This article investigates the uplink performance of reconfigurable intelligent surface (RIS)-aided cell-free (CF) massive multiple-input-multiple-output (mMIMO) systems over spatially correlated Rayleigh fading channels. We consider multiple RISs and low-resolution analog-to-digital converters (ADCs) to improve the system energy efficiency (EE). We first provide an aggregated channel estimation technique with less pilot overhead. By exploiting the statistical channel state information (CSI), we further optimize the RISs’ phase shifts with the goal of minimizing the total normalized mean square error (NMSE) of the estimated aggregated channels. Subsequently, we derive the closed-form expression of the uplink spectral efficiency (SE) for quantization-aware minimum mean-square error (MMSE) combining. Third, based on the closed-form SE expression and power consumption model, we formulate and solve an optimization problem that maximizes the uplink EE under the constraints of transmit power and total ADC quantization bits. Specifically, by leveraging the Dinkelbach transform, Lagrangian dual transform, and fractional programming (FP) techniques, an alternating optimization (AO)-based algorithm is proposed to jointly obtain the bit allocation (BA) scheme among all access points (APs) and the uplink power control (PC) strategy for all users. Finally, numerical results validate the correctness of the closed-form SE expression and show the effectiveness of the proposed optimization methods for phase shift design and EE maximization.

Movable antenna (MA) is a promising technology that can enhance communication performance by properly adjusting the antenna position within a local region at transceivers. To further explore the potential of an antenna array, this article proposes a new rotatable and movable antenna (RMA) architecture where the antenna array at a base station (BS) not only employs multiple MAs but also is capable of being rotated along its yaw, pitch, and roll angles. In this context, we first characterize the wireless channel with respect to different rotation angles and antenna positions and formulate an optimization problem to maximize the downlink sum rate under practical system constraints. Subsequently, we solve the non-convex problem for single-user and multi-user scenarios, respectively. In particular, for the single-user case, we optimize the rotation angles and antenna positions to maximize the user’s rate and propose a gradient ascent (GA) algorithm based on the alternating optimization (AO) framework. For the multi-user scenario with the purpose of maximizing the sum rate of all users, we make the original problem more tractable by exploiting the Lagrangian dual transform and fractional programming (FP) techniques. On this basis, a GA-based algorithm is also proposed to jointly optimize the rotation angles and MAs’ positions together with the precoding matrix at the BS in an iterative manner. Finally, numerical results show that the RMA architecture can improve the sum rate by using the proposed algorithm to adjust rotation angles and antenna positions, compared to the element-level MA, rotatable antenna, and fixed-position antenna. Moreover, the proposed optimization algorithm outperforms its counterparts in achieving a trade-off between performance and computational complexity.

This paper concentrates on cell-free massive multiple-input and multiple-output (MIMO) network with variable-resolution analog-to-digital converters (ADCs). In such an architecture, all ADCs equipping at any access point (AP) can use arbitrary bit resolution to realize adaptive quantization and reduce power consumption. Under this circumstance, we first introduce a quantization-aware channel estimator based on linear minimum mean-square error (LMMSE) theory. On this basis, intra-AP and inter-AP bit allocation problems are investigated to maximize channel estimation quality subject to the total number of quantization bits. By leveraging the statistical characteristics of the estimated channels and estimation errors, we then derive the theoretical expressions of the achievable uplink spectral efficiency (SE) for maximal ratio combining (MRC) and minimum mean-square error (MMSE) combining, respectively. Furthermore, to maximize the sum SE under the constraint of total ADC quantization bits, we also investigate intra-AP and inter-AP bit allocation problems for both single-user and multi-user scenarios. Finally, simulation results confirm that our theoretical analyses are correct and accurate. In addition, we resort to numerical results to achieve some new insights and verify the advantages and conclusions pertinent to the proposed bit allocation techniques.