Generative-Adversarial-Network Enabled Signal Detection for Communication Systems With Unknown Channel Models

We consider detection based on deep learning, and show it is possible to train detectors that perform well without any knowledge of the underlying channel models. Moreover, when the channel model is known, we demonstrate that it is possible to train detectors that do not require channel state information (CSI). In particular, a technique we call a sliding bidirectional recurrent neural network (SBRNN) is proposed for detection where, after training, the detector estimates the data in real-time as the signal stream arrives at the receiver. We evaluate this algorithm, as well as other neural network (NN) architectures, using the Poisson channel model, which is applicable to both optical and molecular communication systems. In addition, we also evaluate the performance of this detection method applied to data sent over a molecular communication platform, where the channel model is difficult to model analytically. We show that SBRNN is computationally efficient, and can perform detection under various channel conditions without knowing the underlying channel model. We also demonstrate that the bit error rate (BER) performance of the proposed SBRNN detector is better than that of a Viterbi detector with imperfect CSI as well as that of other NN detectors that have been previously proposed. Finally, we show that the SBRNN can perform well in rapidly changing channels, where the coherence time is on the order of a single symbol duration.

Recently, semantic communication (SC) has been regarded as one of the most potential paradigms of 6G. Current SC frameworks require the physical layer channel state information (CSI) in order to handle the severe signal distortion induced by channel fading. Since practical CSI cannot be obtained accurately and the overhead of channel estimation cannot be neglected, we therefore propose a generative adversarial network (GAN) based SC framework (Ti-GSC) that doesn’t require CSI. In Ti-GSC, there are two main modules, i.e., an autoencoder-based encoder-decoder module (AEDM) and a GAN-based non-CSI signal distortion suppression (SDS) module (GSDSM), where SDS only relies on learning the syntactic distribution and the semantics of the transmitted data, so no prior information such as CSI is needed by GSDSM. In order to measure signal distortion, a novel loss function is proposed where two terms, i.e., a syntactic distortion loss term and a semantic distortion loss term, are newly added, and a differentiable semantic measurement method is designed based on the intermediate layers of the AEDM decoder. To achieve better training results of Ti-GSC, two training schemes, i.e., the joint optimization based training (JOT) and the alternating optimization based training (AOT) are designed for the proposed Ti-GSC. Experimental results show that JOT is more efficient for Ti-GSC, and Ti-GSC outperforms conventional communication frameworks in terms of bilingual evaluation understudy (BLEU) score in both Rician and Rayleigh fading channels. Moreover, without CSI, the BLEU score achieved by Ti-GSC is about 40% and 62% higher than that achieved by existing SC frameworks in Rician and Rayleigh fading, respectively. Besides, each term of the presented loss function has a great impact on the BLEU performance of Ti-GSC, where in Rician fading syntactic learning has the greatest impact, and in Rayleigh fading, the adversarial learning becomes important.

Convolutional code is a coding scheme used in communication systems including deep space communications and wireless communications.It provides an alternative approach to block codes for transmission over a noisy channel.The block codes can be applied only for the block of data.The Convolutional coding has an advantage over the block codes in that it can be applied to a continuous data stream as well as to blocks of data.Viterbi decoder employed in digital wireless communication plays a rife role in the overall power consumption of trellis coded modulation decoder.Power reduction in Viterbi decoder could be achieved by reducing the number of states.A pre-computation architecture with T-algorithm was implemented for this purpose, and when we compare this result with full Trellis Viterbi decoder, this approach significantly reduces power consumption without degrading decoding speed.Convolutional encoding with viterbi decoding is a powerful FEC technique that is particularly suited to a channel in which the transmitted signal is corrupted mainly by Additive White Gaussian Noise (AWGN).It operates on data stream and has memory that uses previous bits to encode.The Viterbi Algorithm (VA) is proposed, used for decoding a bit stream that has been encoded using FEC code.The Convolutional encoder adds redundancy to a continuous stream of input data by using a linear shift register.A pre-computation architecture with Viterbi algorithm is implemented for this purpose, Viterbi (Convolutional) encoder and Viterbi decoder are designed and implemented using FPGA technology, which are the essential blocks in digital communication systems.It is particularly suited to a channel in which the transmitted signal is corrupted mainly by AWGN.The Viterbi decoder of Constraint length 7 and code rate ½ is considered.The design is implemented using verilog on Xilinx Spartan 3E and advanced Spartan 6 board and the results and Comparisons are presented.

