Ultra-reliable low-latency communication (uRLLC) is a pivotal enabler for B5G/6G networks, yet it faces severe challenges from rare but critical extreme events, which are characterized by heavy tails in the delay distribution. While the cell-free radio access network (CF-RAN) architecture offers essential spatial diversity to combat these uncertainties, conventional user-centric clustering designs typically focus on average metrics, thereby inadequately addressing such tail behaviors. We propose a novel, tail-risk-aware, user-centric clustering framework operating within the finite blocklength (FBL) regime. Our approach employs extreme value theory (EVT), specifically the peaks-over-threshold (POT) model, to accurately quantify the probability of queue latency violations. This framework is applied to formulate an energy efficiency (EE) maximization problem under strict tail latency constraints. The problem is solved via an efficient online algorithm that integrates Lyapunov optimization with successive convex approximation (SCA). Simulation results demonstrate that the proposed scheme, through its dynamic adaptation of cluster formation to mitigate tail risks, achieves a superior reliability-efficiency trade-off and leads to a significant suppression of extreme latency events.

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.

Network-assisted full-duplex (NAFD) cell-free massive MIMO (CF-mMIMO) systems constitute a promising enabler for supporting dynamic downlink (DL) and uplink (UL) traffic demands in forthcoming sixth-generation (6G) wireless networks. In this paper, we analyze channel aging effects on NAFD CF-mMIMO systems. First, we propose an extended beamforming training scheme to relieve heavy pilot overhead for high-mobility channel estimation, significantly enhancing system performance through cross-link interference (CLI) cancellation. Then, we derive novel closed-form expressions for the UL/DL achievable SEs, enabling a comprehensive analysis of channel aging impacts on spectral efficiency (SE) and energy efficiency (EE) across diverse normalized Doppler shiftfDTsscenarios. Furthermore, we develop a joint optimization framework of transmission interval and power control to balance SE-EE tradeoffs while alleviating channel aging effects. We formulate a mixed-integer multi-objective optimization problem (MOOP), which is subsequently transformed into a tractable single-objective formulation via a weighted ℓpscalarizing method. Based on this analytical foundation, we propose a constrained deep reinforcement learning (DRL) algorithm that integrates a primal-dual optimization strategy with multi-agent deep deterministic policy gradient (MADDPG) for safe policy exploitation. Simulation results validate the accuracy of analytical expressions and demonstrate the superiority of the proposed algorithm over the conventional non-dominated sorting genetic algorithm-II (NSGA-II) in achieving Pareto-optimal SE-EE tradeoffs under channel aging.

Phase synchronization among distributed transmission reception points (TRPs) is a prerequisite for enabling coherent joint transmission and high-precision sensing in millimeter wave (mmWave) cell-free massive multiple-input and multiple-output (MIMO) systems. This paper proposes a bidirectional calibration scheme and a calibration coefficient estimation method for phase synchronization, and presents a calibration coefficient phase tracking method using unilateral uplink/downlink channel state information (CSI). Furthermore, this paper introduces the use of reciprocity calibration to eliminate non-ideal factors in sensing and leverages sensing results to achieve calibration coefficient phase tracking in dynamic scenarios, thus enabling bidirectional empowerment of both communication and sensing. Simulation results demonstrate that the proposed method can effectively implement reciprocal calibration with lower overhead, enabling coherent collaborative transmission, and resolving non-ideal factors to acquire lower sensing error in sensing applications. Experimental results show that, in the mmWave band, over-the-air (OTA) bidirectional calibration enables coherent collaborative transmission for both collaborative TRPs and collaborative user equipments (UEs), achieving beamforming gain and long-time coherent sensing capabilities.

Cell-free massive multi-input-multi-output (CF-mMIMO) provides significant improvement owing to the distributed architecture. However, it suffers from the constraints including information constraints, and computation resources constraints. In this paper, we propose 4 ranks of available information in CF-mMIMO and aim to find a distributed precoding exploiting randomly accessible side information, which is one-step without iterations and robust against the unreliable fronthaul between distributed central processing units. Quadratic team learning (QTL) is devised which is derived from team theory to handle the distributed underdetermined quadratic programming. The extensive 1440 experiments validate the superiority of QTL and we believe QTL is a definitely excellent choice for CF-mMIMO distributed precoding. To the best of our knowledge, this is the first work utilizing team theory to help the design of artificial intelligence architecture for wireless communications. To prompt the development of QTL, we have open-sourced the implementation code on https://github.com/hzy238221seu/QTL4CF-Precoding.git.

Cell-free massive MIMO (CF-mMIMO) represents the pinnacle of distributed antenna systems, offering superior service to all users. However, the distributed nature of access points leads to fragmented information processing, limiting performance in practical deployments. Additionally, existing studies often overlook the impact of limited fronthaul capacity, which introduces noise and degrades the reliability of shared information. In this work, we implement a practical CF-mMIMO prototype under fifth generation new radio standards and propose a diffusion-based uplink scheme that outperforms conventional distributed cell-free systems without cooperation. Our approach adapts to arbitrary fronthaul topologies by leveraging the law of large numbers. We further analyze the linear effects of fronthaul noise and the correlation of uploaded data, demonstrating that the diffusion uplink excels in Rician fading environments while maintaining robust performance in Rayleigh fading. To the best of our knowledge, this is the first work to employ a diffusion model for mitigating fronthaul non-idealities, enabling distributed cooperative uplink in a real-world CF-mMIMO system.

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 radio access networks (CF-RANs) with network-assisted free-duplex (NA-FD) architecture unify flexible-duplex, hybrid-duplex, and full-duplex operations, but the coupled uplink/downlink interference and centralized processing impose substantial computational and signaling burdens. To address these challenges, this paper studies distributed transceiver design and access point (AP) duplex mode selection under edge distributed unit (EDU)-level information constraints and per- AP power limitations. We develop a partial distributed block coordinate descent (PDBCD) algorithm that decomposes the original sum-rate maximization into three tractable subproblems and iteratively approximates MMSE performance. The proposed design fully leverages EDU computing resources, reducing the computation load at the cloud computing unit (CCU), and significantly lowering fronthaul signaling overhead through an adaptive inter-EDU information sharing mechanism. Furthermore, by integrating a low-complexity greedy search for duplex mode assignment, the algorithm effectively mitigates cross-link interference (CLI) in NA-FD systems. Simulation results show that the proposed scheme improves spectral efficiency compared with conventional duplex mode, and centralized/distributed MMSE baselines, while maintaining strong scalability under practical deployment constraints.

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 autonomous driving scenarios, limited sensing range of individual autonomous vehicles (AVs) and exponential growth of sensing data have drawn increasing attention. This paper focuses on an integrated system combining communication and computation assistance for sensing enhancement, exploring functional fusion and performance optimization of the autonomous driving integrated sensing, communication, and computation (ISCC) system. Specifically, we first establish a cloud-edge-terminal collaborative ISCC system tailored for autonomous driving. For this system, we model sensing, communication, and computation separately, where the sensing model incorporates task-dependent characteristics, specifically considering the sequential execution of detection and tracking as well as the parallel nature of localization. Given that the collaborative computation between AVs and edge nodes aims to maximize system performance, we formulate a mixed integer nonlinear optimization problem. To solve this problem, we design two independent agents for resource and offloading configuration based on deep reinforcement learning. The former can adaptively allocate resources in each time slot without requiring prior knowledge of task arrival times, while the latter employs a partial offloading strategy to leverage the local computing capabilities of AVs, thereby addressing the limitations of existing approaches that rely on fixed resource allocation or neglect local computation. The simulation results show that the average task completion rate of the proposed scheme is significantly improved, the system cost is notably reduced compared with traditional schemes.

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.

Cell-free is considered a promising technology for the future network, which adopts a large number of distributed antennas to provide a uniformly good service. However, current researches under the long-term evolution standard mostly ignore the problem of asynchronous transmission brought by the different transmission delays due to the geographical distance differences, and assume that the system is perfectly synchronized. On the other, these works often do not consider a distributed method with controllable fronthaul overhead compatible with cell-free. To enable an extensive cell-free in the sixth generation, we derive an asynchronous analysis framework and propose a centralized and a distributed downlink precoding method respectively. What is more important, we have verified that cell-free suffers from inter-carrier-interference and inter-symbol-interference under the 5th generation new radio standard. To the best of our knowledge, this is the first work implementing a distributed asynchronous precoding method in an extensive cell-free, and simulation results demonstrate the effectiveness of the proposed two precoding methods, compared to naive precoding ignoring the asynchronous impact.

