In the context of Internet of Things (IoT) networks, this work investigates an active reconfigurable intelligent surface (RIS)-aided cell-free massive multiple-input multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) system and establishes an integrated analytical frame-work that rigorously characterizes both the inherent double-fading effect of RIS-assisted propagation and the power-gain coupling introduced by the active RIS. A weighted sum-rate (WSR) maximization problem is then formulated under the constraints of access point (AP) power budgets, the RIS power consumption limitations, and the receiver energy harvesting requirements. To manage the resulting highly non-convex structure, the original problem is equivalently reformulated as a weighted minimum mean squared error (WMMSE) minimization problem, which enables the development of an efficient alternating optimization algorithm. At each iteration, the MMSE decoding vectors are firstly updated in closed form, while the energy signal covariance matrices, the AP information beamformers, and the active RIS reflection coefficients are subsequently optimized via semi-definite programming, successive convex approximation, and augmented Lagrangian method, respectively. Extensive simulation results demonstrate that the adoption of an active RIS effectively mitigates the double-fading effect and achieves performance comparable to that of passive RIS with significantly fewer reflecting elements, while the proposed joint optimization strategy yields substantial WSR gains and further enlarges the performance gap between the optimized active and passive RIS schemes.

Thanks to the customization of channel propagation, reconfigurable intelligent surface (RIS)-aided cell-free (CF) massive multiple-input-multiple-output (MIMO) is recognized as a competitive candidate technique for the future communication system. However, only the limited performance gain can be afforded by passive RIS due to the double-fading effect in RIS-aided links. In this article, we consider the CF massive MIMO system with the assistance of the active RIS, which is capable of reflecting and amplifying the incident signal, to enable the Internet of Things network. We analyze the tradeoff between the number of active RIS reflecting elements (REs) and the amplification coefficient. Considering the power constraint at the active RIS, we formulate a sum-rate maximization problem to jointly optimize the user transmission power, the receive beamforming, and the RIS reflecting precoding. Since the original problem is nonconvex, we decouple it into three subproblems and then design an alternating optimization algorithm to solve them iteratively. Using the Lagrangian dual reformulation and generalized Rayleigh quotient theory, we derive the closed-form solutions for both the user transmission power and the uplink receive beamforming. We also develop a low-complexity method to acquire the RIS reflecting precoding based on the primal-dual subgradient theory. Compared to the passive RIS, the active RIS can significantly improve the system performance with fewer REs. Moreover, the proposed optimization scheme effectively mitigates the drawbacks of the active RIS under the high-transmission power regime and enhance its benefits. Finally, the proposed alternating optimization algorithm is validated by numerical results.

We investigate the spectral and energy efficiencies of the uplink in an integrated satellite-terrestrial cell-free massive multiple-input multiple-output (IST-CF-mMIMO) system assisted by rate-splitting multiple access (RSMA). In the IST-CF-mMIMO system, the terrestrial users employ RSMA to transmit a message as a superposition of two parts with different power to the terrestrial access points and low-Earth-orbit satellite. Taking realistic conditions such as the spatially correlated Ricean fading channels, imperfect channel knowledge, and successive interference cancellation into account, we derive rigorous closed-form expressions for uplink achievable spectral and energy efficiencies and evaluate these performance metrics across a range of system configurations. Additionally, to enhance the system energy efficiency, we formulate the design of users’ power control coefficients as an energy efficiency optimization problem and design an efficient algorithm based on Lagrangian dual transformation and quadratic transformation techniques to solve it. Comprehensive simulations validate our theoretical propositions and evaluate the efficacy of the proposed energy efficiency maximization algorithm.

This paper explores the potential of employing rate-splitting multiple access to enhance the achievable rate and energy efficiency (EE) of an uplink cell-free massive multiple-input multiple-output (MIMO) system, where the access points (APs) are configured with low-resolution analog-to-digital converters (ADCs) to minimize the hardware expense and power consumption. Taking the large-scale fading decoding, ADC quantization, and imperfect successive interference cancellation into consideration, a rigorous closed-form rate expression is derived within Ricean fading environments. This analytical framework facilitates an in-depth analysis of the rate performance with respect to various system parameters. To quantify the benefits of low-resolution ADCs, a power consumption model is subsequently incorporated into the analysis, facilitating an evaluation of the system’s EE. Furthermore, the optimization of power control coefficients and receiver weights is tackled through the formulation of weighted sum-rate (WSR) and EE maximization problems. Two efficient alternative algorithms are then proposed to determine their optimal solutions. The theoretical propositions and the efficacy of the proposed WSR and EE optimization algorithms are substantiated through comprehensive simulations.

