this paper, we propose a code division multiple access (CDMA)-based symbiotic radio system to support interference-free simultaneous access for massive IoT devices. In the proposed system, multiple IoT devices first harvest energy from cellular signals, modulate their information onto the incident signal using unique spreading codes, and then transmit the modulated signals to the receiver. Two decoding schemes are proposed to retrieve the information from the IoT devices, and the throughput is analyzed accordingly. Moreover, we derive a closedform expression of the ergodic sum throughput, and reveal that there exists an optimal energy harvesting time that maximizes overall IoT transmission throughput. Finally, simulation results validate the performance of the proposed system and demonstrate the tradeoff between energy harvesting and data transmission.

Symbiotic radio (SR) has emerged as a spectrum- and energy-efficient paradigm to support massive Internet of Things (IoT) connections. Two multiple access schemes are proposed in this paper to facilitate massive IoT connections using the cellular network based on the SR technique, namely, the simultaneous access (SA) scheme and the selection diversity access (SDA) scheme. In the SA scheme, the base station (BS) transmits information to the receiver while multiple IoT devices transmit their information simultaneously by passively backscattering the BS signal to the receiver, while in the SDA scheme, only the IoT device with the strongest backscatter link transmits information. In both of the schemes, the receiver jointly decodes the information from the BS and IoT devices. To evaluate the above two schemes, the closed-form expressions of the ergodic rates in high signal-to-noise ratio (SNR) regimes and outage probabilities for cellular and IoT transmissions are derived by using extreme value theory, generalized-K distribution approximation and Gaussian-Chebyshev quadrature methods. Finally, numerical results are provided to verify the theoretical analysis and compare the proposed multiple access schemes. When the number of IoT devices is small, the SDA scheme is more appealing since it can significantly reduce the computational complexity while achieving equivalent performance to the SA scheme. When the number of IoT devices is large, the SA scheme is preferable since it guarantees a significantly better rate performance and a lower outage probability.

Symbiotic radio (SR) is a promising technique to support cellular Internet-of-Things (IoT) by forming a mutualistic relationship between IoT and cellular transmissions. In this paper, we propose a novel multi-user multi-IoT-device SR system to enable massive access in cellular IoT. In the considered system, the base station (BS) transmits information to multiple cellular users, and a number of IoT devices simultaneously backscatter their information to these users via the cellular signal. The cellular users jointly decode the information from the BS and IoT devices. Noting that the reflective links from the IoT devices can be regarded as the channel uncertainty of the direct links, we apply the robust design method to design the beamforming vectors at the BS. Specifically, the transmit power is minimized under the cellular transmission outage probability constraints and IoT transmission sum rate constraints. The algorithm based on semi-definite programming and difference-of-convex programming is proposed to solve the power minimization problem. Moreover, we consider a special case where each cellular user is associated with several adjacent IoT devices and propose a direction of arrival (DoA)-based beamforming design approach. The DoA-based approach requires only the DoA and angular spread (AS) of the direct links instead of the instantaneous channel state information (CSI) of the reflective link channels, leading to a significant reduction in the channel feedback overhead. Simulation results have substantiated the multi-user multi-IoT-device SR system and the effectiveness of the proposed beamforming design approaches. It is shown that the DoA-based beamforming approach achieves comparable performance as the CSI-based approach in the special case when the ASs are small.

Symbiotic radio (SR) is envisioned to support both Internet-of-Things (IoT) and cellular networks by forming a mutualistic relationship between them. In this paper, we propose a multiuser multi-IoT-device SR system where the base station (BS) transmits information to multiple users, and a number of IoT devices simultaneously backscatter their information to the users via the BS signal. Since the IoT information changes rather quickly compared to the channel variation, the IoT transmission introduces channel uncertainty when the users decoding the cellular information. To leverage the channel uncertainty, we apply the robust design method to design the beamforming vector at the BS. To be specific, the transmit power at the BS is minimized under the cellular transmission outage probability constraints and IoT transmission rate constraints. S-lemma is then utilized to transform the challenging probability constraints into a convex semi-definite programming (SDP) form. To deal with the non-convex rank-one constraint in SDP, we exploit successive convex approximations to transform the SDP problem into a difference-of-convex (DC) problem and solve it iteratively with DC programming. Simulation results have substantiated the multiuser multi-IoT-device symbiotic radio system and the effectiveness of the proposed algorithm.

