With the rapid growth of time-sensitive services, existing infrastructures struggle to guarantee large-scale millisecond-level latency and high reliability. To meet these demands, massive ultra-reliable and low-latency communication (mURLLC) has been introduced as a core scenario in sixth-generation (6G) networks. To address the demands of mURLLC, we propose a hierarchical computing architecture for cell-free (CF) systems, comprising layers for load computation, demand mapping, and resource allocation. Specifically, in the first layer, we develop network load computation methods for CF systems based on the grant-free random access (GFRA) mechanism, where the load is dynamically inferred from subchannel (SC) occupancy patterns. In the second layer, we establish an analytical model that links access failure probability with quality-of-service (QoS) requirements, enabling accurate mapping of subchannel resource demands. In the third layer, based on finite blocklength transmission theory, we establish a joint delay optimization model and formulate the end-to-end (E2E) instantaneous delay minimization problem. To effectively address this problem, we further develop an adaptive blocklength-based resource allocation (ABRA) scheme that integrates with the hierarchical computing architecture for minimizing E2E delay. Numerical results have demonstrated that the proposed architecture can accurately capture network load variations, automatically adjust blocklengths according to users’ QoS requirements, and significantly reduce E2E delay.

Uplink sensing in cell-free integrated sensing and communication (CF-ISAC) systems provides a promising solution by reusing massive communication signals. This approach offers low system overhead and enables wide-area coverage through densely deployed users and distributed access points (APs). However, due to the tight coupling between communication and sensing, accurate extraction of uplink sensing parameters becomes a critical bottleneck: sensing parameter extraction relies on precise demodulation of uplink communication signals, while high-quality channel estimation for data demodulation, in turn, requires accurate sensing results. To address this challenge, we propose an iterative communication-sensing optimization framework under uplink CF-ISAC architecture. This framework establishes dynamic information feedback among the three core modules of data detection, channel reconstruction and target sensing, achieving the collaborative improvement of communication-sensing performance. Specifically, the target sensing module integrates pilot-based sensing and data signal-enhanced sensing to extract target parameters. The channel reconstruction module maps the sensing results to channel state information (CSI). The data detection module recovers data symbols using the reconstructed CSI and feeds back both demodulated symbols and residual errors to the sensing module, enabling iterative correction of target parameter estimation. The simulation results show that the proposed iterative framework achieves simultaneous suppression in communication bit error rate (BER) and enhancement in sensing accuracy through several iterations, thereby effectively breaking through the traditional performance limits.

Semi-grant-free (SGF) access is regarded as a promising technology for next-generation networks, effectively easing the tension between massive access caused by user growth and limited communication resources. However, how to achieve efficient resource utilization through effective pairing of grant-based (GB) users and grant-free (GF) users in SGF access mechanism while ensuring system reliability is a critical challenge for enhancing overall network performance. This paper proposes a scalable SGF access scheme suitable for cell-free massive multiple-input multiple-output (CF-mMIMO) systems. Unlike traditional SGF schemes under centralized cellular systems, this scheme first groups access points (APs) into edge distributed unit (EDU)-centric clusters, then fully leverages the macro-diversity gain and signal power sparsity in the cell-free architecture, and employs a genetic algorithm (GA) to achieve efficient pairing between GB users and GF users. This approach ensures the quality of service (QoS) for GB users while sharing their resources with GF users, optimizing the efficiency of communication resource utilization. Subsequently, closed-form expressions for the signal-to-interference-plus-noise ratio (SINR) and outage probability of GB users and GF users under perfect and imperfect successive interference cancellation (SIC) are derived to evaluate the system reliability. Finally, simulation results demonstrate that the proposed scheme significantly outperforms the other scheduling schemes in terms of outage probability and resource utilization performance, particularly under massive access.

With the evolution from 5G to 6G, ultra-reliable low-latency communication (URLLC) faces increasingly stringent performance requirements. Lower latency constraints demand shorter channel codeword length, which can severely degrade decoding performance. The massive multiple-input multiple-output (MIMO) system is considered a crucial technology to address this challenge due to its abundant spatial degrees of freedom (DoF). While polar codes are theoretically capacity-achieving in the limit of infinite codeword length, their practical applicability is limited by the latency penalty associated with the long codewords. In this paper, we establish a unified theoretical framework and propose a novel spatiotemporal two-dimensional (2-D) polar coding scheme for massive MIMO systems employing minimum mean square error (MMSE) receivers. The polar transform is jointly applied over both spatial and temporal dimensions to fully exploit the large spatial DoF. By leveraging the near-deterministic signal-to-interference-plus-noise ratio (SINR) property of MMSE detection, the spatial domain is modeled as a set of parallel Gaussian sub-channels. Within this framework, we theoretically analyze the 2-D polarization behavior based on the Gaussian approximation method and show that the proposed scheme asymptotically retains its capacity-achieving property, even under finite blocklength constraints and large spatial DoF. Simulation results further demonstrate that, compared to traditional time-domain polar codes, the proposed 2-D scheme can significantly reduce latency while guaranteeing reliability, or alternatively improve reliability under the same latency constraint—offering a capacity-achieving and latency-efficient channel coding solution for massive MIMO systems in future 6G URLLC scenarios.

Future wireless communications require higher reliability, lower latency, and greater connectivity. In this paper, we mainly investigate a multi-dimensional resource-based hybrid quality of service (QoS) massive grant-free random access (GFRA) in the massive MIMO system. First, inspired by the advanced access protocol [1], we develop an extended multi-dimensional resource-based hybrid QoS massive GFRA scheme to address the differentiated requirements of various services, which not only meets the stringent reliability and low latency demands of URLLC users, but also supports the massive concurrent connectivity of mMTC users. Then, based on the extended multi-dimensional resource access scheme, we analyze the access failure probability of URLLC users by comprehensively considering the pilot collision and decoding error in the finite blocklength regime. To achieve massive concurrent connectivity and reliable access for mMTC users, we further propose a multi-slot replica access scheme that leverages time diversity, and investigate the access failure probability of mMTC users by considering the pilot collision and outage probability across multiple slots. Furthermore, by integrating GFRA mechanism and multi-slot replica access scheme, we model the massive access of mMTC users as a Markov state transition process and analyze the access latency of mMTC users from a frame-level perspective. Finally, the effectiveness of the proposed scheme is demonstrated through simulations. The proposed scheme not only meets the differentiated requirements of URLLC and mMTC users, but also further reduces latency of mMTC users compared with repetition transmission.

