Generative-Adversarial-Network-Based Wireless Channel Modeling: Challenges and Opportunities

Intelligent reflecting surface (IRS) is a promising new technology that is able to create a favorable wireless signal propagation environment by collaboratively reconfiguring the passive reflecting elements, yet with low hardware and energy cost. In IRS-aided wireless communication systems, channel modeling is a fundamental task for communication algorithm design and performance optimization, which however is also very challenging since in-depth domain knowledge and technical expertise in radio signal propagations are required, especially for modeling the high-dimensional cascaded base station (BS)-IRS and IRS-user channels (also referred to as the reflected channels). In this paper, we propose a model-driven generative adversarial network (GAN)-based channel modeling framework to autonomously learn the reflected channel distribution, without complex theoretical analysis or data processing. The designed GAN (also named as IRS-GAN) is trained to reach the Nash equilibrium of a minimax game between a generative model and a discriminative model, where the special structure of the reflected channels is incorporated to improve the modeling accuracy. Simulation results are presented to validate the effectiveness of the proposed IRS-GAN framework for IRS-related channel modeling.

The wireless channel model is a deterministric or stochastic numerical description of physical propagation enviroments based on channel measurement or analysis and simulation on the basis of propagation theories. It provides an effective and simple means to approximately express the channel characteristics of the wireless transmission. Channel models can be used for link and system-level performance evaluation, optimization, and comparative studies on wireless communications technologies, algorithms, products and system. Channel model is of great importance to all wireless communications (including 5G mobile communication) research. This chapter focuses on the progress in 5G channel measurement and channel modeling. Section 3.1 introduces the requirements for 5G wireless channel model. Section 3.2 introduces and compares five commonly used channel modeling methods. Section 3.3 introduces the knowledge on channel measurement, channel measurement system and the currently ongoing 5G wireless channel measurement activities. Section 3.4 describes several channel parameter extraction algorithms and statistical analysis of channel parameters. Section 3.5 introduces several existing channel models. Section 3.6 gives an example to show the generating process of stochastic channel models. And the last section is the summary of this chapter.

Coding for the fading channel: a survey

Wireless channel modeling is critical to understanding and optimizing signal transmission. Wireless channels are influenced by many factors, including path loss, reflection, fading, and interference, making them complex and difficult to model and predict. Although some traditional wireless channel modeling methods are effective, they have limitations in handling complex multipath effects and nonlinear characteristics. Generative artificial intelligence (GAI) has the ability to generate robust data and therefore can be potentially used to model realistic wireless channel characteristics. However, there exist some challenges in GAI for wireless channel modeling, including physical layer network security issues, limited generalization ability, and the bandwidth consumption of centralized training. This article introduces a GAI-based wireless channel modeling framework, which leverages blockchain and federated learning to address efficiency, network security, and data privacy concerns in GAI channel modeling. Blockchain technology, through distributed ledgers and smart contracts, enables resource sharing and efficient utilization, enhancing computational efficiency and reducing network security risks. Federated learning allocates training tasks to different devices and nodes, thus allowing model training without centralizing data, protecting user privacy, and reducing network bandwidth usage. This article demonstrates the performance and effectiveness of the proposed framework through numerical results.

In modern wireless communication systems, accurate characterization of wireless propagation channels remains a significant challenge. In multiple-input multiple-output (MIMO) wireless systems, a double-directional channel can be achieved by utilizing multipath components' spatial and temporal properties (MPCs). Grouping the MPC can simplify the parameters as a trade-off between complexity and accuracy. This paper implements k-Deep Auto Encoder (k-DAE), AE+k-means, and K Power Means (KPM) clustering approaches and compares their performance in clustering wireless propagation multipaths in indoor and outdoor scenarios. The results show that AE+k-means performs better than k-DAE in indoor scenarios by 25.48%, while k-DAE performs 24.60 % better in outdoor scenarios. The KPM algorithm performs best in all indoor scenarios among the three algorithms, with a significant increase of 4.38% and 11.062% to AE+k-means and k-DAE, respectively. However, both k-DAE and AE+k-means have quite similar performance in outdoor scenarios. The study also highlights the first use of autoencoders in clustering the MPCs. The results indicate that k-DAE can be used as an alternative clustering method in channel modeling. Future works envisioned applying the approach to other wireless channel models.

