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.

Integrated sensing and communication (ISAC) is a key enabling technology for sixth-generation (6G) mobile communication systems, achieving seamless integration of communication and sensing functions. Cooperative sensing, where the transmitter and receiver are not co-located, serves as a key enabler for ISAC, significantly enhancing the sensing performance while reducing the implementation complexity of the receiver. However, practical deployments of cooperative sensing still face numerous challenges, such as synchronization and interference. This paper presents a set of advanced beam management methods specifically designed for the cooperative sensing system, offering a comprehensive framework to address these challenges. Specifically, we first analyze the strong/weak path effect (SWPE), a critical phenomenon caused by diverse target reflectivities and propagation paths, which severely degrades both synchronization accuracy and target detection. To counteract this, we propose an adaptive path power allocation method compatible with both all-digital and hybrid beamforming architectures. This method intelligently allocates power across different paths to mitigate the SWPE, thereby ensuring reliable synchronization via the direct path while enhancing the detectability of weak targets. As a result, the proposed method improves the target detection probability by over 30%. Furthermore, an adaptive interference suppression method is designed to reduce interference while maintaining sensing/communication quality, which obtains the SINR gain of around 5 dB, compared to the traditional full nulling method. Experimental results validate the effectiveness of robust synchronization and our proposed power allocation. This study lays a solid foundation for beamforming optimization in cooperative sensing systems, facilitating high-accuracy sensing and communication in complex environments.

Next generation cellular deployments are expected to exploit the 6–24 GHz frequency range 3 (FR3) and extremely large-scale multiple-input multiple-output (XL-MIMO) to enable ultra-high data rates and reliability. However, the significantly enlarged antenna apertures and higher carrier frequencies make the far-field and spatial stationarity assumptions in the existing 3rd generation partnership project (3GPP) channel models no longer valid, giving rise to new features such as near-field propagation and spatial non-stationarity (SNS). Despite extensive prior research, incorporating these new features within the standardized channel modeling framework remains an open issue. To address this, this paper presents a channel modeling framework for XL-MIMO systems that incorporates both near-field and SNS features, adopted by 3GPP. For the near-field propagation feature, the framework models the distances from the base station (BS) and user equipment to the spherical-wave sources associated with clusters. These distances are used to characterize element-wise variations of path parameters, such as nonlinear changes in phase and angle. To capture the effect of SNS at the BS side, a stochastic-based approach is proposed to model SNS caused by incomplete scattering, by establishing power attenuation factors from visibility probability and visibility region to characterize antenna element-wise path power variation. In addition, a physical blocker-based approach is introduced to model SNS effects caused by partial blockage. The near-field and SNS channel modeling approaches are validated against ray-tracing simulations. Finally, a simulation framework for near-field and SNS is developed based on the existing 3GPP channel model. Performance evaluations demonstrate that the near-field model captures higher channel capacity potential compablack to the far-field model. Coupling loss results indicate that SNS leads to more pronounced propagation fading relative to the spatial stationary model.

In this paper, we investigate a reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system, where the RIS is modeled using multiport network theory based on theZ-parameter representation. Unlike conventional RIS models based on reflection-coefficient matrices, we characterize RIS reconfigurability with tunable circuit impedances, thus capturing the electromagnetic mutual coupling (MC) effects among RIS elements. Specifically, we jointly optimize the transmit covariance matrix at the base station (BS) and the RIS tunable load impedance matrix to maximize the radar signal-to-noise ratio (SNR). The power budget at the BS and the quality of service (QoS) constraints for the communication users are also satisfied. To highlight the impact of electromagnetic MC on the system, both the no-MC and the MC-aware cases are considered. For the non-convex MC-aware problem, we propose an alternating optimization (AO) algorithm that integrates Neumann series approximation, semidefinite relaxation (SDR), and sequential rank-one constraint relaxation (SROCR) techniques. As a simplified form of the MC-aware case, the no-MC case can be considered as a sub-algorithm embedded in the proposed solution framework. Numerical results show that electromagnetic MC significantly affects the system performance, especially under sub-wavelength spacing.

Cooperative-integrated sensing and communication (C-ISAC) networks have emerged as promising solutions for communication and target sensing. However, imperfect channel state information (CSI) estimation and time synchronization (TS) errors degrade performance, affecting communication and sensing accuracy. This paper addresses these challenges by employing movable antennas (MAs) to enhance C-ISAC robustness. We analyze the impact of CSI errors on achievable rates and introduce a hybrid Cramer-Rao lower bound (HCRLB) to evaluate the effect of TS errors on target localization accuracy. Based on these models, we derive the worst-case achievable rate and sensing precision under such errors. We optimize cooperative beamforming, base station (BS) selection factor and MA position to minimize power consumption while ensuring accuracy. We then propose a constrained deep reinforcement learning (C-DRL) approach to solve this non-convex optimization problem, using a modified deep deterministic policy gradient (DDPG) algorithm with a Wolpertinger architecture for efficient training under complex constraints. To the best of our knowledge, this is the first work that jointly integrates TS errors and imperfect CSI into a unified MA-aided cooperative ISAC framework, providing a physically consistent model for both communication and sensing under dual uncertainties. Simulation results show that the proposed method significantly improves system robustness against CSI and TS errors, where robustness mean reliable data transmission under poor channel conditions. These findings demonstrate the potential of MA technology to reduce power consumption in imperfect CSI and TS environments.

