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.

This paper considers a practical cell-free massive multiple-input multiple-output (CF-mMIMO) architecture within an open radio access network, where edge distributed units (EDUs) and user-centric distributed units (UCDUs) collaboratively handle physical-layer functions (e.g., channel estimation, precoding), while the open radio units (ORUs) are responsible for radio-frequency transmission and reception with the user equipment (UE). Based on large-dimensional random matrix theory, we derive a deterministic equivalent (DE) expression for the ergodic sum SE under imperfect statistical channel state information (S-CSI). Thanks to this DE-assisted result, two optimization problems: 1) sum power minimization, and 2) ergodic sum spectral efficiency (SE) maximization, are addressed through regularized parameter tuning in local partial regularized zero-forcing (LP-RZF), and power control with large-scale fading (LSF)-based EDU-ORU deployment and ORU-UE association. Numerical results validate the tightness of the DE-based ergodic sum SE expression and demonstrate the effectiveness of the LP-RZF scheme compared to the benchmark schemes. Meanwhile, the reduced computational complexity is achieved with acceptable performance loss.

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.

Cell-free massive multiple-input-multiple-output (CF-mMIMO) is regarded as one of the promising technologies for next-generation wireless networks. However, due to its distributed architecture, geographically separated access points (APs) jointly serve a large number of user-equipments (UEs), and there are inevitably discrepancies in the arrival time of transmitted signals. In this paper, we investigate millimeter-wave (mmWave) scalable CF-mMIMO orthogonal frequency division multiplexing (OFDM) systems with asynchronous reception in a wide area coverage scenario, where asynchronous timing offsets may exceed far beyond the cyclic prefix (CP) duration. To address the issue, we propose a novel per-beam timing advance (PBTA) hybrid precoding architecture and derive closed-form expressions of the spectral efficiency (SE) for downlink asynchronous transmission. Both scalable centralized and distributed implementations are taken into account. Furthermore, we formulate the sum rate maximization problem and develop two low-complexity joint beam selection and UE association (BSUA) algorithms considering the impact of asynchronous timing offset. Simulation results demonstrate that asynchronous interference can severely degrade performance in wide-area scenarios, and our proposed PBTA scheme exploits beam-domain synchronization to align signal arrivals, effectively suppressing asynchronous interference and delivering notable performance gains. Additionally, the proposed BSUA algorithms achieve superior SE performance with low computational complexity.

In the 6G era, ultra-reliable and low-latency communications (URLLC) has become a key enabler for mission-critical applications. However, existing approaches predominantly rely on closed-loop communications with hybrid automatic repeat request (HARQ) retransmissions, which struggle to meet the stringent microsecond-level end-to-end (E2E) latency requirements of 6G, particularly during the initial access phase. To bridge this gap, we propose a novel open-loop communication (OLC) framework, integrating grant-free access with frequency diversity under a contention-based transmission paradigm. The framework incorporates a fine-grained resource allocation strategy and detailed mechanisms for collision detection, uplink channel estimation, and signal detection. To evaluate the performance of OLC, we conduct a unified reliability and latency analysis, leveraging the channel hardening property of massive MIMO systems. We derive closed-form approximations for packet loss probability, incorporating factors such as access collisions, channel impairments, and finite blocklength effects. Furthermore, to minimize uplink bandwidth while satisfying reliability and latency constraints, we develop an optimal system parameter configuration method, which is seamlessly integrated with power control in the collision detection process. Simulation results validate the theoretical reliability expressions and demonstrate the superior performance of the proposed OLC framework over conventional closed-loop schemes.

We consider a cell-free massive multiple-input and multiple-output (CF-mMIMO) system, where we derive a deterministic equivalent (DE)-form of the ergodic sum spectral efficiency (SE) based on local partial regularized zero-forcing (LP-RZF) precoding with statistical channel state information (S-CSI) by leveraging large-dimensional random matrix theory. Thanks to this derivation, the previously challenging issue of precoding design based on S-CSI is now resolved, particularly in scenarios where CSI is limited to local information at each access point (AP). Moreover, as the central processing unit (CPU) now only needs to transmit an optimized regularization parameter to the APs, the computational overhead can be reduced, which naturally enhances the system scalability. Driven by these advantages, we then introduce a joint user association, power allocation, and precoding design (i.e., regularization parameter optimization) scheme aimed at maximizing the ergodic sum SE and minimizing the sum power consumption. This is achieved through two optimization problems: one for the ergodic sum SE maximization using weighted minimum mean square error (WMMSE)-based processing and another for the sum power consumption minimization employing a block coordinate descent (BCD)-based algorithm. Numerical results demonstrate the superior performance of the proposed PRO-LPRZF scheme.

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 wireless networks aim to deliver ultra-reliable and low-latency services while containing their rapidly growing energy footprint. In a cell-free radio access network (CF-RAN), this objective translates into a tightly coupled optimisation over access point (AP) activation, user association, precoding design and virtual-CPU provisioning, all under finite-blocklength reliability constraints. We build a detailed power model that includes radio hardware, fronthaul and load-dependent computing, then recast the resulting energy efficiency problem as a mixed-integer second-order cone programming using a tight surrogate for decoding-error probability. A sparsity-promoting convex–concave solver can reach near-optimal solutions but must be run for every channel realisation, making real-time use impractical. To overcome this limitation, we propose a graph neural network (GNN) that represents CF-RAN as a heterogeneous AP-to-user graph, predicts precoding vectors, rates and soft association probabilities in a single forward pass, and then applies a lightweight reliability-enforcement layer to remove any residual violations. Simulation results show that, whenever the constraints are feasible, the learned solver achieves comparable energy efficiency as the optimization-based baseline while operating with only a single-pass inference step per channel realization.

Cell-free massive multiple-input multiple-output (MIMO) systems, leveraging tight cooperation among wireless access points, exhibit remarkable signal enhancement and interference suppression capabilities, demonstrating significant performance advantages over traditional cellular networks. This paper investigates the performance and deployment optimization of a user-centric scalable cell-free massive MIMO system with imperfect channel information over correlated Rayleigh fading channels. Based on the large-dimensional random matrix theory, this paper presents the deterministic equivalent of the ergodic sum rate for this system when applying the local partial minimum mean square error (LP-MMSE) precoding method, along with its derivative with respect to the channel correlation matrix. Furthermore, utilizing the derivative of the ergodic sum rate, this paper designs a successive convex approximation based deployment optimization method to improve system deployment. Simulation experiments demonstrate that under various parameter settings and large-scale antenna configurations, the deterministic equivalent of the ergodic sum rate accurately approximates the Monte Carlo ergodic sum rate of the system. Furthermore, the deployment optimization algorithm effectively enhances the ergodic sum rate of this system by optimizing the positions of access points.

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.

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.

Cell-free massive multiple-input multiple-output (CF-mMIMO) is a promising technology for sixth-generation mobile communication systems. Building upon conventional CF-mMIMO, the cell-free radio access network (CF-RAN) architecture distributes physical-layer functionalities among access points (APs), edge distributed units (EDUs), and user-centric distributed units (UCDUs), striking a balance between complexity and performance. However, prior studies on CF-RANs have primarily focused on the physical-layer, while the scalability of the medium access control (MAC) layer remains insufficiently explored. This paper proposes a novel hierarchical scalable CF-RAN architecture that fully exploits the functional potential of distributed UCDUs, enabling a comprehensive decentralized paradigm spanning from the physical-layer to the MAC-layer. Closed-form uplink spectral efficiency (SE) expressions are derived for maximum ratio (MR), distributed full-pilot zero-forcing (FZF), and distributed joint partial zero-forcing (JP-ZF) combining, explicitly accounting for imperfect channel state information and pilot contamination. The analytical insights reveal the improved scalability and how distributed processing and partial information availability affect system performance. To support large-scale deployments, we further develop a structured massive access scheme, including UCDU-EDU deployment, UE-UCDU association, distributed pilot assignment and AP-UE association, centralized refinement, and distributed power control. Simulation results verify the accuracy of the theoretical analysis and demonstrate the superior SE, favorable fairness, and enhanced scalability of the proposed schemes.

