Motivated by the increasingly dense deployment of satellite constellations and the passive reflection capabilities of metasurface (MS), this paper proposes a novel satellite-mounted MS-enabled communication (MSC) architecture for beyond-line-of-sight transmission. Different from traditional methods that rely on active satellite relays, the proposed approach utilizes passive signal reflection from the satellite-mounted MS to overcome Earth’s curvature and extend communication range. To ensure the stability and efficiency of the highly dynamic reflection link, the architecture incorporates large-scale satellite selection and communication window prediction methods. The large-scale satellite selection method is developed based on three key geometric factors: the line-of-sight condition, the orbital inclination condition, and the specular reflection point condition, selecting satellites that are more likely to serve as the carriers of the MS for stable connections between transceivers. Meanwhile, the communication window prediction method estimates the probability of link outages and identifies reliable transmission periods, thereby avoiding ineffective link establishment attempts. Numerical results demonstrate the feasibility of the proposed MSC framework, achieving data rates on the order of tens of kbps over several thousand kilometers. The findings also highlight the critical roles of the proposed large-scale satellite selection and communication window prediction methods in enhancing both communication duration and data transmission, and further illustrate how frequency, MS size, satellite altitude, and attitude affect the performance of the MSC system.

In this paper, we propose to employ satellite-mounted reconfigurable intelligent surfaces (RIS) to passively reflect transmitted signals, thereby establishing a virtual line-of-sight link between distant ground stations. Specifically, we take into account the non-negligible scattering effects introduced by the large physical dimensions and high reflectivity of satellite payloads, such as antenna arrays (AAs) and solar arrays (SAs), on the performance of the RIS beamforming. To address this issue, we first develop a comprehensive reflection model that jointly considers the scattered fields of the RIS, AA, and SA, integrating wireless channel modeling with bistatic radar cross-section analysis. Based on this reflection model, we then derive the closed-form expressions for the outage probability and coverage performance, considering dynamic satellite motion and time-varying channel conditions. Moreover, we show how the optimal RIS beamforming for such conditions can be obtained. Numerical results show that, when deploying our proposed system onto the Starlink satellites, a rate of 60 kbps and a coverage area of more than 9.05×105km2can be achieved, demonstrating its applicability in real-world scenarios. The results provided in this paper also offer valuable guidelines for the design of practical RIS-enabled satellite systems.

The spectrum shift to high-frequency bands has posed an ever-increasing demand on the paradigm shift from narrowband beamforming to wideband beamforming. Despite recent research efforts, the problem of wideband beamforming design is particularly challenging in reconfigurable intelligent surface (RIS)-assisted systems, due to that RIS is not capable of performing frequency-dependent phase shift, therefore inducing high signal processing complexity. In this paper, we propose the space-frequency Fourier transformation (SFFT)-based wideband beamforming design for RIS-assisted systems. In the proposed design, we exploit SFFT and stationary phase method to yield an approximate closed-form solution of RIS phase shifts, which significantly reduces signal processing complexity. The obtained solution is then used to generate a large and flat beampattern over the desired frequency band to overcome the beam squint effect. Through numerical results, we validate the effectiveness of our proposed beamforming design and demonstrate how it can improve communication and sensing performance. Furthermore, the proposed method can be extended to generate any expected frequency-domain beampattern which provides valuable insights into the design of novel wideband beamforming for RIS-assisted systems.