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.

In this work, we aim to effectively characterize the performance of cooperative integrated sensing and communication (ISAC) networks and to reveal how performance metrics relate to network parameters. To this end, we introduce a generalized stochastic geometry framework to model the cooperative ISAC networks, which approximates the spatial randomness of the network deployment. Based on this framework, we derive analytical expressions for key performance metrics in both communication and sensing domains, with a particular focus on communication coverage probability, radar information rate and coverage probability for sensing. The analytical expressions derived explicitly highlight how performance metrics depend on network parameters, thereby offering valuable insights into the deployment and design of cooperative ISAC networks. In the end, we validate the theoretical performance analysis through Monte Carlo simulation results. Our results demonstrate that increasing the number of cooperative base stations (BSs) significantly improves both metrics, while increasing the BS deployment density has a limited impact on communication coverage probability but substantially enhances the radar information rate. Additionally, increasing the number of transmit antennas is effective when the total number of transmit antennas is relatively small. The incremental performance gain reduces with the increase of the number of transmit antennas, suggesting that indiscriminately increasing antennas is not an efficient strategy to improve the performance of the system in cooperative ISAC networks.

Terahertz (THz) extremely large-scale MIMO (XL-MIMO) is considered a key enabling technology for 6G and beyond due to its advantages such as wide bandwidth and high beam gain. As the frequency and array size increase, users are more likely to fall within the near-field (NF) region, where the far-field plane-wave assumption no longer holds. This also introduces spatial non-stationarity (SnS), as different antenna elements observe distinct multipath characteristics. Conventional far-field stationary models with fixed path parameters fail to capture these variations. Therefore, this paper proposes a THz XL-MIMO channel model that accounts for both NF propagation and SnS, validated using channel measurement data. In this work, we first conduct THz XL-MIMO channel measurements at 100 GHz and 132 GHz using 301- and 531-element ULAs in indoor environments, revealing pronounced NF effects characterized by nonlinear inter-element phase variations, as well as element-dependent delay and angle shifts. Moreover, the SnS phenomenon is observed, arising not only from blockage but also from inconsistent reflection or scattering. Based on these observations, a hybrid NF channel modeling approach combining the scatterer-excited point-source model and the specular reflection model is proposed to capture nonlinear phase variation of different types of non-line-of-sight (NLoS) paths. For SnS modeling, amplitude attenuation factors (AAFs) are introduced to characterize the continuous variation of path power across the array. By analyzing the statistical distribution and spatial autocorrelation properties of AAFs, a statistical rank-matching-based method is proposed for their generation. Finally, the model is validated using measured data. Evaluation across metrics such as entropy capacity, condition number, spatial correlation, channel gain, Rician K-factor, and RMS delay spread confirms that the proposed model closely aligns with measurements and effectively characterizes the essential features of THz XL-MIMO channels.

Reconfigurable intelligent surface (RIS) is a promising technology for 6G wireless communication. To learn from former successful deployed technique, a mature and widely deployed solution in 5G networks, the repeater, is a subject worth comparing. This paper presents a comparative study of RIS and commercial repeater through field trials in a typical indoor parking lot scenario. By providing empirical insights rather than algorithmic contributions, the study focuses on assessing their performance in terms of coverage, signal quality, and deployment characteristics. Field trials were conducted to measure key metrics such as reference signal received power (RSRP) and signal-to-interference-plus-noise ratio (SINR) under both point and area coverage scenarios. The results reveal that RIS achieves localized coverage improvements with enhanced signal reflection, whereas repeater excels in wide-area coverage through cascaded digital remote units (DRUs). The findings highlight the distinct advantages and limitations of each technology, providing practical insights for their deployment in indoor environments.

As generative artificial intelligence (GAI) advances, technologies such as diffusion models provide the foundation for end-edge collaborative computing. Current plug-in network AI capabilities require improvements in efficiency and other aspects. Fortunately, the 6G native AI wireless network offers integrated management capabilities for AI tasks and multidimensional resources, enabling the delivery of high-quality AI services. Therefore, this paper considers an AI service quality optimization scheme for GAI inference in native AI wireless networks, allowing for appropriate resource allocation, expected content quality and inference split point selection. To provide results without subjective preference information, we establish a three-objective integer programming problem to optimize the performance of task delay, energy consumption, and content generation quality. Then, an ASQO algorithm is used to obtain the approximate solution of the three-dimensional Pareto optimal set. Finally, we conduct simulations to verify the feasibility and performance of the proposed scheme.