Cell-free is seen as one of the most important technology for the future wireless communications. In this paper, we adopt a wideband cell-free to implement low-altitude coverage to serve multiple unmanned aerial vehicles (UAVs) in the city playing the core role of low-altitude economy. For practice, distributed computation, asynchronous effects, beam split and imperfect channel state information are considered. We mainly rely on per-beam synchronization (PBS) and discuss different architecture implementations. A wideband asynchronous architecture that reuses the time delay modules exploited in wideband beam split calibration is proposed. In addition, a semi-synchronized path set (SSP-Set) is derived to eliminate asynchronous interference and a geometric scattering graphic convolutional network is used to acquire the (sub)-optimal SSP-Set. Based on these two technologies, a beam-delay alignment transmission (BDAT) scheme is obtained and we implement it with a distributed paradigm. The numerical results demonstrate the proposed BDAT can benefit from the cooperative downlink beamforming and provide a uniformly good service for UAVs.

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.

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

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

This paper investigates the two-timescale design for access point (AP) mode selection and power allocation to realize the full potential of the cooperative bi-static ISAC network with low system overhead, where the beamforming at the APs is adapted to the rapidly-changing instantaneous channel state information (CSI), while the AP mode selection and power allocation are adapted to the slowly-changing statistical CSI. Firstly, the minimum mean square error (MMSE) estimator is applied to estimate the channels between the APs and the channels between the APs and the user equipments (UEs). Then we adopt the low-complexity maximum ratio transmission (MRT) beamforming and maximum ratio combining (MRC) detector, and derive the closed-form expressions of the ergodic rate of the UEs and the sensing signal-to-interference-plus-noise-ratio (SINR). A non-convex mix integer optimization problem is formulated to maximize the minimum sensing SINR under the communication quality of service (QoS) constraints. McCormick envelope relaxation and successive convex approximation (SCA) techniques are applied to solve the challenging non-convex mix integer optimization problem. Extensive simulation results demonstrate the analytical accuracy of the closed-form expressions and validate the convergence and effectiveness of the proposed AP mode selection and power allocation scheme.

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 (CF) networks have attracted increasing attention for their effectiveness in mitigating inter-cell interference through cooperative transmission among distributed access points (APs). However, conventional terrestrial CF networks often lack spatial flexibility and struggle to adapt to dynamic environments. To overcome these limitations, we propose a new CF network served by unmanned aerial vehicles (UAVs) equipped with a three-dimensional (3D) rotatable antenna array. Combined with the UAV’s controllable 3D position, the resulting six-dimensional (6D) spatial reconfigurability enables the active beam steering of such aerial APs, thereby enhancing interference mitigation and dynamic user association. However, this design, referred to as 6D aerial rotatable antenna arrays (6DARAs), faces several critical challenges, such as high-dimensional coupled control variables, time-varying user positions, and increased channel state information (CSI) estimation overhead. To address these issues, we develop a two-timescale optimization framework that separates large-timescale 6DARA control (i.e., clustering, position, and rotation) from small-timescale signal processing. At the small-timescale, a closed-form team minimum mean-squared error decoder is derived using local and statistical CSI. At the large-timescale, 6DARA clustering is modeled as a local altruistic game and solved via a concurrent update algorithm, while 6DARA mobility is managed by an enhanced multi-agent reinforcement learning algorithm for efficient position and rotation adaptation under partial observability. Simulation results demonstrate that the proposed network and optimization framework significantly outperform existing baselines in terms of throughput, scalability, and robustness in dynamic environments.

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.

Uplink integrated sensing and communication (ISAC) systems have recently emerged as a promising research direction, enabling simultaneous uplink signal detection and target sensing. In this paper, we propose the flexible projection (FP)-type receiver that unifies the projection-type receiver and the successive interference cancellation (SIC)-type receiver by using a flexible tradeoff factor to adapt to dynamically changing uplink ISAC scenarios. The FP-type receiver addresses the joint signal detection and target response estimation problem through two coordinated phases: 1) Communication signal detection using a reconstructed signal whose composition is controlled by the tradeoff factor, followed by 2) Target response estimation performed through subtraction of the detected communication signal from the received signal. With adjustable tradeoff factors, the FP-type receiver can balance the enhancement of the signal-to-interference-plus-noise ratio (SINR) with the reduction of correlation in the reconstructed signal for communication signal detection. The pairwise error probability (PEP) expressions are analyzed for both the maximum likelihood (ML) and the zero-forcing (ZF) detectors, revealing that the optimal tradeoff factor should be determined based on the adopted detection algorithm and the relative power of the sensing and communication (S&C) signals. A homotopy optimization framework is first applied for the FP-type receiver with a fixed tradeoff factor. This framework is then extended to develop the dynamic flexible projection (DFP)-type receiver, which iteratively adjusts the tradeoff factor for improved algorithm performance and environmental adaptability. Finally, we show that the length of the jointly processed signal should scale with the antenna size to fully unleash the potential of the uplink ISAC receiver.

This paper investigates the downlink performance of cell-free radio access networks (CF-RAN) employing distributed regularized zero-forcing (RZF) precoding under imperfect channel state information (CSI). We derive a deterministic equivalent expression for the spectral efficiency (SE) lower bound under large-system regime. The proposed bound accurately approximates the system performance while requiring only statistical channel information, formulated based on the principles of large dimensional random matrix theory (RMT). Simulation results demonstrate that the deterministic approximation achieves high accuracy across various configurations, especially in scenarios with a limited number of edge distribution units (EDUs) per user. The derived lower bound provides a tractable and efficient analytical tool for evaluating CF-RAN performance in extreme MIMO (E-MIMO) scenarios, and offers practical guidance for system design, resource allocation, and 6G network planning.

Orthogonal Time Frequency Space (OTFS) modulation can effectively enhance transmission efficiency in doubly selective channels and is considered as one of the modulation techniques for next generation wireless communication. To meet the demands of machine-type communication in high-speed scenarios, this paper conducts research on a massive random access scheme based on OTFS modulation. Specifically, the transmission and reception model of multiple-input multiple-output (MIMO) OTFS signals is analyzed, where channel estimation is formulated as a block-sparse signal recovery problem. Therefore, based on existing superimposed and embedded pilot schemes, we propose a novel hybrid preamble scheme. This scheme utilizes the superimposed preamble and embedded preamble to achieve rough active user detection (AUD) and precise AUD, respectively, enabling accurate detection and channel estimation while supporting a large number of device access. On this basis, this paper employs a generalized approximate message passing with pattern-coupled sparse Bayesian learning (GAMP-PCSBL) algorithm that can capture the block sparsity characteristics of the channel matrix and achieve accurate estimation. The results of numerical simulations verify the effectiveness and superiority of the proposed scheme.

Cell-free is seen as an important implementation for future wireless networks, which eliminates the conventional ‘cell’ concept and enables wide deployment. However, previous works mostly ignore the asynchronous effects in such a large distributed antenna system and assume perfect synchronization which is not practical. In this paper, we proposed a user-centric alignment transmission (UCAT) to settle this problem, which has the analytical beamforming vectors in each access point (AP) being computed locally and fits user-centric cell-free well. With cooperative center processing unit power optimization and AP beamforming computation, an asynchronous downlink method is obtained, and finally, numerical results demonstrate the effectiveness of UCAT.

Abstract Driven by the vision of ubiquitous connectivity and wireless intelligence, the evolution of ultra-dense constellation-based satellite-integrated Internet is underway, now taking preliminary shape. Nevertheless, the entrenched institutional silos and limited, nonrenewable heterogeneous network resources leave current satellite systems struggling to accommodate the escalating demands of next-generation intelligent applications. In this context, the distributed satellite information networks (DSIN), exemplified by the cohesive clustered satellites (CCS) system, have emerged as an innovative architecture, bridging information gaps across diverse satellite systems, such as communication, navigation, and remote sensing, and establishing a unified, open information network paradigm to support resilient space information services. This survey first provides a profound discussion about innovative network architectures of DSIN, encompassing distributed regenerative satellite network architecture, distributed satellite computing network architecture, and reconfigurable satellite formation flying, to enable flexible and scalable communication, computing and control, fundamentally enhancing network resilience. The DSIN faces challenges from network heterogeneity, unpredictable channel dynamics, sparse resources, and decentralized collaboration frameworks. To address these issues, a series of enabling technologies is identified, including channel modeling and estimation, cloud-native distributed MIMO cooperation, new waveform design, grant-free massive access, nonorthogonal multicast, distributed phased array antennas, high-speed inter-satellite communication, network routing, and the proper combination of all these diversity techniques. Furthermore, to heighten the overall resource efficiency, the cross-layer optimization techniques are further developed to meet upper-layer deterministic, adaptive and secure information services requirements. In addition, emerging research directions and new opportunities are highlighted on the way to achieving the DSIN vision.