The scalable cell-free radio access network (CF-RAN), built on cell-free massive multiple-input multiple-output (CF-mMIMO) networks, achieves a critical trade-off between computational complexity and system performance in cooperative transmission through the rational division of physical-layer functionalities. However, due to its distributed transmission architecture, unavoidable propagation delay differences arise in signal arrival times across different receivers during cooperative transmission, leading to asynchronous reception effects that severely degrade system performance. In this paper, we analyze the specific impacts of asynchronous reception effects in scalable CF-RAN systems, including accumulated phase offset on received signals, as well as inter-carrier interference (ICI) and intersymbol interference (ISI). We investigate channel estimation under non-ideal channel state information (CSI) acquisition caused by non-orthogonal pilot sequences and asynchronous reception effects, deriving closed-form expressions for the achievable uplink and downlink spectral efficiency (SE) in scalable CF-RAN systems under asynchronous conditions. To mitigate asynchronous reception effects, we introduce a beam division multiple access (BDMA) transmission scheme into scalable CF-RAN systems, leveraging large-scale antenna arrays at remote radio units (RRUs) to achieve beam-domain multi-user spatial multiplexing. Building on this framework, we implement per-beam time delay compensation (PBTDC) on RRU antenna arrays to approximate asynchronous received signals as synchronized and derive closed-form expressions for the achievable SE of uplink/downlink in asynchronous scalable CF-RAN systems with PBTDC architecture. Numerical simulations demonstrate that asynchronous reception effects significantly degrade channel estimation and data transmission performance in scalable CF-RAN systems. In contrast, the proposed PBTDC architecture based on BDMA transmission effectively mitigates these adverse effects and substantially enhances system performance.

Future industrial 6G applications will impose stringent requirements on ultra-reliable low-latency communications (URLLC) and precision sensing enabled by integrated sensing and communication (ISAC) techniques, motivating a comprehensive study of the communication–sensing (C–S) trade-off under finite blocklength transmission. Therefore, this paper investigates the fundamental C–S performance limits in a network-assisted full-duplex (NAFD) cell-free ISAC system with URLLC users. To address the theoretical gap in the finite blocklength regime, closed-form upper-bound expressions are derived for key communication metrics, including transmission delay and decoding error probability (DEP), and a Cramér–Rao lower bound (CRLB) framework is established for multi-static sensing. Furthermore, to explicitly characterize the C–S trade-off, the ISAC network availability is evaluated and the Pareto frontier is obtained using the non-dominated sorting genetic algorithm II (NSGA-II). The results demonstrate that increasing the blocklength improves sensing accuracy at the cost of higher communication latency. To address this inherent conflict, this study proposes a DDQN-based finite blocklength optimization (FBLO) algorithm that performs blocklength selection under URLLC and sensing quality-of-service requirements to achieve a favorable C–S trade-off. Simulation results validate that the proposed algorithm achieves near-optimal performance with reduced computational overhead.

To realize the potential of integrated sensing and communication (ISAC) in cell-free (CF) massive MIMO system, an ISAC framework is proposed in this paper. With numbers and locations of scatterers (targets) unknown, based on received downlink data signals from the transmit access point (tAP) antenna, multiple receive access points (rAPs) first estimate delays locally. In each outer iteration, by comparing the estimated delays with the delays of the paths through extracted scatterer locations, selected rAPs search out mismatched estimated delays. Through cooperation, the potential locations of the scatterers forming each candidate location set corresponding to each mismatched delay are obtained, and each set will be evaluated in sequence. Specifically, a joint evaluation algorithm is proposed, where the probability model for the joint evaluation problem is established based on delay and limited angular information. Under the expectation maximization (EM) framework, by fusing information from all the rAPs, the extracted scatterer locations and candidate locations are adjusted, and the evaluation results are estimated, which can be regarded as the global probability of scatterers exist at candidate locations. When the evaluation results of all the locations in a candidate location set are low, the corresponding delay will be discarded, otherwise, the candidate location with the highest evaluation result in the set will be extracted. The proposed framework avoids exhaustive search in different associations of scatterers and estimated delays, and is more flexible than extraction from all the candidate locations based on fixed thresholds. Then, based on a simplified model, the impact of system parameters on delay based location sensing accuracy is revealed with the theoretical analysis. Finally, based on the prototype system with CF radio access network (RAN) architecture, the proposed framework is experimentally validated.

Next generation wireless network is expected to provide higher rate, more reliable access and lower latency. Cell-free massive multiple input multiple output (CF-mMIMO) has been considered a potential enabler for massive access. In this paper, we primarily investigate massive grant-free random access (GFRA) in CF-mMIMO ultra-reliable and low latency (URLLC) systems. First, we give a massive GFRA model based on CF-mMIMO URLLC system, focusing on analyzing access reliability and access latency. Next, we derive approximated closed-form expression for the decoding error probability and outage probability of active users in the finite block-length regime by leveraging the approximated distribution of the signal to interference plus noise power. Based on this, by comprehensively considering the random access procedure, we derive the expression for access success probability of the attempted access user. We also jointly optimize the pilot length and data block-length by the access success probability to enhance access reliability. Furthermore, by utilizing the macro diversity of the CF-mMIMO system, we propose a scalable user centric-based multiple access points scheme to improve access reliability. In addition, based on access procedure and the associated GFRA protocol, we analyze the components of access latency. Subsequently, considering the access success probability and the number of retransmissions, we analyze and evaluate access latency for massive GFRA in the CF-mMIMO URLLC system. Finally, simulation results demonstrate the rationality and effectiveness of the proposed massive access model for analyzing the access reliability and access latency.

