Link-level Simulations Research Articles

Antijamming Schemes for the Generalized MIMO Y Channel.

Signal space alignment (SSA) is a promising technique for interference management in wireless networks. However, despite the excellent work done on SSA, its robustness against jamming attacks has not been considered in the literature. In this paper, we propose two antijamming strategies for the SSA scheme applied in the multiple-input-multiple-output (MIMO) Y channel. The first scheme involves projecting the jamming signal into the null space of each source's precoding vectors, effectively eliminating it entirely. The second scheme removes interference originating from the jammer by subtracting the disturbance estimate from the incoming signal. The estimate is derived on the basis of the criterion of minimizing the received signal energy. The block error rate (BLER) performance of the proposed strategies in various channel configurations is verified by link level simulations and is presented to show the efficiency in mitigating jamming signals within the SSA-based MIMO Y channel.

An event-driven link-level simulator for validation of AFDX and Ethernet avionics networks

Aircraft are composed of many electronic systems: sensors, displays, navigation equipment and communication elements. These elements require a reliable interconnection, which is a major challenge for communication networks as high reliability and predictability requirements must be verified for safe operation. In addition, their verification via hardware deployments is limited because these are costly and make difficult to try different architectures and configurations, thus delaying the design and development in this area. Therefore, verification at early stages of the design process is of great importance that has to be supported via simulation. In this context, the present work presents an event-driven link level framework and simulator for the validation of avionics networks. The presented tool supports communication protocols such as Avionics Full-Duplex Switched Ethernet (AFDX), which is a common protocol in avionics, as well as Ethernet, which is used with static routing in such scenarios. The simulator also uses realistic element models to provide accurate results. The proposed platform is evaluated in Clean Sky’s Disruptive Cockpit for Large Passenger Aircraft architecture scenario. The speed of the verification is a key factor, so the computational cost is analyzed, proving that the execution time is linearly dependent on the number of messages sent.

Comprehensive Link-Level Simulator for Terahertz MIMO Integrated Sensing and Communication Systems with TDD Framework

Comprehensive Link-Level Simulator for Terahertz MIMO Integrated Sensing and Communication Systems with TDD Framework

Distributed Machine-Learning for Early HARQ Feedback Prediction in Cloud RANs

In this work, we propose novel HARQ prediction schemes for Cloud RANs (C-RANs) that use feedback over a rate-limited feedback channel (2 - 6 bits) from the Remote Radio Heads (RRHs) to predict at the User Equipment (UE) the decoding outcome at the BaseBand Unit (BBU) ahead of actual decoding. In particular, we propose a Dual Autoencoding 2-Stage Gaussian Mixture Model (DA2SGMM) that is trained in an end-to-end fashion over the whole C-RAN setup. Using realistic link-level simulations in the sub-THz band at 100 GHz, we show that the novel DA2SGMM HARQ prediction scheme clearly outperforms all other adapted and state-of-the-art schemes. The DA2SGMM shows a superior performance in terms of blockage detection as well as HARQ prediction in the no-blockage and single-blockage cases. In particular, the DA2SGMM with 4~bit feedback achieves a more than 200 % higher throughput in average compared to its best alternative. Compared to regular HARQ, the DA2SGMM reduces the maximum transmission latency by more than 72.4 %, while maintaining more than 75 % of the throughput in the no-blockage scenario. In the single-blockage scenario, DA2SGMM significantly increases the throughput for most of the evaluated Signal-to-Noise-Ratios (SNRs) compared to regular HARQ.