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

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

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

As a key technology for the evolution to the sixth generation (6G) systems, cell-free massive multiple-input multiple-output (CF-mMIMO) can effectively improve the spectrum efficiency, peak rate, and reliability of wireless communication systems. Starting from the scalable implementation of CF-mMIMO, we study a cell-free RAN (CF-RAN) with distributed cooperative transceivers under the open RAN (ORAN) architecture. Through theoretical analysis and numerical simulation, we investigate the uplink and downlink spectral efficiencies of CF-mMIMO with the distributed transceivers. We then discuss the implementation issues of CF-RAN under ORAN architecture, including time-frequency synchronization and over-the-air reciprocity calibration, low layer splitting, deployment of ORAN radio units (O-RU), and artificial intelligent-based user associations. Finally, we present some representative experimental results for the uplink distributed reception and downlink coherent joint transmission of CF-RAN with commercial off-the-shelf O-RUs.

In the visionary ideals of “Internet of Everything” and “Digital Twins”, the future 6G will deeply integrate diverse heterogeneous networks such as satellite and aerial networks to support seamless connectivity and efficient interoperability, also known as space-air-ground integrated networks (SAGIN), in which the grant-free uplink random access based on Slotted ALOHA (S-ALOHA) can reduce access latency and complexity for massive Internet of Things (IoT) devices. However, with the increasing number of IoT users, the collision probability of S-ALOHA escalates and further degrades the system performance. In this paper, we focus on the massive IoT device uplink access in SAGIN aided by high altitude platform stations (HAPS), investigating power allocation for IoT devices to maximize system access capability and spectral efficiency (SE). Specifically, we first optimize 3D deployment of HAPS. Then the resilient massive access (RMA) based on flexible fusion of S-ALOHA and non-orthogonal multiple access methods is proposed. To maximize system SE with device power constraints, we model the sequential decision problem as a Markov decision process and solve it with the Advantage Actor-Critic (A2C) algorithm. Simulation results demonstrate the proposed RMA can significantly improve the IoT terminal successful access probability and the resource scheduling based on A2C also significantly increases the system SE with low complexity.

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

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.

Cell-free massive multiple-input multiple-output (MIMO) is a promising technology to address the requirements for higher spectral efficiency and energy efficiency in 6G networks. Downlink beamforming scheme, essential for mitigating multiuser interference and enhancing overall system performance, relies on the estimated uplink channel state information (CSI) in time-division duplex (TDD) mode exploiting channel reciprocity. However, hardware impairments render the bi-directional channel non-reciprocal. This paper focuses on channel calibration for cell-free massive MIMO systems, taking into account both radio frequency (RF) mismatches and nonlinear distortions. We derive the closed-form expression for downlink achievable rate within a specific calibration scheme. To address the calibration challenge, we introduce a novel conceptual model, in which the calibration vector is determined by optimizing the performance of the reference antenna. Expanding on this concept, we propose a novel digital twin (DT)-enabled approach to overcome the limitations in the conceptual model, where the DT model is established to perform calibration task by introducing DT services of virtual reference antennas. By exploiting this method, the calibration vector is computed utilizing the proposed alternating optimization algorithm within the DT model, obviating the need for deploying reference antennas in the real cell-free system, thereby reducing costs. The communication overheads and computation complexity for updating the calibration vector is proportional to the access point (AP) number. Simulation results demonstrate the significant improvement of system performance through channel calibration and verify the higher downlink throughput of our proposed DT-enabled calibration method compared to the existing calibration methods.

The integration of sensing capabilities into communication systems, by sharing physical resources, has a significant potential for reducing spectrum, hardware, and energy costs while inspiring innovative applications. Cooperative networks, in particular, are expected to enhance sensing services by enlarging the coverage area and enriching sensing measurements, thus improving the service availability and accuracy. This paper proposes a cooperative integrated sensing and communication (ISAC) framework by leveraging information-bearing orthogonal frequency division multiplexing (OFDM) signals transmitted by access points (APs). Specifically, we propose a two-stage scheme for target localization, where communication signals are reused as sensing reference signals based on the system information shared at the central processing unit (CPU). In Stage I, we propose a two-dimensional fast Fourier transform (2D-FFT)-based algorithm to measure the ranges of scattered paths induced by targets, through the extraction of delay and Doppler information from the sensing channels between APs. Then, the target locations are estimated in Stage II based on these range measurements. Considering the potential occurrence of ill-conditioned measurements with large error during the extraction of time-frequency information, we propose an efficient algorithm to match the range measurements with the targets while eliminating ill-conditioned measurements, achieving high-accuracy target localization. In addition, based on the transmission configurations defined in the fifth generation (5G) standards, we elucidate the performance trade-offs in both communication and sensing, and extend the proposed sensing scheme for general scenarios. Finally, numerical results confirm the effectiveness of our sensing scheme and the cooperative gain of the ISAC framework.

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.

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

This paper proposes a cooperative integrated sensing and communication (ISAC) scheme for low-altitude sensing scenario, aiming at estimating the parameters of the uncrewed aerial vehicles (UAVs) and enhancing the sensing performance via cooperation. The proposed scheme consists of two stages. In Stage I, we formulate the monostatic parameter estimation problem via using a tensor decomposition model. By leveraging the Vandermonde structure of the factor matrix, a spatial smoothing tensor decomposition scheme is introduced to estimate the UAVs’ parameters. To further reduce the computational complexity, we design a reduced-dimensional (RD) angle of arrival (AoA) estimation algorithm based on generalized Rayleigh quotient (GRQ). In Stage II, the positions and true velocities of the UAVs are determined through the data fusion across the multiple base stations (BSs). Specifically, we first develop a false removing minimum spanning tree (MST)-based data association method to accurately match the BSs’ parameter estimations to the same UAV. Then, a Pareto optimality method and a residual weighting scheme are developed to facilitate the position and velocity estimation, respectively. We further extend our approach to the dual-polarized system. Simulation results validate the effectiveness of the proposed schemes in comparison to conventional techniques.

This paper studies the large-scale cell-free networks where dense distributed access points (APs) serve many users. As a promising next-generation network architecture, cell-free networks enable ultra-reliable connections and minimal fading/blockage, which are much favorable to the millimeter wave and terahertz transmissions. However, conventional beam management with large phased arrays in a cell is very time-consuming in the higher-frequencies, and could be worsened when deploying a large number of coordinated APs in the cell-free systems. To tackle this challenge, the spatial-spectral cell-free networks with the leaky-wave antennas are established by coupling the propagation angles with frequencies. The beam training overhead in this direction can be significantly reduced through exploiting such spatial-spectral coupling effects. In the considered large-scale spatial-spectral cell-free networks, a novel subchannel allocation solution at sub-terahertz bands is proposed by leveraging the relationship between cross-entropy method and mixture model. Since initial access and AP clustering play a key role in achieving scalable large-scale cell-free networks, a hierarchical AP clustering solution is proposed to make the joint initial access and cluster formation, which is adaptive and has no need to initialize the number of AP clusters. After AP clustering, a subchannel allocation solution is devised to manage the interference between AP clusters. Numerical results are presented to confirm the efficiency of the proposed solutions and indicate that besides subchannel allocation, AP clustering can also have a big impact on the large-scale cell-free network performance at sub-terahertz bands.