Toward the sixth generation (6G) Internet of vehicles (IoV) networks, key challenges such as massive connectivity, high mobility and superior energy efficiency have driven the development of advanced wireless technologies. Native artificial intelligence (AI) is expected to support diverse vertical industries and offer numerous emerging AI services for 6G. However, how to efficiently process AI services and improve resource utilization while ensuring quality of service is still a challenging problem. In this paper, non-orthogonal multiple access (NOMA) is applied in the designed three-layer IoV network architecture. Then, an energy efficiency maximization problem is formulated by jointly optimizing NOMA transmission power and AI task deployment decisions. Third, a two-level iterative algorithm is proposed using the Dinkelbac’s method. Simulation results verify that our proposed algorithm outperforms benchmarks in terms of energy efficiency.

Multiple-input-multiple-output (MIMO) optical wireless communication (OWC) is a promising technology capable of providing high data rates. However, interuser interference can negatively impact system performance. Existing studies employ CVX-based beamforming designs to mitigate this issue, but these approaches suffer from high computational complexity. To tackle this challenge, this article studies the low-complexity beamforming design to maximize the sum rate. Specifically, we first utilize the fractional programming (FP) method to decompose the original beamforming problem into a series of convex subproblems and propose a block coordinate descent (BCD)-based beamforming framework. Then, we provide closed-form or semi-closed-form solutions for these subproblems. For the general approximate sum rate maximization, we drive the optimal semi-closed-form beamforming structure based on the Karush-Kuhn–Tucker (KKT) conditions. Furthermore, we propose a closed-form solution for the upper bound maximization by taking advantage of the separability of optical power constraints. Finally, numerical results indicate that the proposed beamforming designs reduce the computational complexity significantly while keeping the sum rate performance.

The massive scale of deployment, hundreds of parameters, differentiated scenarios and interworking with existing mobile networks leads to high complexity and high cost of optimization, operation and maintenance of the 5th generation mobile network (5G), which inspires that 6th generation mobile network (6G) should support high level autonomy at the beginning of deployment. Digital twin network (DTN) technology, with its advantages of intelligent decision making, low-cost experimentation, and preverification, has emerged as a key enabling technology for autonomous network. To address the need for flexibility to fulfill more diverse scenarios and high-level autonomy toward 2030, this article discusses the typical usage cases of DTN, and proposes an innovative and native design for 6G DTN, encompassing logical framework, architecture, functions, and deployment modes. Furthermore, the efficient DTN Model Construction and Intelligent Orchestration and Management are introduced to enable fully automated and high-performance DTN tasks. Finally, the future direction for DTN research is presented.

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.

This paper presents a new multiple-input multiple-output (MIMO) communication system. The system, termed as “pseudo MIMO”, facilitates the transmission of more parallel data streams than the number of receiving radio frequency (RF) chains. The key principle involves connecting multiple antenna elements to each receiving RF chain and employing multiple analog combining patterns for signal reception within the orthogonal frequency division multiplexing (OFDM) sampling period. Through detailing the entire transmission and signal processing procedures by matrix manipulation, the equivalent channel matrix for each OFDM subcarrier is derived in a unified matrix form, which can be further represented by the product of the analog combining matrix and the frequency domain wireless channel matrix. As per the equivalent channel matrix, an optimization problem is formulated and solved to maximize the spectral efficiency by jointly designing the digital precoding and analog combining matrices. Numerical results demonstrate that the performance of a pseudo MIMO system can closely approach that of a fully digital (FD) MIMO system with the same antenna configuration but more RF chains. It is also revealed that a low-precision discrete analog combining scheme is sufficient to achieve nearly optimal performance. Further, the effectiveness of pseudo MIMO technology is validated through prototype testing.

This work aims to tackle the labor-intensive and resource-consuming task of indoor positioning by proposing an efficient approach. The proposed approach involves the introduction of a semi-supervised learning (SSL) with a biased teacher (SSLB) algorithm, which effectively utilizes both labeled and un-labeled channel data. To reduce measurement expenses, unlabeled data is generated using an updated channel simulator (UCHS), and then weighted by adaptive confidence values to simplify the tuning of hyperparameters. Simulation results demonstrate that the proposed strategy achieves superior performance while minimizing measurement overhead and training expense compared to existing benchmarks, offering a valuable and practical solution for indoor positioning.

Abstract A novel multistatic integrated sensing and communication (ISAC) system based on cellular network is proposed. It can make use of widespread base stations (BSs) to perform cooperative sensing in wide area. This system is important since the deployment of sensing function can be achieved upon the mobile communication network at low complexity and cost without modifying the architecture of BSs for full duplexing. In this work, the topology of sensing cell is first provided, which can be duplicated to seamlessly cover the cellular network. Each sensing cell consists of a single central BS transmitting signals and multiple neighboring BSs receiving reflected signals from sensing objects. Then an estimating approach is described for obtaining position and velocity of sensing objects that locate in the sensing cell. Joint data processing with an efficient optimization method is also provided. In addition, key issues in the cellular network based multistatic ISAC system are analyzed. Simulation results show that the multistatic ISAC system can reduce interference power by over 10 dBm and significantly improve position and velocity estimation accuracy of objects when compared with the monostatic ISAC system, demonstrating the effectiveness and promise of implementing the proposed system in the mobile network.