Generative Adversarial Networks (GANs) have received a great deal of attention due in part to recent success in generating original, high-quality samples from visual domains. However, most current methods only allow for users to guide this image generation process through limited interactions. In this work we develop a novel GAN framework that allows humans to be “in-the-loop” of the image generation process. Our technique iteratively accepts relative constraints of the form “Generate an image more like image A than image B”. After each constraint is given, the user is presented with new outputs from the GAN, informing the next round of feedback. This feedback is used to constrain the output of the GAN with respect to an underlying semantic space that can be designed to model a variety of different notions of similarity (e.g. classes, attributes, object relationships, color, etc.). In our experiments, we show that our GAN framework is able to generate images that are of comparable quality to equivalent unsupervised GANs while satisfying a large number of the constraints provided by users, effectively changing a GAN into one that allows users interactive control over image generation without sacrificing image quality.

Terahertz communications are envisioned as a promising technology for the sixth generation and beyond wireless systems, which can support wireless links with Terabits-per-second (Tbps) data rates. As the foundation of designing terahertz communications, channel modeling and characterization are crucial to scrutinize the potential of this spectrum. However, current channel modeling in the terahertz band heavily relies on time-consuming and costly measurements. Here, we propose a transfer learning enabled transformer based generative adversarial network to mitigate this problem in terahertz channel modeling. Specifically, as a fundamental building block, a generative adversarial network is exploited to generate channel parameters. To improve the accuracy, a transformer structure with a self-attention mechanism is incorporated in generative adversarial network. Still incurring errors compared with ground-truth measurement, a transfer learning is designed to solve the mismatch between the formulated network and measurement. The proposed method can achieve high accuracy in channel modeling, while requiring only rather limited amount of measurement, which is a promising complement of current channel modeling techniques.

This paper presents a new study in an attempt to apply Fast Euclidean Direction Search (FEDS) algorithm on adaptive smart antennas. The novelty in this work is the use of FEDS algorithm in a smart antenna system for mobile communications because up to our knowledge this algorithm is not applied on smart antenna so far. In order to properly evaluate the performance of smart antenna for mobile communication systems, an Additive White Gaussian Noise (AWGN) channel model is required that each received signal at each element in the array includes only an additive, zero mean, Gaussian noise . The ability of the FEDS algorithm to operate in a fast changing signal environment is examined by subjecting the input signal to Rayleigh fading with a Jakes power spectral density which is a popular choice for land mobile communications systems. Through simulation results of smart antennas for AWGN channel, the RLS and FEDS start to converge from the initial iteration and 10 respectively, whereas, the LMS and NLMS start to converge after 50 and 10 iterations respectively. In the presence of the Rayleigh fading channel, the LMS, and NLMS algorithms converge after 60 and 25 iterations respectively, while the RLS convergence from the initial iteration and FEDS converge from the iteration number 25. On the other hands, the FEDS algorithm shows better tracking and estimation of the desired input signal compared with both LMS and NLMS algorithms and comparable with RLS algorithm. The comparison of linear plot of array factor using FEDS and RLS , both algorithms generates the same deeper null for undesired signals of about í30 dB towards the interferer using an AWGN channel, while it generates deeper null of about -23 dB and -30 dB respectively using the Rayleigh fading channel.

As a new unsupervised learning algorithm framework, generative adversarial networks(GAN) have been favored by more and more researchers, and it has become a research hotspot now. GAN is inspired by the two-person zero-sum game theory in game theory. Its unique adversarial training idea can generate high-quality samples and has more powerful feature learning and feature expression capabilities than traditional machine learning algorithms. At present, GAN has achieved remarkable success in the field of computer vision, especially in the field of sample generation. Every year, a large number of GAN-related research papers are produced, reflecting the fiery degree of research on GAN model. Aiming at the hot model of GAN, first introduce the research status of GAN; then introduce the theory and framework of GAN, which analyzes in detail why the gradient disappears and the mode collapses during the training of GAN; then discussed some typical GAN improvement models, and summarized their theoretical improvements, advantages, limitations, application scenarios and implementation costs; Finally, the application results of GAN in data generation, image super-resolution, and image style conversion are shown, and the current challenges and future research directions of GAN are discussed.

5G enabled maritime unmanned aerial vehicle (UAV) communication is one of the important applications of 5G wireless network which requires minimum latency and higher reliability to support mission-critical applications. Therefore, lossless reliable communication with a high data rate is the key requirement in modern wireless communication systems. These all factors highly depend upon channel conditions. In this work, a channel model is proposed for air-to-surface link exploiting millimeter wave (mmWave) for 5G enabled maritime unmanned aerial vehicle (UAV) communication. Firstly, we will present the formulated channel estimation method which directly aims to adopt channel state information (CSI) of mmWave from the channel model inculcated by UAV operating within the Long Short Term Memory (LSTM)-Distributed Conditional generative adversarial network (DCGAN) i.e. (LSTM-DCGAN) for each beamforming direction. Secondly, to enhance the applications for the proposed trained channel model for the spatial domain, we have designed an LSTM-DCGAN based UAV network, where each one will learn mmWave CSI for all the distributions. Lastly, we have categorized the most favorable LSTM-DCGAN training method and emanated certain conditions for our UAV network to increase the channel model learning rate. Simulation results have shown that the proposed LSTM-DCGAN based network is vigorous to the error generated through local training. A detailed comparison has been done with the other available state-of-the-art CGAN network architectures i.e. stand-alone CGAN (without CSI sharing), Simple CGAN (with CSI sharing), multi-discriminator CGAN, federated learning CGAN and DCGAN. Simulation results have shown that the proposed LSTM-DCGAN structure demonstrates higher accuracy during the learning process and attained more data rate for downlink transmission as compared to the previous state of artworks.