Evolving next-generation mobile networks is designed to provide ubiquitous coverage and networked sensing. With utility of multi-view sensing and multi-node joint transmission, cell-free is a promising technique to realize this prospect. This paper aims to tackle the problem of access point (AP) deployment in cell-free systems to balance the sensing accuracy and user rate. By merging the D-optimality with Euclidean criterion, a novel integrated metric is proposed to be the objective function for both max-sum and maxmin problems, which respectively guarantee the overall and lowest performance in multi-user communication and target tracking scenario. To solve the corresponding high dimensional non-convex multi-objective problem, the Soft actor-critic (SAC) is utilized to avoid risk of local optimal result. Numerical results demonstrate that proposed SAC-based APs deployment method achieves 20% of overall performance and 120% of lowest performance.

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.

Delay alignment modulation (DAM) is a promising communication technology to mitigate inter-symbol interference (ISI) without relying on sophisticated channel equalization or multi-carrier transmissions. The key ideas of DAM are delay pre-compensation and path-based beamforming, so that the multi-path signal components will arrive at the receiver simultaneously and constructively, rather than causing the detrimental ISI. However, the practical implementation of DAM requires channel state information (CSI) at the transmitter side. Therefore, in this paper, we study an efficient channel estimation method for DAM based on block orthogonal matching pursuit (BOMP) algorithm, by exploiting the block sparsity of the channel impulse response (CIR) vector. Based on the imperfectly estimated CSI, the delay pre-compensations and path-based beamforming are designed for DAM, and the resulting performance is studied. Simulation results demonstrate that with the BOMP-based channel estimation method, the CSI can be effectively acquired with low training overhead, and the performance of DAM based on estimated CSI is comparable to the ideal case with perfect CSI.

This paper considers a decentralized massive random access scheme applicable to a user-centric cell-free massive MIMO system. In this system, a large number of user equipment (UEs) select access points (APs) based on the quality of channel, and adjacent APs can achieve lossless data exchange with a finite data volume. Each AP is equipped with an Edge Distributed Unit (EDU) capable of independently performing Maximum Likelihood (ML) estimation of the large-scale fading coefficients (LSFC) for the UEs associated with. Subsequently, the APs collectively assess the activity of associated UEs based on the estimated LSFC vectors obtained from adjacent APs, and employ a heuristic method to identify primary interference sources. On this basis, each AP's EDU uses the approximate message passing - sparse Bayesian learning (AMP-SBL) algorithm for channel estimation (CE). The proposed scheme concentrates on the associated user set, reducing computational complexity and signaling overhead while maintaining accurate performance. Numerical simulations validate the effectiveness and superiority of the approach presented in this paper.

A cell-free radio access network (CF-RAN) is a novel architecture that can effectively solve the scalability problems and lack of collaboration capabilities of other cell-free networks and achieve a better trade-off between performance and complexity. There are multiple edge distributed units (EDUs) in a CF-RAN system and each EDU mounts multiple APs. This paper inves-tigates the spectral efficiency (SE) performance of a downlink CF-RAN with zero-forcing (ZF) beamforming in the presence of pilot reuse, considering power coefficients and compression noise. An asymptotic expression for the downlink signal-to-interference-plus-noise ratio (SINR) of the system can be derived by using large-scale random matrix theory. To improve the performance of the system, we consider the EDU-AP association strategy and propose a heuristic greedy algorithm. Simulation results verify the accuracy of the asymptotic expression and the effectiveness of the proposed association algorithm.

The demand for higher transmission efficiency and denser user access has been put forth by the next generation of wireless communication systems. To cater to the future communication development, this article focuses on massive random access schemes under the user-centric cell-free massive multiple-input-multiple-output (MIMO) architecture. For uplink transmission, a data frame structure is designed to enable active user detection (AUD), channel estimation (CE), and data transmission. The association between access points (APs) and user equipment (UEs) is presented to facilitate an user-centric cell-free scalable architecture. In this article, a maximum likelihood (ML)-based method is proposed for AUD to obtain the set of active UEs. By setting appropriate thresholds and combining the UE-AP association, accurate active detection results can be obtained. CE can be accomplished with lower computational complexity by utilizing the detected active UE set in AUD module. Specifically, the generalized approximate message passing-based sparse Bayesian learning with Dirichlet process (GAMP-DP-SBL) is adopted as the CE algorithm, leveraging the spatial aggregation and dispersion characteristics of APs to enhance the estimation accuracy. Building upon GAMP-DP-SBL algorithm, a clustered algorithm (GAMP-CDP-SBL) is proposed to reduce the scale of the sensing matrix and improve the accuracy of CE for associated active UEs. Moreover, to enhance system scalability, decentralized AUD and CE algorithms are proposed in this article. Simulation results under various parameter settings and different scenarios exhibit the superior performance of the proposed scheme.

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

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

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

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

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

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

Mainstream lane detection methods often lack flexibility, accuracy, and efficiency in challenging scenarios, especially with occlusion and extreme lighting. To address this, we reframe lane detection as a variational inference problem. Specifically, we propose a Variational Lane Detection Network (VLD-Net) using a Conditional Variational Auto-Encoder (CVAE) as the generative network to produce multiple lane maps as candidates, supervised by the ground-truth lane map. To build a more complex, expressive probability distribution, we incorporate normalizing flows into lane map generation, enhancing realism. Additionally, we develop a Lane-Attention Fusion (LAF) module using attention mechanisms to adaptively fuse generated candidate lane maps. LAF also includes a lane local feature aggregator to enhance local lane keypoint correlation. Experimental results on TuSimple and CULane datasets show our method outperforms previous approaches in challenging scenarios.

Traffic flow prediction is a difficult undertaking in transportation systems, due to the intricate periodicity and real-time dynamics for traffic data, spatial-temporal dependency for road networks, existing prediction approaches fail to yield satisfactory results. We propose a traffic flow prediction method named Extended Multi-component External Interactive Gated Recurrent Graph Convolutional Network (EMGRGCN). The extended multi-component (EMC) module is incorporated into the prediction model to address the periodic temporal diffusion problem. Then, we introduce an encoder-decoder architecture that incorporates attention mechanism to capture spatial-temporal dependencies. Specifically, an External Interactive Gated Recurrent Unit (EIGRU) is utilized to capture crucial temporal features. EIGRU and graph convolutional network are combined in the encoder to extract spatial-temporal correlation, and EIGRU and convolutional neural network based decoder transforms the spatial-temporal characteristics into a sequence to predict future traffic flows. Experiments on public transportation datasets PEMSD8 and PEMSD4 demonstrate that EMGRGCN model achieves the best performance.

This paper investigates the distributed uplink for the hierarchically backhaul-linked cell-free network with distributed processors to maximize the advantages of jointly serving under the same time and frequency resources. It is validated in previous works that the performance of fully centralized uplink in a cell-free network overwhelms uplink methods without or with limited coordination. On the other hand, a fully centralized uplink requires extremely high costs on backhaul links and computation capacity on the central processing unit (CPU), which is impractical in a widely deployed large cell-free network. To handle the mentioned problems, the relation between centralized uplink and group sliced distributed uplink is revealed, firstly. With the uniform framework compatible with previous fully centralized and fully distributed minimal mean square error (MMSE) equalization, two theorems are derived as group-joint MMSE complementary and the column space equivalence, which indicate the relation between the local optimal and the global optimal and can include conclusions achieved in previous works. Both computation and backhaul signaling overheads are distributed among the whole network. Simulation results demonstrate the excellent performance of proposed methods based on the derived complementary kernel.

Distributed precoding is a meaningful topic in the cell-free massive multiple input multiple output system. This system faces challenges in performance degradation due to the absence of knowledge from other antennas and several realistic constraints brought by the distributed implementation of the communication system such as the presence of phase noise (PN). In this paper, a robust cascaded team minimum mean square error (RCT-MMSE) precoding based on a hierarchical fronthaul structure is exploited to handle distributed and robust precoding including not only PN but signaling noise, sharing cost constraints and channel aging uncertainty. Such RCT-MMSE precoding, characterized by its avoidance of iterations because we derive the analytic expressions, mitigates the need for high fronthaul level instantaneous information exchange. It also demonstrates scalability with distributed computation burden and flexible signaling overhead, which offers an advantageous performance-cost tradeoff. Simulation results demonstrate the effectiveness of RCT-MMSE to combat several practical constraints and provide a flexible distributed precoding framework compared with previous ones.

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.