Integrated sensing and communication (ISAC) is emerging as a key enabler of future wireless networks. However, existing analyses of ISAC performance commonly assume discrete-time systems, overlooking the impacts of temporal, spectral, and spatial properties. To address this limitation, we establish a unified information model for band-limited continuous-time ISAC systems. In this model, we employ a novel sensing performance metric called the sensing mutual information (SMI). Our analysis demonstrates how SMI serves as a bridge between the mutual information domain and the minimum mean squared error (MMSE) domain. Additionally, we characterize the communication mutual information (CMI)-SMI and CMI-MMSE regions to identify the performance bounds of practical ISAC systems and reveal the trade-off between communication and sensing performances. Moreover, through analysis and numerical results, we derive two valuable insights for the design of ISAC-enabled systems: i) communication prefers waveforms with random amplitude, sensing prefers waveforms with constant amplitude, and both benefit from waveforms with low correlations and random phases; ii) a linear positive proportional relationship exists between allocated time-frequency resources and the achieved communication rate/sensing accuracy.

Reconfigurable Intelligent Surface (RIS) is a promising low-cost technology for enhancing wireless communication coverage. It consists of passive reflecting elements that can adjust the phase shifts of incident signals. RIS is not only deployed on ground structures but also integrated with unmanned aerial vehicles, high-altitude platforms, and satellites. This enables the establishment of virtual line-of-sight (VLoS) links, thereby over-coming obstacles, extending communication range, and reducing long-distance path loss. However, satellite-mounted RIS faces challenges such as increased launch costs and the need for complex dynamic beamforming optimization. Fortunately, the deployment of large plane array antennas is becoming mainstream in low Earth orbit (LEO) satellite constellations. These large arrays, acting as high-reflectivity meta-surfaces, enable reflection communication via satellites. In this paper, we propose a beyond-line-of-sight long-distance reflection communication system using LEO satellites. Specifically, we introduce a new paradigm where LEO satellite serves as a passive reflector to establish a VLoS path between ground stations. We also propose a satellite selection method to choose suitable satellites as passive reflectors and a communication window prediction method to determine optimal communication opportunities. Simulation results validate the feasibility and effectiveness of the proposed system.

In this paper, we consider an unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) network, where multiple UAVs with caching and computation functionalities are deployed to satisfy the heterogeneous content and service requests from the user equipments (UEs). In order to comprehensively characterize the capability of our considered network in satisfying the UEs’ requests, we define the weighted sum of the content cache hit ratio and the service delay shrinkage ratio as the average quality-of-experience (QoE) of our network and adopt it as the performance metric. Through analysis, we show how the average QoE of our network is dependent on the content cache and service placement decisions at the UAVs, as well as the computation task offloading decisions at the UEs, thus enabling us to formulate an average QoE maximization problem, subject to practical constraints on the UAVs’ caching and computation capabilities. To solve this NP-hard problem, we decompose it into two sub-problems, namely, the content cache and service placement optimization sub-problem and the task offloading optimization sub-problem. Gibbs sampling-based and matching game-based algorithms are proposed to efficiently solve these sub-problems iteratively. Via numerical results, we validate the effectiveness of our proposed algorithms. Compared to various benchmarks, we demonstrate that our proposed algorithms can significantly improve the average QoE of our considered network, especially when the caching and computation resources of the UAVs are limited.

Integrated sensing and communication (ISAC) has been recognized as a key enabler and feature of future wireless networks. In the existing works analyzing the performance of ISAC, discrete-time systems were commonly assumed, which, however, overlooked the impacts of temporal, spectral, and spatial properties. To address this issue, we establish a unified information model for the band-limited continuous-time ISAC systems. In the established information model, we employ a novel sensing performance metric, called the sensing mutual information (SMI). Through analysis, we show how the SMI can be utilized as a bridge between the mutual information domain and the minimum mean squared error (MMSE) domain. In addition, we illustrate the communication mutual information (CMI)-SMI and CMI-MMSE regions to identify the performance bounds of ISAC systems in practical settings and reveal the trade-off between communication and sensing performances. Moreover, via analysis and numerical results, we provide two valuable insights into the design of novel ISAC-enabled systems: i) communication prefers the waveforms of random amplitude, sensing prefers the waveforms of constant amplitude, and both communication and sensing favor the waveforms of low correlations with random phases; ii) There exists a linear positive proportional relationship between the allocated time-frequency resource and the achieved communication rate/sensing accuracy.