The 6th generation mobile communication system (6G) will provide everything as a service (XaaS), where X includes communication, sensing, computing, artificial intelligence (AI), big data and security and more. Novel features such as sensing as a Service (SaaS) will contribute to further realising Internet of Everything (IoE). Integrated Sensing and Communication (ISAC) is identified as one of the six usage scenarios for 6G by the International Telecommunication Union Radiocommunication Sector (ITU-R), and the corresponding studies on the detailed technical performance requirements and evaluation methodologies have begun in 2024. Although ISAC has become a popular topic, there are no systematic performance requirements metrics and corresponding evaluation methodologies defined for SaaS in a mobile communication system, while conventional key performance indicators (KPI) for radar systems have been borrowed currently. Therefore, to fill this gap, this paper proposes SensCAP, a systematic CAPability performance metric composed of Sensing Capacity, Accuracy and Probability. The sensing capacity reflects the comprehensive sensing performance, which can be expressed as the number of targets that can be detected per unit area within unit time, given Sensing Quality of Service (QoS) requirements consisting of sensing accuracy and probability. Furthermore, the performance evaluation of the SensCAP is conducted through system simulation using proposed evaluation methodologies, and the KPI values are suggested as the guidelines for further study in ITU-R.

Visible light communication (VLC) is considered a promising technology for enabling Internet of Everything (IoE) applications in the sixth generation (6G), owing to its specific advantages over radio frequency (RF) communications. A comprehensive understanding of VLC channel characteristics and models is imperative for optimizing VLC technology, designing systems, and evaluating performance. This article presents an overview of ongoing research in channel characterization and modeling for VLC-IoE applications in the context of 6G. Recent advancements are systematically summarized, encompassing channel modeling methods, application scenarios, emerging combining technologies, such as reconfigurable intelligent surfaces (RISs) and integrated sensing and communication (ISAC), and distinctive channel characteristics. Additionally, future research directions in these domains are outlined to provide insights into forthcoming investigations for VLC-IoE applications in 6G.

The combination of communication and sensing is envisioned as a novel feature in the forthcoming sixth-generation (6G) wireless communication. The conventional approach to the joint sensing and communication (JSAC) system is utilizing one base station (BS) as both a sensing transmitter and a sensing receiver, which is known as monostatic sensing. However, the resulting self-interference issue requires additional hardware promotion to achieve full-duplexing. To overcome this issue, in this paper, we focus on cooperative sensing where the transmitter and receivers are non-co-located, which includes the bistatic and multistatic sensing. Specifically, the system model of cooperative sensing based on mobile networks is established. To demonstrate the feasibility of cooperative sensing, the bistatic radar cross section (RCS) is provided. As for the sensing method, a refined orthogonal matching pursuit (R-OMP) method is proposed to estimate the channel parameters and data fusion is also provided to derive the objects’ positions and velocities. Considering the non-negligible interference in the cooperative JSAC networks, we also discuss interference management in this paper. Simulation results show that the proposed cooperative sensing system improves the position and velocity estimation accuracy by over 20% when compared with monostatic sensing. The preliminary experiment results also verify the feasibility of the proposed system.

Cell-free (CF) networks are a promising architecture poised to revolutionize future wireless communication systems. To enhance performance, designing effective transmission strategies for CF networks is of practical significance. In this paper, we study the downlink beamforming design to maximize the resource efficiency (RE) of CF networks, which encompasses both energy efficiency (EE) and spectral efficiency (SE) optimization, thereby enabling the realization of an EE-SE tradeoff. Specifically, we formulate a RE maximization problem taking into account both the quality of service (QoS) requirements of the users and the power constraint of the network. The RE optimization problem is a non-convex fractional program. To solve it efficiently, we first equivalently decompose the challenging RE problem into two more tractable problems, a subproblem and a primary problem. Then, we show that the subproblem is equivalent to a power minimization problem and propose two effective methods to obtain the optimal primal and dual solutions simultaneously. After that, we derive the gradient of the optimal value function of the subproblem exploiting the obtained primal and dual solutions that facilitates the design of efficient algorithms with rapid convergence to address the primary problem. Finally, numerical results demonstrate that the proposed algorithms can effectively balance the EE-SE tradeoff, surpassing existing approaches in terms of either EE or SE.

Flight safety is a key issue in the aviation industry. Recently, with the prevalence of flight data recording systems, some deep learning-based studies have been devoted to predicting safety incidents based on flight data. However, these studies, although they exhibit higher prediction accuracy, have largely neglected the interpretability analysis of safety incidents which is of great concern to airlines and pilots. To address this issue, we define flight safety prediction as a multiscale time series classification problem and propose an interpretable model named IMTCN to provide both accurate predictions and high interpretability of flight safety. First, multiple temporal convolutional networks (TCNs) are utilized to capture local representations and long effective histories from multivariate flight data. Because different flight parameters are collected with diverse sampling frequencies, multiple TCNs are used to handle these parameters separately. Then, we creatively adapt the class activation mapping (CAM) method, which has been used for interpretation in image classification, and combine it with the TCN to provide flight data interpretability. The established model can pinpoint key flight parameters and corresponding moments that contribute most to safety incidents. Experimental results on a real-world dataset with 37,943 Airbus A320 aircraft flights show that our model outperforms the baselines on the task of exceedance classification and prediction 2 seconds and 4 seconds in advance, and case studies demonstrate its superb interpretability for flight safety analysis.