In this paper, the cooperative edge caching problem is studied in fog radio access networks (F-RANs). Given the non-deterministic polynomial hard (NP-hard) nature of the problem, a dueling deep Q network (Dueling DQN) based caching update algorithm is proposed to make an optimal caching decision by learning the dynamic network environment. In order to protect user data privacy and solve the problem of slow convergence of the single deep reinforcement learning (DRL) model training, we propose a communication-efficient federated deep reinforcement learning (CE-FDRL) method to implement cooperative training of models from multiple fog access points (F-APs) in F-RANs. To address the excessive consumption of communication resources caused by model transmission, we propose to prune and quantize the shared DRL models to reduce the number of transferred model parameters. The communication interval is increased and the communication round is reduced by periodic model aggregation. The global convergence and computational complexity of our proposed method are also analyzed. Simulation results verify that our proposed method can offer better performance in reducing user request delay and improving cache hit rate and the transmitted parameters of our proposed method can drop to 60% compared to the existing benchmark schemes. Our proposed method is also shown to have faster training speed and higher communication efficiency.

In recent years, with the improvement of mobile communication network performance, cell-free mMIMO with fully integrated and distributed processing has been widely studied, and wireless access networks have also become a widely studied topic in the academic community. This article analyzes the spectral efficiency of a new full spectrum cell-free wireless access network architecture with multiple EDUs and derives the performance upper and lower bounds of its traversal achievable rate. The full set formula and distributed processing can be used as two special cases; Secondly, the article further considers the distribution of users and large-scale fading models and studies the location distribution of RRUs. It is concluded that a uniform distribution of RRUs is beneficial for user traversal and speed improvement, and RRUs in multiple EDUs need to be as intertwined as possible, which is different from traditional multi-node clustering centralized collaborative processing; The article further proposes an modified genetic algorithms (GA), which simulates the performance of a new full spectrum cellfree wireless network with pilot pollution. The performance is compared with that of multi-RRU clustering group collaboration processing through Monte Carlo simulation.

This paper considers a cell-free (CF) massive multiple-input multiple-output (MIMO) with network-assisted full-duplex (NAFD) system for the ultra-reliable and low-latency (URLLC) communications, jointly optimizing duplex mode selection and transceiver design. To reduce the cross-link interference (CLI) and improve the weighted URLLC sum-rate of downlink and uplink users, we propose an optimization problem to maximize the achievable sum rates for the cell-free massive MIMO with NAFD system under a finite blocklength, limited by data rates and transmission power constraints. Then, a concave-convex procedure (CCCP) algorithm is used to solve this optimization problem. Simulation results show that the proposed algorithm has better performance than the traditional co-frequency co-time full-duplex (CCFD) and half-duplex (HD) schemes.

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.

Downlink coherent multiuser transmission is an essential technique for cell-free massive multiple-input multiple-output (MIMO) systems, and the availability of channel state information (CSI) at the transmitter is a basic requirement. To avoid CSI feedback in a time-division duplex system, the uplink channel parameters should be calibrated to obtain the downlink CSI due to the radio frequency circuit mismatch of the transceiver. In this paper, a design of a reference signal for over-the-air reciprocity calibration is proposed. The frequency domain generated reference signals can make full use of the flexible frame structure of the fifth-generation (5G) new radio, which can be completely transparent to commercial off-the-shelf (COTS) remote radio units (RRU) and commercial user equipments. To further obtain the calibration of multiple RRUs, an interleaved RRU grouping with a genetic algorithm is proposed, and an averaged Argos calibration algorithm is also presented. We develop a cell-free massive MIMO prototype system with COTS RRUs, demonstrate the statistical characteristics of the calibration error and the effectiveness of the calibration algorithm, and evaluate the impact of the calibration delay on the different cooperative transmission schemes.

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.

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.

Cell-free massive multiple-input multiple-output (MIMO) is an attractive network in 6G communications that significantly increases the spectral efficiency. Operating in time-division duplex (TDD) mode, the downlink beamforming is achieved by the estimated uplink channel, which is equal to the downlink channel due to the property of channel reciprocity. However, the involvement of different radio frequency (RF) gains in transceiver antennas renders the whole channel non-reciprocal. Therefore, it is of great necessity to calibrate the bi-directional channel. In this paper, we focus on the issue of over-the-air channel calibration in cell-free system. Taking a toy scenario as an example, we examine the performance differences between the calibration methods of ‘Direct Process’ and ‘Indirect Process’. A novel low-cost calibration method based on spanning tree model is proposed specifically for this distributed AP scenario, where a calibration tree is established to calculate calibration coefficients. Numerical results manifest that higher accuracy of our method is achieved compared to the existing calibration methods in the literatures. Our method is less sensitive to the location of master AP compared to Argos, and practical applications under the impact of phase noise show the priority of our method compared to LS method.

In this paper, we investigate the problem of optimizing the overall system secrecy spectral efficiency of cell-Free massive multiple-input multiple-output (CF-mMIMO) with network-assisted full-duplexing (NAFD) system, considering resisting interception by superimposing artificial noise (AN) on the transmitted signal. The access points (APs) are selected by binary mode selection vectors as transmitting or receiving APs flexibly to serve both uplink and downlink users simultaneously. Since optimization variables are tightly coupled, a double-loop strategy is developed to solve the non-covex combinatorial optimization problem. The outer loop is constructed according to greedy search for duplex mode selection, while the inner loop based on the proposed successive convex approximation (SCA) algorithm aims to optimize the transceivers and AN. Simulation results show that the proposed solution is superior to the fixed-mode duplex scheme in terms of secure spectral efficiency and able to achieve similar performance to that of the optimal exhaustive search scheme.

This is the demonstration description for a cloud- based cell-free distributed massive MIMO system, which is based on our already published work. Distributed massive MIMO antennas will result in design and implementation challenges regarding synchronization, calibration, real-time baseband processing, and so on. The contributors propose a cloud-based cellfree distributed massive MIMO system, which cannot only meet 5G NR requirements but also can be easily extended to different application scales. For this demostration, a 128 × 128 distributed MU-MIMO system with frequency 100 [email protected] GHz is given. Test results show that 10.185 Gbps throughput and more than 100 bps/Hz spectrum utilization can be obtained. On-site applications such as HD video transmission and virtual reality (VR) are offered for visitor experiences and interacts.

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.

The emerging space-air-ground integrated network has attracted intensive research and necessitates reliable and efficient aeronautical communications. This paper investigates terahertz Ultra-Massive (UM)-MIMO-based aeronautical communications and proposes an effective channel estimation and tracking scheme, which can solve the performance degradation problem caused by the uniquetriple delay-beam-Doppler squint effectsof aeronautical terahertz UM-MIMO channels. Specifically, based on the rough angle estimates acquired from navigation information, an initial aeronautical link is established, where the delay-beam squint at transceiver can be significantly mitigated by employing a Grouping True-Time Delay Unit (GTTDU) module (e.g., the designedRotman lens-based GTTDU module). According to the proposed prior-aided iterative angle estimation algorithm, azimuth/elevation angles can be estimated, and these angles are adopted to achieve precise beam-alignment and refine GTTDU module for further eliminating delay-beam squint. Doppler shifts can be subsequently estimated using the proposed prior-aided iterative Doppler shift estimation algorithm. On this basis, path delays and channel gains can be estimated accurately, where the Doppler squint can be effectively attenuated via compensation process. For data transmission, a data-aided decision-directed based channel tracking algorithm is developed to track the beam-aligned effective channels. When the data-aided channel tracking is invalid, angles will be re-estimated at the pilot-aided channel tracking stage with an equivalent sparse digital array, where angle ambiguity can be resolved based on the previously estimated angles. The simulation results and the derived Cramér-Rao lower bounds verify the effectiveness of our solution.

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

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

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

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

Mobile edge computing (MEC) raises the issue of resisting selfish edge attackers that use less computation resources than promised to process offloading tasks or provide faked computation results. In this paper, we present a blockchain based trust mechanism to help MEC address selfish edge attacks and faked service record attacks. This mechanism evaluates the computational performance of the edge devices and broadcasts such information to the neighboring edge devices and mobile devices. By building a reputation assignment method for the edge devices, the edge reputation system chooses the miner of the blockchain, which applies the joint Proof-of-Work and Proof-of-Stake consensus protocol to append a block recording the new service reputations onto the MEC blockchain. We propose a reinforcement learning (RL) based edge central processing unit (CPU) allocation algorithm without knowing the mobile service generation model and the network model in the dynamic edge computing process and a deep RL version to further improve the computational performance. The security performance is analyzed and the performance bound of the edge utility is provided. Experimental results show that this framework suppresses the selfish edge attacks, decreases the response latency and saves the energy compared with a benchmark MEC scheme.

