arXiv subjects

Yong Zeng

Publications and source records attributed to Yong Zeng.

At least 55 records · Page 3Linked to original sources

arXiv2025

Affine Frequency Division Multiplexing for Communication and Channel Sounding: Requirements, Challenges, and Key Technologies

Channel models are crucial for theoretical analysis, performance evaluation, and deployment of wireless communication systems. Traditional channel sounding systems are insufficient for handling the dynamic changes of channels in the next-generation space-air-ground-sea integrated networks (SAGSIN), which often results in outdated channel models that fail to provide reliable prior information for communication systems. To address this challenge, this paper proposes an integrated channel sounding and communication (ICSC) method as a practical solution. Unlike orthogonal frequency division multiplexing, affine frequency division multiplexing (AFDM) provides a full delay-Doppler representation of the channel, achieving optimal diversity in time-frequency doubly dispersive channels and effectively addressing the aforementioned challenges. Thus, we investigate the fundamental principles of AFDM, showing how it enables simultaneous communication and channel sounding, and explore key performance metrics for both functionalities. We also clarify the distinction and relationship between channel sounding, estimation, tracking and scatterer sensing. Additionally, several potential application scenarios for AFDM-ICSC are explored. Finally, we highlight the key challenges in implementing AFDM-ICSC, outline future research directions, and provide valuable insights for the continued development of this technology.

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arXiv2025

Full-Dimensional Beamforming for Multi-User MIMO-OFDM ISAC for Low-Altitude UAV with Zero Sensing Resource Allocation

Low-altitude unmanned aerial vehicles (UAVs) are expected to play an important role for low-altitude economy with a wide range of applications like precise agriculture, aerial delivery and surveillance. Integrated sensing and communication (ISAC) is a key technology to enable the large-scale deployment and routine usage of UAVs by providing both communication and sensing services efficiently. For UAV ISAC systems, as UAV often acts as both a communication user equipment (UE) and a sensing target, traditional ISAC systems that usually allocate dedicated TF resources for sensing are inefficient due to the severe degradation of communication spectral efficiency. To address this issue, in this paper, we propose a novel multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM)-based ISAC framework for UAVs that eliminates the need for dedicated sensing TF resources, achieving zero TF sensing overhead. By designing the transmit beamforming to meet the requirements for both communication and sensing tasks, our proposed approach enables the communication TF resources to be fully reused for sensing, thereby enhancing both the communication sum rate and the sensing performance in terms of resolution, unambiguous range, and accuracy. Additionally, we introduce a low-complexity target searching beamforming algorithm and a two-stage super-resolution sensing algorithm, which ensure efficient implementation. Simulation results demonstrate that the proposed MIMO-OFDM-ISAC framework not only improves the communication sum rate but also outperforms traditional ISAC systems in sensing performance, making it a promising solution for future ISAC systems to support low-altitude UAVs.

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arXiv2025

Data Fusion for BS-UE Cooperative MIMO-OFDM ISAC

Integrated sensing and communication (ISAC) is a promising technique for expanding the functionalities of wireless networks with enhanced spectral efficiency. The 3rd Generation Partnership Project (3GPP) has defined six basic sensing operation modes in wireless networks. To further enhance the sensing capability of wireless networks, this paper proposes a new sensing operation mode, i.e., the base station (BS) and user equipment (UE) cooperative sensing. Specifically, after decoding the communication data, the UE further processes the received signal to extract the target sensing information. We propose an efficient algorithm for fusing the sensing results obtained by the BS and UE, by exploiting the geometric relationship among BS, UE and targets as well as the expected sensing quality in the BS monostatic and BS-UE bistatic sensing. The results show that the proposed data fusion method for cooperative sensing can effectively improve the position and velocity estimation accuracy of multiple targets, and provide a new approach on the expansion of the sensing pattern.