This work explores the maximization of energy efficiency (EE) in cell-free ultra-reliable low-latency communication (URLLC) systems, specifically addressing the challenges presented by hardware impairments (HWIs). We introduce ratesplitting multiple access (RSMA) as an effective technique to enhance EE by managing the distortions caused by HWIs during downlink transmission. The optimization problem is formulated to maximize EE by optimizing precoding vectors for both common and private streams, while adhering to rate, power, and URLLC constraints. To address the non-convex and dynamic nature of this optimization problem, we propose a graph neural network (GNN) model that facilitates scalable and data-efficient solutions. Simulation results demonstrate that the proposed RSMA-GNN method consistently outperforms baseline approaches, including space-division multiple access (SDMA) and successive convex approximation (SCA) methods, particularly in scenarios characterized by severe HWIs.

Cell-free (CF) massive multiple-input multipleoutput (MIMO) system based on local operations is recognized as a viable next-generation network architecture with high performance. Leveraging large-dimensional random matrix theory, we have derived a deterministic equivalent performance expression for the performance of a CF MIMO system employing local regularized zero-forcing (L-RZF) precoding, and based on this performance expression, we have designed a high-performance regularization parameter optimization method, termed statistical channel state information aided L-RZF (SCSI-L-RZF). Under SCSI-L-RZF method, the CPU only needs to optimize the regularization parameters for each AP by utilizing statistical channel state information (CSI), while each AP only needs to design precoding by combining the regularization parameter and local CSI to achieve good performance gains. Simulation results demonstrate that our SCSI-L-RZF scheme outperforms the current mainstream maximum ratio transmission (MRT) precoding schemes, L-RZF and local team minimum mean square error (LT-MMSE), further narrowing the performance gap between local and global precoding.

To meet the stringent requirements of latency, reliability and energy efficiency (EE), we introduce rate splitting multiple access (RSMA) into ultra-reliable and low-latency communication (URLLC) IIoT networks, where RSMA has a good flexibility in interference management. By jointly optimizing beamforming design and common rate allocation, we investigate the worst-case EE maximization problem with imperfect channel state information (CSI). Although the problem is nonconvex, we exploit the monotonicity of the problem to develop an optimal solution, where a monotonic optimization framework based on polyblock outer approximation (PA) and boundary searching is proposed to find the optimal points on the Pareto boundary. Simulation results show the convergence of the proposed optimal algorithm, and RSMA can achieve higher EE than space division multiple access (SDMA) and non-orthogonal multiple access (NOMA) in URLLC IIoT scenarios.

The convergence of hyper-reliable and low-latency communication (HRLLC) with industrial networking has been envisioned as the potential candidate for cyber-physical systems deployments, which increases the flexibility without moderating the stringent requirements of latency and reliability. Throughout the industrial network, event-triggered (ET) traffic bears serious performance degradation of timeliness, stemming from the resource preemption by time-critical time-triggered (TT) traffic under prioritized scheduling mechanisms. Furthermore, wireless links changes and finite blocklength regime in HRLLC result in transmission error, violation of latency requirement, and conservative resource allocation. Consequently, a cross-layer optimization framework is established to compress the total end-to-end (E2E) latency and bandwidth of ET flows under quality-of-service (QoS) constraints. The reliability is characterized with the decoding error probability with frequency diversity in finite blocklength regime, and the timeliness is characterized with the experienced E2E latency considering time offsets and routing of TT flows. Through spatial non-overlapping path planning that balances shorter length and fewer links contention, and physical optimal resource allocation that refines QoS-aware assignments of bandwidth, the number of subchannels, and decoding error probability threshold, the total latency compression and spectrum compaction can be achieved. Simulation results show the distinct performance gain of proposed cross-layer optimization framework compared with other methods.

In this paper, we investigate the cross-layer design in hybrid reconfigurable intelligent surface (RIS)-assisted cell-free systems. To maximize long-term energy efficiency while ensuring network stability, we formulate a queue-aware stochastic problem that jointly optimizes the beamforming matrices at the remote antenna units (RAUs) and hybrid RIS, along with the operation modes of RIS elements. By utilizing the Lyapunov theory, the formulated stochastic optimization problem is decomposed into a sequence of slot-level problems that maximize energy efficiency while minimizing the Lyapunov drift. To tackle the formulated non-convex slot-level problem, we propose an alternating optimization algorithm to transform the slot-level problem into transmission and element mode switching subproblems. In particular, for efficient RIS element selection, we first derive the number upper bound of the elements that can be activated given the RIS power budget, and then the element selection optimization is transformed into multiple tractable smaller problems that can be solved in parallel by utilizing a greedy algorithm. Simulation results demonstrate that the proposed algorithm outperforms the baseline schemes in terms of energy efficiency and can effectively improve system stability as well as user fairness.

To meet the quality demands of industries for future communication networks, it is crucial to explore wireless communication technologies with low latency and high reliability. In wireless networks, multi-connectivity (MC) technologies can improve performance in terms of data rate, reliability, and latency. To realize the vision of ultra-reliable and low latency communications (URLLC), the design and performance analysis of transmission schemes for short packet communication are required. In this paper, we propose MC transmission schemes based on packet splitting (SP) for the multiple-input multipleoutput (MIMO) eigenbeam-space division multiplexing (E-SDM) system architecture over quasi-static fading channels in the finite blocklength regime, accompanied by theoretical performance evaluations. Analytical expressions for the decoding error probability across distinct eigenbeams are derived to enable comparative analysis of the transmission performance. Simulation results validate the theoretical derivations, demonstrating that the flexible design of transmission mechanisms leveraging eigenbeam characteristics can further unlock the reliable performance of short packet communication.

Ultra-reliable and low-latency communications (URLLC) is crucial for Internet of Things (IoT) applications, while cell-free massive MIMO (mMIMO) is a promising paradigm for URLLC in IoT, as it can improve the reliability of cell-edge users. However, URLLC typically uses short packet transmission, requiring pilot reuse among users to reduce pilot overhead, which inevitably leads to pilot contamination. There-fore, efficient pilot assignment and power control are essential. In this paper, we consider a cell-free mMIMO system for a large-scale smart factory, and formulate a problem to maximize sum rate by jointly optimizing pilot assignment and power control. Meanwhile, the lower bound of the ergodic data rate under finite blocklength is derived to provide an explicit expression for subsequent algorithm design. Then, we transform the non-convex sum-rate maximization problem into a Markov decision process and design a multi-agent proximal policy optimization algorithm. Finally, simulation results demonstrate the superiority of the proposed algorithm over the baselines in both static and dynamic environments.

Hybrid digital-analog precoding is a pivotal transmission technique to balance communication performance and hardware costs associated with radio frequency (RF) chains in millimeter wave (mmWave) massive multiple-input multipleoutput (MIMO). However, most existing designs utilize Shannon rate and assume an infinite blocklength, which is impractical for emerging finite blocklength (FBL) applications, such as massive machine-type communications. To fill in this gap, this paper investigates hybrid precoding optimization in the FBL regime. The aim is to maximize the weighted sumrate (WSR), while fulfilling the transmit power budget at the base station (BS) and users' minimum rate requirements. The formulated optimization problem is highly challenging to solve, particularly due to the complex and nonconcave FBL rate function and the intricate coupling between analog and digital precoders. To tackle these issues, we propose a computationally efficient solution based on the penalty dual decomposition (PDD) method, which is guaranteed to converge to the Karush-KuhnTucker (KKT) solutions under mild conditions. Simulation results demonstrate that our proposed hybrid precoding design significantly outperforms several baseline schemes, especially those ignoring the impact of blocklength and adopting Shannon rate as the performance metric.

