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.

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.

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.

Accurate positioning is a prerequisite for Harmonized Communication and Sensing. Most existing fingerprint positioning methods require a large amount of data for training, which is a labor-intensive and time-consuming task. Consequently, the issue of few samples in positioning tasks is inevitable. To accurately position with a limited number of samples, a new few-shot positioning framework is proposed, named CK-GAT. Specifically, the framework initially extracts features from the limited labeled data through a meticulously designed neural network to construct high-dimensional Channel Knowledge (CK), which characterizes the features of the current environment. Subsequently, the Channel State Information (CSI) that need to be predicted are processed through the same neural network to extract features, which are then embedded into the CK. These features are enhanced by leveraging the environmental characteristics learned by Graph Attention Networks (GAT) to achieve precise positioning. Experimental results demonstrate that under the condition of limited labeled data, the proposed CK-GAT framework outperforms the current state-of-the-art and their semi-supervised variants, achieving an improvement in positioning accuracy by 0.85 m in outdoor scenarios and 0.19 m in indoor scenarios.

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.

Hand Pose Estimation (HPE) is a crucial technique for human-computer interaction perception. Recent works have shown that leveraging temporal information yields significant importance in the stability of the HPE system. However, existing sequential optimization methods are mainly designed for specific frameworks, limiting their applicability and generalization potential. To solve these problems, we propose a model-agnostic Sequential hand pose Optimization Network (SO-Net), which can be applicable to various HPE methods. Specifically, SO-Net first utilizes a feature-heterogeneous pre-embedding module that unifies multiple types of features in a coherent manner and facilitates their effective interactions. Then it adopts a transformer-based spatial-temporal network to capture the long-range information across frames. Finally, to enhance the robustness of the model, a dense refinement process is employed for further optimization. Our framework outperforms state-of-the-art methods on NYU and DexYCB datasets and also provides optimization advantages across various frameworks.

Microservice architectures, characterized by their loosely coupled services and complex call patterns, have become predominant in cloud applications, benefiting from elastic scalability and development agility. However, they face challenges in anomaly propagation and root cause analysis (RCA), often depending on the operational knowledge and system familiarity. FlowRCA, a non-invasive RCA framework, addresses these challenges by leveraging common monitoring metrics like CPU load, memory usage, and container latency to facilitate RCA in microservice environments. By analyzing causal relationships between metrics, FlowRCA clarifies fault propagation complexities, enabling accurate and comprehensive failure diagnosis. Experimental evidence shows FlowRCA’s superiority over existing algorithms, effectively identifying faulty microservices and root cause metrics in simulated environments.

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.

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.

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.

Orthogonal Time Frequency Space (OTFS) is a two-dimensional modulation technique designed in the delay-Doppler (DD) domain. Each symbol experiences a sparse channel with low variability in the DD domain rather than the time-varying multi-path channel in conventional orthogonal frequency division multiplexing systems. In practical applications, fractional Doppler shifts is unavoidable due to the limited frame duration. Channel dispersion caused by fractional Doppler shifts makes the channel estimation more difficult and causes data aliasing. In this paper, we propose an adaptive scheme to reduce fractional Doppler shifts. Doppler shift compensation is performed on time-domain signals and the number of time-domain symbols is changed to approximate the quantized Doppler shifts integers. With the proposed adaptive scheme, the overhead of the reference signal can be significantly reduced with the fractional Doppler shifts eliminated. Simulation results show that block error rate performance is not degraded and system throughput is improved with the proposed scheme.

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 a candidate technique to achieve sixth-generation wireless communication (6G), reconfigurable intelligent surface (RIS) has become popular in both academia and industry. For better exploration of the advantages of RIS, we compare the performances of RIS and network-controlled repeater (NCR) in 3GPP release-18. We first theoretically analyze the received signal power and signal-to-noise ratio performances for both RIS and NCR. Then, we simulate the reference signal received power and signal-to-interference-and-noise ratio performances at the system level for both RIS and NCR in the frequency range 1 and frequency range 2 bands. Finally, several insights on engineering applications are given based on the comparison between RIS and NCR.

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).

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.

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.

Reconfigurable intelligent surface (RIS) is an emerging technique employing metasurface to reflect the signal from the source node to the destination node. Essentially, RIS can be considered as a passive relay between the source and destination node. By contrast, relay node in a traditional network has to be active, consuming energy when it is relaying the signal or information. It is worth investigating the differences between RIS and relay. In this paper, we compare the performances between RIS and active relay for a general multiple-input multiple-output (MIMO) system. To make the comparison fair and comprehensive, both the performances of RIS and active relay are optimized with best-effort, by formulating a non-convex optimization problem respectively. In case of RIS, transmit beamforming and reflecting coefficients are jointly optimized. In case of active relay including half-duplex relay and full-duplex relay, transmit beamforming for both source and relay are optimized. The original nonconvex optimization problems are transformed into to a weighted mean-square error (MSE) minimization problem and solved by the proposed alternating optimization algorithm, respectively. Numerical results are presented to demonstrate the validity of the proposed algorithm.

Orthogonal time frequency space (OTFS) is a multi-carrier technique where a time-varying channel can be converted into a time-invariant channel in the delay-Doppler (DD) domain through a two-dimensional transformation. In practical wireless systems, due to the non-biorthogonal waveforms, the inter-carrier interference (ICI) will bring up phase shifts related to Doppler shifts, so that the channel in the DD domain is no longer invariant. Moreover, the limited frame durations will lead to channel dispersion, which makes it more difficult to estimate Doppler shifts. However, existing channel estimation schemes for OTFS are unable to accurately estimate the Doppler shifts. In this paper, we propose a novel channel estimation scheme. Firstly, we estimate Doppler shifts by linear fitting based on the channel dispersion in the DD domain. Then we calculate the power of dispersion paths as combining coefficients to obtain channel fading. Simulation results show that the proposed scheme can significantly reduce the normalized mean square error compared with conventional schemes, especially if Doppler shifts are large.

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.

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.

In LTE downlink multiple-input multiple-output (MIMO), eigen based beamforming (EBB) is a promising approach for single-user scenario, while block diagonalization based beamforming (BDBF) eliminates intra-cell interferences significantly for multi-user scenario. However, both of them neglect inter-cell interferences which may lead system performance degradation. This paper presents an interference suppression based beamforming (ISBF) scheme to suppress inter-cell interferences. We first introduce interference suppression matrix (ISM) into EBB and BDBF to propose a novel beamforming matrix construction method. Then the ISM reporting scheme is discussed to support ISBF, and a precoded uplink pilot based (PUPB) reporting scheme is presented exploiting the channel reciprocity in TDD system. Finally, the simulation results are provided using dynamic system level simulator. Our results show that ISBF improves cell average and cell edge spectral efficiency significantly compared with traditional EBB and BDBF.