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arXiv2025

Digital Twin Channel-Aided CSI Prediction: An Environment-Based Subspace Extraction Approach for Achieving Low Overhead and High Robustness

To meet the robust and high-speed communication requirements of the sixth-generation (6G) mobile communication system in complex scenarios, sensing- and artificial intelligence (AI)-based digital twin channel (DTC) techniques become a promising approach to reduce system overhead. In this paper, we propose an environment-specific channel subspace basis (ECB)-aided partial-to-whole channel state information (CSI) prediction method (ECB-P2WCP) for realizing DTC-enabled low-overhead channel prediction. Specifically, we introduce a wireless environment knowledge (WEK) construction method that extracts ECB from the digital twin environment via subspace estimation. This ECB characterizes the static statistical properties of the electromagnetic environment and serves as environment information prior to the prediction task. Then, we fuse ECB with real-time estimated local CSI to predict the entire spatial-frequency domain channel for both the present and future time instances. Hence, an ECB-based partial-to-whole CSI prediction network (ECB-P2WNet) is designed to achieve a robust channel prediction scheme in various complex scenarios. Simulation results indicate that incorporating ECB provides significant benefits under low signal-to-noise ratio and pilot ratio conditions, achieving a reduction of up to 50\% in pilot overhead. Additionally, the proposed method maintains robustness against multi-user interference, tolerating 3-meter localization errors with only a 0.5 dB normalized mean square error increase, and predicts CSI for the next channel coherent time within 1.3 milliseconds.

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arXiv2025

Full-Angle Ray Antenna Array and Omnicell Wireless Communication System

Ray antenna array (RAA) was recently proposed as a novel multi-antenna architecture that arranges multiple massive cheap antenna elements into simple uniform linear arrays (sULAs) with different orientations. Compared with traditional architectures like hybrid analog/digital beamforming with uniform linear array (ULA) and uniform circular array (UCA), RAA has several promising advantages such as significantly reduced hardware cost, higher beamforming gains and the ability of providing uniform angular resolution for all directions. In this paper, we propose a full-angle RAA architecture and an innovative omnicell wireless communication paradigm enabled by full-angle RAA. The proposed full-angle RAA expands RAA's orientation angle to the full angle domain, such that the RAA's advantages can be exploited to all directions. This further enables the new concept of omnicell wireless communication system, with the base station equipped by full-angle RAA and deployed at the center of each cell. Compared to the conventional cell sectoring wireless communication system, the proposed omnicell system is expected to not only significantly reduce the inter-user interference, but also improve the cost efficiency. Extensive analytical and numerical results are provided to compare those key performance indicators such as the spatial resolution and the communication rate of the proposed full-angle RAA based omnicell wireless communication system against the conventional ULA/UCA-based cell sectoring systems.

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arXiv2025

Flexible XL-MIMO via Array Configuration Codebook: Codebook Design and Array Configuration Training

XL-MIMO emerges as a promising technology to achieve unprecedented enhancements in spectral efficiency and spatial resolution, via orders-of-magnitude increase in the antenna array size. However, the practical issues of high hardware cost and power consumption pose great challenges towards the cost-effective implementation of XL-MIMO. To address such challenges, this paper proposes a novel concept called array configuration codebook (ACC), which enables flexible XL-MIMO cost-effectively and improves the system performance compared with conventional antenna selection (AS) schemes with limited number of RF chains. Specifically, ACC refers to a set of pre-designed array configuration codewords, where each codeword specifies the positions of activated antenna pixels. Then, flexible XL-MIMO architecture can be enabled via dynamical pixel activation based on the designed ACC, without having to exhaustively try all possible combinations of the antenna pixels activations. As an illustration, we give a specific codebook design, encompassing the classic compact array (CA), uniform sparse array (USA), modular array (MoA), nested array (NA), and co-prime array (CPA), and each codeword is specified by one array configuration parameter. With the designed ACC, array configuration training is considered for multi-UE communication to maximize the sum rate. To reduce the training overhead of exhaustive scanning, a two-stage scanning scheme is proposed, including the array- and pixel-level scanning. For comparison, the greedy AS scheme is proposed, where the resulting incremental SINR expression by activating antenna pixel sequentially is derived in closed-form. Subsequently, array configuration training is extended to the wireless localization scenario. Simulation results demonstrate the effectiveness of codeword optimization for scenarios of multi-UE communication and wireless localization.

cs.IT↗

arXiv2025

BS-1-to-N: Diffusion-Based Environment-Aware Cross-BS Channel Knowledge Map Generation for Cell-Free Networks