Enhanced mobile broadband (eMBB) and ultra-reliable low-latency communications (URLLC) are two critical service types in industrial automation. In a closed-loop control system, device-to-device (D2D) communication is typically employed for direct transmission due to its low-latency requirements. However, this approach does not leverage the large-scale antenna gains of massive MIMO cellular systems. To address this limitation, we introduce a multi-connectivity network that integrates both cellular and D2D links to serve URLLC sensors while accommodating the transmission needs of general eMBB traffic. Since the D2D link serves as the primary link for URLLC transmission in a multi-connectivity setup, we first analyze the packet loss probability components for single-D2D URLLC link. Then, we formulate an optimization problem to maximize the sum channel capacity of eMBB sensors while satisfying URLLC QoS requirements. A sub-optimal power and spectrum allocation scheme is proposed to solve this coexistence problem of single-D2D URLLC and cellular eMBB transmission. For multi-connectivity, we examine the packet loss probability and present two transmission frameworks based on selection combining (SC) and maximal ratio combining (MRC). Simulation results validate the properties of the optimal solution for the relaxed problem and demonstrate the performance gains of multi-connectivity over single-D2D links.

In this paper, we study the transmit beamformer design to minimize the weighted sum finite blocklength (FBL) decoding error probability for a downlink multiuser multiple-input single-output (MISO) ultra reliable and low-latency communication (URLLC) system. Since the Gaussian Q-function in the design objective does not admit a closed form, the considered problem is challenging to solve. In order to handle the complicated problem, we first obtain its reformulation via a tight Gaussian Q-function approximation, which is then solved via the majorization-minimization (MM) technique tailored for the reformulated problem. Moreover, in order to further reduce the computational complexity, we first obtain the optimal structure of the transmit beamformer for the original problem, based on which we establish a neural network solution which avoids solving convex problems. Furthermore, we also investigate the extension to the statistically robust beamforming design with the imperfect channel state information (CSI). Simulation results verify the clear performance advantages of the proposed solutions over several state-of-the-art schemes as well as widely used beamforming schemes such as regularized zero-forcing (RZF) precoding in terms of the FBL decoding error probability. Moreover, the low-complexity solutions can effectively approach the proposed MM-based algorithms with much reduced computational costs.

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.

Ultra-reliable and low-latency communications (URLLC) has great potential in wireless industrial automation, which increases flexibility without moderating the stringent quality-of-service (QoS). In URLLC-enabled Industrial Internet of Things (IIoT), mixed traffic is essential to support applications with differentiated latency requirements, where event-triggered (ET) traffic requires bounded end-to-end (E2E) latency, and time-triggered (TT) traffic demands deterministic latency. This article proposes a mixed traffic scheduling scheme with the preemption mode, in which TT traffic with a higher priority is allowed to preempt the transmission of ET traffic to ensure TT deterministic latency. However, this preemption may cause ET latency to exceed its requirements with constrained resources. To characterize the impact of TT preemption on the ET latency and the deterministic metrics of TT traffic, we derive the ET delay violation probability bound and the TT deterministic delay probability using the stochastic network calculus. To further achieve bandwidth-saving scheduling while ensuring the QoS requirements of mixed traffic, the credit-based shaper (CBS) configurations, radio resources and delay components are assigned under constraints of ultra-reliability, ET bounded latency and TT deterministic latency. Then, a three-step method is proposed to find the global optimal solution to minimize the total bandwidth. Simulation results validate our analysis and show the bandwidth-saving performance gain by jointly optimizing uplink and downlink resources with the proposed preemption-based scheduling scheme.

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.

Sparse Bayesian Learning (SBL) is recognized for its efficacy in sparse signal recovery, the computational demand escalates significantly with increasing data dimensionality due to the matrix inversion at each iteration. An Inverse-Free sparse Bayesian Learning (IF-SBL) approach has been introduced to mitigate computational complexity. However, IF-SBL converges easily to a sub-optimal solution with false peaks due to the neglect of the correlation between atoms. In this paper, we analyze causes of false peaks in IF-SBL. Subsequently, a novel dynamically updated embedded Bayesian threshold is designed to mitigate the interference caused by false peaks. This innovative approach retrieves the stability and reliability without significantly increasing signal recovery complexity compared with IF-SBL. Simulation experiments validate the results.

In this paper, we focus on the decoding error probability based beamforming design for a downlink multiantenna multiuser ultra reliable and low-latency communication (URLLC) system. We first optimize the transmit beamformer to minimize the maximum finite blocklength (FBL) decoding error probability under block fading channels, which turns out to be a complicated nonconvex problem with a non-closed-form objective function. A successive convex approximation (SCA)-based algorithm is developed to determine a high-quality solution. Moreover, in order to reduce the computational complexity of the proposed algorithm, we perform the downlink beamforming optimization by solving a simpler virtual uplink problem. The complexity of the resultant solution only scales linearly with the number of transmit antennas. Furthermore, a special case with quasi-static channels is investigated, where the proposed SCA and the uplink-downlink duality based solutions for the block fading channel can be simplified in a non-trivial manner. Simulation results verify the performance superiorities of the proposed algorithms in the context of short packet communication. In particular, the proposed designs outperform conventional regularized zero-forcing (RZF) and max-min signal-to-interference-plus-noise ratio (SINR) beamforming by evident gains in terms of the FBL decoding error probability.

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.

As a critical component of beyond fifth-generation (B5G) and sixth-generation (6G) mobile communication systems, ultra-reliable and low-latency communication (uRLLC) imposes stringent requirements on both latency and reliability. In recent years, with the evolution of mobile communication networks, centralized and distributed processing schemes for cell-free massive multiple-input multiple-output (CF-mMIMO) have attracted significant attention. This paper investigates the performance of a novel scalable cell-free radio access network (CF-RAN) architecture featuring multiple edge distributed units (EDUs) under the finite block length regime. Closed-form expressions for the upper and lower bounds of the expected sum spectral efficiency (SE) are derived, where centralized and fully distributed deployment can be treated as two special cases, respectively. Furthermore, the spatial distributions of user equipment (UE) and remote radio units (RRUs) are analyzed, revealing that interleaving RRUs deployment associated with the EDUs can enhance SE performance under finite block length constraints with a specified transmission error probability. This paper also compares Monte-Carlo simulation results with multi-RRU clustering-based collaborative processing, validating the accuracy of the space-time exchange theory in the scalable CF-RAN scenario. By deploying scalable EDUs, a practical tradeoff between latency and reliability can be achieved through the spatial degree-of-freedom (DoF), thereby offering a distributed and scalable realization of the space-time exchange theory.