This paper investigates an intelligent reflecting surface (IRS) assisted integrated sensing and communication (ISAC) with high-mobility systems, where the orthogonal time frequency space (OTFS) modulation is employed to leverage the Delay- Doppler (DD) spread. We propose a subspace-based beamforming design algorithm, which optimizes the phase shifts at the IRS and the combining vector at the base station (BS) to enhance the communication performance subject to the constraint on the sensing accuracy. Moreover, we derived closed-form solutions for the optimization problems. Numerical results affirm the effectiveness of our proposed beamforming design algorithm in high-mobility scenarios.

This paper introduces an intelligent reflecting surface (IRS)-aided integrated sensing and communications (ISAC) framework for joint signal demodulation and localization in multiuser millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. A time block is divided into uplink estimation stage and downlink transmission stage. In the uplink estimation stage, a joint active beamforming at the BS and passive beamforming at the IRSs are designed based on estimated angle of arrival (AoA) information. In the downlink transmission stage, a joint signal demodulation and location sensing algorithm at the users is proposed by exploiting the statistical properties of the received signals. Numerical results demonstrate that the proposed ISAC framework can achieve centimeter-level localization accuracy while maintaining communication performance compared to communication-only systems with perfect channel state information (CSI).

This paper delves into the potential of intelligent reflecting surfaces (IRSs) in enabling integrated sensing and communication (ISAC) in multi-user multi-path scenarios. We introduce a three-dimensional (3D) multi-user ISAC framework with distributed IRSs, which offers simultaneous signal demodulation, channel estimation, and localization. The transmission is divided into a user access stage and a downlink transmission stage. In the first stage, we propose an algorithm for simultaneous uplink signal demodulation and angles of arrival (AoA) estimation at the semi-passive IRS. Moreover, a joint active and passive beamforming scheme inspired by radar-communication, is proposed to enhance both communication and localization performance in the downlink stage, while eliminating the need for distinct localization reference signals. Numerical results demonstrate that the proposed ISAC framework achieves centimeter-level localization accuracy while maintaining comparable communication performance to communication-only systems, thus validating its effectiveness.

An integrated sensing and communication (ISAC) system assisted by distributed passive intelligent reflecting surfaces (IRSs) is proposed in this paper, which enables simultaneous signal demodulation and location sensing, without channel state information (CSI). A detailed workflow of the proposed IRS-based ISAC system is designed, including transmission protocol, joint location sensing and demodulation, as well as beamforming optimization. Specifically, each coherent block consists of two stages, and each stage is divided into two time blocks, where the signal demodulation, channel estimation, and location sensing are conducted by the proposed integrated localization and demodulation (I-LAD) algorithm, simultaneously. Simulation results show that the signal demodulation performance of the proposed I-LAD algorithm is close to that of the benchmark scheme assuming perfect CSI. At the same time, the channel estimation performance is comparable to that of the parallel factor decomposition (PAPRFAC) method or the compressive sensing-based channel estimation (CS-EST) method. In addition, the proposed location sensing scheme almost achieves the Cramér-Rao lower bound (CRLB) for distributed IRS-assisted localization-only systems, confirming the effectiveness of the proposed I-LAD algorithm.

In this paper, we investigate integrated sensing and communication (ISAC) in high-mobility systems with the aid of an intelligent reflecting surface (IRS). To exploit the benefits of Delay-Doppler (DD) spread caused by high mobility, orthogonal time frequency space (OTFS)-based frame structure and transmission framework are proposed. In such a framework, we first design a low-complexity ratio-based sensing algorithm for estimating the velocity of mobile user. Then, we analyze the performance of sensing and communication in terms of achievable mean square error (MSE) and achievable rate, respectively, and reveal the impact of key parameters. Next, with the derived performance expressions, we jointly optimize the phase shift matrix of IRS and the receive combining vector at the base station (BS) to improve the overall performance of integrated sensing and communication. Finally, extensive simulation results confirm the effectiveness of the proposed algorithms in high-mobility systems.