Channel knowledge map (CKM) inference across base stations (BSs) is the key to achieving efficient environmentaware communications. This paper proposes an environmentaware cross-BS CKM inference method called BS-1-to-N based on the generative diffusion model. To this end, we first design the BS location embedding (BSLE) method tailored for cross-BS CKM inference to embed BS location information in the feature vector of CKM. Further, we utilize the cross- and self-attention mechanism for the proposed BS-1-to-N model to respectively learn the relationships between source and target BSs, as well as that among target BSs. Therefore, given the locations of the source and target BSs, together with the source CKMs as control conditions, cross-BS CKM inference can be performed for an arbitrary number of source and target BSs. Specifically, in architectures with massive distributed nodes like cell-free networks, traditional methods of sequentially traversing each BS for CKM construction are prohibitively costly. By contrast, the proposed BS-1-to-N model is able to achieve efficient CKM inference for a target BS at any potential location based on the CKMs of source BSs. This is achieved by exploiting the fact that within a given area, different BSs share the same wireless environment that leads to their respective CKMs. Therefore, similar to multi-view synthesis, CKMs of different BSs are representations of the same wireless environment from different BS locations. By mining the implicit correlation between CKM and BS location based on the wireless environment, the proposed BS-1-to-N method achieves efficient CKM inference across BSs. We provide extensive comparisons of CKM inference between the proposed BS-1-to-N generative model versus benchmarking schemes, and provide one use case study to demonstrate its practical application for the optimization of BS deployment.

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arXiv2025

Coverage Probability and Average Rate Analysis of Hybrid Cellular and Cell-free Network

Cell-free wireless networks deploy distributed access points (APs) to simultaneously serve user equipments (UEs) across the service region and are regarded as one of the most promising network architectural paradigms. Despite recent advances in the performance analysis and optimization of cellfree wireless networks, it remains an open question whether large-scale deployment of APs in existing wireless networks can cost-effectively achieve communication capacity growth. Besides, the realization of a cell-free network is considered to be a gradual long-term evolutionary process in which cell-free APs will be incrementally introduced into existing cellular networks, and form a hybrid communication network with the existing cellular base stations (BSs). Such a collaboration will bridge the gap between the established cellular network and the innovative cellfree network. Therefore, hybrid cellular and cell-free networks (HCCNs) emerge as a practical and feasible solution for advancing cell-free network development, and it is worthwhile to further explore its performance limits. This paper presents a stochastic geometry-based hybrid cellular and cell-free network model to analyze the distributions of signal and interference and reveal their mutual coupling. Specifically, in order to benefit the UEs from both the cellular BSs and the cell-free APs, a conjugate beamforming design is employed, and the aggregated signal is analyzed using moment matching. Then, the coverage probability of the hybrid network is characterized by deriving the Laplace transforms and their higher-order derivatives of interference components. Furthermore, the average achievable rate of the hybrid network over channel fading is derived based on the interference coupling analysis.

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arXiv2025

Ambiguity Function Analysis of AFDM Signals for Integrated Sensing and Communications

Affine frequency division multiplexing (AFDM) is a promising chirp-based waveform with high flexibility and resilience, making it well-suited for next-generation wireless networks, particularly in high-mobility scenarios. In this paper, we investigate the ambiguity functions (AFs) of AFDM signals, which fundamentally characterize their range and velocity estimation capabilities in both monostatic and bistatic settings. Specifically, we first derive the auto-ambiguity function (AAF) of an AFDM chirp subcarrier, revealing its "spike-like" local property and "periodic-like" global property along the rotated delay and Doppler dimensions. This structure naturally forms a parallelogram for each localized pulse of the AAF of the AFDM chirp subcarrier, enabling unambiguous target sensing. Then, we study the cross-ambiguity function (CAF) between two different AFDM chirp subcarriers, which exhibits the same local and global properties as the AAF but with an additional shift along the Doppler dimension. We then extend our analysis to the AF of various typical AFDM frames, considering both deterministic pilot and random data symbols. In particular, we demonstrate that inserting guard symbols in AFDM facilitates interference-free sensing. Simulation results validate our theoretical findings, highlighting AFDM's strong potential for ISAC applications.