To mitigate the delay caused by large-scale devices access, the grant-free (GF) technology has been widely used in massive Ultrareliable-Low-Latency Communications (mURLLCs). However, the absence of a grant-based scheduling handshake with the base station leads to significant collisions when different users select the same resources, thereby compromising the reliability of the communication. To improve the reliability of the network, we propose a GF scheme assisted by hybrid automatic repeat request (HARQ) in Cell-Free massive Multiple Input-Multiple Output (CF mMIMO) system. Distinct from the traditional HARQ strategies employed in centralized cellular system, the proposed scheme first clusters user devices and access points based on Poisson cluster process (PCP) spatial distribution characteristics, and then fully exploits the macro diversity gain and spatial sparsity of the CF mMIMO system, effectively mitigating the impact of interference on the communication between devices. Subsequently, the HARQ strategy is introduced, and its round-trip delay is analyzed to achieve the maximum number of retransmissions under delay constraints. To further validate the effectiveness of the proposed scheme, the closed-form expression of uplink signal to interference plus noise ratio (SINR) with maximal ratio combining (MRC) receiver is deduced as well as the approximate outage probability expression. Finally, simulation results confirm the precision of the derived expressions and the enhancement of the proposed scheme on system reliability under stringent latency constraints.

Cooperative cell-free (CF) integrated sensing and communication (ISAC) systems emerge as a promising architecture for supporting 6G dynamic Internet of Things (IoT) scenarios. However, the mobility of user equipment (UE) and the inherent non-scalability of CF networks pose critical challenges to the practical deployment of CF ISAC systems. This paper presents a comprehensive mobility management framework for cooperative CF ISAC systems to enhance their deployability and scalability. This framework not only establishes a foundational operation paradigm to obtain the mutual promotion of communication and sensing (C&S) performance in multi-static ISAC, but also employs the dynamic cooperative clustering method and dynamic management mechanism to ensure seamless service for mobile UEs. First, we establish the mathematical signal model of the proposed mobility management framework and conduct the analysis of mobility-aware C&S performance in CF ISAC systems. Subsequently, a distributed, low-complexity initial access scheme is designed to tackle the tightly coupled challenges of access point (AP) clustering and AP mode selection, which can ensure communication reliability and sensing accuracy in static scenarios. Furthermore, to achieve the trade-off between mobility-induced handover loss and per-slot C&S performance, a dynamic access scheme is introduced for dynamic scenarios, comprising the dynamic adaptive hysteresis handover strategy and the kinematic information-based dynamic clustering update algorithm. Theoretical and numerical analyses validate that the proposed access schemes significantly enhance the operability and practicality of CF ISAC systems through low complexity and flexible operations. Meanwhile, simulation results demonstrate that the framework empowers cooperative CF ISAC systems to achieve superior mobility-aware C&S performance, ensuring the stable and efficient system support in 6G dynamic IoT scenarios.

The deployment of cell-free massive multiple-input multiple-output (CF-mMIMO) systems in urban areas enriches wireless channel characteristics, making it a promising approach for fingerprint-based localization. However, the high cost of label collection and the unreliable generalization performance significantly limit its widespread application. Recently, model-agnostic meta-learning (MAML) has achieved remarkable success in few-shot learning by extracting common knowledge from existing tasks. But the use of a forcibly shared meta-parameter for model initialization often struggles with task heterogeneity in practical applications. To address these challenges, we propose a novel gradient-based task-aware meta-learning (GTML) framework for fingerprint-based localization. We first model the localization problem in a new environment with limited fingerprint data as a meta-learning problem for new task adaptation. Then, we propose an improved embedded task-aware method based on training gradients to reduce the overhead of task-specific feature extraction. The proposed GTML includes two paradigms using task-specific information to customize the global meta-learner: for a few historical tasks, a weighted paradigm is introduced to compensate for task heterogeneity; for a larger set of historical tasks, a clustered paradigm is used to capture the distribution of training tasks and learn group-specific meta-parameter. The simulation results using Wireless Insite software demonstrate that the proposed GTML enables rapid adaptation to a new environment with a few training samples. Moreover, compared to the vanilla MAML, GTML improves the localization accuracy by over 11% while maintaining low computational overhead.

Multi-static Integrated Sensing and Communication (ISAC) is a potential technology for future sixth-generation (6G) and cell-free (CF) network is a suitable architecture to integrate it. Current research on multi-static ISAC and full-duplex communication (MIFC) CF systems is scarce, and the adoption of full-duplex (FD) access points (APs) inevitably leads to significant self-interference (SI) and exorbitant deployment costs. Utilizing network-assisted full-duplex (NAFD) technology to implement MIFC CF systems can effectively avoid the above issues. However, in addition to the challenge posed by highly coupled cross-link interference (CLI) and multi-user interference, NAFD-based MIFC CF systems must also address the mutual interference between sensing signals and communication signals. This paper proposes a practical MIFC CF system based on NAFD technology and introduces a four-stage interference management mechanism, which integrates direct interference suppression with indirect interference suppression techniques. Within this mechanism, we initially derive the data transmission estimated channel state information (CSI), the maximum a posteriori ratio test (MAPRT) target detector and inter-AP estimated CSI. Then, we furnish the expressions for communication achievable rate and sensing signal-to-interference-plus-noise ratio (SINR) after direct interference cancellation based on the estimated CSI. Furthermore, a deep learning (DLN)-based joint communication and sensing precoding (JCSP) algorithm is devised for indirect interference suppression. Simulation results demonstrate the effectiveness of the direct interference suppression strategy and DLN-based JCSP algorithm in the proposed interference management mechanism, which can achieve the trade-off between communication and sensing performance.