This paper presents a study on the integrated sensing and communication (ISAC) for symbiotic radio systems in mobile scenarios, where the receiver shared by the primary and secondary systems is capable of locating the moving object of secondary systems via estimating the direction of arrival (DoA), and detecting the symbols for both systems. For the considered symbiotic radio systems, the strong interference from the primary systems and moving object pose significant challenges for location sensing. Moreover, the symbol detection for symbiotic radio systems typically relies on channel state information (CSI), which would reduce the spectrum efficiency substantially. To address these challenges, this paper proposes a low-complexity and high-accuracy two-dimension DoA estimator based on the ratio of dual points on the main lobe using a single snapshot. The theoretical performance in terms of effective sensing probability and mean square error for the proposed estimator are provided in closed-form. To eliminate the dependence of symbol detection on CSI, we propose to fully capture the characteristic of the ISAC systems and introduce two types of DoA-assisted detectors to detect both the primary and secondary symbols. Specifically, the reciprocal linear detectors that use heuristic linear combiners and iterative detection to achieve mutual benefit for both transmissions, as well as a Manchester detector that can separate the primary and secondary signals, are proposed. Finally, the numerical results are provided to validate the correctness of the theoretical analysis, and demonstrate the superiority of the proposed DoA estimator and DoA-assisted detectors.

Orthogonal time frequency space (OTFS) modulation is a promising technique for the next-generation communications in high-mobility scenarios. However, in the delay-Doppler (DD) domain, the received signals suffer from a decrease in power due to the non-coherent superposition of all symbols transmitted through wireless channels. To address this issue, this paper proposes the incorporation of an intelligent reflecting surface (IRS) to assist the transmission for the OTFS systems, and jointly designs the OTFS frame structure and IRS phase shifts to achieve a coherent combination of the received signals. To address the problem of outdated channel state information in high-mobility systems, we propose a location-aided channel estimation strategy at the IRS. Additionally, to mitigate the adverse effects of the fractional Doppler shifts, a delay and shifted-Doppler domain-based channel estimation method is designed at the base station. By utilizing the well-designed OTFS frame structure and IRS phase shifts, we propose a low-complexity iterative interference cancellation (IIC) detector, and analyze the lower bound for its symbol error probability. To provide a clear understanding for the process of the considered systems, we describe a two-stage transmission protocol. Finally, the numerical results are provided to evaluate the effectiveness and superiority of the proposed estimation methods and IIC detector.

Recently, reconfigurable intelligent surface (RIS) has gained growing research interests in sensing fields. While extensive efforts have been devoted to designing RIS space beamforming for improving sensing accuracy, the potential of RIS in improving sensing resolution has so far not been fully dug. In this paper, we investigate the fundamental performance of RIS-enabled multi-target sensing, and exploit the potential of RIS in simultaneously boosting resolution and accuracy. Specifically, based on the identification that sensing is to obtain target state information from the received echo signals, we adopt the sensing mutual information as the performance metric, thus enabling comprehensive evaluation of both sensing resolution and accuracy. Then, we derive the analytical expression of the sensing mutual information in our considered systems, revealing that in addition to providing beamforming gains to enhance sensing accuracy in the space domain, RIS can also flexibly vary its beamforming in the time domain to improve sensing resolution. Based on this result, we propose a novel space-time beamforming, in which space-domain and time-domain beamforming gains are utilized for enhancing sensing accuracy and resolution, respectively. Numerical results demonstrate the significant advantages of the proposed space-time beamforming scheme over the traditional space-only beamforming scheme in terms of sensing resolution.

