Integrating reconfigurable intelligent surface (RIS) into cell-free massive multiple-input multiple-output (MIMO) is a promising approach to enhance the coverage quality, spectral efficiency (SE), and energy efficiency. In this paper, an RIS-assisted cell-free massive MIMO downlink system suffering from the transceiver hardware impairments (T-HWIs) is investigated. To improve the accuracy of the direct estimation (DE) scheme, a modified ON/OFF estimation (MOE) with moderate pilot overhead is proposed. Relying on the knowledge of imperfect channel state information, we derive closed-form expressions of the lower-bound achievable SE with T-HWIs under both DE and MOE schemes. The closed-form results facilitate the investigation of how RIS improves the downlink SE under various system settings and allow us to explore the trade-off strategies between using more hardware-impaired APs and low-cost RISs in terms of the downlink SE and power consumption. Numerical results validate the theoretical analysis and show that the proposed MOE scheme outperforms the DE scheme in terms of the downlink SE. Moreover, the benefits of introducing RIS into hardware-impaired cell-free massive MIMO systems are also illustrated.

This paper considers an uplink cell-free massive multi-input multi-output (mMIMO) system with multi-antenna access points (APs) and users, assuming low-resolution analog-to-digital converter (ADC) architecture is employed at the APs. Leveraging on the additive quantization noise model (AQNM), we derive a tight approximate expression for uplink spectral efficiency (SE). This trackable finding provides us with a tool for easily quantifying the impacts of the number of antenna arrays and the number of quantization bit of low-resolution ADCs. We find 5-bit is required in a cell-free mMIMO with low-resolution ADCs to achieve the same SE as a cell-free mMIMO with full-precision ADCs. Besides, when the number of antennas of the user is small, deploying more antennas at the users can boost the sum SE. Then, to further highlight the potential of low-resolution ADCs architecture, we also investigate the tradeoff between the SE and energy efficiency (EE) with design issues surrounding the quantization bit of low-resolution ADCs and the number of antenna arrays. The resulting observations reveal that by choosing a proper quantization bit, the cell-free mMIMO with low-resolution ADCs has the capability to enjoy a better SE-EE tradeoff compared to the perfect ADCs counterpart.

This paper investigates the secure transmission in downlink cell-free massive multiple-input multiple-output (MIMO) systems in the presence of an active multi-antenna eavesdropper (Eve) over Rician fading channels, assuming that each access point (AP) possesses multiple antennas which are connected with low-resolution digital-to-analog converters (DACs). Closed-form expressions of the achievable secrecy rate relied on the additive quantization noise model are derived. Based on these analytical results, we quantify the impacts of key system parameters, such as the antenna array number, DAC resolution, Rician $\mathcal K$ -factor, and balance factor between data and artificial noise power on secrecy enhancement. Several interesting insights are attained by assuming that Eve can or cannot perfectly remove inter-mobile-terminal interference. Moreover, we also propose a power control algorithm that maximizes the achievable secrecy rate, which can be represented as a series of second-order-cone programs for which efficient solvers exist. All the theoretical analyses and the effectiveness of the proposed algorithm are corroborated by simulation experiments.

In the realm of industrial Internet of Things, the imperative for ultra-reliable and low-latency communication (URLLC) is underscored by the demand for up to 99.999% reliability and sub-microsecond latency. In this paper, we delve into a downlink cell-free massive multiple-input multiple-output (MIMO) system designed to facilitate URLLC, operating over spatially correlated Rician fading channels with inherent phase shifts. Utilizing short-packet transmission and accounting for imperfect channel state information, we derive stringent closed-form expressions for the lower-bound achievable rates, considering both phase-aware and phase-unaware minimum mean squared error estimations. Employing these expressions, we execute an in-depth performance analysis across diverse system configurations, including the availability of phase shifts and the counts of access points (APs), connected devices, antennas per AP, and pilot sequences. Additionally, we propose a path-following power control algorithm that employs geometric programming to enhance the downlink sum-rate. This algorithm is meticulously designed to meet the stringent latency and reliability requirements of URLLC for all connected devices. The theoretical underpinnings and the efficacy of the proposed power control algorithm are substantiated through extensive simulations.

This letter investigates the physical layer security of reconfigurable intelligent surface (RIS) aided integrated-sensing-and-communication (ISAC) systems, in which the high-power radar signal is used as the interference to disturb the eavesdropper. We formulate a joint beamforming optimization problem of active beamforming of base station and passive beamforming of RIS to minimize the maximum eavesdropping signal-to-interference-plus-noise (SINR). To find its solution, we decompose the original problem into two subproblems and then use the alternating optimization (AO) technique based on the fraction programming (FP) and semi-definite programming (SDP) methods. Simulation results show that the proposed FP-SDP-AO algorithm has good convergence performance and can effectively suppress the SINR of eavesdroppers while maintaining a satisfactory quality of service for legitimate users.