Hybrid digital-analog precoding is essential for balancing communication performance, energy efficiency, and hardware costs in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. However, most existing designs rely on the Shannon capacity and assume infinite blocklengths, which are impractical for emerging applications, such as massive machine-type communications, operating with finite blocklength (FBL). To address this gap, this paper pioneers a novel hybrid precoding design for mmWave massive MIMO in the FBL regime. We meticulously optimize hybrid precoding based on both the weighted sum-rate (WSR) and the max-min fairness (MMF) criteria, while fulfilling the transmit power budget and users’ minimum rate requirements. Both continuous and discrete phase shifters are considered for analog precoding. The formulated optimization problems are highly challenging to solve due to the nonconvex objective functions and nonconvex constraints. These challenges are further intensified by the nonconcave FBL rate function and the intricate coupling between analog and digital precoders. By proposing novel problem transformation and decomposition techniques, we reformulate the original complex problems into forms solvable with the penalty dual decomposition (PDD) method. We then develop two efficient iterative algorithms with parallel, and even closed-form variable updates, and guaranteed convergence to solve the WSR and MMF optimization problems, applicable to both continuous and discrete phase shifters. Simulation results show that our proposed hybrid precoding designs significantly outperform several baseline schemes, especially those adopting the Shannon capacity and infinite blocklength. Additionally, our proposed optimization algorithms enable hybrid precoding exploiting discrete phase shifters with limited quantization resolution (e.g., 3-bit) to closely match the performance of fully digital precoding in FBL scenarios.

One of the primary challenges in ultra-reliable and low-latency communications (URLLC) is to achieve accurate channel estimation and data detection while minimizing latency. Given the small packet size in URLLC, relying solely on pilot-assisted (PA) coherent detection is almost impossible to meet the seemingly contradictory requirements of high channel estimation accuracy, high reliability, low training overhead, and low latency. In this paper, we explore both frequency domain differential modulation (FDDM) and time domain differential modulation (TDDM), enabling non-coherent short packet URLLC with mini-slot structures. The minimum achievable block error rate and the maximum achievable rate for all three modes (i.e., FDDM, TDDM and PA modes) are derived using non-asymptotic information-theoretic bounds. Furthermore, we show that FDDM can more than compensate for the training overhead inadequacy and performance degradation of PA mode in medium-to-high-mobility scenarios, thereby improving the performance of short packet transmission with mini-slot by dynamically activating FDDM. Simulation results validate the feasibility and effectiveness of the proposed low overhead FDDM mini-slot transmission scheme.

Vehicle-to-everything (V2X) applications are usually powered by vehicular batteries and thus are power limited in general. Reconfigurable intelligent surfaces (RISs) are capable of improving the spectral efficiency and conserving energy of the wireless communications, due to the planar array architecture of which is superior beamforming gain and energy-efficient. In this paper, we study a novel design scheme where a double-RIS-enabled integrated sensing and communication (ISAC) system in vehicular network performs both a single target sensing and multi-user communications synchronously. Specifically, two transmit power budget minimization problems are formulated based on Cramér-Rao bound (CRB)-based framework under the known target location model, and radar signal-to-noise ratio (SNR)-related framework under the uncertain target location model, respectively. For the former, we propose an efficient solver based on alternative direction method of multipliers (ADMM) technique to obtain high-quality solutions for transmit beamforming and phase shifts. For the latter, an efficient algorithm based on penalty-dual-decomposition (PDD) and second order cone programming (SOCP) approaches is proposed. Simulation results demonstrate the effectiveness of two proposed algorithms and also show the superiority of our developed schemes over state-of-the-art benchmark ISAC schemes.

Ultra-Reliable and Low-Latency Communications (URLLC) is essential for sixth generation communications, with Cell-Free massive Multiple-input-Multiple-Output (CF mMIMO) being a promising architecture to support these demands. This paper addresses the challenge of optimizing energy efficiency in CF mMIMO systems for URLLC, focusing on the probabilistic delay bounds and finite blocklength communications. We propose a theoretical framework that considers tail distributions to evaluate extreme reliability and latency requirements, instead of relying on asymptotic analysis. In particular, a closed-form expression for the signal-to-interference-plus-noise ratio (SINR) distribution is derived, accommodating imperfections in channel state information caused by pilot contamination. Then, the paper also presents a comprehensive reliability analysis, incorporating both delay violation probability and average decoding error probability, utilizing stochastic network calculus for accurate statistical modeling. Finally, an innovative power control algorithm is proposed to maximize effective energy efficiency (EEE), the ratio of the effective data rate to total power consumption, while meeting stringent Quality-of-Service (QoS) constraints and power limits. Extensive simulations validate the theoretical framework and the efficacy of the proposed algorithm, demonstrating its ability to enhance EEE in various scenarios and providing insights into the interplay between EEE, delay, and reliability metrics.

The Industrial Internet of Things (IIoT) is envisioned as the future paradigm for the next-generation industrial system. To provide low-latency and high-reliable communication for a large number of battery-limited industrial equipments, this paper proposes a cell-free network-assisted full-duplex (NAFD)-enabled short packet communications (SPC) scheme in IIoT networks, where access points (APs) operating in the uplink (UL) mode or downlink (DL) mode simultaneously serve UL sensors and DL actuators on the same frequency resource. Considering the UL-to-DL error propagation as well as the dynamic between latency, reliability and energy efficiency (EE), we take the effective EE as the performance metric and investigate the effective EE maximization problem by jointly optimizing UL power, DL beamforming matrices, data rates and duplex mode selection. The formulated problem is a mixed-integer fractional programming and decomposed into three subproblems. We develop the optimal and suboptimal algorithms for joint power control and beamforming design. A semi-closed-form solution is derived for the optimal data rates, and an efficient duplex selection algorithm is also proposed to find the computationally optimal duplex mode. Simulation results demonstrate the convergence of proposed algorithms, and the flexible cell-free NAFD scheme can achieve better EE performance than benchmark schemes such co-time co-frequency full duplex (CCFD), frequency division duplex (FDD) and greedy algorithm (GA) schemes.

The efficient implementation of massive multiple-input-multiple-output (MIMO) transceivers is essential for the next-generation wireless networks. To reduce the high computational complexity of the massive MIMO transceiver, in this paper, we propose a new massive MIMO architecture using finite-precision arithmetic. First, we propose a mixed-precision architecture for massive MIMO systems based on blocked matrix computations. Then the corresponding analysis of rounding errors and computational costs is derived. Finally, simulation results underscore the superiority of the proposed mixed-precision architecture to the conventional structure.

In this paper, we investigate the energy efficiency (EE) optimization in the user-centric Cell-Free massive MIMO (CF mMIMO) system for Ultra-Reliable Low-Latency Communications (URLLC), where access points (APs) use maximum ratio transmission for downlink transmission. We first formulate an optimization problem to maximize the system EE while taking the finite blocklength achievable rate in URLLC into consideration. To deal with the intractable achievable rate in the objective function and the constraint, we derive a convex lower bound of it using successive convex approximation (SCA), and then reformulate the original problem into a second-order cone programming (SOCP). Next, we propose a low-complexity iterative algorithm to solve the SOCP by applying SCA. Simulation results show that the proposed method provides near-optimal performance in terms of Branch-and-Bound (BnB), and provide insights into the influences of blocklength, system parameters and AP clustering schemes on system EE.

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.

Ultrareliable low-latency communication (URLLC) has been envisioned as the paradigm shift for wireless industrial automation in the coming industry 4.0. Despite the flexibility in conjunction with stringent requirements of latency and reliability, URLLC cannot guarantee the determinism demanded by industrial automation to provide an in-order and low-jitter delivery. Motivated by the evolvements in Rel-17 that strengthen the intrinsic determinism of 5G system, this article studies deterministic URLLC to achieve low-latency and bandwidth-saving scheduling of time-triggered traffic in Industrial Internet of Things (IIoT), instead of using time-sensitive networking (TSN) or hybrid TSN-URLLC. However, due to the queueing congestion and wireless links changes, it is challenging to achieve deterministic scheduling with bounded end-to-end (E2E) latency and low-loss probability. To ensure the determinism and schedulability of URLLC, offset s in gate control lists (GCLs) and radio resources are assigned under constraints of zero congestion, sequential arrival, bounded latency, and ultra reliability. To further improve the latency reduction and bandwidth saving of deterministic URLLC, we find the feasible transmission delays subject to the contradiction on monotonicity of the E2E latency and bandwidth with respect to the transmission delay, then propose a scheduling method to obtain the best solution of three cases by optimizing transmission delay, offset, bandwidth, and the number of subchannel. Simulation results validate the analysis and show the performance gain of the proposed method in three cases.