Bağlantı Seviyesi Simülasyon Üzerinde LLRnet'in 5G-NR'ye Uygulanması

Modern receivers apply smooth demodulation and demapping processes to received symbols, using bit log-likelihood ratios (LLRs). Known as “LLRnet" demodulator architecture is offered that is a global educatable neural network attributed in this paper. The calculation of the optimal LLR algorithm includes the calculation of each bit in the LLR value and requires the assessment of whole lattice points as high dimensional which is unpractical for the QAM modulation. Known the most in the literature the Maximum Likelihood (ML) detector shows very high computational complexity that is used in the QAM scheme. Besides LLRnet developed achievement importantly, all calculational complexity is also reduced. Via estimating exact log-likelihood, how to create symbols, and channel corruptions for training a neural network LLRNet is shown in this paper. New and contemporary radio communication systems, such as 5G- NR (New Radio) and DVB (Digital Video Broadcasting) for satellite, DVB (S.2 Second Generation) utilize the LLR approach that calculates soft bit values with FEC (Forward Error Correction) algorithms and utilizes demodulated smooth bit values. This article aims a link-level simulation study to implement of LLRnet to DVB S2 and 5G-NR. The motivation of this study is seen that performing machine learning techniques on physical layer scheme, makes LLRNet a powerful example for practicability. This paper offers to compare Max-Log Approximate LLR, Exact LLR, and LLRNet methods for 16, 32 and 128 QAM.

Model-Based Deep Learning Receiver Design for Rate-Splitting Multiple Access

Effective and adaptive interference management is required in next generation wireless communication systems. To address this challenge, Rate-Splitting Multiple Access (RSMA), relying on multi-antenna rate-splitting (RS) at the transmitter and successive interference cancellation (SIC) at the receivers, has been intensively studied in recent years, albeit mostly under the assumption of perfect Channel State Information at the Receiver (CSIR) and ideal capacity-achieving modulation and coding schemes. To assess its practical performance, benefits, and limits under more realistic conditions, this work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods, which aims to unite the simple structure of the conventional SIC receiver and the robustness and model agnosticism of deep learning techniques. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS), and average training overhead. Also, a comparison with the SIC receiver, with perfect and imperfect CSIR, is given. Results reveal that the MBDL receiver outperforms by a significant margin the SIC receiver with imperfect CSIR, due to its ability to generate on demand non-linear symbol detection boundaries in a pure data-driven manner.

A comprehensive study on the synchronization procedure in 5G NR with 3GPP-compliant link-level simulator

The 5G New Radio synchronization procedure is the first step that the user must complete to access the mobile network. It mainly consists of the detection of the primary and secondary synchronization signals (PSS and SSS, respectively) and the decoding of the physical broadcast channel (PBCH). Our goal is to provide a comprehensive study of the synchronization procedure and investigate different techniques and approaches, through the implementation of a 5G New Radio-compliant simulator. Of significant interest is the investigation of impairments such as the fading channel, the frequency offset, and the delay spread. The results are provided in terms of detection probability for the PSS and SSS detection, and in terms of block error rate for the PBCH. From the data collected, there is evidence that choosing M-sequences for the PSS leads to an appreciably robust solution against frequency offset. The structure of the Gold sequences for SSS generation can be exploited to reduce the detection complexity, and different approaches can be chosen to improve reliability against delay spread. Moreover, the polar coding for 5G PBCH outperforms the former 4G coding technique, but they are still sensible to frequency offset. Finally, the simulator functionalities are validated through real captures of 5G signals.

5G-RCOLAB: A system level simulator for 5G and beyond in rural areas

This paper describes 5G-RCOLAB, an open source simulation tool for performance evaluation of 5G/6G wireless networks in rural areas. The tool is designed with a layered architecture, based on three modules: (i) a link-level simulator of a communication channel for remote areas, with a mapping Link to System (L2S) interface; (ii) a Long-Term Evolution (LTE)-based physical layer adapted for remote areas, together with a Medium Access Control (MAC) layer that includes a novel Collaborative Spectrum Sensing (CSS) technique with security features for Byzantine attacks and spectrum usage efficiency; and (iii) an application oriented network layer that emulates the behavior of typical users in rural areas. The tool has the goal to reduce simulation complexity of large scale 5G rural networks and at the same time, to represent coherently the features of the physical and MAC layers, that include new channel models that cover long distances with the use of TV White Spaces (TVWS) in the Ultra High Frequency (UHF) and Very High Frequency (VHF) bands, as well as novel CSS techniques to improve spectrum efficiency. The 5G-RCOLAB was validated both by field test measurements from a Proof of Concept (PoC) prototype and by the simulation and evaluation of three 5G common core use cases in rural areas. The results show that the 5G-RCOLAB features allow a better evaluation of novel technologies implemented in the physical and MAC layers and provide paths for improvements and deployments of cost effective solutions of 5G in rural or remote areas.