This article presents a 32-GHz frequency modulated continuous wave (FMCW) modulator based on the phase-locked loop (PLL) with nested sub-PLL structure in a 65-nm CMOS process. With the sub-PLL, the low-pass effect in phase domain is realized, reducing the noise folding effect, quantization noise, and spurs due to the delta sigma modulator (DSM). To achieve good stability and phase noise performance, both the phase-domain model and the phase noise model are analyzed and simulated. Based on these models, the chirp linearity is discussed and simulated, which helps to determine the design parameters and verifies the linearity improvement. The measurement results illustrate that in fractional-N mode, the nested-PLL achieves the phase noise of -91 dBc/Hz at 1-MHz offset frequency and the fractional spurs of less than -54 dBc at 30.78-GHz output frequency. In FMCW mode, the proposed modulator achieves a triangular chirp with 1.08-2.16-GHz bandwidth at about 32-GHz center frequency. In addition, the measured root mean square (rms) frequency errors of 400 and 770 kHz are achieved with the ramp slopes of 1.08 GHz/93 μs and 2.16 GHz/93 μs, respectively. Measurement results prove the improvements of the phase noise and chirp linearity with the sub-PLL. Including all pads, the chip occupies a silicon area of 1.5 mm2, and consumes 62-mW dc power.

In this paper, we study the low-complexity iterative soft-input soft-output (SISO) detection algorithm in a large-scale distributed multiple-input multiple-output (MIMO) system. The uplink interference suppression matrix is designed to decompose the received multi-user signal into independent single-user receptions. An improved minimum-mean-square-error iterative soft decision interference cancellation (MMSE-ISDIC) based on eigenvalue decomposition (EVD-MMSE-ISDIC) is given to perform low-complexity detection of the decomposed signals. Furthermore, two iteration schemes are given to improve receiving performance, which are iterative detection and decoding (IDD) scheme and iterative detection (ID) scheme. While IDD utilizes the external information generated by the decoder for iterative detection, the output information of the detector is directly exploited with ID. In particular, the performance of the schemes is evaluated in a 128×128 (16 remote antenna units (RAUs) and 16 users, each equipped with 8 antennas) large-scale distributed MIMO prototyping system, which is also a cell-free massive MIMO. The experimental results show that the proposed iterative receiver greatly outperforms the linear MMSE receiver, since it reduces the average number of error blocks of the system significantly.

Distributed phased arrays based multiple-input multiple-output (DPA-MIMO) is a recently proposed highly reconfigurable architecture enabling both spatial multiplexing and beamforming in millimeter-wave (mmWave) systems. In this work, we focus on coping with the hybrid precoding for the wideband DPA-MIMO system with orthogonal frequency division multiplexing (OFDM) modulation. More specifically, we propose an alternating direction method of multipliers (ADMM) enabled hybrid precoding approach based on an alternating optimization framework, abbreviated to ADMM-AltMin, for such cooperative array-of-subarrays structures. Simulation results show that the proposed ADMM-AltMin method achieves favourable performance with practical quantization of phase shifters taken into account.

In this paper, a large-scale distributed antenna system (DAS) with network-assisted full duplexing (NAFD) is investigated. Our model assumes that imperfect channel state information (CSI) is available and channels have independent spatial correlations. Based on large dimensional random matrix theory (RMT), the deterministic equivalent expressions for the ergodic uplink (UL) sum-rate with minimum-mean-square-error (MMSE) receiver as well as the ergodic downlink (DL) sum-rate with regularized zero-forcing (RZF) beamforming are derived. With the cancellation of downlink-to- uplink interference, the spectral efficiency of NAFD system is further improved. Numerical simulations indicate that under various environment settings, the deterministic equivalent results are accurate in both large-scale system and system with finite number of antennas.

By supporting asynchronous transmission and flexible subband (SB) setting, filtered orthogonal frequency division multiplexing (f-OFDM) has been identified as one of the most promising waveforms for future wireless communications, which makes the deep investigation on the f-OFDM an urgent mission. Therefore, this paper investigates the downlink interference of asynchronous f-OFDM systems under different SB configurations. We initially classify the overall interference into two categories, termed as the inner-SB and inter-SB interferences. Subsequently, the closed-form expressions for interference and its variances are derived on the basis of interference structures and are then verified via simulations. Some important influencing factors, such as the timing offset between the user of interest and the interfering user, the width of guard band, as well as the choice of SB filter, are simulated and analyzed. Our simulations and comparisons explicitly reveal the interference characteristic of the f-OFDM systems, which will benefit the design of the f-OFDM systems in the future.

This paper presents a 28-GHz low phase noise voltage-controlled oscillator (VCO) in a 65-nm CMOS process for 5G communication applications. With the capacitive splitting and transformer feedback techniques, theoretical analysis and simulations are undertaken for the proposed oscillator, proving that its negative Gmcan be enhanced over the interested frequency range and reduced at the harmonic frequencies. Moreover, the proposed oscillator startup performance is analyzed, and its tank quality factor, transient and phase noise performance are simulated. With 4-bit switch capacitors and varactors, the proposed VCO achieves a tuning range from 25.7 to 29.7 GHz, consuming 10.8 mW from a 0.9-V supply voltage. With a wideband common-source buffer, the output signal power is around 1 dBm. At 26.5 GHz, the proposed VCO achieves low phase noise of -105.8 dBc/Hz at 1-MHz offset and -130 dBc/Hz at the 10-MHz offset, respectively.

To cope with the growing trend of asymmetric data traffic, the bidirectional dynamic networks (BDNs) dynamically allocate the number of uplink and downlink remote radio heads (RRHs), which facilitates simultaneous uplink and downlink communications. In this study, the authors derive the asymptotic approximations of the achievable uplink and downlink rates using maximum ratio transmission precoder and maximum ratio combination receiver, as the RRH antenna number ( M ) approaches infinity. Considering an optical fibre connected backhaul network, a practical power consumption model is presented to study the system energy efficiency (EE). Based on the asymptotic analysis, they exploit the power scaling laws that both the uplink and downlink powers should scale down to 1/ M to maintain a desirable uplink or downlink rate. Numerical results verify that when M is large, the BDN system outperforms the dynamic time division duplex system in both the spectral efficiency and EE.

This paper investigates the performance of bidirectional dynamic networks (BDN) with massive multiple input multiple output (MIMO) systems. In BDN, dynamic allocation of the number of uplink and downlink remote radio heads (RRHs) is proposed, which offers a flexible solution to balance the data traffic asymmetry without requiring the time synchronization. Intuitively, the interference between the downlink and uplink RRHs is one of the main challenges in BDN. However, we prove that the massive MIMO strategy can effectively reduce a certain portion of the downlink-to-uplink interference. We derive the approximations of the achievable uplink and downlink rates using a maximum ratio transmission (MRT) precoder and a maximum ratio combination (MRC) receiver. Based on the asymptotic analysis, we exploit the power scaling laws that both the uplink and downlink power should scale down to 1/M (M is the antenna number) to ensure a desirable uplink or downlink rate. Furthermore, simulations show that BDN outperforms traditional time division duplex (TDD) systems in terms of the spectral efficiency.

Scaling up the conventional multiple-input multiple- output (MIMO) by orders of magnitude, massive MIMO technique brings huge improvement in spectrum efficiency and energy efficiency. However, under time- division duplex (TDD) operation, the reciprocity calibration need to be necessarily investigated, since the mismatches of the transceiver radio frequency (RF) circuits at both sides of the link will make the whole communication channels non-reciprocal. In this paper, by utilizing the strong mutual coupling between adjacent antennas, a new calibration algorithm called adjacent mutual coupling method (AdjMC) is proposed for massive MIMO systems with zero forcing (ZF) precoding. Exploiting this method, the base station (BS) can perform the reciprocity calibration without the involvement of user equipments (UEs). Theoretical analysis and simulation results show that, comparing with the several methods in the least squares (LS) framework, the AdjMC method significantly reduces the complexity and achieves the high performance.