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.

Ultra-reliable and low-latency communications (URLLC) plays a critical role for the coming era of wireless industrial automation, which increases the flexibility without moderating the stringent requirements of latency and reliability. The task for URLLC is to deliver short packets from sensors to actuators via the central controller reliably and timely, during which large bandwidth is required for ensuring stringent quality-of-service (QoS) metrics. Due to the scarce spectrum resource shared by a large number of devices, and the dynamic channel changes over wireless links, it is challenging to achieve bandwidth-saving URLLC with one single method. Motivated by the observation that groups of devices working in close proximity to each other can form device-to-device (D2D) communications, this paper considers multi-connectivity (MC) together with other cross-layer methods including grant-free access, data replication, broadcasting, and processor-sharing server to minimize the total bandwidth under the QoS constraints of URLLC. With the cross-layer design, we fisrt derive the packet loss probability including the factors of collision due to the contention-based access scheme and decoding error due to the dynamic wireless channel, and hence the overall reliability of MC is provided. Then, we establish a framework to minimize the total bandwidth of MC, where the relationship of collision and packet loss probabilities, the monotonicity of collision and decoding error probabilities, and the relationship of the third type blocklength and size rule are proved to find the optimal resource allocation. Simulation results validate the analysis and show the performance gain by optimizing resource allocation with the considered cross-layer design.

Grant-free random access is a critical enabling technology for massive ultra-reliable and low-latency communications (mURLLC), and user activity detection (UAD), determining which users are active based on received signals, is indispensable in grant-free access. The cell-free massive multiple-input multiple-output (MIMO) system, providing macro diversity and supporting more users, is an architecture suitable for mURLLC. This paper investigates the UAD problem with changeable pilot sequences in cell-free massive MIMO systems, and proposes deep learning based UADFormer, where users are assigned to access points (APs) for detection using user grouping algorithm and transformer-based neural networks (NNs) in APs are employed to output user activity state by exploiting the correlation between received signals and pilot sequences. For users detected by multiple APs, a fusion NN based on transfer learning in the central processing unit is utilized to enhance UAD accuracy. Additionally, considering limited computational and storage resources in APs, sparse attention mechanism is used in NNs to reduce computational complexity and residual attention score is employed to improve NN performance. Simulation results show that UADFormer outperforms other schemes in various evaluation metrics and reduces computational complexity.

The receiver design for multi-input multi-output (MIMO) ultra-reliable and low-latency communication (URLLC) systems can be a tough task due to the use of short channel codes and few pilot symbols. Consequently, error propagation can occur in traditional turbo receivers, leading to performance degradation. Moreover, the processing delay induced by information exchange between different modules may also be undesirable for URLLC. To address the issues, we advocate to perform joint channel estimation, detection, and decoding (JCDD) for MIMO URLLC systems encoded by short low-density parity-check (LDPC) codes. Specifically, we develop two novel JCDD problem formulations based on the maximuma posteriori(MAP) criterion for Gaussian MIMO channels and sparse mmWave MIMO channels, respectively, which integrate the pilots, the bit-to-symbol mapping, the LDPC code constraints, as well as the channel statistical information. Both the challenging large-scale non-convex problems are then solved based on the alternating direction method of multipliers (ADMM) algorithms, where closed-form solutions are achieved in each ADMM iteration. Furthermore, two JCDD neural networks, called JCDDNet-G and JCDDNet-S, are built by unfolding the derived ADMM algorithms and introducing trainable parameters. It is interesting to find via simulations that the proposed trainable JCDD receivers can outperform the turbo receivers with affordable computational complexities.

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.

One key feature of ultra-reliable low-latency communications (URLLC) in 5G is to support short packet transmission (SPT). However, the pilot overhead in SPT for channel estimation is relatively high, especially in high Doppler environments. In this paper, we advocate the adoption of differential modulation to support ultra-low latency services, which can ease the channel estimation burden and reduce the power and bandwidth overhead incurred in traditional coherent modulation schemes. Specifically, we consider a multi-connectivity (MC) scheme employing differential modulation to enable URLLC services. The popular selection combining and maximal ratio combining schemes are respectively applied to explore the diversity gain in the MC scheme. A first-order autoregressive model is further utilized to characterize the time-varying nature of the channel. Theoretically, the maximum achievable rate and minimum achievable block error rate under ergodic fading channels with PSK inputs and perfect CSI are first derived by using the non-asymptotic information-theoretic bounds. The performance of SPT with differential modulation and MC schemes is then analysed by characterizing the effect of differential modulation and time-varying channels as a reduction in the effective SNR. Simulation results show that differential modulation does offer a significant advantage over the pilot-assisted coherent scheme for SPT, especially in high Doppler environments.

We study the resource allocation for an uplink ultra reliable and low-latency communication (URLLC) system. The receive beamformer at the base station (BS) and the transmit power of users are jointly optimized to minimize the finite block-length (FBL) decoding error probability. To solve the difficult nonconvex problem, we first acquire a tractable reformulation by approximating the Gaussian Q-function. Although the resultant problem is still nonconvex, we propose an efficient algorithm where the optimal receive beamformer is obtained in closed form and the transmit powers of users are optimized by employing the majorization-minimization (MM) technique. Simulation results and complexity analysis verify the superiority of the proposed algorithm over existing benchmark schemes.

The explosive growth of data results in more scarce spectrum resources. It is important to optimize the system performance under limited resources. In this paper, we investigate the weighted throughput (WPT) maximization for cell-free (CF) multiuser (MU) MIMO multicarrier (MC) systems through resource allocation (RA) in finite blocklength regime (FBL) while ensuring the quality of service (QoS) of each user under the constraints of total power consumption. Since the channels vary in different subcarriers and inter-user interference strengths, the WPT can be maximized by scheduling the best users in each time-frequency (TF) resource and advanced beamforming design (BF). With this motivation, we propose a joint user scheduling (US) and BF algorithm to address an mixed integer nonlinear programming (MINLP) problem. Numerical results demonstrate that the proposed RA scheme outperforms the comparison schemes. And the CF system in our scenario is capable of achieving higher spectral efficiency (SE) than the centralized antenna systems (CAS).

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.

Ultra-reliable and low-latency communications (URLLC) is firstly proposed in 5G networks, and expected to support applications with the most stringent quality-of-service (QoS). However, since the wireless channels vary dynamically, the transmit power for ensuring the QoS requirements of URLLC may be very high, which conflicts with the power limitation of a real system. To fulfil the successful URLLC transmission with finite transmit power, we propose an energy-efficient packet delivery mechanism incorparated with frequency-hopping and proactive dropping in this paper. To reduce uplink outage probability, frequency-hopping provides more chances for transmission so that the failure hardly occurs. To avoid downlink outage from queue clearing, proactive dropping controls overall reliability by introducing an extra error component. With the proposed packet delivery mechanism, we jointly optimize bandwidth allocation and power control of uplink and downlink, antenna configuration, and subchannel assignment to minimize the average total power under the constraint of URLLC transmission requirements. Via theoretical analysis (e.g., the convexity with respect to bandwidth, the independence of bandwidth allocation, the convexity of antenna configuration with inactive constraints), the simplication of finding the global optimal solution for resource allocation is addressed. A three-step method is then proposed to find the optimal solution for resource allocation. Simulation results validate the analysis and show the performance gain by optimizing resource allocation with the proposed packet delivery mechanism.