Successive Cancellation Integer Forcing via Practical Binary Codes

A new multiple-input multiple-output (MIMO) receiver scheme for practical binary codes is proposed that provides consistent gains over conventional linear receivers. We first develop a practical successive integer forcing (IF) scheme based on practical binary codes rather than lattice codes. We then present the successive cancellation integer forcing (SC-IF) scheme, which combines and enhances successive IF and minimum mean squared error successive interference cancellation (MMSE-SIC). In this scheme, the receiver first decides whether individual decoding or IF sum decoding is appropriate for each data stream, and then conducts successive IF sum decoding only for selected streams while decoding the remaining streams using MMSE-SIC. The proposed SC-IF methodology mitigates the performance loss caused by mismatched IF filtering in fading channels, while attenuating the noise amplification caused by MMSE filtering. Extensive link-level simulations demonstrate that the proposed successive IF significantly improves the basic IF, and the SC-IF improves both the successive IF and MMSE-SIC, offering uniform improvements over conventional linear receivers for most channel correlation and variation parameters and modulation orders at comparable computational costs. These results illustrate the viability of SC-IF as a fundamental building block for high-performance MIMO receivers in 5G-Advanced and/or subsequent-generation communication systems.

Link-Level Simulator for 5G Localization

Channel-state-information-based localization in 5G networks has been a promising way to obtain highly accurate positions compared to previous communication networks. However, there is no unified and effective platform to support the research on 5G localization algorithms. This paper releases a link-level simulator for 5G localization, which can depict realistic physical behaviors of the 5G positioning signal transmission. Specifically, we first develop a simulation architecture considering more elaborate parameter configuration and physical-layer processing. The architecture supports the link modeling at sub-6GHz and millimeter-wave (mmWave) frequency bands. Subsequently, the critical physical-layer components that determine the localization performance are designed and integrated. In particular, a lightweight new-radio channel model and hardware impairment functions that significantly limit the parameter estimation accuracy are developed. Finally, we present three application cases to evaluate the simulator, i.e. two-dimensional mobile terminal localization, mmWave beam sweeping, and beamforming-based angle estimation. The numerical results in the application cases present the performance diversity of localization algorithms in various impairment conditions.

Non-Stationarity Characterization and Geometry-Cluster-Based Stochastic Model for High-Speed Train Radio Channels

In time-variant high-speed train (HST) radio channels, the scattering environment changes rapidly with the movement of terminals, leading to a serious deterioration in communication quality. In the system-and link-level simulation of HST channels, this non-stationarity should be characterized and modeled properly. In this paper, the sizes of the quasi-stationary regions are quantified to measure the significant changes in channel statistics, namely, the average power delay profile (APDP) and correlation matrix distance (CMD), based on a measurement campaign conducted at 2.4 GHz. Furthermore, parameters of the multi-path components (MPCs) are estimated and a novel clustering-tracking-identifying algorithm is designed to separate MPCs into line-of-sight (LOS), periodic reflecting clusters (PRCs) from power supply pillars along the railway, and random scattering clusters (RSCs). Then, a non-stationary geometry-cluster-based stochastic model is proposed for viaduct and hilly terrain scenarios. Furthermore, the proposed model is verified by measured channel statistics such as the Rician K factor and the root mean square delay spread. The temporal autocorrelation function and the spatial cross-correlation function are presented. Quasi-stationary regions of the model are analyzed and compared with the measured data, the standardized IMT-Advanced (IMT-A) channel model, and a published non-stationary IMT-A channel model. The good agreement between the proposed model and the measured data demonstrates the ability of the model to characterize the non-stationary features of propagation environments in HST scenarios.