When both access points (APs) and user equipments (UEs) have multiple antennas, the block diagonalization (BD) precoding is preferred for the distributed large-scale multiple-input multiple-output (MIMO) systems. In time division duplexing (TDD) operation, the APs can exploit the estimated uplink channel for downlink joint precoding transmission to simultaneously serve multiple UEs, due to the principle of channel reciprocity. However, the non-symmetric hardware radio frequency (RF) circuits at both sides of the link disable the channel reciprocity and result in a system performance loss. Based on a low complex BD precoding method, this paper designs a scheme for both the downlink and the uplink transmissions with the partial calibration. Theoretical analysis and simulation results show that the inter-stream interference (ISI) can be canceled out at the UEs through the minimum mean square error (MMSE) receiver in the downlink transmission. While in the uplink transmission, the APs can distinguish each data stream of every UE via the maximal ratio combining (MRC) receiver. Besides, the interference suppression precoding matrix at the APs can be used for both the downlink and uplink transmissions, which need to be calculated only once.

Channel coding for the next generation communication system needs to support higher data throughput and transmission rate. Due to the inherent parallel feature, low density parity check (LDPC) codes comply with the target. In this paper, we propose a complete channel coding scheme based on structured LDPC codes. In our design, the correlation among different parity check matrices for different code rates is established, meanwhile the correlation between adjacent rows is established, then matrix design should be restricted by these two type defined correlations in order to make great progress in the support of multiple code rates and layered decoding algorithm. Our scheme includes the definition of the four matrices and five constraint features of these matrices, also the benefits of these features are analyzed. Simulation result shows that the performance of the proposed scheme is the same as or a little better than that of 11ad LDPC codes. The proposed LDPC scheme obviously decreases route complexity and increases decoder throughput. So, this scheme is very suitable for high speed decoder with more than 1Gbps throughput in various future communication systems.

5G sets stringent requirement on delay, which means large packets have to be segmented into shorter ones. However, the gain of turbo coding can be severely degraded due to short code size. In this paper, a novel coding and encoding scheme is proposed to obtain high coding gain under short size. The idea is to encode multiple short code blocks cooperatively to obtain a long code size. In the process of encoding, an XOR operation is conducted on certain parts of previously encoded blocks to introduce correlation. The mechanism of successive interference cancelation (SIC) receiver is applied in the decoding procedure and each code block utilizes the extrinsic information from the correlated parts from other code blocks. The scheme incurs no extra delays at reduced complexity. Simulation results show the scheme can yield equivalent or better performance than using traditional turbo code on long code block.

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

Massive multiple-input multiple-output (MIMO) is a promising technique to greatly increase the spectral efficiency and may be adopted by the next generation mobile communication systems. Base stations (BSs) equipped with large-scale antennas can serve multiple users simultaneously by exploiting the downlink precoding in time division duplex (TDD) mode. However, channel state information (CSI) of uplink transmissions cannot be simply used for downlink precoding, because the gain mismatches of the transceiver radio frequency (RF) circuits disable the channel reciprocity. In this paper, we focus on antenna calibration for massive MIMO systems with maximal ratio transmit (MRT) precoding to solve the channel nonreciprocity problem. A new calibration method, called mutual coupling calibration, is proposed by using the effect of mutual coupling between adjacent antennas. By exploiting this method, the BS can perform the calibration without extra hardware circuit and users' involvement. We also build up the model of calibration error and derive the closed-form expressions of the ergodic sum-rates for evaluating the impact of calibration error on system performance. Simulation results verify the high calibration accuracy of the proposed method and show the significant improvement of system performance by performing antenna calibration.

In this paper, a channel estimation (CE) and precoding scheme by using H-infinity (H-inf) criterion for mitigation of pilot contamination (PC) in Massive multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems is reconsidered. Firstly, different thresholds in H-inf CE and precoding are considered. Secondly, asymptotic analysis of the large system is applied to simplify the H-inf precoding, which shows that the complexity of an order of magnitude is reduced. Thirdly, approximate downlink achievable data rates per user are presented for different CE and precoding schemes, such as H-inf and minimum mean square error (MMSE) CE, MMSE, zero-forcing (ZF) and H-inf precoding. The analytic work shows that the proposed scheme can provide dual mitigation to the PC. That is, the H-inf CE mitigates the PC by adjusting its thresholds, and the H-inf precoding is utilized to suppress the PC by considering inter- cell interference. The numerical results show that the joint use of H-inf CE and H-inf precoding outperforms existing several schemes in terms of mitigation to the PC.

Cloud radio access networks (C-RANs) have drawn considerable interests due to the significant improvements of spectral and energy efficiencies. Since most signal processing functions are moved to the centralized baseband unit (BBU), remote radio heads (RRHs) in C-RANs can be regarded as soft relays to transfer the received signals. The centralization characteristics in C-RANs make traditional channel estimation and training design approaches inefficient, and the requirements of perfect channel state information (CSI) would not be satisfied in turn. To solve this problem, a segment training based individual channel estimation scheme and the corresponding training design are proposed for C-RANs in this paper. Particularly, the channel estimator in terms of the sequential minimum mean-square-error (MMSE) is developed through a prior knowledge of long-term channel correlation statistics and previous channel estimates. The optimal training design for the developed estimator is derived by minimizing the estimation mean-square-error (MSE). Further, the optimal training design for the channel estimation of radio access links is computed by applying the eigenvalue decomposition (EVD). Numerical results show that performance gains of the proposed channel estimation and training design schemes are significant.

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, we study the performance of ultradensely distributed antenna system in multi-floor buildings with high user density. To reduce the pilot overhead, we consider multi-floor pilot reuse. We derive the closed-form approximations of the sum-rate for the system using linear receivers, including the linear minimum-mean-squared-error receiver and the maximal ratio combining (MRC) receiver. We demonstrate the spectral efficiency per unit volume of the system and show that the ultradensely distributed antenna system is a promising way to achieve the spectral efficiency target of 5G.

In this paper, we study the large-scale multi-user distributed antenna system (DAS) for hot-spot coverage in the future 5G wireless network. Firstly, the multi- user system model of large- scale DAS is introduced and a simple random pilot reuse scheme is presented to reduce the overhead of pilot. Then, the sum-rate of system is derived, and its asymptotical performance is studied when the number of antennas goes to infinity. Moreover, an attractive method for large-scale DAS using matrix sparsification is presented to simplify the signal processing, which is of great significance for the future research. Finally, we study the detection bit error ratio (BER) and area spectral efficiency of large-scale DAS.

In massive multiple-input multiple-output (MIMO) systems, idle users may passively overhear the signal transmitted to the intended user, known as internal eavesdroppers. In order to improve the secrecy performance, artificial noise (AN) is used jointly with maximum ratio transmission (MRT) precoding. Since the idle users can be anywhere in the cell, we investigate the secrecy outage probability for the legitimate user by taking an expectation over the positions of all internal eavesdroppers. Furthermore, according to the secrecy performance of the legitimate user in the given location, we define a new concept Secrecy Area centered on the base station (BS) where all the legitimate users satisfy the secrecy quality-of-service (QoS) requirements i.e. secrecy outage probability. Then, in order to achieve the maximum Secrecy Area under the given secrecy QoS requirements, we derive the optimal power allocation factor for AN, which is irrelevant to the channel state information (CSI) of both the legitimate user and the eavesdropper. The analytical and simulation results show that, by AN-assisted MRT precoding, the secrecy performance is significantly improved and the Secrecy Area can be efficiently enlarged with a modest increase of the transmit power.

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

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

We study the power allocation for incremental redundancy (IR) hybrid automatic repeat request (HARQ) in block fading channel where causal channel state information is known at both the transmitter and the receiver. In IR-HARQ, the traffic is assigned into consecutive transmission rounds, and at each round, the instantaneous mutual information that is required for successful decoding is upper bounded by the data rate that is to be delivered. We propose an HARQ power allocation method that maximizes the average of incremental mutual information at each round, and its throughput quickly converges to the ergodic capacity as the number of retransmissions increases. The numerical results show that the proposed power allocation method achieves almost the full channel capacity with moderate average transmission delay and that it maintains good throughput under stringent delay requirement.

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.