In this paper, we propose a novel non-orthogonal multiple access (NOMA) based integrated localization and communication (ILAC) signal transmission scheme, where communication and localization signals of different user equipments (UEs) are superimposed respectively. We analyze the performance of localization and communication in terms of position error bound (PEB) and effective data rate (EDR) theoretically. We further compare the proposed NOMA-ILAC method with current NOMA-orthogonal multiple access (OMA) method where different UEs’ communication signals are superposed while their localization signals are transmitted orthogonally, both from theoretical analysis and simulations. Performance trade-off is then carried out w.r.t. the time-domain resource allocation. Numerical results demonstrate that by adapting the time allocation ratio, the proposed method is able to improve the communication performance by up to 33%, when the PEB of the two methods are equal.

The Internet of Bio-Nanothings (IoBNT) is one of the potential application scenarios in the 6th generation (6G) mobile network, which envisions the interaction between biological cells or nanodevices and the Internet. Molecular communication (MC) may offer an appropriate communication method for the nanodevices using chemical molecules as the information carriers. However, due to the complexity of experiments and insufficient interdisciplinary cooperation, MC study mainly focuses on theoretical research, which seriously hinders the MC’s advancement and further applications. Therefore, it is crucial to explore methods for constructing transmitter and receiver nanomachines to realize practical MC systems. Based on the research progress of micro/nanomachines (MNMs) in recent years, this article summarizes the possible technologies for implementing MNMs used for MC systems within 6G application scenarios.

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.

Multi-user multiple-input multiple-output (MU-MIMO) is one of the key techniques to meet high spectral efficiency requirements for the fifth generation (5G) and beyond 5G wireless network. To achieve the high capacity of uplink MU-MIMO, a generalized polar precoding scheme based on rotation and permutation (GP-RP) is proposed in this paper, which can enhance the reliability distinctions between users’ channels. Via the three-stage channel transformation, the binary polar coding, signal modulation and the original multi-user channel are successively partitioned into multiple bit polarized channels. To further amplify the polarization effect, an unitary precoding scheme is proposed for the uplink MU-MIMO system without changing the communication capacity. The simulation results show that the proposed precoding scheme can further improve the polarization between the synthesized user channels and achieve lower block error ratio (BLER).

As a visible light communications (VLC) modulation method, the layered asymmetrically clipped optical orthogonal frequency division multiplexing (LACO-OFDM) is promising. The layers in LACO-OFDM provide a high flexibility for power allocation and modulation selection. In this paper, we analyze the impacts of LACO-OFDM layer selection, layer power allocation and modulation selection. Firstly, we compare the BER performance of different power allocation methods and investigate the impact of layers on BER and optical power. On this basis, we propose a joint layer selection and power allocation algorithm. Simulation results show that the proposed power allocation scheme has a better bit error ratio (BER) performance than the common schemes. Finally, we verify the layer modulation selection and propose a search method with low time complexity.

As the transmitter in visible light communications (VLC) system, when the electrical current signal exceeds the input range of light emitting diode (LED), it will cause a nonlinear distortion. Since the electrical current is proportional to the optical power, the nonlinear distortion of electrical current signal is equivalent to the nonlinear distortion of optical power signal. This paper studies the impacts of optical power signal nonlinear distortion on signal distribution and capacity. Firstly, we derive the maximum entropy distribution of the optical power signal and obtain the corresponding capacity lower bound using the entropy power inequality (EPI). Then the nonlinear distortion is modelled as the signal clipping noise, and we analyze the impacts of upper-clipping and lower-clipping on system performance. Numerical results show that a certain degree of signal clipping is acceptable at low optical signal-to-noise ratio (OSNR). However, the clipping should be minimum at high OSNR.

Integrated Sensing and Communication (ISAC) is an emerging technology that realizes both communication and sensing functionalities on the same sets of hardware as well as same frequency bands. Such technique will bring tremendous benefits such as spectrum efficiency improvement and cost reduction. In this paper, we firstly define three different classes of ISAC with the integration of both resources and functionalities for communication and sensing as the ultimate form. Furthermore, target at the full-integration level, we propose a downlink-pilot-based OFDM ISAC signal processing algorithm for base-station-orientated sensing. The OFDM architecture guarantees forward compatibility, and downlink reference signals including DMRS and CSI-RS ensure the communication throughput and address privacy concerns as well. Simulation results show that the proposed method is able to achieve outstanding sensing performance with range estimation error of 0.15m and velocity estimation error of 0.2m/s in the low SNR region.

As one of the potential candidate technologies for sixth generation (6G), visible light communication (VLC) can provide indoor communication services with high data rate when combined with multiple-input multiple-output (MIMO). In this paper, we first study the impact of precoding on zonotopes model of channel and then derive the capacity of VLC-MIMO with precoding when the number of transmitters is greater than receivers. Based on the zonotopes graph, two design principles for VLC-MIMO optimal precoding are obtained. Simulation results show that the commonly used radio frequency (RF)-MIMO precodings can hardly catch the capacity lower bound. The optimal precoding in RF may not be optimal in VLC.

