Realistic Wireless Channel Research Articles

Training of Deep Joint Transmitter-Receiver Optimized Communication System without Auxiliary Tools

Deep Joint transmitter-receiver optimized communication system (Deep JTROCS) is a new physical layer communication system. It integrates the functions of various signal processing blocks into deep neural networks in the transmitter and receiver. Therefore, Deep JTROCS can approach the optimal state at the system level by the joint training of these neural networks. However, due to the non-differentiable feature of the channel, the back-propagation of Deep JTROCS training gradients is hindered which hinders the training of the neural networks in the transmitter. Although researchers have proposed methods to train transmitters using auxiliary tools such as channel models or feedback links, these tools are not available in many real-world communication scenarios, limiting the application of Deep JTROCS. In this paper, we propose a new method to use undertrained Deep JTROCS to transmit the training signals and use these signals to reconstruct the training gradient of the neural networks in the transmitter, thus avoiding the use of an additional reliable link. The experimental results show that the proposed method outperforms the additional link-based approach in different tasks and channels. In addition, experiments conducted on real wireless channels validate the practical feasibility of the method.

Evaluation of User-Centric Cell-Free Massive Multiple-Input Multiple-Output Networks Considering Realistic Channels and Frontend Nonlinear Distortion

Future 6G networks are expected to utilize Massive Multiple-Input Multiple-Output (M-MIMO) and follow a user-centric cell-free (UCCF) architecture. In a UCCF M-MIMO network, the user can be potentially served by multiple surrounding Radio Units (RUs) and Distributed Units (DUs) controlled and coordinated by a single virtualized Centralized Unit (CU). Moreover, in an M-MIMO network, each transmit frontend is equipped with a Power Amplifier (PA), typically with nonlinear characteristics, that can have a significant impact on the throughput achieved by network users. This work evaluates a UCCF M-MIMO network within an advanced system-level simulator considering multicarrier transmission, using Orthogonal Frequency-Division Multiplexing (OFDM), realistic signal-processing steps, e.g., per resource block scheduling, and a nonlinear radio frontend. Moreover, both idealistic independent and identically distributed (i.i.d.) Rayleigh and 3D ray-tracing-based radio channels are evaluated. The results show that under the realistic radio channel, the novel user-centric network architecture can lead to an almost uniform distribution of user throughput and improve the rate of the users characterized by the worst radio conditions by over 3 times in comparison to a classical, network-centric design. At the same time, the nonlinear characteristics of the PA can cause significant degradation of the UCCF M-MIMO network’s performance when operating close to its saturation power.

Multiple attributes based physical layer authentication through propagation scenario identification in the internet of vehicles

The Internet of Vehicles (IoV) systems improve road safety through coordination among vehicles, but they are vulnerable to impersonation attack due to their broadcast communication nature. Existing cryptographic authentication approaches are complex for resource-limited IoV devices, which motivated the development of less complex Physical Layer Authentication (PLA) approaches. However, the existing PLA approaches use all channel attributes for all communication scenarios and do not update reference attributes used to authenticate newly observed attributes during authentication, which leads to poor performance when the attributes become insensitive to communication scenarios. To address these limitations, we propose a multiple attributes-based PLA scheme that flexibly selects effective attributes according to current communication scenarios for improved authentication. First, the historical channel attributes including the ricean K-Factor (KF), the Delay Spread (DS), and the Azimuth Spread of Arrival (ASA) together with the communication scenarios (i.e., Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS)) information of transmitters are used to establish a relationship and train a Long Short-Term Memory (LSTM) neural network to identify current communication scenarios. The softmax function is then used to evaluate the scenario identification performance of each attribute and the attributes with maximum softmax values are dynamically selected for authentication. Second, to track and update the reference attributes, we use the historical attributes together with the past location and communication scenarios information of transmitters and train our model to predict future attributes for authentication. Finally, we use the Euclidean distance to measure the similarity between the selected and the predicted attributes to authenticate the transmitters. We validate the effectiveness of our approach and compare its performance with the existing methods through extensive simulations using realistic radio channel characteristics generated from the Quasi Deterministic Radio channel Generator (QuaDRiGa) platform. The results of the evaluation show that our system improves authentication performance and its false alarm and miss detection are respectively 19.19% and 52% lower than the existing approaches.