Towards 5G new radio sidelink communications: A versatile link-level simulator and performance evaluation

Sidelink in cellular networks enables direct exchange of data packets between devices without the need for network infrastructure, resulting in various benefits, including communication in out-of-coverage areas and possible decrease in latency by a considerable extent. Thus, sidelink is a favorable choice for applications like public safety communications and Vehicle-to-Everything (V2X) communications. As 4G Long Term Evolution (LTE) advanced to 5G New Radio (NR) under the Third Generation Partnership Project (3GPP), several new features were introduced to sidelink, such as two-stage Sidelink Control Informations (SCIs), data and control multiplexing, and feedback-based Hybrid Automatic Repeat Request (HARQ) with a configurable maximum number of transmissions. To conduct extensive NR sidelink link-level evaluations, a comprehensive simulation platform is essential. In this paper, we introduce the first publicly accessible 5G NR Link-Level Simulator (LLS) that supports major 5G NR sidelink features and complies with the 3GPP standards. This MATLAB-based simulator allows for flexible control over various Physical Layer (PHY) configurations, facilitating customized simulations on algorithm development and performance evaluations. We discuss the simulator’s structure and the sidelink features implemented in detail and evaluate the 5G NR sidelink performance using the developed simulator. Our simulation results indicate that the Block Error Rate (BLER) curves are insensitive to error-prone 2nd-stage Sidelink Control Information (SCI2) and number of Resource Blocks (RBs) allocated; however, the sidelink communication range is sensitive to the deployment environment. We also highlight the need for a careful choice of numerology, device power class and HARQ configuration to balance performance metrics for a variety of services based on 5G NR sidelink deployment scenarios.

Demodulation Reference Signal (DM-RS) based channel estimation for non-terrestrial networks to support high mobility

This paper provides an adaptive 5G-NR based solution for non-terrestrial networks (NTN) to support the high mobility of the user equipment (UE). A front-loaded demodulation reference signal (DM-RS) symbol pattern with additional orthogonal symbols is proposed to support UE mobility 500 km/h. We develop a 5G-NR link-level simulator (LLS) to analyze the performance of physical downlink shared channel (PDSCH) under the proposed front-loaded DM-RS symbol pattern, tapped delay line (TDL) channel model, and precoding-based beamforming/MIMO schemes. The simulation results show that the proposed channel estimation method based on front-loaded DM-RS helps to improve the link reliability and achieves the maximum spectral efficiency up to 5.05 bps/Hz under 64-QAM, which can also contribute to the evolution of NTN in 6G networks to support the transonic speed mobility of UE.

5G-NR Physical Layer-Based Solutions to Support High Mobility in 6G Non-Terrestrial Networks

Non-terrestrial network (NTN) systems can offer wide area coverage for applications requiring high mobility, which is expected in the sixth generation (6G) of telecommunication systems. This paper proposes a high-mobility support system based on the 5G-NR physical layer components for NTN connectivity. In this paper, we propose the optimization of 5G-NR numerologies and the impact of various modulation and coding schemes (MCS), 3GPP NR-NTN channel models, and MIMO/beamforming schemes with link-level simulation under pilot-aided-based perfect and DM-RS-based practical channel estimation at stationary UE and high mobility of 500 km/h, respectively. This paper also develops a link-level simulation of the 5G-NR physical downlink shared channel (PDSCH) under the 3GPP NR-NTN tapped delay line (TDL) channel model to support UE mobility up to 500 km/h. The bit error rate (BER), maximum achievable throughput (Mbps), and spectral efficiency (bps/Hz) are analyzed for the 5G-NR-based potential elements to be utilized in the evolution of NTN. Furthermore, the denser DM-RS symbol pattern is proposed for utilization in channel estimation to support high mobility, as simulation results prove their capability of fast decoding while using the front-loaded symbol structure. The simulation results show that the large 5G-NR numerologies, such as 120 kHz and DM-RS-based channel estimation, support the high UE mobility by providing high link reliability and the maximum achievable throughput of 368.832 Mbps and spectral efficiency of 3.68 bps/Hz under 64-QAM for TDL-E (LOS) channel model, which can also be a potential solution to support transonic speed mobility in the NTN of 6G services.