As an important part of beyond the fifth-generation (B5G) and the sixth-generation (6G) mobile communication systems, ultra-reliable and low latency communications (uRLLC) puts forward strict requirements for delay and reliability (e.g., 99.9999% reliability and$500 \mu \text{s}$latency). At present, the evaluation measures of delay and reliability are usually based on infinite block length and rely on long-term statistics, which cannot meet the requirement of low latency. The cell-free system, with a very large number of distributed antennas, has the characteristics of macro-diversity and spatial sparsity, which can further enhance the performance of uRLLC. In this paper, the downlink multidevice cell-free system with hard deadlines is considered and analyzed in the finite block length (FBL) regime. The communication’s delay and reliability are described based on two instantaneous evaluation measures: transmission error (TE) and time overflow (TO) probability. From the perspective of information theory, this paper analyzes the analytic expression of TE probability for a single device and the performance impact of FBL on the traditional channel capacity analysis. Considering the multidevice TO probability in a cell-free system, the closed-form expressions of upper and lower bounds are derived and compared with the gamma approximation results. This paper further provides three methods, namely, transmission rate selection, device grouping and space division multiplexing, to balance the delay and reliability of the system and analyzes the performance.

In this paper, we investigate the content popularity prediction problem in cache-enabled fog radio access networks (F-RANs). In order to predict the content popularity with high accuracy and low complexity, we propose a Gaussian process based regressor to model the content request pattern. Firstly, the relationship between content features and popularity is captured by our proposed model. Then, we utilize Bayesian learning to train the model parameters, which is robust to overfitting. However, Bayesian methods are usually unable to find a closed-form expression of the posterior distribution. To tackle this issue, we apply a stochastic variance reduced gradient Hamiltonian Monte Carlo (SVRG-HMC) method to approximate the posterior distribution. To utilize the computing resource of fog access points (F-APs) and also reduce the communication overhead, we propose a quantized federated learning (FL) framework combining with Bayesian learning. The proposed quantized federated Bayesian learning framework allows each F-AP to send gradients to the cloud server after quantizing and encoding. It can achieve a tradeoff between prediction accuracy and communication overhead effectively. Simulation results show that the performance of our proposed policy outperforms the considered baseline policies.

It is supposed that ultra-reliable low latency communication (uRLLC) would continue to evolve in the future sixth generation (6G) network, to provide enhanced capability towards extreme connectivity, with the aid of well established multiple-input multiple-output (MIMO) technology. Since the latency constraint can be represented equivalently by the blocklength of a codeword, channel coding theory at a finite blocklength plays an important role in theoretic analysis of uRLLC. Based on Polyanskiy’s and Yang’s asymptotic results on maximal achievable rate, we first derive the proximate closed-form expressions for the expectation and variance of channel dispersion. Then, the upper bound of average maximal achievable rate is obtained for massive MIMO systems under ideal independent and identically distributed fading channels. Since almost all the fundamental parameters, including the spatial degree-of-freedom (DoF), are considered, this expression can be viewed as a performance bound of the spatiotemporal two-dimension channel coding to some extent. Moreover, it is shown by simulation and analysis, as the DoF goes to infinity, MIMO systems reveal a nature of deterministic transmission, since the average maximal achievable coding rate per antenna can be achieved at each transmission. In this case, the inversely proportional law observed therein implies that the blocklength in the time domain can be further shortened at the expense of spatial DoF. This exchangeability of space and time, to support a given coding rate, paves a solid and feasible road for us to further reduce latency in 6G uRLLC.

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.

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.

This paper focuses on the channel estimation in cell-free hybrid massive multiple-input multiple-output (MIMO). Efficient uplink channel estimation and data detection with reduced number of pilots can be performed based on low-rank matrix completion. However, such a scheme requires the central processing unit (CPU) to collect received signals from all access points (APs), which may enable the CPU to infer the private information of user locations. We therefore develop privacy-preserving channel estimation schemes under the framework of differential privacy (DP). As the key ingredient of the channel estimator, a joint differentially private noisy matrix completion algorithm based on Frank-Wolfe iteration is presented. We provide an analysis on the tradeoff between the privacy and the channel estimation error. In particular, we characterize the scaling laws of the estimation error in terms of data payload size. Simulation results demonstrate the tradeoff between privacy and channel estimation performance, and show that the estimation error can be mitigated by increasing the payload size while keeping the pilot size fixed.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

In this article, delay and energy efficiency (EE) are investigated in fog radio access networks (F-RANs) with hybrid caching. With multiple caching and transmission strategies, hybrid caching offers great flexibility for file placement and file fetching. By using tools from stochastic geometry, we firstly derive tractable expressions of delay for coded cached, non-partitioned cached and uncached files. Then, we derive tractable expressions of EE by jointly considering power consumed in circuits, transmissions and fronthaul links. To balance delay and EE, the corresponding multi-objective optimization problem is formulated to obtain the optimal hybrid caching strategy. Furthermore, considering the NP-hard complexity of the problem, we first theoretically analyze the optimal structure of the caching result. Then, we convert the original problem into a classification problem. We further propose a gradual-replacement greedy algorithm to obtain a near optimal hybrid caching strategy, which ensures high accuracy with low complexity. Numerical results show a significant performance gain of the proposed near optimal hybrid caching strategy over baselines and flexibility in delay-sensitive and EE-sensitive scenarios.

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.

We consider a cell-free hybrid massive multiple-input multiple-output (MIMO) system with K users and M access points (APs), each with Naantennas and Nraradio frequency (RF) chains. When Ka, efficient uplink channel estimation and data detection with reduced number of pilots can be performed based on low-rank matrix completion. However, such a scheme requires the central processing unit (CPU) to collect received signals from all APs, which may enable the CPU to infer the private information of user locations. We therefore develop and analyze privacy-preserving channel estimation schemes under the framework of differential privacy (DP). As the key ingredient of the channel estimator, two joint differentially private noisy matrix completion algorithms based respectively on Frank-Wolfe iteration and singular value decomposition are presented. We provide an analysis on the tradeoff between the privacy and the channel estimation error. In particular, we show that the estimation error can be mitigated while maintaining the same privacy level by increasing the payload size with fixed pilot size; and the scaling laws of both the privacy-induced and privacy-independent error components in terms of payload size are characterized. Simulation results are provided to further demonstrate the tradeoff between privacy and channel estimation performance.

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.

Through offloading the computing tasks of the task nodes (TNs) to the fog nodes (FNs) located at the network edge, the fog network is expected to address the unacceptable processing delay and heavy link burden existed in current cloud-based networks. Unlike most existing researches based on the command-mode offloading and full capability report, this paper develops a general analytical model of the task scheduling among voluntary nodes (VNs) in fog networks, wherein the VNs voluntarily contribute their capabilities for serving their neighboring TNs. A novel delay-optimal task scheduling (DOTS) algorithm is proposed to obtain the delay-optimal offloading solution according to the reported capabilities of the VNs. Extensive simulations are carried out in a fog network, and the numerical results indicate that the proposed DOTS algorithm can effectively provide the optimal set of the helper nodes, subtask sizes, and the TN transmission power to minimize the overall task processing delay. Moreover, compared with the command-mode offloading, the voluntary-mode achieves more balanced offloading and a higher fairness level among the FNs.

In this paper, we consider a three-point relay system, in which the relay has no fixed energy supplies and thus needs to replenish energy from RF signals transmitted by the source via wireless energy transfer (WET). We propose optimal selection relaying scheme when the source knows partial channel state information (CSI). To maximize the ergodic throughput under the peak/total power constraints of source and energy causality constraint of relay, the joint optimization of power control and information/energy transfer scheduling is formulated as a non-convex optimization of functional. By introducing new variables and constraints into the problem, the problem is solved by combining the fractional programming, convex optimization and linear search. Our results provide useful guidelines for the efficient design of relay systems with relay powered by WET.