Hard landing is a typical flight safety incident, and interpretability plays an important role in flight safety research. However, existing studies failed to provide good interpretability of the reasons for hard landing incidents and suffer from low prediction accuracy. To address the above problems, in this paper we propose CurveCluster+, a curve clustering method based on quick access recorder (QAR) data for hard landing risk evaluation. Specifically, we first conduct an in-depth analysis on hard landing flights by comparing key QAR parameter curves with the group behavior, based on which we establish a two-level hierarchical classification of hard landing incidents according to the hard landing patterns. Then we extract curve-level features from key QAR parameters through interpolation and resampling. After that we turn the classic K-means clustering into a semi-supervised algorithm by incorporating some expert experience and apply it on the curve-level features to automatically recognize the hard landing patterns. Finally, we propose a risk evaluation model based on the clustering results to discover high-risk flights from normal ones. We evaluate our method on a QAR dataset of 37,943 Airbus 320 aircraft flights. The results show that compared with other state-of-the-art data-driven methods, CurveCluster+ provides strong interpretability of hard landing incidents and exhibits good performance in recognizing hard landing patterns (the overall accuracy of our method reaches up to 92.99%). Moreover, it only requires a handful of hard landing samples to discover high-risk flights from tremendous normal landing flights, which is critical for flight safety warnings.

Visible light communication (VLC), as an auxiliary method of radio-frequency (RF) communication, can solve the problem of spectrum shortage in 6G. Due to the channel correlation caused by the layout of light-emitting diodes (LEDs) and photo detectors (PDs) in indoor scenario, the performance of VLC MIMO systems may get a significant degradation. This paper proposes a lamp selection scheme to select the optimal lamp subset to maximize the energy efficiency (EE) while meeting the illumination requirement. Simulation results verify that the performance of the proposed lamp selection scheme is close to that of the optimal scheme while reducing the complexity significantly.

Visible light communication (VLC) multiple-input multiple-output (MIMO) technology is a promising technology in 6G. However, the existing capacity formulae for radio frequency (RF) cannot be directly used for VLC due to the constraints of transmitted signals and unique channel characteristics. In this paper, we make a comparison of different signal distribution. Firstly, we analyze the relationship between transmitted and received signals based on zonotopes model. Then the capacity gap between the truncated Gaussian distribution and uniform distribution is analyzed. Simulations show that the uniform distribution has a better performance than truncated Gaussian, which may promote new signal design for VLC.

For higher energy and spectrum efficiency, simultaneous wireless information and power transfer (SWIPT) and full-duplex relay (FDR) have been recognized as essential techniques in the 6th-generation wireless communications. This paper investigates a decode-and-forward (DF) network using an energy-constrained FDR for energy harvesting from the source node and information transmission to the destination node via the well-known time switching (TS) scheme. Under multiple-input single-output (MISO) channel, the beamforming for information transmission and time allocation for wireless power transfer are jointly designed by system throughput maximization. However, the problem is nonconvex and hard to solve. By means of semidefinite relaxation (SDR) and restrictive convex approximation, we propose a successive convex approximation (SCA) algorithm that can yield a stationary-point solution to the SDR of the original problem. Finally, some simulation results are presented to demonstrate the effectiveness of the proposed algorithm.

With the development of mobile Internet and the Internet of Everything (IoE) for beyond the fifth generation (BSG)/sixth generation (6G) mobile network, the short-packet communication have attracted much attention. In order to improve the utilization of sporadic spectrum and to reduce the peak-to-average power ratio (PAPR) and out-of-band emission (OOBE), a filtering and windowing based orthogonal frequency division multiplexing (FW-OFDM) waveform is proposed in this paper. An optimization problem is formulated to minimize the power spectral density (PSD) in the stopband, and subject to the constraints of the PAPR of the FW-OFDM signal. Meanwhile, both windowing and filtering operation will not cause energy degradation of the transmitted signal. The suboptimal coefficient vectors of windowing and filtering are obtained by the genetic algorithm (GA). Simulation results show that FW-OFDM and cyclic prefix-orthogonal frequency division multiplexing (CP-OFDM) perform better than filter bank multi-carrier (FBMC) in terms of time efficiency. Besides, because of better OOBE and PAPR performance, the proposed FW-OFDM waveform is more suitable for the IoE scenario.

In this paper, we propose the robust transmission design for an intelligent reflecting surface (IRS) aided distributed multi-antenna system under imperfect cascaded channel state information at transmitter (CSIT). The active transmit beamforming at access points and reflection coefficients at IRS are jointly optimized to maximize the worst-case sum rate subject to the transmit power constraint and unit-modulus reflection coefficients for all possible channel realizations in the uncertainty region. We propose the conservative approximation (CA) based robust optimization. Under the framework of alternative optimization algorithm, the transmit beamforming is first solved via weighted minimum mean square error method with the fixed reflection coefficients, and then the reflection coefficients are optimized via nearest point projection method. Since the CA method can introduce the rate saturation for non-scaling CSIT as signal-to-noise ratio scales, we investigate another robust optimization algorithm, cutting-set (CS) method, which solves the problem by alternating between an optimization step for a finite subset of the uncertainty region, and a pessimization step for updating the subset. The numerical simulation results illustrate that the CS method can reap better sum-rate performance than CA, and the deleterious effect of imperfect cascaded CSIT on the performance becomes more significant with the increase of the reflecting elements.