Deep Learning-Based Automatic Modulation Classification Using Robust CNN Architecture for Cognitive Radio Networks

Automatic modulation classification (AMC) is an essential technique in intelligent receivers of non-cooperative communication systems such as cognitive radio networks and military applications. This article proposes a robust automatic modulation classification model based on a new architecture of a convolutional neural network (CNN). The basic building convolutional blocks of the proposed model include asymmetric kernels organized in parallel combinations to extract more meaningful and powerful features from the raw I/Q sequences of the received signals. These blocks are connected via skip connection to avoid vanishing gradient problems. The experimental results reveal that the proposed model performs well in classifying nine different modulation schemes simulated with different real wireless channel impairments, including AWGN, Rician multipath fading, and clock offset. The performance of the proposed system systems shows that it outperforms its best rivals from the literature in recognizing the modulation type. The proposed CNN architecture remarkably improves classification accuracy at low SNRs, which is appropriate in realistic scenarios. It achieves 86.1% accuracy at −2 dB SNR. Furthermore, it reaches an accuracy of 96.5% at 0 dB SNR and 99.8% at 10 dB SNR. The proposed architecture has strong feature extraction abilities that can effectively recognize 16QAM and 64QAM signals, the challenging modulation schemes of the same modulation family, with an overall average accuracy of 81.02%.

ELM‐based timing synchronization for OFDM systems by exploiting computer‐aided training strategy

AbstractDue to the implementation bottleneck of training data collection in realistic wireless communications systems, supervised learning‐based timing synchronization (TS) is challenged by the incompleteness of training data. To tackle this bottleneck, the computer‐aided approach is extended, with which the local device can generate the training data instead of generating learning labels from the received samples collected in realistic systems, and then construct an extreme learning machine (ELM)‐based TS network in orthogonal frequency division multiplexing (OFDM) systems. Specifically, by leveraging the rough information of channel impulse responses (CIRs), i.e. root‐mean‐square (r.m.s) delay, the loose constraint‐based and flexible constraint‐based training strategies are proposed for the learning‐label design against the maximum multi‐path delay. The underlying mechanism is to improve the completeness of multi‐path delays that may appear in the realistic wireless channels and thus increase the statistical efficiency of the designed TS learner. By this means, the proposed ELM‐based TS network can alleviate the degradation of generalization performance. Numerical results reveal the robustness and generalization of the proposed scheme against varying parameters.

Over-the-Air Adversarial Attacks on Deep Learning Wi-Fi Fingerprinting

Empowered by deep neural networks (DNNs), Wi-Fi fingerprinting has recently achieved astonishing localization performance to facilitate many security-critical applications in wireless networks, but it is inevitably exposed to adversarial attacks, where subtle perturbations can mislead DNNs to wrong predictions. Such vulnerability provides new security breaches to malicious devices for hampering wireless network security, such as malfunctioning geofencing or asset management. The prior adversarial attack on localization DNNs uses additive perturbations on channel state information (CSI) measurements, which is impractical in Wi-Fi transmissions. To transcend this limitation, this paper presents FooLoc, which fools Wi-Fi CSI fingerprinting DNNs over the realistic wireless channel between the attacker and the victim access point (AP). We observe that though uplink CSIs are unknown to the attacker, the accessible downlink CSIs could be their reasonable substitutes at the same spot. We thoroughly investigate the multiplicative and repetitive properties of over-the-air perturbations and devise an efficient optimization problem to generate imperceptible yet robust adversarial perturbations. We implement FooLoc using commercial Wi-Fi APs and Wireless Open-Access Research Platform (WARP) v3 boards in offline and online experiments, respectively. The experimental results show that FooLoc achieves overall attack success rates of about 70% in targeted attacks and of above 90% in untargeted attacks with small perturbation-to-signal ratios of about -18 dB.

A General Channel Model for Integrated Sensing and Communication Scenarios

Aiming to the vision of 6G, integrated sensing and communication (ISAC), as the potential techniques to deal with the increasing demands of native perception ability, has recently attracted a lot of attention. In order to guide system optimization and evaluate novel ISAC techniques, accurate and realistic wireless channel model for ISAC is essential. This article proposes a framework of ISAC channel modeling extended based on 3GPP channel model, which can be used for system simulation and performance analysis of 6G ISAC techniques. We first overview the principles and characteristics of 3GPP channel model, and then we detail the generation of sensing channels as well as communication channels. The birth and death process from communication clusters to sensing clusters is presented in the channel modeling, and the sensing channel modeling approach is proposed for ISAC channels. Implementation of ISAC channel model is presented and the simulation results are discussed.

FIR-SIMO channel order determination by invariant-signal-subspace matching

We present a novel and computationally simple solution to the problem of determining the order of finite impulse response (FIR) single-input multiple-output (SIMO) channel. The solution is applicable to ideal and realistic wireless channels, with both white and colored noise. It is based on a non-asymptotic goodness-of-fit metric, referred to as invariant-signal-subspace matching (ISSM), aimed at matching pairs of invariant-signal-subspaces, created by exploiting the shift-invariance property of the block-Toeplitz data matrix. A pair of projection matrices corresponding to these subspaces is created for every potential FIR-SIMO order, and the order is then determined as that for which this metric is minimized. Simulation results are included, demonstrating the performance of the proposed solution for both ideal and realistic channels, with both white and colored noise.

