In this paper, we investigate a novel uplink reconfigurable intelligent surface (RIS) based multi-user multi-input multi-output symbiotic radio system. It indicates that each RIS, as an Internet-of-Things (IoT) device, enhances the primary transmission from a nearby user to the base station (BS), and simultaneously transmits its own information to the BS by backscattering modulation. By embedding environmental sensors on the RISs, the proposed system enables the IoT transmission of locally collected environmental data to the BS while assisting the primary communications from the users to the BS. We consider both the case of perfect and imperfect channel state information (CSI), and design the active beamforming at the BS and the passive beamforming at the RISs jointly to maximize the weighted sum-rate of both the primary and IoT transmissions. For the perfect CSI case, we propose an algorithm based on the block coordinate descent (BCD) method to solve the problem. We also propose another algorithm with a similar framework to reduce the computational complexity. For the imperfect CSI case, an algorithm based on BCD and the online successive convex approximation technique is proposed. Simulation results show that the proposed system achieves significant performance gain over a number of baseline schemes for both the perfect and imperfect CSI cases. Furthermore, when the channel estimation error is small, the performance loss due to imperfect CSI is insignificant.

In this paper, we investigate a reconfigurable in-telligent surface (RIS)-based uplink massive multi-input multi-output symbiotic radio system, where each RIS, as an IoT device, enhances the primary transmission from a nearby user to the base station (BS) and simultaneously transmits its own infor-mation to the BS by backscattering modulation. By embedding environmental sensors on the RISs, the proposed system enables the IoT transmission of locally collected environmental data to the BS while assisting the primary transmission. Assuming imperfect channel state information (CSI), we jointly design the active beamforming at the BS and the passive beamforming at the RISs to maximize the weighted sum-rate of both the primary and IoT transmissions. An algorithm based on the block coordinate descent method is proposed to solve it. Simulation results show that the proposed system achieves significant performance gain compared with different baseline schemes. Besides, when the channel estimation error is small, the performance loss due to imperfect CSI is insignificant.

To support massive access for future wireless communications, we propose a novel intelligent reflecting surface (IRS) enhanced downlink multi-user multi-input single-output (MU-MISO) symbiotic radio (SR) system, where each IRS, acting as a reflecting Internet-of-Things (IoT) device, transmits its message to a nearby primary receiver (PR) by reflecting the RF signals from the primary transmitter (PT), and simultaneously enhances the transmission from the PT to the associated PR. Thus, each PR jointly decodes its own message as well as the one from the corresponding IRS. We are interested in maximizing the weighted sum-rate of both primary and IoT transmissions by jointly designing the active transmit beamforming at PT and the passive beamforming at each IRS, subject to the maximum transmit power constraint at PT. Besides, as the passive elements at IRS can only reflect the incident signal with discrete phase shifts in practice, the discrete reflection coefficient (RC) constraint is further considered at the IRSs. Due to the non-convexity of the formulated problems, we solve them with fractional programming (FP) technique and alternating optimization (AO) method. Simulation results have verified the effectiveness of the proposed algorithms compared to different benchmark schemes.

To support massive access for future wireless networks, we propose a novel reconfigurable intelligent surface (RIS)-enhanced downlink multi-user multi-input single-output (MU-MISO) symbiotic radio (SR) system. In the proposed system, each RIS not only enhances the primary transmission from the primary transmitter (PT) to the associated primary receiver (PR) nearby, but also acts as an Internet-of-Things (IoT) device to enable IoT transmissions to the same PR. Therefore, each PR needs to jointly decode the information from both the PT and its corresponding RIS. We are interested in maximizing the weighted sum-rate of both primary and IoT transmissions by jointly designing the active transmit beamforming at PT and the passive beamforming at each RIS under the maximum transmit power constraint at the PT and various constraints on the reflection coefficients (RCs), which include the ideal, continuous-phase and the discrete-phase cases. The formulated problem is non-convex, which cannot be solved directly. Thus, fractional programming (FP) method and alternating optimization (AO) technique are adopted to tackle the problem. In particular, three low-complexity algorithms are proposed to trade off between computational complexity and convergence rate. Compared to different benchmark schemes, simulation results demonstrate that with the aid of the RISs, the PRs can benefit from the enhanced primary transmission from the PT, and receive information from the associated RISs via IoT transmission.