Small cell networks (SCNs) have emerged as a promising solution to meet the demand for increasing data traffic for the sixth generation and beyond wireless networks. However, power consumption and two-tier interference issues are two bottlenecks that hinder further development. This paper proposes a novel reconfigurable intelligent surface (RIS)-based SCN in which an RIS serves multiple micro users as a small cell base station while assisting the macro user’s transmission. Compared to the conventional SCNs, the RIS-based SCN can achieve significant power reduction. Meanwhile, the reflected signal can be regarded as a multipath component instead of interference to the macro user. We propose two transmission schemes and formulate the design of the phase shift matrix at the RIS and the beamforming vector at the macro base station as an optimization problem. The alternating optimization algorithm is developed to optimize the phase shift matrix and the beamforming vector to minimize the total power consumption under the user rate and phase shift constraints. Simulation results show that the total power consumption can be reduced significantly by deploying the RIS in the SCN when the number of reflective elements is sufficiently large.

Symbiotic radio (SR) has emerged as a spectrum-and energy-efficient paradigm to support massive Internet of Things (IoT) connections. Two multiple access schemes are proposed in this paper to facilitate the massive IoT connections using the cellular network based on the SR technique, namely, the simultaneous access (SA) scheme and the selection diversity access (SDA) scheme. In the SA scheme, the base station (BS) transmits information to the receiver and multiple IoT devices simultaneously transmit their messages by passively backscat-tering the BS signal to the receiver, while in the SDA scheme, only the IoT device with the strongest backscatter link transmits information to the receiver. The receiver jointly decodes the information from the BS and the IoT devices. To evaluate the above two schemes, we derive the closed-form expressions of the ergodic rates for both schemes. Finally, numerical results are provided to verify the theoretical analysis and compare the proposed two multiple access schemes. When the number of IoT devices is small, the SDA scheme is more appealing since it can significantly reduce computational complexity while achieving equivalent performance to the SA scheme.

In a backscatter communication system, the backscatter device (BD) transmits its messages to the backscatter receiver (BR) by reflecting the incident signal from an external radio frequency (RF) emitter, instead of using power-hungry active RF components themselves. Thus, backscatter communication has shown great potential for achieving low-power communication. The double-fading effect associated with the backscatter link, however, is a major limiting factor to achieve efficient backscatter communication. Reconfigurable intelligent surfaces (RISs), a recently developed technology, can be applied at the BD to enhance the backscatter link thanks to the fact that both RIS and backscatter communication share the same reflective principle. Such a design can also allow the backscatter communication system to capture the desired RF signal as a reflective carrier in a complex radio environment. In this article, a comprehensive overview of backscatter communication assisted by RIS is given. We first introduce the basics of backscatter communication, which covers the antenna scattering principle, backscatter modulation, and link budget calculation. Then, the details of RIS are discussed, which include antenna-based RIS and metamaterial-based RIS, followed by the discussion of the roles of RIS in backscatter communication. After that, we provide an overview of three types of backscatter communication systems assisted by RIS, including RIS-assisted unmodulated backscatter communication, RIS-assisted ambient backscatter communication, and RIS-assisted symbiotic radio. Emerging applications of these systems, technical challenges, and future opportunities in this emerging field are also presented.