In this paper, the robust transceiver design is investigated for "cell-free" massive MIMO system which is deemed as one of the promising technology for the future mobile communication network. When constructing the optimization problem, different access point (AP) sets are considered for different user equipment (UE) to simulate the true "UE-centric" system. Besides, the idea of interference leakage is used so that the overall optimization problem could be separated into several subproblems that can be carried out in parallel. To make the algorithm more practical, a low complexity robust design is proposed based on alternating direction method of multipliers (ADMM) algorithm. At the end of the paper, it is demonstrated by the simulation that the proposed robust algorithm has better performance than the non-robust design.

Three-dimensional (3D) multiple input and multiple output (3D-MIMO) with massive antennas is a key technology to achieve high spectral efficiency and user experienced data rate for the fifth generation (5G) mobile communication system. To implement 3D-MIMO in 5G system, practical constraints on the product design should be considered. This paper proposes a systematic design for the 3D-MIMO product by considering the restrictions of both base band and the hardware, including cost, size, weight, and heat dissipation. The design has been implemented for 2.6-GHz time-division duplex band, and field trials have been conducted for performance validation with practical intercell interference in commercial network. The trial results show that this 3D-MIMO design can meet the spectral efficiency requirement of the 5G enhanced mobile broadband services. The performance gain of 3D-MIMO varies with the traffic load. When the traffic load is heavy, 3D-MIMO can enhance the cell throughput by 4~6.7 times. When the traffic load is low, the performance gain of this 3D-MIMO design decreases. The results from field trial also show that the performance of 3D-MIMO degrades in mobility scenarios, where further enhancement on acquiring instant channel status information are necessary to improve the robustness of 3D-MIMO to mobility.

3D-MIMO, using two dimensional antenna array at base station, demonstrates promising throughput gain over conventional antenna system during recent academic and industry studies. To realize the performance gain, accurate channel state information (CSI) feedback is one essential aspect. One effective way to obtain this CSI is taking advantage of channel reciprocity between uplink and downlink in TDD system. This paper analyzes the uplink pilot contamination problem in practical TDD LTE system using a novel pilot contamination model, besides, pilot contamination elimination methods in terms of increasing pilot power and reducing pilot collision probability are evaluated. The simulation results show that system performance degradation is less than 21% considering the practical pilot contamination, which still outperform the traditional 2D-MIMO system. Moreover, about 15% performance gain could be benefit from pilot contamination reduction approaches.

Estimating the criticality of each link in a transportation network is a crucial step for guiding the design and deployment of vulnerability reduction measures. Using exhaustive simulations based on user-equilibrium assignment to evaluate the effect of the failure of each link (or set of links) can be prohibitively time-consuming for reasonably-sized transportation networks. In this paper, we propose an alternative heuristic approach to the estimation the vulnerability of network links employing efficient graph-theoretical algorithms. In particular, our model estimates the cost of single link failure based on the increase in shortest path travel time taking into account the effect of congestion. Results show that the proposed method can reliably be used to estimate the relative effect of each link failure on the system travel time and rank links accordingly.

In real environments, channel reciprocity cannot be directly exploited between uplink and downlink in time-division duplex (TDD) due to antenna gain mismatch. Previous work about antenna calibration mainly focused on single cell. This work proposes an adaptive scheme for antenna calibration between two cooperative BSs. The proposed scheme aims at achieving an equal ratio between antenna transmitter and receiver analog gains among cooperative BSs in flat fading channels. The essential idea is to relay calibration parameter through a calibration path. This procedure can be controlled by a certain BS dubbed primary BS. Evaluation of the proposed scheme is carried out by computer simulation.

We investigate linear detection and precoding for denoising-based physical network coding (D-PNC) in two-way multi-input multi-output relay channels. We propose an MMSE-based detector, which first gives a coarse detection to the two source messages using MMSE detector and then detects the product of the two coarse detected messages. The advantage of such detector is to randomize the interference. A simple precoder is also proposed for relay message transmission, which provides fairness. Simulation results show that D-PNC with the proposed detector and precoder has similar pair error rate performance to other schemes while keeps simple structure and lower complexity.

LTE-Advanced extends the number of transmit antennas of evolved NodeB (eNB) up to 8, which will provide substantial spatial degrees of freedom. However, the user is usually equipped with a small number of antennas due to space constraints. Thus, multi-user beamforming (MU-BF) was proposed to dramatically boost the spectrum efficiency by transmitting multiple users' data on the same time-frequency resource. The co-channel interference (CCI) among users scheduled on the same resource can be eliminated by block diagonalization (BD); however, the performance of downlink MU-BF with BD relies on the accuracy of channel estimation to a great extent. Therefore, the effect of imperfect channel estimation on MU-BF for the downlink of LTE-Advanced system is studied in this paper. The reference signal targeting data demodulation (DM-RS) and uplink sounding reference signal (SRS) for deriving the downlink channel in TDD system are considered with different error models respectively. The estimation errors are modeled through link level simulations. It is shown that the imperfect SRS and DM-RS channels degrade the system performance in different degree through system level simulation.