In the sixth generation (6G), ultra-reliable low-latency communications (URLLC) will be further developed to achieve TKμ extreme connectivity. On the premise of ensuring the same rate and reliability, the spatial domain advantage of multiple-input multiple-output (MIMO) has the potential to further shorten the time-domain code length and is expected to be a key enabler for the realization of TKμ. Different coded MIMO schemes exhibit disparities in exploiting the spatial domain characteristics, so we consider two extreme MIMO coding schemes, namely, time-domain coding in which the codewords on multiple spatial channels are independent of each other, and spatiotemporal coding in which multiple spatial channels are jointly coded. By analyzing the statistical characteristics of information density and utilizing the normal approximation, we provide explicit performance bounds for finite blocklength coded MIMO under time-domain coding and spatiotemporal coding. It is found that, different from the phenomenon in time-domain coding where the performance degrades as the blocklengths decrease, spatiotemporal coding can effectively compensate for the performance loss caused by short blocklengths by improving the spatial degrees of freedom (DoF). These results indicate that spatiotemporal coding can fully exploit the spatial dimension advantages of MIMO systems, enabling extremely low error-rate communication under stringent blocklengths constraint.

In the low-altitude three-dimensional coverage scenario with unmanned aerial vehicles (UAVs), user data requirements change quickly and the asymmetry of uplink and downlink traffic is hard to address. We utilize cell-free radio access networks (CF-RAN) to support dynamic scenario due to its cooperative capability and scalability, and adopt network-assisted full-duplex (NAFD) to support flexible duplex communication by selecting appropriate node duplex modes according to the traffic in real-time. Considering the scalable minimum mean square error (MMSE) receiver and regularized zero-forcing (RZF) precoding scheme, the closed-form expressions of uplink spectral efficiency with infinite block-length regime and the lower bound of downlink spectral efficiency with finite block-length communication (FBLC) regime are derived. Based on these expressions, we propose a multi-objective optimization problem (MOOP) to maximize and balance the spectral efficiency of upink and downlink by optimizing the duplex mode of remote radio units (RRUs) and solving it with deep Q-learning (DQN) algorithm. Simulation results verify the accuracy of derived expressions, the effectiveness of the proposed optimization algorithm and the superiority of NAFD technique in low-altitude CF-RAN system.

This article proposes an uplink transmission scheme for cell-free (CF) massive multiple-input-multiple-output (MIMO) systems in high-speed railway communications to mitigate the negative effects of fast time-varying multipath channels with fewer antennas and insufficient angular resolution. To enhance the estimation performance by utilizing the possible similarity of the relatively slow time-varying channel parameters, the Dirichlet process (DP) is introduced to construct the probabilistic model of the doubly selective multiparameter channel estimation problem, which is solved by adopting the expectation-maximization (EM) framework. Furthermore, by introducing successive approximation, an enhanced channel estimation scheme is proposed. Finally, for the data transmission utilizing the orthogonal frequency-division multiplexing (OFDM) modulation, based on estimated channel parameters, a distributed intercarrier interference (ICI) elimination algorithm with local channel quality-based combining is proposed, and the performance of the system under different architectures and ICI elimination algorithms is analyzed. Simulation results show that DP can improve estimation performance by utilizing implicit similarity, the combination of first-order Taylor expansion and successive approximation can effectively improve estimation performance at higher speeds under relatively higher pilot power, and compared to centralized processing, the proposed distributed ICI elimination algorithm with local channel quality-based combining has a relatively small performance loss.

Massive ultrareliable and low-latency communications (mURLLCs) have emerged as a dominating 6G-standard service. Fast uplink grant is an effective means to solve the uplink access in mURLLC due to the advantages of low signaling cost and collision-free. However, as two key challenges in fast uplink grant, active set prediction and optimal scheduling still lead to high resource wastage and low access success probability. Based on the special spatial sparsity of cell-free networks, we propose the spatial-separable nonorthogonal multiple access (SSNOMA). Compared with nonorthogonal multiple access (NOMA), SSNOMA allows multiple users to share same 3-D resources composed of time-frequency resources and pilots, so as to reduce the resource wastage caused by prediction errors. Furthermore, we design an intelligent hierarchical fast uplink grant framework. In this framework, the upper controller is responsible for scheduling users from the predicted active user set to ensure the optimal Quality of Service (QoS) and active probability, while the lower controller strictly controls the allocation of uplink grants among the scheduled users to maximize spectral efficiency. In addition, considering the limited ability to collect information in massive user access, the upper confidence bound (UCB)-based multiarmed bandit (MAB) algorithm is used in the upper layer to schedule fast grant users, while the multiagent deep deterministic policy gradient (MADDPG) is used in the lower layer to perform specific grant allocation. Simulation results show that the proposed SSNOMA-based intelligent hierarchical framework can significantly improve the utilization of limited resources, and track long-term scheduling experience as well as QoS, effectively supporting mURLLC.