Small cell network (SCN) is a promising solution to meet the demand for increasing data traffic for the sixth generation and beyond wireless networks. However, the power consumption and two-tier interference issues are two bottlenecks that hinder its further development. In this paper, we propose a novel intelligent reflecting communication (IRC) system in which a reconfigurable intelligent surface (RIS) is used to serve multiple micro users in an SCN while assisting the transmission from a macro base station (MBS) to a macro user. Compared to the conventional SCN, the RIS can achieve significant power reduction as it transmits the information by passively reflecting the incident signals. In addition, the reflected signal can be regarded as a multipath component instead of an interference to the macro user. We are interested in minimizing the total power consumption by jointly designing the phase shift matrix at the RIS and the beamforming vector at the MBS under the user rate constraints and the practical phase shift constraints. The solution is obtained by alternating optimization to iteratively solve two subproblems, one to optimize the phase shift matrix, and the other to optimize the beamforming vector. A TDMA transmission scheme is also proposed as an alternative to serve multiple users. Simulation results demonstrate that the total power consumption can be reduced significantly by deploying the RIS in the SCN when the number of reflecting elements is sufficiently large.

In this paper, we consider a reconfigurable intelligent surface (RIS)-assisted two-way relay network, in which two users exchange information through the base station (BS) with the help of an RIS. By jointly designing the phase shifts at the RIS and beamforming matrix at the BS, our objective is to maximize the minimum signal-to-noise ratio (SNR) of the two users, under the transmit power constraint at the BS. We first consider the single-antenna BS case, and propose two algorithms to design the RIS phase shifts and the BS power amplification parameter, namely the SNR-upper-bound-maximization (SUM) method, and genetic-SNR-maximization (GSM) method. When there are multiple antennas at the BS, the optimization problem can be approximately addressed by successively solving two decoupled subproblems, one to optimize the RIS phase shifts, the other to optimize the BS beamforming matrix. The first subproblem can be solved by using SUM or GSM method, while the second subproblem can be solved by using optimized beamforming or maximum-ratio-beamforming method. The proposed algorithms have been verified through numerical results with computational complexity analysis.

Reconfigurable Intelligent Surface (RIS) is a new and promising technique to solve the energy-efficiency, spectral-efficiency and hardware-cost problem faced by beyond-5G wireless networks. In this paper, we consider a RIS-assisted two-way relay network in which two users exchange information via the base station (BS) with the help of a RIS. By jointly designing the beamforming matrix at the BS and the phase shifts introduced by the RIS, the minimum SNR of the two users is maximized, under the transmit power constraint at the BS. The formulated problem is non-convex and difficult to solve in general. To start with, we first study the single BS antenna case. The design problem is reduced to the problem of how to choose the phase shifts for the RIS, in addition to optimizing the BS power amplification parameter. A Channel-Gain-Maximization (CGM) algorithm is proposed to solve the problem. For the multiple BS antenna case, we decouple the phase shifts and the beamforming matrix by taking an upper bound of the SNR. The way to obtain the RIS phase shifts is similar to the single BS antenna case while the beamforming matrix is obtained by utilizing an existing solution. A Channel-Gain-Maximization Maximal-Ratio-Beamforming (CGM-MRB) algorithm is thus developed. Finally, numerical results are presented to show the effectiveness of the proposed algorithms.

Intelligent reflecting surface (IRS), with a large number of reflective elements, is a promising technology to achieve both spectrum and energy efficient wireless communication. The IRS can reflect the incident electromagnetic wave passively and steer it to the desirable way before reaching the intended receiver by adjusting the phase shift on the reflective elements. In order to better improve communication quality, the beamforming vector at the base station (BS) and the phase shift induced by the IRS should be jointly designed carefully. However, thus far, previous works on IRS have assumed that the channel state information (CSI) is perfectly known at the BS, which is not available in the practical systems. In this paper, we study an IRS-enhanced multi-user multiple-input single-output (MISO) downlink communication system assuming imperfect CSI. An optimization problem is formulated to jointly optimize the beamforming vector at the BS and the phase shift at the IRS such that the total transmit power is minimized under the individual outage probability constraints. An algorithm based on alternating optimization (AO) and semi-definite relaxation (SDR) is proposed to solve this challenging non-convex problem. Finally, numerical results have validated the effectiveness of the proposed algorithm.