A Feasibility Study for Signal-in-Space Design for LEO-PNT Solutions With Miniaturized Satellites

The global navigation satellite systems (GNSSs) are increasingly suffering from interferences, such as coming from jammers and spoofers, and their performance is still modest in challenging urban and indoor scenarios. Therefore, there are efforts worldwide to develop complementary positioning, navigation, and timing (PNT) solutions. One such complementary method under current research is the so-called LEO-PNT, namely, PNT solutions based on low-Earth orbit (LEO) satellites, and in particular on small-sized or miniaturized satellites. Such satellites have low-to-moderate costs of building, launching, and maintenance. Several challenges are to be overcome when designing a new LEO-PNT solution, concerning all three satellite segments: 1) the signal-in-space (SIS) or space segment; 2) the ground segment; and 3) the user/receiver segment. This article presents a survey of the SIS design challenges under the inherent constraints of wireless-channel propagation impairments as well as some design recommendations for SIS features. We address different constellation types, achievable coverage limits, and geometric dilution of precision (GDOP) bounds, as well as achievable carrier-to-noise ratios (CNRs) under a realistic wireless channel model, based on a MATLAB Quadriga simulator. We also discuss several optimization criteria regarding LEO-PNT SIS design, by taking into account the tradeoff between a low cost/low number of satellites in orbit on the one hand, and a sufficient coverage and good CNR for PNT purposes on the other hand.

Traffic-aware gateway placement and queue management in flying networks

Unmanned Aerial Vehicles (UAVs) have emerged as adequate platforms to carry communications nodes, including Wi-Fi Access Points and cellular Base Stations. This has led to the concept of flying networks composed of UAVs as a flexible and agile solution to provide on-demand wireless connectivity anytime, anywhere. However, state of the art works have been focused on optimizing the placement of the access network providing connectivity to ground users, overlooking the backhaul network design. In order to improve the overall Quality of Service (QoS) offered to ground users, the placement of Flying Gateways (FGWs) and the size of the queues configured in the UAVs need to be carefully defined to meet strict performance requirements. The main contribution of this article is a traffic-aware gateway placement and queue management (GPQM) algorithm for flying networks. GPQM takes advantage of knowing in advance the positions of the UAVs and their traffic demand to determine the FGW position and the queue size of the UAVs, in order to maximize the aggregate throughput and provide stochastic delay guarantees. GPQM is evaluated by means of ns-3 simulations, considering a realistic wireless channel model. The results demonstrate significant gains in the QoS offered when GPQM is used.

Co-Optimization of Motion, Communication, and Sensing in Real Wireless Channel Environments via Monte Carlo Tree Search

We consider the problem where a robot navigates from a start position to a destination and needs to sense some sites along the way. The robot collects data when sensing each site and needs to transmit all collected data to a remote station by the end of its trip, as it moves along its path, under time/energy constraints, and while operating in real wireless fading environments. Our goal is to minimize the robot's total motion and communication energy costs by co-optimizing its path, data transmission along the path, and sensing decisions, under given constraints and while considering the stochastic wireless channel. In this paper, we show how to solve this co-optimization problem efficiently and with performance guarantees. More specifically, we formulate a specially-designed Markov Decision Process (MDP) and utilize Monte Carlo Tree Search (MCTS) to efficiently and optimally solve it. While the co-dependence of communication, sensing, and motion decisions makes this joint optimization challenging, we show that by considering the transmission optimization in the terminal reward and motion actions in the state transitions, we can iteratively optimize the sensing/motion and the communication parts in different stages of MCTS, in a way that allows us to equivalently solve the original co-optimization problem efficiently. We mathematically prove the convergence of our approach, characterize its convergence speed, and derive key properties of the optimum solution. We extensively evaluate our approach in realistic wireless environments where the channel experiences path loss, shadowing, and multi-path fading and is unknown to the robot.

Deep reinforcement learning based joint partial computation offloading and resource allocation in mobility-aware MEC system

Mobile edge computing (MEC) emerges as a paradigm to free mobile devices (MDs) from increasingly dense computing workloads in 6G networks. The quality of computing experience can be greatly improved by offloading computing tasks from MDs to MEC servers. Renewable energy harvested by energy harvesting equipments (EHQs) is considered as a promising power supply for users to process and offload tasks. In this paper, we apply the uniform mobility model of MDs to derive a more realistic wireless channel model in a multi-user MEC system with batteries as EHQs to harvest and storage energy. We investigate an optimization problem of the weighted sum of delay cost and energy cost of MDs in the MEC system. We propose an effective joint partial computation offloading and resource allocation (CORA) algorithm which is based on deep reinforcement learning (DRL) to obtain the optimal scheduling without prior knowledge of task arrival, renewable energy arrival as well as channel condition. The simulation results verify the efficiency of the proposed algorithm, which undoubtedly minimizes the cost of MDs compared with other benchmarks.