Network-assisted full-duplex (NAFD) cell-free (CF) network emerges as a promising solution for enabling massive ultra-reliable and low-latency communications (mURLLC). In the massive Internet-of-Things (mIoT) scenarios where users’ active statuses change, the existing NAFD resource scheduling schemes require frequent invocation of optimization algorithms, causing significant energy loss and operational delays. So they are not conducive to mURLLC. This paper proposes a dual time-scale resource scheduling scheme, which combines the improved long-term AP duplex mode optimization method with the short-term power allocation optimization method to further enhance the mURLLC ability of NAFD CF networks. In the proposed long-term AP duplex mode optimization method, we first derive the closed-form expressions of active users’ time overflow (TO) probability as the service latency indicator. Operating on a superframe as a large time-scale unit, the improved long-term AP duplex mode optimization method initially employs a long-term active user prediction algorithm to forecast active users in an upcoming superframe and then leverages the long-term AP duplex mode optimization algorithm based on multi-agent deep reinforcement learning to achieve optimal long-term AP mode selection which minimizes the TO probability. In the proposed short-term power allocation optimization method, we design a heuristic algorithm to ensure active users in each coherence time can receive high-reliable and low-latency service. Simulation results demonstrate the effectiveness of the proposed scheme. Compared with the short-term AP mode and power joint optimization methods, the dual time-scale resource scheduling scheme achieves similar spectral efficiency and a much lower TO probability, while also avoiding the frequent AP mode optimization and switching, making it more suitable for mURLLC.

Cell-free radio access network (CF-RAN) with network-assisted full-duplex (NAFD) possesses scalability and enables reception and transmission simultaneously, which is suitable for large-scale ultra reliable and low-latency communication (URLLC). Achieving strict requirements of URLLC for each terminal with a fixed infrastructure is challenging, and unmanned aerial vehicles (UAVs) have been considered as promising enablers to handle this issue due to its flexible deployment, low cost and large coverage. In this paper, we investigate a multi-UAV aided CF-RAN with NAFD that use UAVs as aerial access points (APs). We firstly propose a multi-UAV deployment algorithm based on user distribution and quality of service (QoS) requirement. Then, we derive the closed-form expressions for uplink achievable rate with long block length regime and the lower bound of downlink achievable rate with finite block length communication (FBLC) regime. Based on those expressions, we formulate a weighted sum spectral efficiency maximization problem and solve it by Deep Q-Network (DQN) algorithm. Numerical results verify the accuracy of the derived closed-form expressions and illustrate the impact of system parameters on spectral efficiency. The effectiveness of proposed UAV deployment algorithm and the performance advantages of the weighted sum spectral efficiency optimization algorithm are demonstrated.

Confronted with the challenges of interruptions and blockages caused by dense obstacles in millimeter-wave (mmWave) communication, we propose to employ a flexible distributed reconfigurable intelligent surface (RIS) assisted massive multiple-input multiple-output (MIMO) system to improve performance in areas with poor coverage. In a scenario featuring multi-antenna user equipments (UEs), we consider the practical additive hardware impairments (AHIs) at transceivers and conduct a comprehensive analysis of their impact on the system performance. Leveraging the pronounced beam directivity inherent in mmWave MIMO, we design phase shifts of RISs and analog beamformers of transceivers to achieve beam alignment. In light of this, we explore a linear minimum mean-square error (LMMSE) equivalent channel estimation method. Furthermore, we derive the closed-form expressions for downlink achievable spectral efficiency (SE) in the presence of AHIs, utilizing statistical channel state information (CSI) and maximum ratio transmission (MRT). Based on the derived closed-form expressions, we propose an efficient power allocation strategy relying on an intelligent algorithm known as primal-dual optimization based deep deterministic policy gradient (PDO-DDPG), which can ensure safe exploration of the agent. Numerical results confirm the accuracy of the derived closed-form expressions, unveil the impact of AHIs on the achievable SE, and verify the effectiveness of the PDO-DDPG based power allocation.

Cellular-connected unmanned aerial vehicles (UAVs) is considered as integral components for sixth generation (6G) cellular networks. Cell-free radio access network (CF-RAN) with network-assisted fullduplex (NAFD) possesses global collaborative capabilities and enable uplink and downlink transmission simultaneously, which can be considered as a potential technology for supporting air-ground communication. In this paper, we investigate the performance of cellular-connected UAV in CF-RAN with NAFD. We propose a modified beamforming training scheme to mitigate cross-link interference (CLI) and a location-aware access point (AP) clustering strategy to reduce fronthaul overhead. We derive closed-form expressions for uplink and downlink achievable rates of GUEs and UAVs, respectively. Based on these expressions, we propose an efficient global spectral efficiency optimization scheme by solving a multi-objective optimization problem (MOOP) aiming to maximize the uplink sum rates and downlink sum rates simultaneously with deep Q-network (DQN). Numerical results verify the accuracy of the derived closed-form expressions. The effectiveness of the modified beamforming training scheme and location-aware AP clustering strategy are proved. In addition, the impact of system parameters and the advantages of NAFD system are analyzed. We also illustrate the convergence and benefits of DQN-based optimization scheme on different type of users.

In order to cope with the demand of explosively increasing service diversity and quality, network slicing has become the key technology of next-generation mobile communication. Mobile edge computing (MEC) can provide multi-dimensional resources and network functions at the edge of the network and reduce the delay of wireless networks. At the same time, non-orthogonal multiple access (NOMA) allows traffic to share resources and improve the spectral efficiency and energy efficiency of wireless networks. In this paper, we propose a hierarchical NOMA-based network slicing architecture in the 6G novel full-spectrum scalable cell-free radio access network with MECs and conduct joint allocation of communication, computing and caching resources at different resource granularity to meet the requirements of latency-critical applications with different latency. In order to realize the hierarchical joint resource allocation to improve system efficiency, we propose to address the optimal computing resource allocation and cache placement problem firstly through conventional optimization methods to reduce the action space and then use the multi-agent deep reinforcement learning algorithm for solving other complex coupling strategies. Simulation results further verify the effectiveness of the proposed intelligent network slicing scheme.