Communication-Based Train Control (CBTC) systems use wireless communication to confirm safe train operation for train-ground transmission. CBTC consists of four subsystems, and the Data Communication System (DCS) is one of the most important parts, which plays a vital role in train-ground transmission. Many devices are exposed to the environment in DCS, trains operation in bad weather easily leads to a series of hardware failures. When DCS occurs one mistake, it will threaten the efficiency of CBTC and even the safety of passengers. Since DCS occurs failures inevitably, it is necessary to analyze and identify the mistakes more quickly and accurately. This paper proposes a GAN-Bert based edge-to-edge model to identify DCS faults. Bidirectional Encoder Representations from Transformers (BERT) extracts text features from fault diagnosis with Masked Language Model(MLM) and Next Sentence Prediction(NSP). Meanwhile, Generative Adversarial Network (GAN) is used to perform DCS fault diagnosis. At the same time, edge-to-edge collaboration contributes to training a better model. We use raw DCS logs and compare prediction results between the GAN-Bert and Bert-only models. The simulation results represent that the GAN-Bert based model presents higher accuracy.

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.

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.

Distributed, networked communication systems, such as relay beamforming networks are typically designed without considering how the positions of the respective nodes might affect the quality of the communication. That is, network nodes are either assumed to be stationary in space, or, if some of them are moving while communicating, their trajectories are assumed to be independent of the respective communication task. However, in most cases, the Channel State Information (CSI) observed by each network node, per channel use is both spatially and temporally correlated. One could then ask whether the performance of the communication system could be improved by predictively controlling the positions of the network nodes (e.g., the relays), based on causal CSI estimates and by exploiting the spatiotemporal dependencies of the communication medium. In this talk, we address the problem of enhancing Quality-of-Service (QoS) in power constrained, mobile relay beamforming networks, by optimally exploiting relay mobility. We consider a time slotted system, where the relays update their positions before the beginning of each time slot. Adopting a spatiotemporal stochastic field model of the wireless channel, we propose a novel 2-stage stochastic programming formulation for specifying the relay positions at each time slot, such that the QoS of the network is maximized on average, based on causal CSI and under a total relay transmit power budget. The motion control problem considered is shown to be approximately equivalent to a set of simple subproblems, which can be solved in a distributed fashion, one at each relay. Numerical simulations are presented, corroborating the efficacy of the proposed approach and confirming its properties.

This chapter is attempted to provide a survey of the advanced concepts and related issues involved in Multiple Input Multiple Output (MIMO) systems. MIMO system technology has been considered as a really significant foundation on which to build the next and future generations of wireless networks. The chapter addresses advanced MIMO techniques such as polarization diversity and antenna selection. We gradually provide an overview of the MIMO features from basic to more advanced topics. The first sections of this chapter start by introducing the key aspects of theMIMO theory. TheMIMO systemmodel is first presented in a genericway. Then, we proceed to describe diversity schemes used in MIMO systems. MIMO technology could exploit several diversity techniques beyond the spatial diversity. These techniques essentially cover frequency diversity, time diversity and polarization diversity. We further provide the reader with a geometrically based models for MIMO systems. The virtue of this channel modeling is to adopt realisticmethods for modeling the spatio-temporal channel statistics from a physical wave-propagation viewpoint. Two classes for MIMO channel modeling will be described. These models involve the Geometry-based Stochastic ChannelModels (GSCM) and the Stochastic channel models. Besides the listedMIMO channel models already described, we derive and discuss capacity formulas for transmission over MIMO systems. The achieved MIMO capacities highlight the potential of spatial diversity for improving the spectral efficiency of MIMO channels. When Channel State Information (CSI) is available at both ends of the transmission link, the MIMO system capacity is optimally derived by using adaptive power allocation based on water-filling technique. The chapter continues by examining the combining techniques for multiple antenna systems. Combining techniques are motivated for MIMO systems since they enable the signal to noise ratio (SNR) maximization at the combiner output. The fundamental combing techniques are the Maximal Ratio Combining (MRC), the Selection Combining (SC) and the Equal Gain Combining(EGC). Once the combining techniques are analyzed, the reader is introduced to the beamforming processing as an optimal strategy for combining. The use of multiple antennas significantly improves the channel spectral efficiency. Nevertheless, this induces higher system complexity of the communication system and the communication system performance is effected due to correlation between antennas that need to be deployed at the same terminal. As such, the antenna selection algorithm for MIMO systems is presented. To elaborate on this point, we introduce Space time coding techniques for MIMO systems and we evaluate by simulation the performance of the communication system. Next, we emphasis on multi polarization techniques for MIMO systems. As a background, we presume that the reader has a thorough understanding of antenna theory. We recall the basic antenna theory and concepts that are used throughout the rest of the chapter. We rigorously introduce the 3D channel model over the Non-Line of Sight (NLOS) propagation channel for MIMO system with polarized antennas. We treat the depolarization phenomena and we study its effect on MIMO system capacity. The last section of the chapter provides a scenario for collaborative sensor nodes performing distributed MIMO system model which is devoted to sensor node localization in Wireless Sensor Networks. The localization algorithm is based on beamforming processing and was tested by simulation. Our chapter provides the reader by simulation examples for almost all the topics that have been treated for MIMO system development and key issues affecting achieved performance.