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arXiv2025

AI-based Environment-Aware XL-MIMO Channel Estimation with Location-Specific Prior Knowledge Enabled by CKM

Accurate and efficient acquisition of wireless channel state information (CSI) is crucial to enhance the communication performance of wireless systems. However, with the continuous densification of wireless links, increased channel dimensions, and the use of higher-frequency bands, channel estimation in the sixth generation (6G) and beyond wireless networks faces new challenges, such as insufficient orthogonal pilot sequences, inadequate signal-to-noise ratio (SNR) for channel training, and more sophisticated channel statistical distributions in complex environment. These challenges pose significant difficulties for classical channel estimation algorithms like least squares (LS) and maximum a posteriori (MAP). To address this problem, we propose a novel environment-aware channel estimation framework with location-specific prior channel distribution enabled by the new concept of channel knowledge map (CKM). To this end, we propose a new type of CKM called channel score function map (CSFM), which learns the channel probability density function (PDF) using artificial intelligence (AI) techniques. To fully exploit the prior information in CSFM, we propose a plug-and-play (PnP) based algorithm to decouple the regularized MAP channel estimation problem, thereby reducing the complexity of the optimization process. Besides, we employ Tweedie's formula to establish a connection between the channel score function, defined as the logarithmic gradient of the channel PDF, and the channel denoiser. This allows the use of the high-precision, environment-aware channel denoiser from the CSFM to approximate the channel score function, thus enabling efficient processing of the decoupled channel statistical components. Simulation results show that the proposed CSFM-PnP based channel estimation technique significantly outperforms the conventional techniques in the aforementioned challenging scenarios.

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arXiv2025

Near-Field Codebook-Based 3D Spherical Channel Estimation for UCA XL-MIMO Systems

Extremely large-scale multiple input multiple output (XL-MIMO), a key technology for 6G communications, faces challenges in near-field channel estimation due to spherical wavefronts and the need for three-dimensional (3D) spatial characterization, particularly with uniform circular arrays (UCAs). This letter proposes a spherical-domain simultaneous orthogonal matching pursuit (S-SOMP) based scheme tailored for near-field 3D channel estimation in UCA-equipped XL-MIMO systems. We establish a sparse channel representation based on the near-field spherical wave model. Then, a novel spherical-domain transform matrix codebook is designed via joint discrete sampling of distance, azimuth, and elevation parameters, leveraging analytical approximations to ensure low correlation between steering vectors. This structured codebook enables accurate sparse signal recovery using the S-SOMP algorithm for efficient joint estimation of channel path gains, spatial angles, and distances. Simulation results demonstrate significant channel estimation accuracy improvements compared to existing benchmarks.

cs.IT↗

arXiv2025

Intelligent Rotatable Antenna for Integrated Sensing, Communication, and Computation: Challenges and Opportunities

Integrated sensing, communication, and computation (ISCC) has emerged as a promising paradigm for enabling intelligent services in future sixth-generation (6G) networks. However, existing ISCC systems based on fixed-antenna architectures inherently lack spatial adaptability to cope with the signal degradation and dynamic environmental conditions. Recently, non-fixed flexible antenna architectures, such as fluid antenna system (FAS), movable antenna (MA), and pinching antenna, have garnered significant interest. Among them, intelligent rotatable antenna (IRA) is an emerging technology that offers significant potential to better support the comprehensive services of target sensing, data transmission, and edge computing. This article investigates a novel IRA-enabled ISCC framework to enhance received signal strength, wider coverage, and spatial adaptability to dynamic wireless environments by flexibly adjusting the boresight of directional antennas. Building upon this, we introduce the fundamentals of IRA technology and explore IRA's benefits for improving system performance while providing potential task-oriented applications. Then, we discuss the main design issues and provide solutions for implementing IRA-based ISCC systems. Finally, experimental results are provided to demonstrate the great potential of IRA-enabled ISCC system, thus paving the way for more robust and efficient future wireless networks.

cs.IT↗

arXiv2025

A Novel Cost-Effective MIMO Architecture with Ray Antenna Array for Enhanced Wireless Communication Performance