Implementation of Deep-Learning-Based CSI Feedback Reporting on 5G NR-Compliant Link-Level Simulator

Advances in machine learning have widened the range of its applications in many fields. In particular, deep learning has attracted much interest for its ability to provide solutions where the derivation of a rigorous mathematical model of the problem is troublesome. Our interest was drawn to the application of deep learning for channel state information feedback reporting, a crucial problem in frequency division duplexing (FDD) 5G networks, where knowledge of the channel characteristics is fundamental to exploiting the full potential of multiple-input multiple-output (MIMO) systems. We designed a framework adopting a 5G New Radio convolutional neural network, called NR-CsiNet, with the aim of compressing the channel matrix experienced by the user at the receiver side and then reconstructing it at the transmitter side. In contrast to similar solutions, our framework is based on a 5G New Radio fully compliant simulator, thus implementing a channel generator based on the latest 3GPP 3-D channel model. Moreover, realistic 5G scenarios are considered by including multi-receiving antenna schemes and noisy downlink channel estimation. Simulations were carried out to analyze and compare the performance with current feedback reporting schemes, showing promising results for this approach from the point of view of the block error rate and throughput of the 5G data channel.

Model for Interference Evaluation in 5G Millimeter-Wave Ultra-Dense Network with Location-Aware Beamforming

Location-Aware Beamforming (LAB) in Ultra-Dense Networks (UDN) is a breakthrough technology for 5G New Radio (NR) and Beyond 5G (B5G) millimeter wave (mmWave) communication. Directional links with narrow antenna half-power beamwidth (HPBW) and massive multiple-input multiple-output (mMIMO) processing systems allows to increase transmitter and receiver gains and thus facilitates to overcome high path loss in mmWave. Well known problem of pencil beamforming (BF) is in construction of precoding vectors at the transmitter and combining vectors at the receiver during directional link establishing and its maintaining. It is complicated by huge antenna array (AA) size and required channel state information (CSI) exchange, which is time consuming for vehicle user equipment (UE). Knowledge of transmitter and receiver location, UE or gNodeB (gNB), could significantly alleviate directional link establishment and space division multiple access (SDMA) implementation. Background of SDMA is in efficient maintenance of affordable level of interference, and the purpose of this research is in signal-to-interference ratio (SIR) evaluation in various 5G UDN scenarios with LAB. The method, used to evaluate SIR, is link level simulation, and results are obtained from publicly released open-source simulator. Contribution of research includes substantiation of allowable UE density, working with LAB. Practical implications include recommendations on terrestrial and angular separation of two UE in 5G UDN scenarios.