In the time-division-duplexing (TDD)-based cell-free (CF) massive multiple-input multiple-output (MIMO) system, the channel reciprocity needs to be recovered via a reciprocity calibration operation due to the random circuit impact on the transceiver radio frequency. In this paper, we study the effect of the calibration error on the TDD CF massive MIMO system under non-ideal calibrations. Assuming the spatially correlated Ricean fading channel, we derive the closed-form expression of the downlink achievable rate, which takes both the channel estimation error and the calibration error into account. Some novel insights of the calibration error in the CF massive MIMO system are gathered from the analytical results. It is shown that the downlink achievable rate is more sensitive to the calibration error at the user side. In order to provide a uniformly good service for each user, we employ the geometric programming (GP) to solve the max-min power optimization problem to maximize the minimum user rate. Additionally, we utilize the scaled alternating direction method of multipliers to develop a calibration error-aware beamforming scheme to mitigate the impact of calibration errors, improving the downlink sum-rate. Numerical results demonstrate that the proposed GP-based algorithm significantly improves the 95%-likely per-user downlink achievable rate with a fast convergence behavior. Moreover, the proposed calibration error-aware beamforming scheme enhances the downlink sum-rate and outperforms other benchmark schemes.

We explore the synergistic advantages of integrating intelligent reflecting surface (IRS) with rate splitting multiple access (RSMA) in a downlink cell-free massive multiple-input multiple-output (MIMO) system to meet the stringent requirements of ultra-reliable and low-latency communications. Taking into account the estimation errors, statistical channel knowledge, finite blocklength, and spatial correlation among IRS elements, a tight closed-form expression for the achievable rate is derived, which serves as a tool for evaluating the achievable rate across various system configurations. To enhance the weighted sum-rate (WSR) while adhering to the latency and reliability constraints, we formulate a joint WSR maximization problem with respect to both IRS phase shifts and power control coefficients. Given the non-convex nature of this problem, we develop an alternating optimization strategy that decouples the original problem into two distinct sub-problems. Specifically, the IRS phase shift design is reformulated as a min-max normalized mean squared error problem, enabling an efficient closed-form solution, whereas the power control optimization is addressed using a geometric programming approach. Numerical results validate the synergistic gain of integrating IRS with RSMA in terms of achievable rate and demonstrate that the proposed optimization scheme significantly enhances the WSR while fulfilling the latency and reliability requirements.

This paper investigates secure transmission in the uplink of a cell-free massive multiple-input multiple-output (MIMO) system employing three distinct multiple access strategies: rate-splitting multiple access (RSMA), non-orthogonal multiple access (NOMA), and space-division multiple access (SDMA). RSMA, functioning as a unifying paradigm, merges the merits of both NOMA and SDMA, and holds substantial promise for enhancing system secrecy. We derive closed-form expressions for secrecy spectral efficiency (SE) under Rician fading channels and imperfect channel knowledge assumptions. The secrecy SE is subsequently evaluated across a range of system configurations, encompassing varying access point (AP) and user numbers, AP and eavesdropper antenna dimensions, line-of-sight probabilities, successive interference cancellation conditions, and multiple access protocols. Harnessing these expressions, we establish an optimization framework for the users’ power control coefficients and APs’ receiving weights to maximize the sum secrecy SE while ensuring quality-of-service secrecy requirements for users. Additionally, an alternative optimization algorithm is proposed to ascertain a high-quality solution. Comprehensive simulations substantiate our theoretical propositions and evaluate the efficacy of the proposed sum secrecy SE maximization algorithm.