This paper proposes a novel multi-antenna architecture, termed ray antenna array (RAA), which practically enables flexible beamforming and also enhances wireless communication performance for high frequency systems in a cost-effective manner. RAA consists of a large number of inexpensive antenna elements and a few radio frequency (RF) chains. These antenna elements are arranged in a novel ray like structure, where each ray corresponds to one simple uniform linear array (sULA) with a carefully designed orientation. The antenna elements within each sULA are directly connected, so that each sULA is able to form a beam towards a direction matching the ray orientation without relying on any analog or digital beamforming. By further designing a ray selection network (RSN), appropriate sULAs are selected to connect to the RF chains for subsequent baseband processing. Compared to conventional multi-antenna architectures such as the uniform linear array (ULA) with hybrid analog/digital beamforming (HBF), the proposed RAA enjoys three appealing advantages: (i) finer and uniform angular resolution for all signal directions; (ii) enhanced beamforming gain by using antenna elements with higher directivity, as each sULA is only responsible for a small portion of the total angle coverage range; and (iii) dramatically reduced hardware cost since no phase shifters are required, which are expensive and difficult to design in high-frequency systems such as mmWave and THz systems. To validate such advantages, we first present the input-output mathematical model for RAA-based wireless communications. Efficient algorithms for joint RAA beamforming and ray selection are then proposed for single-user and multi-user RAA-based wireless communications. Simulation results demonstrate that RAA achieves superior performance compared to the conventional ULA with HBF, while significantly reducing hardware cost.

cs.AR↗

arXiv2025

Ray Antenna Array Achieves Uniform Angular Resolution Cost-Effectively for Low-Altitude UAV Swarm ISAC

Ray antenna array (RAA) is a novel multi-antenna architecture comprising massive low-cost antenna elements and a few radio-frequency (RF) chains. The antenna elements are arranged in a novel ray-like structure, where each ray corresponds to a simple uniform linear array (sULA) with deliberately designed orientation and all its antenna elements are directly connected. By further designing a ray selection network (RSN), appropriate sULAs are selected to connect to the RF chains for further baseband processing. RAA has three appealing advantages: (i) dramatically reduced hardware cost since no phase shifters are needed; (ii) enhanced beamforming gain as antenna elements with higher directivity can be used; (iii) uniform angular resolution across all signal directions. Such benefits make RAA especially appealing for integrated sensing and communication (ISAC), particularly for low-altitude unmanned aerial vehicle (UAV) swarm ISAC, where high-mobility aerial targets may easily move away from the boresight of conventional antenna arrays, causing severe communication and sensing performance degradation. Therefore, this paper studies RAA-based ISAC for low-altitude UAV swarm systems. First, we establish an input-output mathematical model for RAA-based UAV ISAC and rigorously show that RAA achieves uniform angular resolution for all directions. Besides, we design the RAA orientation and RSN. Furthermore, RAA-based ISAC with orthogonal frequency division multiplexing (OFDM) for UAV swarm is studied, and efficient algorithm is proposed for sensing target parameter estimation. Extensive simulation results demonstrate the significant performance improvement by RAA system over the conventional antenna arrays, in terms of sensing angular resolution and communication spectral efficiency, highlighting the great potential of the novel RAA system to meet the growing demands of low-altitude UAV ISAC.

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arXiv2025

Ray Antenna Array: A Novel Cost-Effective Multi-Antenna Architecture for Enhanced Wireless Communication

This paper proposes a novel multi-antenna architecture, termed ray antenna array (RAA), which aims to enhance wireless communication performance in a cost-effective manner. RAA is composed of massive cheap antenna elements and a few radio frequency (RF) chains. The massive antenna elements are arranged in a novel ray-like structure, with each ray corresponding to a simple uniform linear array (sULA) with a carefully designed orientation. The antenna elements of each sULA are directly connected to an RF combiner, so that the sULA in each ray is able to form a beam towards a direction matching the ray orientation without relying on any analog or digital beamforming. By further designing a ray selection network (RSN), appropriate sULAs are selected to connect to the RF chains for further baseband processing. Compared to conventional multi-antenna architectures like hybrid analog/digital beamforming (HBF), the proposed RAA has two major advantages. First, it can significantly reduce hardware costs since no phase shifters, which are usually expensive especially in high-frequency systems, are required. Besides, RAA can greatly improve system performance by configuring antenna elements with higher directionality, as each sULA only needs to be responsible for a portion of the total coverage angle. To demonstrate such advantages, in this paper, we first present the input-output model for RAA-based wireless communications, based on which the ray orientations of the RAA are designed. Furthermore, efficient algorithms for joint ray selection and beamforming are proposed for single-user and multi-user RAA-based wireless communications. Simulation results demonstrate the superior performance of RAA compared to HBF while significantly reducing hardware cost.