QR Decomposition-Based Cyclic Prefixed Single-Carrier Transmissions for Cooperative Communications: Concepts and Research Landscape

As an alternative transmission scheme to orthogonal frequency division multiplexing (OFDM), cyclic prefixed single-carrier (CP-SC) transmissions have been widely adopted to overcome the shortcomings of OFDM. How to exploit the achievable full diversity in frequency selective fading channels is the key question in applying CP-SC-based transmissions. Thus, in this tutorial, we focus on recent research in the areas related to CP-SC transmissions for cooperative wireless systems. After explaining the basic concept and operation of CP-SC transmissions, we introduce various types of diversity that are achievable by CP-SC transmissions. To achieve these types of diversity, it is necessary to employ QR decomposition (QRD)-M-based data detection in the receiver. To verify the benefits of CP-SC transmissions in terms of diversity gain, we exemplify various wireless systems for relaying, spectrum sharing, physical layer security, and distributed cyclic delay diversity, and provide link-level simulations. Especially, as alternative metrics of reliability, which is one of the key features of 5G and beyond 5G (B5G) systems, we evaluate the average symbol error rate and outage probability. Finally, we discuss potential research directions involving CP-SC transmissions in emerging wireless communication systems.

A Deep Learning Receiver for Non-Linear Transmitter

Non-linearity of wireless transceivers, specifically power amplifier (PA) non-linearity, could pose major limitations towards having high throughput, and cost and energy efficient wireless communication systems. Such limitations from the PA is typically compensated in the transmitter, e.g. by applying power back-off or performing digital-pre-distortion (DPD) aiming to linearize the transmitter. However, applying PA power back-off leads to lower energy efficiency, and lower output power, and hence lower coverage; and performing DPD results in higher complexity of the transmitters. This paper presents an alternative approach based on a receiver method to perform signal detection in the presence of distortions due to PA non-linearity. We propose a receiver technique using artificial neural networks (ANN) to compensate for the PA non-linearity at the receiver side. The paper presents link-level simulation results using pre-trained neural network models based on synthesized training data. The simulation results confirm that the designed receiver can tolerate higher distortions, hence allow the PA output power back-off to be reduced, leading to higher output power improving coverage, spectral efficiency, energy efficiency, and throughput.

3GPP-Compatible Channel Generation Framework for FR2-2 Indoor Short-Range Communication

This study proposes a 3GPP-compatible channel generation framework for ultra-wideband and indoor shortrange communication realized by the fifth-generation (5G) new radio operating in the frequency range (FR) of 52.6-71.0 GHz. The addition of this band, coined “FR2-2”, to the 5G specification allows foreseeing the use cases of shorter-range communications with ultra-wide bandwidth. However, the current focus of the 3GPP channel model on longer-range communication hinders link-level simulations of this use case. To fill this void, we develop a channel generation framework compatible with the 3GPP channel model and applicable to the FR2-2 indoor short-range use case based on real channel measurements. We highlight that even with appropriate parameters for this use case, the 3GPP channel model does not necessarily reproduce similar characteristics of the measured channel impulse response (CIR) owing to the lack of uniformity of cluster angle of arrival (AoA) and power dispersion in intra-cluster subpaths. Based on this finding, an amendment is proposed, where cluster AoA is uniformly distributed, and the intra-cluster power dispersion term is introduced. We demonstrate the feasibility of the proposed framework for reproducing the real CIR characteristic by showcasing an agreement of root-mean-square delay and angular spreads with the measured CIR.

Research on improved multiple users detection algorithm in pattern division multiple access system

Receiver detection algorithm has always been a research hotspot in communication system and the key point to determine the performance of 5G system. Both industry and academia have been committed to finding detection algorithms with lower complexity and higher detection performance. This paper will study how to solve the problems of low detection performance and high complexity of classical algorithms. Taking Pattern Division Multiple Access (PDMA) as an example, this paper improves the existing receiver detection algorithms such as Minimum Mean Square Error-Serial Interference Cancellation (MMSE-SIC), Message Passing Algorithm (MPA) and Expectation Propagation Algorithm (EPA), and puts forward corresponding improved algorithms with specific formulas, i.e. Enhanced-MMSE-SIC, Turbo-like MPA (MPA-Turbo) and Turbo-like EPA with hybrid parallel interference cancellation (EPA-Turbo-PIC). Link-level simulation results and analysis show that the proposed improved algorithms have performance advantages over the corresponding traditional algorithms.