Wireless energy transfer is a promising solution to provide convenient and steady energy supplies for low-power relays. This paper investigates the simultaneous information and energy transfer in fading relay channels, where the relay has no fixed energy supply and replenishes energy from radio frequency signals transmitted by the source. Assume that the relay can switch among energy harvesting, information decoding, and information retransmission in each channel fading state. Our objective is to maximize the ergodic throughput by optimizing the mode switching rule and transmit power jointly under the data and energy causality constraints. When the source knows channel state information (CSI) of all links, to make the problem tractable, for the relay, we neglect the causality constraints during the transmission, and only consider the total data and energy constraints. We thus obtain an upper bound on the ergodic throughput by solving a convex optimization problem. Numerical results show that the achievable rate is very close to the upper bound when we apply the optimized parameters to a practical system. When the source only knows CSI of partial links, the whole transmission process is divided into two phases: the source transmits in the first phase and the relay decodes and forwards received bits using the harvested energy in the second phase. The throughput maximization problem is solved by combing convex optimization, fractional programming, and linear search. We also consider the simplified network topology when a direct link between the source and destination is unavailable. In this network, we propose algorithms based on bisection method to obtain the optimal parameters in information/energy transfer scheduling and power control when the source knows full or partial CSI. The simulation results reveal that the throughput gain brought by wireless powered relaying in different system configurations when the source knows full or partial CSI. Moreover, the effect of the relay position is discussed.

The replica method originated from statistical mechanics has been successfully applied to analyzing performance of the global maximum-likelihood (GML) MIMO detector in the large-system limit. In this paper, the analysis is extended to the local maximum-likelihood (LML) detectors. A bit error rate (BER) formula for the LML detectors with a fixed neighborhood size is obtained by the replica method and interestingly by the method of Gaussian approximation as well. It is shown that the LML BER is always one of the solutions to the GML BER in any system configuration. Furthermore, the LML BER is the only solution of the GML BER in a broad range of system parameters of practical interest. In the high signal-to-noise ratio regime, both LML and GML detectors achieve the AWGN channel performance when the channel load is up to 1.51 bits/dimension with an equal-energy distribution, and the load can be higher with an unequal-energy distribution. This analytical result is verified by simulation that the sequential likelihood ascent search detector, which is a linear-complexity LML detector, can approach the BER of the NP-hard GML detector predicted by the analysis. This result might be practically useful in large MIMO systems.

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.

In this paper, a resource allocation algorithm for maximizing energy efficiency (EE) is studied in multi-cell orthogonal frequency division multiple access (OFDMA) wireless networks. The resource allocation is designed based on imperfect channel state information (CSI). We formulate the resource allocation problem as a mixed non-convex probabilistic optimization problem. The user scheduling, data rate adaptation and power allocation are jointly designed to maximize the system EE, under the maximum transmitted power constraint and the outage probability constraint. An iterative algorithm is proposed in which the EE keeps improving until algorithm convergence. In each iteration, the energy-efficient power allocation optimization problem is solved by a lower bound problem and a parameterized transformation. Numerical results illustrate the convergence and the effectiveness of the proposed algorithm.

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.

Coordinated multipoint has been widely introduced to enhance the capacity, especially that of users at cell edge. Recently energy efficient transmission techniques are increasingly important due to the increase of energy consumption in wireless communication systems. In this paper, an energy efficient cooperative transmission algorithm using coordinated beamforming is proposed for multi-cell MIMO networks. We formulate the problem as a non-convex optimization problem in a fractional form. Then we transform the problem into an equivalent form and propose an iterative algorithm to solve it. In each iteration, a simplified zero-forcing coordinated beamforming using beam tracing and an optimal power allocation algorithm for maximizing energy efficiency are derived. Simulation results demonstrate that the proposed algorithm has a better energy efficiency performance than the traditional capacity maximizing method. With the lower complexity, the proposed method using beam tracing approaches the energy efficiency performance of the subspace decomposition method in slowly varying channels.

This paper addresses the analog feedback of channel state information (CSI) in multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) based wideband wireless communication systems. In MIMO-OFDM systems, the amount of CSI to be fed back grows prohibitively with the number of subcarriers. Aiming at reducing the feedback overhead, we design efficient analog feedback schemes. In our approach, the user equipment (UE) samples the CSI so that only a small fraction of the CSI needs to be fed back to the base station (BS). Based on the sampled feedback information, the BS reconstructs full CSI using some interpolation method. Low complexity clustering/interpolation based schemes are first considered, but these schemes only provide limited feedback quality. To improve the performance, we analyze the sparsity of the time-domain channel impulse response and propose a time-domain filtering scheme based on the sparsity property. Simulation results show that the time-domain filtering scheme efficiently decreases the interpolation distortion and feedback error, and achieves much better performance than the clustering/interpolation based schemes.

Energy efficiency(EE) has caught more and more attention in future wireless communications due to steadily rising energy costs and environmental concerns. In this paper, we propose an EE scheme with proportional fairness for the downlink multiuser distributed antenna systems (DAS). Our aim is to maximize EE, subject to constraints on overall transmit power of each remote access unit (RAU), bit-error rate (BER), and proportional data rates. We exploit multi-criteria optimization method to systematically investigate the relationship between EE and spectral efficiency (SE). Using the weighted sum method, we first convert the multi-criteria optimization problem, which is extremely complex, into a simpler single objective optimization problem. Then an optimal algorithm is developed to allocate the available power to balance the tradeoff between EE and SE. We also demonstrate the effectiveness of the proposed scheme and illustrate the fundamental tradeoff between energy- and spectral-efficient transmission through computer simulation.

In this paper, we compare EE of the distributed MIMO (D-MIMO) and co-located MIMO (C-MIMO) in the uplink cellular systems since mobile stations are battery powered. The total energy consumption includes both the circuit energy consumption and the transmission energy. We get the closed-form expression for EE of D-MIMO and C-MIMO systems. What's more, an optimization algorithm is proposed to get the optimal EE values while satisfying given spectral efficiency (SE) requirement for both D-MIMO and C-MIMO systems. Simulation results show that the D-MIMO systems are more energy efficient than C-MIMO systems in composite fading channel, and the optimal EE value can be obtained by the proposed algorithm while satisfying given SE requirement.

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.

Transmit beamforming and receive combining, which is used in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, is investigated in this paper. The complexity of linear spherical interpolation algorithm is too large and it can only be applied to unquantized beamforming, so we propose a general spherical interpolation method. This method feeds back pilot subcarrier beamforming vectors to the transmitter, then using the channel correlation in frequency domain, the beamforming vectors for non-pilot subcarriers are reconstructed according to that of the two neighboring pilot subcarriers using the general spherical interpolation algorithm. Simulation results show that this algorithm is applicable not only for unquantized beamforming, but also for quantized beamforming. Considering the realization of the actual system, we propose a phase quantized method. Simulations demonstrate that the performance of the phase quantization method outperforms existing MIMO-OFDM beamforming interpolation algorithms, and is easy to realize in actual system.

Cell edge effect has been recently paid much attention in the development of new generation mobile communications systems because it can cause serious performance degradation in cell edge. In this paper, the cell edge effects of traditional cellular systems and distributed cellular systems are evaluated and compared in environments with or without inter-cell interference (ICI). Three performance metrics are proposed to quantify the cell edge effect of cellular systems. A lower bound is derived on the location-specific spectral efficiency of the collocated antenna system (CAS). Both an approximate expression and an iterative method to quantify the location-specific spectral efficiency of the distributed antenna system (DAS) are presented. Moreover, based on these results, the proposed performance metrics are analyzed and discussed. Finally, numerical results for typical configurations of the cellular systems are presented to validate the theoretical results, and it is also shown that the cell edge performance of the DAS is better than that of the CAS.