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arXiv2025

CKMDiff: A Generative Diffusion Model for CKM Construction via Inverse Problems with Learned Priors

Channel knowledge map (CKM) is a promising technology to enable environment-aware wireless communications and sensing with greatly enhanced performance, by offering location-specific channel prior information for future wireless networks. One fundamental problem for CKM-enabled wireless systems lies in how to construct high-quality and complete CKM for all locations of interest, based on only limited and noisy on-site channel knowledge data. This problem resembles the long-standing ill-posed inverse problem, which tries to infer from a set of limited and noisy observations the cause factors that produced them. By utilizing the recent advances of solving inverse problems with learned priors using generative artificial intelligence (AI), we propose CKMDiff, a conditional diffusion model that can be applied to perform various tasks for CKM constructions such as denoising, inpainting, and super-resolution, without having to know the physical environment maps or transceiver locations. Furthermore, we propose an environment-aware data augmentation mechanism to enhance the model's ability to learn implicit relations between electromagnetic propagation patterns and spatial-geometric features. Extensive numerical results are provided based on the CKMImageNet and RadioMapSeer datasets, which demonstrate that the proposed CKMDiff achieves state-of-the-art performance, outperforming various benchmark methods.

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arXiv2025

Integrated Super-resolution Sensing and Symbiotic Communication with 3D Sparse MIMO for Low-Altitude UAV Swarm

Low-altitude unmanned aerial vehicle (UAV) swarms are expected to play important role for future intelligent aerial systems due to their great potential to cooperatively accomplish complicated missions effectively. However, there are important challenges to be addressed to enable their efficient operation: the large-scale nature of swarms usually leads to excessive spectrum consumption, and ultra-low cost requirements for individual UAVs renders it necessary to develop more cost-effective communication modules. In addition, the densely located swarm UAVs require high resolution for localization and sensing. To address the above challenges and simultaneously achieve spectrum and energy-efficient communication and accurate sensing, we investigate low-altitude UAV swarm with integrated super-resolution sensing and symbiotic communication technology. Specifically, one leading UAV may act as a primary transmitter (PT) to transmit communication signals to the base station (BS), and the remaining nearby UAVs in the swarm act as passive backscatter devices (BDs), which can modulate their information by efficiently backscattering the radio frequency (RF) signals from the PT without consuming extra spectrum or power. In addition, to achieve efficient three-dimensional (3D) super-resolution sensing for the densely located UAV swarm, 3D sparse multiple-input multiple-output (MIMO) technology and super-resolution signal processing algorithms are further exploited, where both L-shaped nested array (LNA) and planar nested arrays (PNA) are considered at the BS. To evaluate the communication and sensing performance for the UAV-symbiotic radio (SR) system, the achievable rates of UAV swarm are derived and the beam patterns of sparse LNA, PNA and the benchmarking compact uniform planar array (UPA) are compared.

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arXiv2025

Supporting Urban Low-Altitude Economy: Channel Gain Map Inference Based on 3D Conditional GAN

The advancement of advanced air mobility (AAM) in recent years has given rise to the concept of low-altitude economy (LAE). However, the diverse flight activities associated with the emerging LAE applications in urban scenarios confront complex physical environments, which urgently necessitates ubiquitous and reliable communication to guarantee the operation safety of the low-altitude aircraft. As one of promising technologies for the sixth generation (6G) mobile networks, channel knowledge map (CKM) enables the environment-aware communication by constructing a site-specific dataset, thereby providing a priori on-site information for the aircraft to obtain the channel state information (CSI) at arbitrary locations with much reduced online overhead. Diverse base station (BS) deployments in the three-dimensional (3D) urban low-altitude environment require efficient 3D CKM construction to capture spatial channel characteristics with less overhead. Towards this end, this paper proposes a 3D channel gain map (CGM) inference method based on a 3D conditional generative adversarial network (3D-CGAN). Specifically, we first analyze the potential deployment types of BSs in urban low-altitude scenario, and investigate the CGM representation with the corresponding 3D channel gain model. The framework of the proposed 3D-CGAN is then discussed, which is trained by a dataset consisting of existing CGMs. Consequently, the trained 3D-CGAN is capable of inferring the corresponding CGM only based on the BS coordinate without additional measurement. The simulation results demonstrate that the CGMs inferred by the proposed 3D-CGAN outperform those of the benchmark schemes, which can accurately reflect the radio propagation condition in 3D environment.

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