The key performance indicators (KPIs) of the sixth generation (6G) will increase by orders of magnitude compared to the fifth generation (5G), promising extreme connectivity performance with Tbps-scale data rate, Kbps/Hz-scale spectral efficiency (SE) and$\mu \text {s}$-level latency. Cell-free massive MIMO (CF-mMIMO) with rich spatial dimension resources is expected to be a key architecture to realize$\text {TK}\mu $extreme connectivity, but the existing research has not yet given a compact and closed-form approximation to describe the relationship between the spatial dimension and system performance, which makes it difficult to evaluate the KPIs of$\text {TK}\mu $intuitively. This paper derives explicit closed-form expressions for the relationship between system performance and system configuration parameters for finite blocklength CF-mMIMO systems and analyzes the relationship between system performance and spatial dimensions. Based on this, we perform parameter selection and performance evaluation of specific implementations in the three$\text {TK}\mu $KPIs in CF-mMIMO systems. Both theoretical analysis and simulation results show that increasing the spatial degree of freedom (DoF) and deploying antennas more dispersedly can realize latency reduction while guaranteeing the system performance, and the joint collaboration of multi-users and multiple access points (APs) with large DoFs can achieve a continuous increase in SE and data rate.

Massive ultrareliable and low-latency communications (mURLLC) is an emerging and dominate traffic service in 6G. To reduce the signaling overhead and access delay, grant-free random access (GFRA) is widely used in mURLLC. As the first step in GFRA, active user detection (AUD) is aimed to identify the set of active users accurately and timely. Conventional AUD schemes relying on iterative computations over massive users bring redundant computing overload and processing delay, which seriously affect the system scalability in the mURLLC scenario. Considering the near-real-time requirement of mURLLC, we propose a scalable deep learning-based AUD approach utilizing similar channel sparsity in cell-free (CF) massive multiple-input–multiple-output (mMIMO) systems. By exploiting the distributed computing unit, i.e., space expansion unit (SEU), we design an SEU-assisted CF mMIMO to improve the scalability of the traditional centralized CF computing architecture. In the proposed system, all access points (APs) are divided into several clusters, and the SEU in each cluster provides a reliable distributed AUD scheme through a 1-D convolutional network (1-D CNN). In addition, a transfer learning-based ensemble model is established at the CPU to achieve a better global detection decision. Simulation results demonstrate the superiority of our scalable deep learning-based approach, and reveal that through the transfer learning-based model fusion at the CPU, our proposed scalable SEU-assisted approach can obtain success probability close to that of the centralized CF computing scheme with less access delay. In addition, our scheme requires fewer pilots than other compressed sensing-based schemes.

In this paper, a channel estimation problem for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems with hybrid structures is studied. Firstly, a beamspace multiple signal classification (MUSIC) algorithm for mmWave wideband channels is proposed to simultaneously estimate the angles of arrival (AOAs), angles of departure (AODs) and transmission delays. Since the traditional spectral peak search method has high complexity, a multi-spectral peak search method is skillfully designed to search for multiple spectral peaks on the MUSIC spatial spectrum more quickly and accurately. Then, the proposed channel estimator is extended to more actual systems equipped with uniform planar arrays (UPAs). Finally, the Cramér–Rao bound (CRB) results of these channel parameters are derived for evaluating the performance of the proposed channel parameter estimator. Simulation results demonstrate that the proposed channel estimator has greatly high channel estimation accuracy.

In this work, a study is conducted on the coexistence of two services, enhanced mobile broadband (eMBB) and massive machine-based communication (mMTC), in a fifth generation (5G) wireless communication system. Coexistence of these two services can be achieved by network slicing and non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) decoding to improve spectral efficiency, but the block error rate (BLER) of mMTC devices needs to be considered for short packet communications (SPC). In this paper, we combine deep reinforcement learning (DRL) proximal policy optimization (PPO) algorithm to efficiently accommodate mMTC devices into available random access (RA) slots while guaranteeing the throughput of eMBB devices to achieve dynamic scheduling of resources to improve system throughput. The performance of the proposed method is compared with recent solutions in the literature, and simulation results show that the proposed method can guarantee a low outage probability for eMBB services and effectively improve the throughput of mMTC services.

This paper studies the secure computation offloading for multi-user multi-server mobile edge computing (MEC)-enabled internet of things (IoT). A novel jamming signal scheme is designed to interfere with the decoding process at the Eve, but not impair the uplink task offloading from users to APs. Considering offloading latency and secrecy constraints, this paper studies the joint optimization of communication and computation resource allocation, as well as partial offloading ratio to maximize the total secrecy offloading data (TSOD) during the whole offloading process. The considered problem is nonconvex, and we resort to block coordinate descent (BCD) method to decompose it into three subproblems. An efficient iterative algorithm is proposed to achieve a locally optimal solution to power allocation subproblem. Then the optimal computation resource allocation and offloading ratio are derived in closed forms. Simulation results demonstrate that the proposed algorithm converges fast and achieves higher TSOD than some heuristics.

Satellite communication is an important technology for the coverage of open country, and plays a vital role of link channel in remote Internet of Things (IoT). However, various kinds of IoT terminals may suffer from the limited battery capacity and computing capability. Therefore, this article proposes a new multiuser IoT system design, in which the satellite link is used to provide communication service for access points (AP), and allow terminals to download and upload data through APs. Then, the AP will exploit simultaneous wireless information and power transfer (SWIPT) and mobile edge computing (MEC) technologies to alleviate the deficiencies mentioned above. Moreover, the AP will equip with the full duplex (FD) and multi-input multi-output (MIMO) technologies to further improve the spectrum efficiency. Besides, a hybrid energy storage is assumed in the AP, i.e., the energy may come from power grid or renewable energy. Taking into account all issues above, we optimize the uplink achievable rate through jointly optimizing the CPU frequency, computation tasks, terminal transmitting power and the ratio of MEC task. In order to solve this optimization problem, we first decouple it into two sub-problems. For the first one, we can obtain the closed-form solution of CPU frequency. For the second one, we continue to decompose it into two non-convex problems, and then the iterative active-set method is used to solve these two problems. Numerical simulations demonstrate the effectiveness of the proposed design.