In modern wireless communication systems, wireless channel modeling has always been a fundamental task in system design and performance optimization. Traditional channel modeling methods, such as ray-tracing and geometry- based stochastic channel models, require in-depth domain-specific knowledge and technical expertise in radio signal propagations across electromagnetic fields. To avoid these difficulties and complexities, a novel generative adversarial network (GAN) framework is proposed for the first time to address the problem of autonomous wireless channel modeling without complex theoretical analysis or data processing. Specifically, the GAN is trained by raw measurement data to reach the Nash equilibrium of a MinMax game between a channel data generator and a channel data discriminator. Once this process converges, the resulting channel data generator is extracted as the target channel model for a specific application scenario. To demonstrate, the distribution of a typical additive white Gaussian noise channel is successfully approximated by using the proposed GAN-based channel modeling framework, thus verifying its good performance and effectiveness.

This chapter discusses fundamentals of information theory and channel coding (error correction coding ECC, Forward error correction FEC). First, key results of Shannon, including channel capacity and the relationship between transmit power and bandwidth, are discussed. Block codes multiply a block of data with an encoding matrix. For systematic codes, the resulting codeword consists of systematic bits and parity check bits. Decoding is achieved by multiplication with a parity check matrix and determination of the syndrome vector. Correction of errors within the correction sphere can be done, e.g., through lookup tables. Convolutional codes send the source data stream through shift registers and adders, a process described by a trellis diagram. Maximum-likelihood sequence estimation (Viterbi algorithm) determines the (transmit) bit sequence with best distance metric (Hamming or Euclidean) from the received signal. Methods for combining coding and higher-order modulation include trellis coded modulation (using set partitioning for code design) and bit-interleaved coded modulation (BICM). Turbo codes are close to capacity-achieving. They are based on transmitting differently interleaved convolutional codes; receivers use soft decoding of the constituent codes and iterative exchange of soft information (log-likelihood ratios). Low-density parity check codes (LDPCC) are decoded by belief propagation (message passing), where variable nodes and constraint nodes exchange beliefs about the codeword. In fading channels, (block or convolutional) interleaving is essential to provide diversity. From an information-theoretic point of view, in fading channels we can distinguish between the ergodic capacity and the outage capacity. If the channel state information is known at the transmitter, waterfilling is the optimum power allocation strategy. Automatic Repeat Request ARQ) and Hybrid ARQ (including chase combining or incremental redundancy) are described in the appendix.

Recently, research on generative adversarial networks (GANs) in deep learning has advanced rapidly. For instance, in the field of image recognition, using a GAN, the number of training data was increased. GAN have also been used to create new similar images using training images, which can help designers such as car designer, character designer, game designer and more. In this study, we consider a method to automatically generate a game stage based on a dot-picture using GAN. However, the images generated by GAN using the original game stages are blurred. To solve this problem, we perform preprocessing to the original images. Specifically, we painted out colors for each type of object, and deleted backgrounds and unnecessary objects. In our experiments, we automatically generated game stages using DC-GAN for two 2D action games with different scroll directions (vertical or horizontal). As a result, the proposed method can generate game stages clearly in some game systems.