In this paper, we consider the cell-free massive multiple-input multiple-output (MIMO) system for supporting ultra-reliable and low-latency communication (URLLC) transmission, where a large number of access points (APs) serve a small number of users in the short packet regime. Assuming channel aging and channel spatial correlation, we derive the closed-form expression of the downlink achievable rate with the normalized conjugate beamforming (NCB). Under the goal of maximizing the minimum user rate, we formulate a max-min power optimization problem with a power constraint at each AP. However, it is challenging to solve this problem because the objective function is a complicated function of power coefficients. To tackle this difficulty, we use a path-following method to approximate the objective function to a logarithmic function and transform the polynomial constraint into a monomial. Thus, we can iteratively solve the original problem by reformulating it as a series of geometric programming problems. Numerical results verify the tightness of the closed-form expression for the downlink achievable rate in the short packet regime. Both channel aging and channel spatial correlation significantly degrade the system performance of CF massive MIMO URLLC systems. Moreover, Using NCB and the proposed max-min power allocation can effectively alleviate this impairment and improve the system performance.

In the context of Internet of Things, this letter considers a cell-free (CF) massive multiple-input–multiple-output (MIMO) system for ultrareliability and low-latency communication (URLLC) assisted by multiple reconfigurable intelligent surfaces (RISs). We derive the closed-form expression of the downlink achievable rate under multiple correlated RISs and pilot contamination. To mitigate the impact of pilot contamination and improve the fairness among users, we minimize the maximum normalized mean-squared error (NMSE) of the channel estimation by jointly optimizing the pilot power coefficient and the RIS phase shifts. Due to the nonconvexity of the original problem, we design an alternating optimization algorithm to solve the substitutable two subproblems using fractional programming and sequential convex approximation. Numerical results validate the proposed algorithm in terms of decreasing the maximum user NMSE and converging. Moreover, the 95%-likely per-user downlink achievable rate is also improved.

This letter establishes a novel transmission framework that amalgamates downlink (DL) training with rate-splitting multiple access, thereby being expected to enhance the spectral efficiency (SE) of a cell-free massive multiple-input multiple-output enabled Internet of Things (IoT) system. Considering a correlated Ricean fading environment coupled with imperfect channel knowledge, we derive a closed-form expression for the achievable SE and evaluate the DL SE under a variety of system configurations. Our comprehensive simulations corroborate the theoretical findings and yield critical insights pertinent to the system’s architectural design.

Rate-splitting multiple access (RSMA) has emerged as a potent paradigm shift in wireless communications, demonstrating resilience to channel state information (CSI) inaccuracies and significant rate enhancements. This work investigates RSMA’s application within the context of ultra-reliable and low-latency communication (URLLC) for the forthcoming Internet-of-Everything networks. Specifically, we integrate RSMA with a cell-free massive multiple-input multiple-output (MIMO) architecture to support URLLC demands. Considering the imperfect CSI, attributable to pilot contamination and thermal noise, we derive rigorous lower-bound expressions for the downlink achievable rates. These expressions are applicable to short-packet communication scenarios and RSMA strategy over spatially correlated Rician fading channels. Utilizing these analytical expressions, we perform an exhaustive rate performance evaluation, varying system parameters such as the numbers of pilots, access points (APs), devices, and antennas per AP, alongside different multiple access techniques. Furthermore, we address the power control coefficient design for both common and private streams, framing it as an optimization problem aimed at maximizing the weighted sum-rate and enhancing URLLC service quality. To tackle this non-convex challenge, we introduce a geometric programming-based path-following algorithm, which iteratively converges to the solution. The theoretical underpinnings and the efficacy of the proposed power optimization algorithm are corroborated through extensive simulation results.

This paper investigates the achievable rate and spectral efficiency (SE) of an uplink cell-free massive multiple-input multiple-output Internet-of-Things (mMIMO-IoT) network over Ricean fading channels, where both access points and user equipments (UEs) are equipped with multiple antennas. We derive tight closed-form expressions for the lower-bound achievable rate and SE under maximum ratio combining and imperfect channel state information (CSI). Moreover, we propose a target-signal-to-interference-plus-noise-ratio-tracking opportunistic power control (TOPC) algorithm with gradual soft UE removal to mitigate the effects of unsupported UEs. The proposed TOPC algorithm is fully distributed, as each UE updates its transmit power based on local CSI. Numerical results show that adding more antennas at the UEs can enhance the achievable rate, but may degrade the achievable SE due to the increased pilot overhead. Moreover, the Ricean fading channels offer much higher achievable rate and SE than the Rayleigh fading channels, and our TOPC algorithm exhibits satisfactory performance in various aspects.