Reconfigurable intelligent surface (RIS) has shown its great potential in facilitating device-based integrated sensing and communication (ISAC), where sensing and communication tasks are mostly conducted on different time-frequency resources. While the more challenging scenarios of simultaneous sensing and communication (SSC) have so far drawn little attention. In this paper, we propose a novel RIS-aided ISAC framework where the inherent location information in the received communication signals from a blind-zone user equipment is exploited to enable SSC. We first design a two-phase ISAC transmission protocol. In the first phase, communication and coarse-grained location sensing are performed concurrently by exploiting the very limited channel state information, while in the second phase, by using the coarse-grained sensing information obtained from the first phase, simple-yet-efficient sensing-based beamforming designs are proposed to realize both higher-rate communication and fine-grained location sensing. We demonstrate that our proposed framework can achieve almost the same performance as the communication-only frameworks, while providing up to millimeter-level positioning accuracy. In addition, we show how the communication and sensing performance can be simultaneously boosted through our proposed sensing-based beamforming designs. The results presented in this work provide valuable insights into the design and implementation of other ISAC systems considering SSC.

In this paper, we establish an integrated sensing and communication (ISAC) system based on a distributed semi-passive intelligent reflecting surface (IRS), which allows location sensing and data transmission to be conducted on the same time-frequency resources. The detailed working process of the proposed IRS-based ISAC system is designed, including the transmission protocol, location sensing and beamforming optimization. Specifically, each coherence block consists of the channel estimation period, the ISAC period with two time blocks, and the pure communication (PC) period. During the channel estimation period, the low-dimensional effective user-BS channel is estimated. During each time block of the ISAC period, data transmission and user positioning are carried out simultaneously. The estimated user location in the first time block will be used for beamforming design in the second time block. During the PC period, only data transmission is conducted, by invoking the user location estimated in the second time block of the ISAC period for beamforming design. Simulation results show that a millimeter-level positioning accuracy can be achieved by the proposed location sensing scheme. Besides, the proposed two beamforming schemes based on the estimated location achieve similar performance to the benchmark schemes assuming perfect channel state information, verifying the effectiveness of beamforming design using sensed location information.

In this paper, we exploit the potential benefits of intelligent reflecting surface (IRS) in achieving integrated sensing and communication (ISAC) at the same frequency and time resources. To this end, we establish a novel framework, in which a single-antenna user transmits to a multi-antenna base station, with the aid of a distributed semi-passive IRS. In the established framework, the transmission period is divided into two time blocks. At each time block, the distributed semi-passive IRS conducts the location sensing and data transmission simultaneously. Simple-yet-efficient location sensing and beamforming design schemes are respectively proposed. Particularly, the estimated user location in the first time block is used to facilitate the beamforming design of the IRS in the second time block. Through numerical results, we demonstrate that the proposed location sensing scheme can achieve a millimeter-level positioning accuracy, even when the number of semi-passive reflecting elements is small and the allocated sensing time is short. In addition, we show that, utilizing the imperfect location information obtained from our proposed location sensing scheme, the proposed beamforming design scheme can achieve almost the same performance as the optimal beamforming scheme assuming the perfect channel state information, thus verifying the effectiveness of our proposed framework and providing valuable insights into the design of other IRS-enabled ISAC systems.

In this paper, we propose a novel semi-passive elements-aided channel estimation framework for intelligent reflecting surface (IRS), where a small portion of IRS reflecting elements are able to process the received signal for facilitating the channel estimation. Specifically, the BS-IRS channel is estimated by applying the estimation of signal parameters via rotational invariance technique (ESPRIT), while the user-IRS channels are estimated by combining the use of total least square (TLS) ESPRIT and multiple signal classification (MUSIC) methods. The required training time of the proposed channel estimation scheme is irrelevant to the number of IRS reflecting elements, thus substantially reducing the training overhead. Simulation results show the great advantages of our proposed scheme over both the conventional compressed sensing (CS)-based channel estimation and cascaded channel estimation schemes.