Under the assumption of single bounce channel model, the position and velocity of a mobile station (MS) can be determined by time of arrival (TOA), angle of arrival (AOA) and doppler-shifted frequency (DSF) measurements at three base stations (BSs) when line of sight (LOS) paths between the three BSs and the MS are all blocked. The equations relating the measured TOAs, AOAs and DSFs to the location parameters are nonlinear and under-determined which are hard to solve. A novel grid search method which requires only two-dimensional search in x-y coordinates and achieves good location accuracy is presented. Simulation results verify the effectiveness of the proposed method.

Based on the joint estimations of time of arrival (TOA), angle of arrival (AOA) and angle of departure (AOD) of two or more single-bounced multipaths from a single base station (BS) and an mobile station (MS) in an MIMO system, two schemes, closed-form total least squares solution (CF-TLS) and constraint-based grid-search approach (CB-GSA) are presented to locate an MS. The CF-TLS method is time saving and provides good location accuracy when two or more resolvable single-bounced multipaths are available. However, the CB-GSA method always provides better location accuracy than the CF-TLS method at a cost of large computational burden. When only two single-bounced multipaths can be observed or the range and angle measurement errors are large, the location accuracy of the CB-GSA method outperforms that of the CF-TLS method significantly. By executing the CF-TLS method to give an initial estimation of an MS position, the search area of the CB-GSA method is narrowed, and the location time is significantly reduced. Simulations verify the effectiveness of the two proposed methods.

We study the symbol error rate (SER) of maximum ratio transmission, transmit antenna selection, and codebook-based beamforming in correlated Rayleigh fading channels. Assuming maximum ratio combining is performed at the receiver, we derive a universal upper bound on the average SER of the three schemes, and prove that the bound is asymptotically tight in high signal-to-noise ratio (SNR) regions. However, numerical results show that at medium SNR, the tightness of the bound depends on the condition number of the channel correlation matrix.

This paper addresses a per user unitary precoding and rate control (PU2RC) system with adaptive multi-level quadrature amplitude modulation (QAM) modulation, where both the PU2RC operation and adaptive modulation are based on finite rate feedback. The system supports two transmission modes (single user transmit beamforming and multiuser transmission with orthogonal beamforming), and switches between the two modes to maximize the average throughput. We first study single user transmit beamforming, and derive closed-form expressions for the average throughput and bit-error rate (BER) with outdated channel information. Then, multiuser transmission with orthogonal beamforming is investigated. We analyze the throughput and BER performance without feedback delay in the normal signal noise ratio (SNR) regime, and the performance with feedback delay in the interference-limited regime. Simulation results demonstrate the effect of time delay, and the preferred regions of the single user transmission mode and multiuser transmission mode.

This paper investigates a quantized beamforming system with feedback delay. A linear channel predictor is used to cope with the feedback delay. We derive an upper bound on the symbol error rate (SER) of phase shift keying (PSK) signal. Based on the bound, we design a predictor that provides good error performance. We also demonstrate that the beamformer design methods developed in a delay-free scenario are applicable to the system with feedback delay.

Single carrier frequency division multiple access (SC-FDMA) is adopted as uplink multiple access scheme in 3G long-term evolution (LTE). Combining SC-FDMA with MIMO (multiple-input multiple-output) can increase capacity and throughput but detection technique is required at the base station (BS). Interference cancellation (IC) based on cyclic redundancy check (CRC) is a conventional detection algorithm. In this paper, we divide IC into useful IC and useless IC. Simulation results indicate that CRC-based IC algorithm performs too many useless ICs, which increases the complexity. Thresholded IC algorithm is proposed to reduce the complexity. In the proposed algorithm, IC is performed only when the probability that the IC is useful exceeds a threshold. We select 0.5 as the threshold. With the threshold, the proposed algorithm reduces the complexity significantly and keeps the performance loss negligible.

Codebook-based transmit beamforming with receive combining is a simple and efficient strategy for multiple- input multiple-output (MIMO) wireless systems. However, as a closed-loop technique, codebook-based transmit beamforming suffers from the performance degradation due to feedback delay. In this paper, we design quantizers robust to the feedback delay for codebook-based beamforming systems. Aiming at maximizing the average receive signal-to-noise ratio (SNR) or minimizing the average bit error rate (BER), optimal codeword selection rules are derived for the quantizer. Numerical results reveal that the proposed quantizers well compensate the performance loss due to feedback delay.

In a multiple-input multiple-output (MIMO) beam- forming system with finite rate feedback, the receiver sends back quantized channel information to the transmitter via a feedback channel. The overall system performance is degraded due to feedback errors. In this paper, we treat the feedback of the beamforming vector as a generalized vector quantizer (VQ), and adopt index assignment (IA) technique to cope with feedback errors. A lower bound to the average symbol error rate (SER) is derived, and an IA design criterion is proposed to minimize this bound, which is in accord with the pseudo-Gray coding principle in conventional IA design literature. Numerical results show that well-designed IA schemes improve the SER considerably.

In a single-input single-output (SISO) downlink with multiple users, to obtain the multiuser diversity gain, the full channel state information (CSI) for all users is required for selecting the 'best' user and transmission mode. However, feedback channels are often capable of carrying only a limited number of bits. With such rate-limited feedback links, we intend to maximize the sum-rate of systems with average power and bit-error rate (BER) constraints by varying the transmission mode according to feedback information. Althrough in multiuser systems with finite-rate feedback, the scheduled user is not necessarily the 'best' user, this work turns out to be a design of transmission modes based on CSI of the 'best' user. A nested iterative algorithm is proposed to obtain optimal thresholds and discrete transmission modes. Simulation results demonstrate the throughput with finite-rate feedback approaches that with perfect CSI when the feedback rate increases. As the number of users increases, the multiuser diversity gain is achieved and the average feedback load is reduced without the decrease of throughput.

In a single-input single-output (SISO) downlink with multiple users, to obtain the multiuser diversity gain, the full channel state information (CSI) of all users is required for selecting the desired user and transmission mode. However, feedback channels are often capable of carrying only a limited number of bits. With such rate limited feedback links, we intend to maximize the average throughput of each user by designing transmit power levels, signal constellations and feedback thresholds jointly. Both un-coded adaptive M-ary quadrature amplitude modulation (MQAM) and adaptive trellis-coded modulation (TCM) are investigated. A nested iterative algorithm is proposed to obtain a finite number of transmission modes and thresholds. Optimization in the heterogeneous network is also considered. Simulation results demonstrate a small number of feedback bits make the throughput approach to that with perfect CSI. As the number of users increases, the multiuser diversity gain is achieved and the average feedback load of each user is reduced.

Index assignment (IA) technique is introduced to quantized beamforming systems. The feedback channel in these systems is modeled as a discrete memoryless channel (DMC), and the diversity and array gains with feedback errors are derived. Based on the analytical results, IA scheme is designed to provide a redundancy-free protection against feedback errors. Simulation results show that good IA scheme improves the array gain and symbol error rate (SER).

In this paper, we consider transmit beamforming systems with finite rate feedback and feedback errors. We model the feedback channel as a uniform symmetric channel and analyze the effects of feedback errors on outage probability, bit error rate (BER), diversity gain, and array gain. Both analytical and simulation results show that feedback error with small probability will make the system behave badly at high signal-to-noise ratio (SNR).