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.

In this paper, we investigate the secure transmission in the downlink of a cell-free massive multiple-input multiple-output (mMIMO) system that relies on rate-splitting multiple access (RSMA). We specifically evaluate the impact of hardware impairments (HWIs) originating from non-ideal access points (APs), user equipments (UEs), and Eavesdroppers (Eves) on the system’s secrecy performance. The investigation encompasses scenarios with both colluding and non-colluding Eves orchestrating pilot spoofing attacks against a designated UE, subsequently intercepting transmissions from both common and private streams. By taking into account a spatially correlated Ricean fading channel model and imperfect channel state information, we derive closed-form expressions for both legitimate and secrecy rates. The secrecy performance is scrutinized across different system configurations, including varying HWI levels, power splitting ratios, AP/Eve transmission powers, spatial correlations, line-of-sight components, and the presence of colluding versus non-colluding Eves. To enhance the secrecy rate for the compromised UE, we propose a secure power control strategy for adjusting the downlink transmission powers of the common and private streams. A sequential convex approximation-based algorithm is introduced to iteratively address this non-convex problem. Through comprehensive simulations, we validate our theoretical propositions and extract pivotal insights for system design.

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 investigates the uplink performance of a general cell-free massive multiple-input multiple-output (CF-mMIMO) system, in which all access points (APs) and user equipments (UEs) suffer from hardware impairments (HWIs). Besides, there are several reconfigurable intelligent surfaces (RISs) that aim to improve the coverage quality, spectral efficiency (SE), and energy efficiency (EE). Relying on the knowledge of only imperfect channel state information, a tight closed-form expression for the lower-bound achievable SE is derived. Based on this expression, we quantitatively investigate the impacts of different system parameters on uplink SE and EE, and conduct a tradeoff analysis between using more APs versus using more RISs with respect to the above performance metrics. In addition, we also design a max-min SE algorithm that takes into account both large-scale fading decoding weights and power control coefficients to guarantee UE fairness. Specifically, the proposed algorithm admits a closed-form solution and is therefore memory-efficient and time-saving. Both the theoretical analysis and the effectiveness of the proposed max-min SE algorithm are verified via extensive simulations.

Abstract Here, we investigate the precoding schemes for the downlink data transmission of a time‐division duplex cell‐free massive multiple‐input multiple‐output (MIMO) system with channel aging, which arises from the user mobility. Closed‐form spectral efficiency (SE) expressions of the downlink with the normalized conjugate beamforming (NCB), and the full‐pilot zero‐forcing (FZF), are derived, which are used for the analytical system performance evaluation. Then, a novel combinatorial precoding scheme with enhanced system SE performance, which adopts either NCB or FZF according to each user channel aging condition, is proposed. Moreover, a pilot allocation strategy is proposed to alleviate the extra interference brought by the combinatorial precoding scheme. Also, a statistical channel cooperative power control is employed to further improve the performance for all the above precoding schemes. Numerical results show that the proposed precoding scheme can substantially improve the average downlink SE.

In this article, the performance of simultaneous wireless information and power transfer (SWIPT) in downlink (DL) Internet of Things (IoT) networks relying on the cell-free massive multiple-input–multiple-output (CF-mMIMO) technique is investigated. In such a network, the access points (APs) beam the radio-frequency (RF) energy toward IoT sensors during the DL wireless power transfer phase. Tight closed-form expressions for DL harvested energy (HE) and achievable rate with conjugate beamforming (CB) and normalized CB (NCB) are, respectively, derived, which enable us to analyze the behaviors of CB and NCB schemes in terms of both HE and achievable rate. Apart from this, to guarantee sensor fairness with respect to the HE and achievable rate, a max–min power control strategy based on the accelerated projected gradient (APG) method is proposed. Specifically, the proposed APG-based power control is able to determine the optimal solution in closed form and is more memory efficient than the convex-solver-based counterpart. These analytical results as well as the effectiveness of the proposed power control policy are verified by experimental simulations.