As one of the key technologies in TD-LTE-Advanced downlink system, MIMO-OFDMA (Multi-Input and Multi-Output Orthogonal Frequency Division Multiplexing Access) achieves high system throughput by using multiple antennas at both the transmitter and receiver while eliminating intra-cell interference by utilizing closely-spaced orthogonal sub-carriers. Nonetheless, the inter-cell interference among several UEs remains unsolved. Therefore CoMP(Coordinated Muti-Point transmission/reception) is introduced to solve this problem. This paper studies different MU(Multi User) CoMP schemes based on two different coordinated topologies and transparent/non-transparent MU(Multi User)-MIMO. The simulation results shows the CoMP schemes achieve different gains in average sector throughput and 5% edge-user throughput as compared to the performance of conventional pre-coding scheme.

This paper proposes a new approach to apply singular value decomposition (SVD) in fractionally spaced frequency domain equalizer (FSFDE) for the single carrier MIMO-CDMA systems. Multiple data streams can be reliably transmitted through the parallel subchannels by using SVD, which can improve the capacity effectively without increasing frequency bandwidth or the average transmit power. Fractional sampling at the receiver is equivalent to having multiple virtual receive antennas with correlated channel gains and colored noise, which may be seen as a virtual receive diversity. Therefore, each polyphase component is denoted as a virtual antenna, and it is also called fractionally spaced system as virtual antennas system. Simulations show that high performance can be achieved by using SVD with virtual antennas.

In this paper, we propose a novel fractionally spaced frequency domain equalization scheme for the downlink of single carrier cyclic prefix assisted MIMO-CDMA system over frequency selective channels. This scheme combines the fractionally spaced frequency domain equalizer with parallel interference cancellation (PIC) in the frequency domain to cancel the inter chip interference (ICI), inter antenna interference (IAI) and multiple access interference (MAI). It is shown that our proposed scheme significantly outperforms the conventional frequency domain equalization algorithms with low complexity.

The use of oversampling at the receiver results in colored noise when the bandwidth of the noise-limiting filter is not sufficiently large. This paper proposes a fractionally-spaced single-carrier linear MMSE frequency domain equalizer (LMMSE-FDE) over frequency-selective multiple input multiple output (MIMO) channels. The performance of the fractionally-spaced LMMSE-FDE with colored noise samples is analyzed and the relation between the time domain correlation and the frequency domain correlation of noise samples is presented. Simulation results show that the proposed technique significantly outperforms the conventional frequency domain equalization by exploiting the frequency domain correlation of noise samples.

In this paper, a frequency-domain equalization architecture for the downlink over a space-time block coded MIMO-CDMA system is proposed. This architecture exploits transmit diversity by equally dividing theNT(NT=2Q,Qis a positive integer) transmit antennas intoQgroups and implementing an Alamouti-like space-time block coding scheme for each group. At the receiver, the single-carrier minimum- mean-square-error frequency-domain equalization is combined with the successive interference cancellation to combat various interferences resulting from frequency-selective fading channels under multi-antenna and multi-user conditions. It is shown that our proposed architecture significantly outperforms the conventional MIMO-CDMA structures.

This paper proposes a new approach to apply fractionally spaced frequency-domain equalizer (FSFDE) for the single carrier MIMO-CDMA systems. The complexity of this equalizer can be significantly reduced with the help of performing the equalization in the frequency domain. Analogous to the previously developed layered space-time architectures, we proposed layered space-frequency detection (LSFD) which is performed layer by layer in a successive way. In this work, the concept of virtual antennas is also introduced. Oversampling at the receiver is equivalent to having multiple virtual receive antennas with the correlated channel gains and the colored noise. This may be viewed as a virtual receive diversity, and therefore, each polyphase component is denoted as a virtual antenna. Simulations demonstrate that high performance can be achieved by using the proposed detection with virtual antennas.

Oversampling at each receive antenna is equivalent to having multiple polyphase components with correlated channel gains and colored noise, provided that the signal bandwidth is higher than Nyquist bandwidth. To exploit the potential of these polyphase components in the MIMO systems, this letter advocates the approach of using deliberately designed asynchronous transmission instead of the synchronous transmission usually employed. Simulations show that the asynchronous transmission can easily outperform its synchronous counterparts. Moreover, it opens up the possibility of using fewer antennas at the receiver, particularly in a subscriber unit where there are severe constraints on size, weight, and power consumption.

This paper proposes a new approach to apply fractionally spaced frequency-domain equalizer for the single carrier broadband wireless systems in the presence of colored noise. The complexity of this equalizer can be significantly reduced with the help of performing the equalization in the frequency domain, when the channel is highly dispersive. In addition, the overlap-cut method, which can be applied to the existing single carrier air interfaces directly, takes the place of the well-known cyclic-prefix method. It is shown that the fractionally spaced frequency- domain equalizer is superior to the conventional baud spaced ones.