In this paper, the uplink sum-rate of the multi-cell multi-user massive MIMO system is studied under correlated Rayleigh fading channels. First, by considering pilot contamination effect, the equivalent system model of the massive MIMO is given with MMSE channel estimation. Then, the lower bound of the sum-rate is derived, and its asymptotical performance is studied when the base station antenna number goes to infinity. The result is general and is very accurate under the physical channel models.

The spectral efficiency of multi-cell multi-user distributed antenna system with imperfect channel state information (CSI) is studied in this paper. First, considering the pilot contamination and minimum mean-squared-error (MMSE) channel estimation, the equivalent channel model is presented and the sum-rate of the uplink transmission is derived. Under a physical channel model, the equivalent channel model can be seen as a special case of the joint correlated MIMO channel model proposed by Gao [1]. Then, the closed-form approximation of the spectral efficiency is derived. The spectral efficiency of the system with large number of antennas is also studied. Finally, the theoretical results are compared with Monte-Carlo simulation, and the performances of DAS and co-located massive MIMO are also compared. It is shown that similar to the systems with perfect CSIs, DAS still has large performance gains compared with massive MIMO.

Distributed multi-input multi-output (D-MIMO) system is a promising system to greatly improve the spectral efficiency and power efficiency of the cellular system. The performance analysis of the spectral efficiency of D-MIMO system is a fundamental problem for both theoretical study and technique evaluation. In this paper, the theoretical performances of spectral efficiency for D-MIMO system and traditional collocated MIMO (C-MIMO) system are studied and compared. First, a composite D-MIMO channel including path loss, shadow fading and multipath fading is given. Conditioned on the desired user position, by using the tight bounds and central limit theory, the analytical approximations of the mean and the cumulative distribution function (CDF) of the mutual information (MI) are derived for C-MIMO and D-MIMO channels at both high signal to noise ratio (SNR) and low SNR. Assuming that the users are randomly distributed in the cell, the CDFs of the spectral efficiency are also given for the C-MIMO and D-MIMO cellular system, and the closed-form expressions for the mean spectral efficiency, mean outage spectral efficiency are derived. Finally, the theoretical comparisons between C-MIMO and D-MIMO for large number of antennas are given, and simulation results are presented which validate the analytical results.

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.

Distributed antenna system (DAS) can improve the spectral efficiency of the cellular system, especially at the cell edge. In the paper, we consider the cognitive radio technique to further improve the cell edge performance of the DAS. We consider the following scenario: multiple secondary users (SUs) share the same spectrum with the primary user (PU) at the cell edge, and one or two SUs are selected to forward signals for the PU. First, we study the transmit scheme using MRT, and the downlink capacity of the PU is analyzed. Then, we generalize the idea of dynamic relay selection by allowing one or two SUs to cooperate. The dynamic selection scheme can be realized based on the optimal signal noise ratio (SNR). Simulation results show that dynamic selection of one relay can achieve better performance than the static selection of one, and dynamically selecting one SU outperforms the dynamic selection of two because of the increasing of the resource consumption along with the number of relay nodes. With different choices of the channel state information (CSI), the large-scale fading achieves a better tradeoff between the performance gain and processing complexity. The effect of different selection threshold on the average capacity is also given.

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.

In this paper, accurate approximations of the spectral efficiency are presented for co-located multiple-input multiple-output (C-MIMO) and distributed MIMO (D-MIMO) cellular systems in the composite channel model, which includes path loss, shadow fading, and multipath fading. Firstly, conditioned on the desired user position, the analytical approximations of the mean and variance of the mutual information are derived for C-MIMO and D-MIMO at high SNR. Because the exact distribution of the mutual information in a composite Rayleigh- lognormal environment is difficult to analyze, we apply Gaussian approximation to the distribution of the mutual information. Then, the growth in ergodic capacity and outage capacity of a D-MIMO channel with the number of antennas as well as the variance of the shadow fading is well understood. Assuming that the users are randomly distributed in the cell, the closed-form expressions for the mean spectral efficiency and mean outage spectral efficiency are derived. Finally, the numerical results are presented which validate the analytical results.

A ratio-adjustable time-frequency training sequence and the corresponding carrier frequency offset (CFO) estimator are proposed for orthogonal frequency division multiplexing (OFDM) systems over frequency-selective fading channels. The proposed method estimates the coarse and fine CFO through the training sequence in frequency domain and time domain separately. Computation complexity can be greatly decreased by employing predefined lookup table. Simulation results show that the proposed estimator has better performance with almost the same overhead compared with Lei's method in (2004).

This paper addresses the problem of the soft decision equalization. We show the relation between the iterative soft decision interference cancellation (ISDIC) and probabilistic data association (PDA) multiuser detector (PDA-MUD). We prove that ISDIC is equivalent to PDA-MUD, and give the block-wise implementation of them. We also present a soft interference cancellation algorithm for the polynomial expansion linear detector. With its low complexity, simple implementations, and impressive performance offered by iterative soft-decision processing, it is an attractive candidate to deliver efficient reception solutions to practical wireless transmission systems. Simulation comparisons of the new method with ISDIC are presented under the frequency selective channels.

In this paper, a low complexity space-frequency iterative detection and decoding scheme is developed for multiuser space-time block-coded (STBC) block transmission systems, such as the cyclic prefix based single-carrier block transmission (CP-SCBT) system and OFDM system. Using the algebraic properties of such systems, the MMSE-based turbo detection algorithm can be implemented in the frequency domain and then the complexity of the matrix inversion can be reduced greatly. The performance of the iterative receiver for multiuser STBC block transmission systems with bit-interleaved coded modulation (BICM) is evaluated in mobile multipath frequency selective channels through computer simulations. It has been shown that the proposed receiver significantly outperforms the conventional noniterative receiver. Moreover, at high signal-to-noise ratio, the detrimental effects of multiple-access interference (MAI) and intersymbol interference (ISI) in the channel can almost be completely overcome by the turbo processing, and the performance is very close to that of BICM in a Gaussian channel.

We study the low complexity space-time turbo detection and decoding schemes for MIMO block transmission systems such as cyclic prefix based single-carrier block transmission (CP-SCBT) system and OFDM system. Because of the circulant property of the channel matrices, the detectors can he implemented by FFT/IFFT. Simulation results show that the proposed receivers significantly outperform the traditional, non-iterative receivers.

We study the low complexity turbo detector for cyclic prefix based single-carrier block transmission (CP-SCBT) systems with multiple receive antennas. It has the following characteristics: firstly, it can be implemented by FFT/IFFT with low complexity; secondly, along with SISO decoder, turbo detection can be applied. Simulation results show that the iterative receiver provides much better performance than the conventional CP-SCBT system. It also outperforms its coded OFDM counterpart. The most attractive advantage is that it can be implemented by FFT/IFFT without significantly increasing the complexity of the system.

In a CDMA system, the RAKE receiver is commonly used to attain the diversity gain by taking advantage of the good correlation properties of the spreading codes. However, at low spreading gains the good correlation properties of the spreading codes are lost and the RAKE receiver performance is severely degraded by interpath interference (IPI). In the case of multi-code CDMA system, the multi-code interference (MCI) exists in the system. In order to suppress MPI and MCI, a novel receiver based on soft-output Viterbi algorithm (SOVA) equalization is proposed in this paper. The SOVA equalization is applied to symbol sequences after RAKE combining and MCI cancellation to effectively eliminate the IPI during transmission of high rate data in wideband DS-CDMA systems. Simulation results show that the proposed receiver significantly outperform the traditional RAKE and RAKE-VA receivers.

In this paper we propose low complexity turbo detection and decoding schemes for space-time block-coded block transmission (STBC-BT) systems. Because of the circulant property of the channel matrices, the detectors can be implemented in the frequency domain. Simulation results show that the performance of the proposed receivers is better than that of the traditional, noniterative receivers.

In the broadband communication, channel impulse response usually exhibits sparse behavior (i.e.. many nearly zero taps). This paper considers the channel estimation of the sparse channel for broadband multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems. Three algorithms for MIMO-OFDM sparse channel estimation are presented and compared: least square channel estimation (LSCE), constraint least square channel estimation (CLSCE) with ideal delay estimation, matching pursuit based channel estimation (MPCE). Mean square error (MSE) performances an also analyzed. Simulation results show that MPCK has better performance than LSCE and is very close to CLSCE under the high SNR, and also MPCE does not require the a priori of channel. Using the MP-based channel estimator, the detection performance is nearly optimal.