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 this paper, we consider a downlink cell-free massive multi-input multi-output (mMIMO) system, assuming few-bit analog-digital converters (ADCs) and digital-analog converters (DACs) are implemented at the access points (APs). Leveraging on the linear additive quantization noise model (AQNM), we derive a tight approximate rate expression, which provides insights into the impacts of the imperfect quantization error and channel estimation error. Thanks to the trackable result, we quantitatively compare the performance differences between the two quantization models, namely the AQNM and the Bussgang theorem. In particular, the AQNM can offer analytical tractability for few-bit quantization while the Bussgang theorem only characterizes 1-bit quantization since the multi-bit quantization under the Bussgang theorem is difficult to deal with. Simulation results show that under the same 1-bit quantization, the rate performance with the Bussgang theorem is roughly identical to the case of the AQNM.

In this study, the uplink performance of cell‐free massive multi‐input multi‐output (mMIMO) system with multi‐antenna access points (APs) and users is investigated, assuming low‐resolution analogue–digital converters (ADCs) are employed at the APs. By exploiting the additive quantisation noise model, a tight closed‐form rate expression is derived. This tractable finding characterises the impacts of the multi‐antenna APs and users, the imperfect quantisation error and the channel estimation error. In order to maximise the uplink sum‐rate, a joint quantisation bit and power control problem is formulated, subjecting to the backhaul capacity and each user power constraints. The original resource optimisation problem is non‐convex and it is decomposed into two sub‐problems, namely quantisation bit design and power allocation problem, to alleviate the difficulties. In particular, the resultant two sub‐problems can be efficiently determined by utilising the Lagrange Multiplier and sequential convex approximation methods, respectively. Finally, numerical simulations are presented to examine the analytical findings and evaluate the effectiveness of the proposed algorithm.

We investigate the downlink rate performance of cell-free massive multiple-input multiple-output (mMIMO) network with conjugate beamforming precoder when the access points (APs) are equipped with one-bit analog-digital converters (ADCs) and digital-analog converters (DACs). Based on Buss-gang decomposition theory, we derive a rigorous closed-form rate expression, which covers the impact of multi-antenna APs, the imperfect quantization error, and channel estimation error. Then, by exploring this closed-form result, we show that the quantization interferences resulted from one-bit quantization can significantly decrease the downlink rate performance. In addition, we also analyze the performance gain provided by adding the total number of antennas or increasing the total transmitted power. We observe that, adding the total number of antenna arrays is a promising way to compensate for the quantization losses. However, these losses cannot be compensated by infinitely increasing the total transmitted power.

This paper explores the potential of physical layer security for K- tier heterogeneous networks (HetNets) with nonbest user association (UA) scheme. By modeling the spatial positions of network elements in each tier as homogeneous Poisson point processes (PPPs), we first present the non-best UA probability of a typical user equipment associating with the m-th average biased received power (ABRP) with passive eavesdroppers, then the expression of total secrecy probability for the K-tier HetNets is derived with stochastic geometry. Both the analytical and numerical results show that the implementation of the non-best UA scheme can significantly improve the secrecy probability, which indicates that non-best UA could be a promising solution for safeguarding K-tier HetNets. Moreover, we show that the secrecy probability with non-best UA scheme is not always decreasing over the transmission power of the base stations, which is quite different from the best ones.

In this paper, a multigroup multicast cell-free massive MIMO (CF-mMIMO) system with conjugate beamforming (CB) precoding is considered. In this system, M N-an-tennas access points (APs) distribute in the serving area and coherently serve J×K single-antenna users. All users are randomly divided into J multicast groups. A novel closed-form downlink rate's expression for any M, N, J, and K is derived, which motivates us to propose a weighted max-min power optimization algorithm. In designing this algorithm, the high rate requirements of the high priority groups and the egalitarianism principle are considered. Numerical results demonstrate that the performance improvement achieved by increasing N is visibly better than increasing M. Furthermore, the proposed weighted maxmin fairness algorithm performs well in many respects.