Cel: The purpose of this article is to present the results of ongoing theoretical research – the evaluation of normative and legal documents in the area of emergency voice communication. The research was aimed at analysing and comparing the operation of emergency communication systems (SKA) in selected countries and the possibility of their wider use in construction objects in Poland. The utilitarian purpose of the publication is a discussion that should contribute to the development of appropriate standards for ensuring the proper level of reliability of such installations and the safety of those who use them. Wprowadzenie: Emergency communication systems enable communication between locations in a building that are critical for conducting evacuation and rescue, as well as firefighting operations and a central location in the building object (e.g. the fire equipment service room) that provides control and support for these processes. They can be used to communicate with people with different types and degrees of disabilities, who are in survival areas, firefighters/rescuers in the fire vestibules of lifts for rescue teams or stairwells, and stewards at sporting events. Their use in Poland is not common, but increasingly required by the investors. Metodologia: The authors used theoretical research, such as analysis of literature and legal documents, synthesis, generalization, inference, comparison and analogy. Documents from Poland, the United Kingdom, Germany and the United States were analysed. The selection of individual countries was guided by the level of development of these systems in the given country and the availability of source documents. Wnioski: Up until now SKA installations are not widespread in our country. However, an increasing demand for such installations is to be expected, especially given changes in the attitudes toward ensuring the safety of people with various disabilities, as well as, perhaps to a somewhat lesser extent, the emerging need to support the communication of the rescue teams, especially in buildings with very high heights. So far, emergency communication systems have been used mainly for utility purposes under normal operating conditions of a construction object. Thus there are no national regulations or standards on the requirements for such installations to function under fire conditions. Słowa kluczowe: evacuation of buildings, safety of evacuation, fire intercoms, emergency communication systems

A new nano-based architectural design of multiple-stream convolutional homeomorphic error-control coding will be conducted, and a corresponding hierarchical implementation of important class of the homeomorphic Viterbi algorithm within convolutional homeomorphic error-control coding using lattice networks via nano carbon-based field emission controlled-switching will be achieved. Error-correction coding is highly important in modern wireless networking and digital data transaction since channel coding is required to counteract data errors that are encountered in wireless data networking due to the corresponding existence of unavoidable channel noise. Further, the new lattice nano-based implementation will be useful for enhancing the error-control system performance such as the corresponding enhancements within error-correcting capability, regular synthesis, speed improvement and the minimization of power consumption. Logic homeomorphism describes properties-preserving logic mapping that is intrinsically bijective. Homeomorphic property in error-control coding is important since it is shown that the homeomorphism relationship between multiple-streams of data can be used for further correction of errors that are uncorrectable using the implemented decoding algorithm such as in the case of triple-errors that are uncorrectable using the classical Viterbi algorithm. Applications of the new regular nano-based homeomorphic architecture include low-power design of circuits and systems for enhanced and more reliable wireless data networking and transaction.

Publication in the conference proceedings of EUSIPCO, Tampere, Finland, 2000

Molecular communication is a promising nanoscale communication paradigm that enables nanomachines to exchange information by using molecules as communication carrier. Up to now, the molecular communication channel between a transmitter nanomachine (TN) and a receiver nanomachine (RN) has been modeled as either concentration channel or timing channel. However, these channel models necessitate exact time synchronization of the nanomachines and provide a relatively low communication bandwidth. In this paper, the Molecular ARray-based COmmunication (MARCO) scheme is proposed, in which the transmission order of different molecules is used to convey molecular information without any need for time synchronization. The MARCO channel model is first theoretically derived, and the intersymbol interference and error probabilities are obtained. Based on the error probability, achievable communication rates are analytically obtained. Numerical results and performance comparisons reveal that MARCO provides significantly higher communication rate, i.e., on the scale of 100 Kbps, than the previously proposed molecular communication models without any need for synchronization. More specifically, MARCO can provide more than 250 Kbps of molecular communication rate if intersymbol time and internode distance are set to 2 μs and 2 nm, respectively.