Network-assisted full-duplex (NAFD) distributed massive multiple-input multiple-output (MIMO) systems enable simultaneous uplink and downlink communications by dynamically allocating the numbers of uplink and downlink remote antenna units (RAUs), which potentially improve the spectral efficiency in wireless communications. In such systems, channel state information (CSI) plays a critical role in uplink reception and downlink transmission, as well as the cross link interference cancelation caused by downlink RAUs to uplink RAUs. Moreover, downlink terminals need to estimate CSI to reliably decode the received signals due to the reduced channel hardening effect. However, high training overhead makes it generally impossible to directly estimate CSI. This paper proposes to estimate effective CSI (inner products of beamforming and channel vectors) instead based on beamforming training scheme. Under this scheme, we derive closed-form expressions for uplink and downlink achievable rates with different receivers and beamforming. Given these expressions, we propose an efficient power allocation scheme which is only dependent on slowly varying large-scale fading from the perspective of multi-objective optimization. Numerical results verify the accuracy of the derived closed-form expressions and effectiveness of beamforming training based CSI estimation. Moreover, trade-off regions between the considered optimization objectives under various system parameters offer numerous flexibilities for system optimization.

In this paper, we investigate the problem of sum rate maximization by guaranteeing the quality of service (QoS) of both uplink and downlink in cell-free with network-assisted full Duplexing (NAFD), where the users operate in half-duplex mode and access points (APs) operate in either full-duplex mode or half-duplex mode. In the considered network, the central processor unit (CPU) sends the compressed beamformed signals to the transmit-APs (T-APs) over the downlink fronthaul, and the T-APs forward the signals to downlink users. At the same time, the receive-APs (R-APs) compress the signals transmitted by uplink users and forward them to the CPU via uplink fronthaul. We aim to maximize the spectral efficiency and the number of users that should be admitted by the network, where users’ requirements of both downlink and uplink signal-to-interference-plus-noise (SINR) constraints, fronthaul capacity constraints, energy harvesting constraints and simultaneous wireless information and power transfer (SWIPT) ratio design are considered. A successive convex approximation-based algorithm is proposed to solve the highly coupled problem, which is guaranteed to converge to the Karush-Kuhn-Tucker (KKT) conditions. We conduct a comprehensive comparison between the NAFD scheme and the traditional co-frequency co-time full duplex (CCFD) scheme and time division duplex (TDD) scheme and offer some valuable opinions about the system design.

This paper studies transceiver design for a large-scale distributed antenna system (L-DAS) with network assisted full duplexing (NAFD), where all the users and remote antenna units (RAUs) operate in either half-duplex (HD) or full-duplex (FD) mode. In the considered network, transmitting-RAUs (TRAUs) transmit information to downlink users (DUs) while receiving-RAUs (R-RAUs) receive signal from uplink users (UUs). All the T-RAUs and R-RAUs are connected to the central processor (CP) via high-speed backhaul links. T-RAUs obtain DUs' data from the CP via downlink backhaul (D-backhaul), and forward the data to DUs by sparse beamforming. Meanwhile, R-RAUs detect the signal transmited by UUs, and forward the signal to the CP via uplink backhaul (U-backhaul). We aim to maximize the the spectral efficiency subject to quality of service (QoS) constraints and backhaul constraints. Since various design parameters, such as the downlink sparse beamformers, the uplink transmit power, and the receiver, are tightly coupled together in both the subject function and the constraints, the solution of the problem is challenging. By converting the object function to the difference between two convex functions (D.C.) structure with semi definite relax (SDR), an iterative SDR-block coordinate descent (SDR-BCD) method is proposed. Simulation results show that the proposed algorithm yield a higher spectral efficiency (SE) gain compared with the traditional time-division duple (TDD) scheme.

In this paper, the spectral efficiency of network assisted full-duplex communications (NAFD) in cell-free (CF) massive multiple-input multiple-output (MIMO) network with imperfect channel state information is investigated under spatial correlated channels. Based on large dimensional random matrix theory, the deterministic equivalents for the uplink sum-rate with minimum-mean-square-error receiver as well as the downlink sum-rate with zero-forcing and regularized zero-forcing beamforming are presented. Numerical results show that under various environmental settings, the deterministic equivalents are accurate in both a large-scale system and system with a finite number of antennas. It is also shown that with the downlink-to-uplink interference cancellation, the uplink spectral efficiency of CF massive MIMO with NAFD could be improved. The spectral efficiencies of NAFD with different duplex configurations such as in-band full-duplex, and half-duplex are compared. With the same total numbers of transmit and receive antennas, NAFD with half-duplex remote antenna units offers a higher spectral efficiency. To alleviate the uplink-to-downlink interference, a novel genetic algorithm based user scheduling strategy (GAS) is proposed. Simulation results show that the achievable downlink sum-rate by using the GAS is greatly improved compared to that by using the random user scheduling.

This paper studies the energy-efficient power allocation problem for physical-layer security in multi-user (MU) distributed massive multiple-input multiple-output (MIMO) systems. A new metric called global average secrecy energy efficiency (GASEE) is proposed to measure the MU secrecy energy efficiency (SEE) with a single eavesdropper (Eve). We first derive closed-form expressions for the signal to interference-plus-noise ratios (SINRs) of legitimate users and the Eve with pilot contamination. Under a power consumption model that incorporates transmit power, backhaul power, remote antenna unit (RAU) circuit and signal processing power, and with transmit power constraints as well as SINR constraints for both users and the Eve, the GASEE maximization problem is formulated as a joint optimization of power allocation, RAU clustering, RAU selection and artificial noise (AN) selection. The formulated problem is a mixed integer nonlinear program (MINLP), which is solved by a double-loop procedure. In the outer loop, the denominator of objective is approximated as a linear function. In the inner loop, an efficient algorithm is proposed to find a near-optimal solution to the approximated problem by solving a sequence of sub-problems. Simulation results demonstrate that the proposed algorithm converges fast and achieves a higher GASEE than some heuristics.