This paper considers an angle-domain intelligent reflecting surface (IRS) system. We derive maximum likelihood (ML) estimators for the effective angles from the base station (BS) to the user and the effective angles of propagation from the IRS to the user. It is demonstrated that the accuracy of the estimated angles improves with the number of BS antennas. Also, deploying the IRS closer to the BS increases the accuracy of the estimated angle from the IRS to the user. Then, based on the estimated angles, we propose a joint optimization of BS beamforming and IRS beamforming, which achieves similar performance to two benchmark algorithms based on full CSI and the multiple signal classification (MUSIC) method respectively. Simulation results show that the optimized BS beam becomes more focused towards the IRS direction as the number of reflecting elements increases. Furthermore, we derive a closed-form approximation, upper bound and lower bound for the achievable rate. The analytical findings indicate that the achievable rate can be improved by increasing the number of BS antennas or reflecting elements. Specifically, the BS-user link and the BS-IRS-user link can obtain power gains of order N and NM2, respectively, where N is the antenna number and M is the number of reflecting elements.

This paper proposes an angle domain framework for the design of an intelligent reflecting surface (IRS) system. The maximum likelihood (ML) principle is applied to derive the estimators for the effective angles among the base station (BS), IRS and user. It is demonstrated that the accuracy of the estimated angles improves with the number of BS antennas. Also, deploying the IRS closer to the BS increases the accuracy of the estimated angle from the IRS to the user. Then, exploiting the estimated angles, we propose a joint design of BS beamforming and IRS beamforming. Simulation results show that our proposed algorithm, which only needs few angle information, achieves nearly the same performance as the algorithm requiring full channel state information (CSI). Moreover, the optimized BS beam becomes more focused towards the IRS direction as the number of reflecting elements increases.

This paper proposes a novel location information aided design framework for multiple intelligent reflecting surface (IRS) systems. Assuming practical and imperfect user location information, the effective angles from the IRS to the users are estimated, which is then used to design the transmit beam and IRS beam. Furthermore, closed-form expressions for the achievable rate are derived. The analytical findings indicate that the achievable rate can be improved by increasing the number of base station (BS) antennas or reflecting elements. Specifically, a power gain of order NM2is achieved, where N is the number of BS antennas and M is the number of reflecting elements. Moreover, with a large number of reflecting elements, the individual signal to interference plus noise ratio (SINR) is proportional to M. Also, it has been shown that high location uncertainty would significantly degrade the achievable rate. Besides, IRSs should be deployed at distinct directions (relative to the BS) and be far away from each other to reduce the interference from multiple IRSs.

This paper considers a multi-antenna multicast system with programmable metasurface (PMS) based transmitter. Taking into account of the finite-resolution phase shifts of PMSs, a novel beam training approach is proposed, which achieves comparable performance as the exhaustive beam searching method but with much lower time overhead. Then, a closed-form expression for the achievable individual rate is presented, which is valid for arbitrary system configurations. Besides, assuming a large number of reflecting elements, a simple approximated expression for the multicast rate is derived. A closed-form solution is obtained for the optimal power allocation scheme, and it is shown that equal power allocation is optimal when the number of reflecting elements is sufficiently large. The analytical findings indicate that, increasing the number of radio frequency (RF) chains or reflecting elements can significantly improve the multicast rate, and as the phase shift number becomes larger, the multicast rate improves first and gradually converges to a limit. Moreover, increasing the number of users would significantly degrade the multicast rate, but this rate loss can be compensated by implementing a large number of reflecting elements.