The 5th Generation (5G) of wireless mobile communication systems is currently the focus of many research projects and standardization bodies, such as the 3rd Generation Partnership Project (3GPP). Accurate modeling of the radio propagation channel is important for evaluating the performance of candidate technologies for 5G. However, the state-of-the-art radio channel models, such as the 3GPP 3D channel model, are not appropriate for analyzing all use-cases and scenarios commonly considered for 5G mainly due to its inherent stochastic nature. In particular, the difficulty in dealing with long-term spatial consistency and lack of correlation of small-scale parameters motivates using channel models based on ray-tracing. In fact, channel models that provide spatially consistent observations are of fundamental importance in assessing the performance of multiuser and massive MIMO schemes, by taking into account the spatial correlation of small-scale parameters, as well as of continuous ultra-dense networks (UDNs). This paper shows, by means of extensive numerical results, that the 3GPP 3D channel model consistently underestimates the achievable SIR in LoS multiuser MISO settings. However, in NLoS scenarios and urban micro-cell deployments, the 3GPP 3D channel model significantly overestimates the SIR experienced by the users. Hence, the achievable performance of massive MIMO schemes and continuous UDNs can only be assessed with channel models that take into account the spatial correlation of small-scale parameters.

In the upcoming 6G era, with the deployment of massive Multi-input Multi-output (MIMO) systems, collecting and capturing 6G channel data through traditional channel modeling methods is very expensive. In addition, wireless communication carriers continuously propose and use artificial intelligence (AI) and deep learning (DL) based wireless communication solutions. Implementing such AI and DL based solutions requires a certain amount of high-quality channel data as a prerequisite. Traditional channel modeling methods cannot meet the requirements of simulating or collecting channel data rapidly and efficiently. In this paper, a generative network for channel modeling and signal generation, two data augmentation methods and a training technique are proposed. In short, this paper covers how to improve the performance of generative networks and how to generate high quality data with the premise that a large amount of channel samples are limited. Finally, the experimental results show that our proposed network could effectively and quickly generate 6G channel data by achieving the highest final score on both simple and complex testset. And the simulation results show that the generated data by our proposed structure has consistent normalized power with the real data. And the generated data can support a wide variety of AI-based wireless communication tasks.

Many robotic tasks require accurate shape models in order to properly grasp or interact with objects. However, it is often the case that sensors produce incomplete 3D models due to several factors such as occlusion or sensor noise. To address this problem, we propose a semi-supervised method that can recover the complete the shape of a broken or incomplete 3D object model. We formulated a hybrid of 3D variational autoencoder (VAE) and generative adversarial network (GAN) to recover the complete voxelized 3D object. Furthermore, we incorporated a separate classifier in the GAN framework, making it a three player game instead of two which helps stabilize the training of the GAN as well as guides the shape completion process to follow the object class labels. Our experiments show that our model produces 3D object reconstructions with high-similarity to the ground truth and outperforms several baselines in both quantitative and qualitative evaluations.

A number of radio channel models based on either geometrical or statistical methods have been proposed in literature. Many such models tend to simplify the scattering environment in indoor radio medium by assuming uniform distribution of scatterers and thereby overestimate the performance of the communication system. In this paper, we present a clustering based radio channel model for a wideband frequency selective Single Input Single Output (SISO) indoor wireless channel. With the transmitter and the receiver located at the foci of an ellipse, the scatterers are modeled to be present in form of clusters of scatterers distributed randomly inside the ellipse. Further, the size and hence number of scatterers inside the cluster may vary from cluster to cluster. Such representations of the scatterers are more realistic and therefore the proposed model is expected to provide better approximation to the actual indoor environment. The proposed model has been used to evaluate the performance of a generic SISO wireless link and the results are compared with those obtained using existing channel models. The proposed channel model can be extended to a frequency selective MIMO channel simulator enabling the performance evaluation of multi-antenna wideband communication systems.

A novel generative adversarial network for estimation of trip travel time distribution with trajectory data