This study investigates the joint power allocation and energy cooperation problem in a multiuser downlink distributed antenna system with hybrid energy supply. The authors focus on the energy efficiency (EE) maximization problem for three different types of precoding, zero‐forcing (ZF), general beamforming method and conjugate‐beamforming. For ZF precoding, they apply fractional programming and reform the optimization problem to a convex one. An iterative algorithm to deal with the fractional objective function is presented. Whereas the problem is non‐convex for general beamforming with user interference, they take maximum ratio transmission as an example and adopt a set of transformation and approximation based on the difference of convex (DC) programming. Furthermore, for conjugate‐beamforming, they first transform the quadratic signal to interference plus noise power ratio into a trackable form and then DC programming is applied. A two‐loop algorithm for the general beamforming and conjugate‐beamforming is presented with fractional programming in the inner loop and DC programming in the outer loop. Simulations show that the proposed algorithm improved EE significantly and indicate that energy cooperation can contribute to a higher EE. It also reveals that ZF achieves better EE with small noise variance while conjugate beamforming in high noise circumstance.

This paper considers the uplink of a multicell multiuser multiple‐input‐multiple‐output (MIMO) time‐division duplexing system, where K mobile users in the target cell send their uncoded M‐ary phase shift keying (M‐PSK) signals to the target base station (BS) equipped with a linear minimum mean square error (MMSE) receiver in the presence of receiver antenna correlations. By employing the equivalent channel model, the uplink is analysed in terms of symbol error rate (SER), giving approximated closed‐form expressions. The complicated functions of the SER can be simplified for the case where all the eigenvalues of the correlation matrix of the target BS are identical or distinct. It is proved that in the high signal to noise ratio (SNR) region, the SER is independent of the correlations between the BS antennas. And due to the existence of pilot contamination and intercell interference, the SER tends to a constant whose value is depended on the number of mobile users in each cell and the interference strength between the cells as well. Finally computer simulations are conducted to show that our approximated expressions have good performance in low SNR even for not large K.

In this paper, the downlink spectral efficiency of multi-cell multi-user large-scale distributed antenna systems (DASs) is studied in the presence of pilot contamination. Based on the properties of Gamma distribution, the closed-form expression is derived for the downlink achievable rate with maximum ratio transmission (MRT). The ultimate rate is also given when the ratio of the total number of base station (BS) antennas to the number of users goes to infinity. Finally, the results are validated via numerical simulations. It is shown that the closed-form expression is very accurate, and the downlink achievable rate of large-scale DAS is much larger than that of co-located massive multiple-input multiple-output (MIMO) with the same antenna configuration.

We consider the downlink multi-user multi-cell massive MIMO systems, assuming that the number of antennas at base station (BS) and the number of users are large. Our system model accounts for channel estimation, pilot contamination, and uniformly random user location distribution. We derive the approximation of area spectral efficiency (ASE) with regularized zero-forcing (RZF) precoding technique which are proven to be accurate via simulation results. With a realistic power consumption model considering not only transmit power but also the fundamental power for operating the circuit at transmitter and receiver, we analyze the performance of area energy efficiency (AEE). Finally, based on the proposed power consumption model, we determine the optimal number of antennas at BS aimed at maximizing AEE when transmit power is given.

In this study, the downlink spectral efficiency of multi‐cell multi‐user large‐scale distributed antenna systems (DASs) with pilot contamination is studied in a composite correlated Rayleigh fading channel. Firstly, under a physical channel model, the equivalent channel model of large‐scale DAS with pilot contamination is given. Secondly, based on the equivalent channel model, the closed‐form expression is derived for the downlink achievable rate with maximum ratio transmission, and the ultimate rate is also given when the ratio of the total number of base station antennas to the number of users goes to infinity. Thirdly, the results are validated via numerical simulations. It is shown that the closed‐form expression is very accurate, and the downlink achievable rate of the large‐scale DAS is much larger than that of the co‐located massive multiple‐input multiple‐output (MIMO) with the same antenna configuration.

The spectral efficiency of single‐cell multi‐user large‐scale distributed antenna system (DAS) is studied. Firstly, the closed‐form expressions are derived for the uplink achievable rate with maximum ratio combining and the downlink achievable rate with maximum ratio transmission. Secondly, the limiting rates are also given when the ratio of the total number of base station (BS) antennas to the number of users goes to infinity. Thirdly, the results are validated via numerical simulations. It is shown that the closed‐form expressions are very accurate with respect to the simulation results over a wide range of the ratio of the total number of BS antennas to the number of users, both the uplink and downlink achievable rates of the large‐scale DAS are much larger than that of the co‐located massive multiple‐input multiple‐output with the same antenna configuration and the theoretical results get more and more close to the limiting rates with increasing the ratio of the total number of BS antennas to the number of users.

Uplink–downlink duality has emerged as an attractive approach to optimise the downlink beamforming problem with fixed cooperation clusters where either all base stations serve all terminals or each base station serves only its own terminals. Although easily implementable for co-located base stations, the performance is still limited by out-of-cluster interference. To address these concerns, this study establishes an uplink–downlink duality for the multi-cell multi-user system with dynamic cooperation clusters where each base station has responsibility for the interference leaked to a set of terminals while only serving a subset of them with data. The multi-cell downlink problem of minimising the total transmit power subject to individual signal-to-interference-and-noise ratio requirements under per-base station power constraints is solved via a dual uplink problem. Conditions for beamforming optimality and the optimal downlink beamforming design are derived using Lagrange duality theory. The convergence behaviour of proposed algorithm is shown. The percentage of power saved by proposed algorithm is calculated subject to the user-specific SINR value achieved by zero-forcing (ZF), maximum-ratio transmit (MRT), virtual SINR (VSINR) and layered virtual SINR (LVSINR) under different cooperation scenarios and the sum rate performance is compared.