This paper considers a multi-antenna multicast system with programmable metasurface (PMS) based transmitter. Taking into account of the finite-resolution phase shifts of PMSs, a novel beam training approach is proposed, which achieves comparable performance as the exhaustive beam searching method but with much lower time overhead. Then, a closed-form expression for the achievable multicast rate is presented, which is valid for arbitrary system configurations. In addition, for certain asymptotic scenario, simple approximated expressions for the multicase rate are derived. Closed-form solutions are obtained for the optimal power allocation scheme, and it is shown that equal power allocation is optimal when the pilot power or the number of reflecting elements is sufficiently large. However, it is desirable to allocate more power to weaker users when there are a large number of RF chains. The analytical findings indicate that, with large pilot power, the multicast rate is determined by the weakest user. Also, increasing the number of radio frequency (RF) chains or reflecting elements can significantly improve the multicast rate, and as the phase shift number becomes larger, the multicast rate improves first and gradually converges to a limit. Moreover, increasing the number of users would significantly degrade the multicast rate, but this rate loss can be compensated by implementing a large number of reflecting elements.

This article proposes a novel location information aided multiple intelligent reflecting surface (IRS) systems. Assuming imperfect user location information, the effective angles from the IRS to the users are estimated, which is then used to design the transmit beam and IRS beam. Furthermore, closed-form expressions for the achievable rate are derived. The analytical findings indicate that the achievable rate can be improved by increasing the number of base station (BS) antennas or reflecting elements. Specifically, a power gain of order N M2is achieved, where N is the antenna number and M is the number of reflecting elements. Moreover, with a large number of reflecting elements, the individual signal to interference plus noise ratio (SINR) is proportional to M, while becomes proportional to M2as non-line-of-sight (NLOS) paths vanish. Also, it has been shown that high location uncertainty would significantly degrade the achievable rate. Besides, IRSs should be deployed at distinct directions (relative to the BS) and be far away from each other to reduce the interference from multiple IRSs. Finally, an optimal power allocation scheme has been proposed to improve the system performance.

This paper investigates the performance of angle-domain millimeter-wave (mmWave) multi-input multi-output (MIMO) non-orthogonal multiple access (NOMA) systems in the presence of angular estimation error. A closed-form expression for the achievable rate of the system is derived. Based on which, a simple asymptotic approximation is obtained. The findings of paper suggest that, with a large number of BS antennas, the user rate is mainly constrained by the antenna number to beam number ratio (ANTBNR) and the spatial direction distance. In particular, increasing the ANTBNR would cause a severe rate loss, and the achievable rate is an increasing function with respect to the spatial direction distance. Capitalizing on this key observation, a novel cluster grouping scheme is designed to reduce the inter-cluster interference, which shows significant performance gain over a random cluster grouping scheme. Finally, simulation results are provided to corroborate the analytical results.

This paper investigates the achievable performance of cell-free massive multiple-input multiple-output (MIMO) systems with low resolution analog-to-digital converters (ADCs) at both the access points (APs) and users. A closed-form expression for the achievable rate is derived, which enables the study of the effects of AP number, antenna number per AP, user number, and ADC resolution on the achievable rate. In addition, a simple asymptotic approximation for the individual user rate is presented, which shows that the user rate is mainly constrained by the ADC resolution at the user. Moreover, we propose an ADC resolution bits allocation scheme aiming at maximizing the sum rate subject to the total ADC resolution bits constraint, which substantially outperforms the equal ADC resolution bits allocation scheme. Furthermore, a max-min power control scheme is proposed, which not only ensures user fairness, but also improves the achievable rate. Finally, simulation results are provided to corroborate the analytical results.

We study the effects of low resolution analog-to-digital converters (ADCs) on cell-free massive multiple-input multiple-output (MIMO) systems. We first derive a closed-form expression for the achievable rate, which enables efficient evaluation of the impact of key parameters on system performance. Then, we give a simple approximate closed-form expression for the individual user rate, which indicates that the ADC resolution at the user is the main constraining factor for the user rate. Furthermore, we study the allocation of ADC resolution bits among different access points (APs) with fixed total resolution bits. It has been shown that our proposed ADC resolution bits allocation scheme is substantially superior to the equal ADC resolution bits allocation scheme. Finally, we provide simulation results to verify our analytical results.