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Register a new userErgodic Rate Analysis of Reconfigurable Intelligent Surface-Aided Massive MIMO Systems with ZF Detectors
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This letter investigates the reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) systems with a two-timescale design. First, the zero-forcing (ZF) detector is applied at the base station (BS) based on instantaneous aggregated CSI, which is the superposition of the direct channel and the cascaded user-RIS-BS channel. Then, by leveraging the channel statistical property, we derive the closed-form ergodic achievable rate expression. Using a gradient ascent method, we design the RIS passive beamforming only relying on the long-term statistical CSI. We prove that the ergodic rate can reap the gains on the order of $mathcal{O}left(log_{2}left(MNright)right)$, where $M$ and $N$ denote the number of BS antennas and RIS elements, respectively. We also prove the striking superiority of the considered RIS-aided system with ZF detectors over the RIS-free systems and RIS-aided systems with maximum-ratio combining (MRC).
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We investigate a reconfigurable intelligent surface (RIS)-aided multi-user massive multiple-input multi-output (MIMO) system where low-resolution digital-analog converters (DACs) are configured at the base station (BS) in order to reduce the cost and power consumption. An approximate analytical expression for the downlink achievable rate is derived based on maximum ratio transmission (MRT) and additive quantization noise model (AQNM), and the rate maximization problem is solved by particle swarm optimization (PSO) method under both continuous phase shifts (CPSs) and discrete phase shifts (DPSs) at the RIS. Simulation results show that the downlink sum achievable rate tends to a constant with the increase of the number of quantization bits of DACs, and four quantization bits are enough to capture a large portion of the performance of the ideal perfect DACs case.
Reconfigurable intelligent surfaces (RISs) have emerged as a promising technique to enhance the system spectral efficiency. This letter investigates the ergodic channel capacity (ECC) of an RIS-aided multiple-input multiple-output channel under the assumption that the transmitter-RIS, RIS-receiver, and transmitter-receiver channels contain deterministic line-of-sight paths. Novel expressions are derived to characterize the upper and lower bounds of the ECC. To unveil more system insights, asymptotic analyses are performed to the system ECC in the limit of large signal-to-noise ratio (SNR) and number of reflecting elements (REs). Theoretical analyses suggest that the RISs deployment can shape the ECC curve by influencing its high-SNR power offset and the ECC can get improved by increasing the number of REs.
This paper investigates the two-timescale transmission design for reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) systems, where the beamforming at the base station (BS) is adapted to the rapidly-changing instantaneous channel state information (CSI), while the passive beamforming at the RIS is adapted to the slowly-changing statistical CSI. Specifically, we first propose a linear minimum mean square error (LMMSE) estimator to obtain the aggregated channel from the users to the BS in each channel coherence interval. Based on the estimated channel, we apply the low-complexity maximal ratio combining (MRC) beamforming at the BS, and then derive the ergodic achievable rate in a closed form expression. To draw design insights, we perform a detailed theoretical analysis departing from the derived ergodic achievable rate. If the BS-RIS channel is Rician distributed, we prove that the transmit power can be scaled proportionally to $1/M$, as the number of BS antennas, $M$, grows to infinity while maintaining a non-zero rate. If the BS-RIS channel is Rayleigh distributed, the transmit power can be scaled either proportionally to $1/sqrt{M}$ as $M$ grows large, or proportionally to $1/N$ as the number of reflecting elements, $N$, grows large, while still maintaining a non-zero rate. By capitalizing on the derived expression of the data rate under the statistical knowledge of the CSI, we maximize the minimum user rate by designing the passive beamforming at the RIS. Numerical results confirm that, even in the presence of imperfect CSI, the integration of an RIS in massive MIMO systems results in promising performance gains. In addition, the obtained results reveal that it is favorable to place the RIS close to the users rather than close to the BS.
This paper investigates the reconfigurable reflecting surface (RIS)-aided multiple-input-single-output (MISO) systems with imperfect channel state information (CSI), where RIS-related channels are modeled by Rician fading. Considering the overhead and complexity in practical systems, we employ the low-complexity maximum ratio combining (MRC) beamforming at the base station (BS), and configure the phase shifts of the RIS based on long-term statistical CSI. Specifically, we first estimate the overall channel matrix based on the linear minimum mean square error (LMMSE) estimator, and evaluate the performance of MSE and normalized MSE (NMSE). Then, with the estimated channel, we derive the closed-form expressions of the ergodic rate. The derived expressions show that with Rician RIS-related channels, the rate can maintain at a non-zero value when the transmit power is scaled down proportionally to $1/M$ or $1/N^2$, where $M$ and $N$ are the number of antennas and reflecting elements, respectively. However, if all the RIS-related channels are fully Rayleigh, the transmit power of each user can only be scaled down proportionally to $1/sqrt{M}$ or $1/N$. Finally, numerical results verify the promising benefits from the RIS to traditional MISO systems.
In this work, we investigate a novel simultaneous transmission and reflection reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output downlink system, where three practical transmission protocols, namely, energy splitting (ES), mode selection (MS), and time splitting (TS), are studied. For the system under consideration, we maximize the weighted sum rate with multiple coupled variables. To solve this optimization problem, a block coordinate descent algorithm is proposed to reformulate this problem and design the precoding matrices and the transmitting and reflecting coefficients (TARCs) in an alternate manner. Specifically, for the ES scheme, the precoding matrices are solved using the Lagrange dual method, while the TARCs are obtained using the penalty concave-convex method. Additionally, the proposed method is extended to the MS scheme by solving a mixed-integer problem. Moreover, we solve the formulated problem for the TS scheme using a one-dimensional search and the Majorization-Minimization technique. Our simulation results reveal that: 1) Simultaneous transmission and reflection RIS (STAR-RIS) can achieve better performance than reflecting-only RIS; 2) In unicast communication, TS scheme outperforms the ES and MS schemes, while in broadcast communication, ES scheme outperforms the TS and MS schemes.
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