Study on precoding optimization algorithms in massive MIMO system with multi-antenna users

The paper studies the multi-user precoding problem as a non-convex optimization problem for wireless multiple inputs and multiple outputs (MIMO) systems. In our work, we approximate the target Spectral Efficiency function with a novel computationally simpler function. Then, we reduce the precoding problem to an unconstrained optimization task using a special differential projection method and solve it by the Quasi-Newton L-BFGS iterative procedure to achieve gains in capacity. We are testing the proposed approach in several scenarios generated using Quadriga-open-source software for generating realistic radio channel impulse response. Our method shows monotonic improvement over heuristic methods with reasonable computation time. The proposed L-BFGS optimization scheme is novel in this area and shows a significant advantage over the standard approaches. The proposed method has simple implementation and can be a good reference for other heuristic algorithms in this field.

End-to-End Learning for OFDM: From Neural Receivers to Pilotless Communication

The benefits of end-to-end learning has been demonstrated over AWGN channels but has not yet been quantified over realistic wireless channel models. This work aims to fill this gap by exploring the gains of end-to-end learning over a frequency- and time-selective fading channel using OFDM. With imperfect channel knowledge at the receiver, the shaping gains observed on AWGN channels vanish. Nonetheless, we identify two other sources of performance improvements. The first comes from a neural network-based receiver operating over a large number of subcarriers and OFDM symbols which allows to reduce the number of orthogonal pilots without loss of BER. The second comes from entirely eliminating orthogonal pilots by jointly learning a neural receiver together with either superimposed pilots (SIPs), combined with conventional QAM, or an optimized constellation. The learned constellation works for a wide range of signal-to-noise ratios, Doppler and delay spreads, has zero mean and does hence not contain any form of SIP. Both schemes achieve the same BER as the pilot-based baseline with 7% higher throughput. Thus, we believe that a jointly learned transmitter and receiver are a very interesting component for beyond-5G communication systems which could remove the need and associated overhead for demodulation reference signals.

Joint Link Rate Selection and Adaptive Forward Error Correction for High-Rate Wireless Multicast

Multicast communication over wireless networks has many potential applications, such as real-time audiovisual content distribution or digital signage systems with multiple remote terminals. However, today’s common 802.11 networks cannot fully support such applications at the link layer due to the technical challenges in achieving both high transmission rate and high reliability for multicast data transfers. Those challenges primarily emerge from the inapplicability of immediate acknowledgment mechanisms of unicast transmissions to the multicast case and the need for finding a common transmission rate for all receivers with diverse channel conditions. The research literature in this domain mainly addresses the problem at the higher layers of the protocol stack. In this article, we present a novel approach that combines wireless link rate selection with an adaptive packet-level Forward Error Correction (FEC) mechanism in order to achieve high-rate and highly reliable multicast in 802.11 wireless networks. The integration of FEC into the 802.11 MAC layer allows direct interaction between the transmission rate and the coding scheme. As a result, potential packet losses caused by higher link rates can be compensated by the adjustable redundancy provided by FEC. In combination with an aggregated receiver feedback mechanism, this yields improved transmission efficiency and reliability for wireless multicast. We investigate this approach in a simulation environment under a realistic wireless channel model in various application scenarios with up to 50 receivers. The results represent significant performance improvements, in terms of throughput, channel utilization, and packet loss, over the state-of-the-art methods for reliable wireless multicast.

An efficient adaptive modulation technique over realistic wireless communication channels based on distance and SINR

Abstract A growing trend has been observed in recent research in wireless communication systems. However, several limitations still exist, such as packet loss, limited bandwidth and inefficient use of available bandwidth that needs further investigation and research. In light of the above limitations, this paper uses adaptive modulation under various parameters, such as signal to interference plus noise ratio (SINR), and communication channel’s distances. The primary goal is to minimize bit error rate (BER), improve throughput and utilize the available bandwidth efficiently. Additionally, the impact of Additive White Gaussian Noise (AWGN), Rayleigh and Rician fading channels on the performance of various modulation schemes are also studied. The simulation results demonstrate that our proposed technique optimally improves the BER and spectral efficiency in the long-range communication as compared to the fixed modulation schemes under the co-channel interference of surrounding base stations. The results indicate that the performance of fixed modulation schemes is suitable only either at high SINR and low distance or at low SINR and high distance values. Moreover, on the other hand, its performance was suboptimal in the entire wireless communication channel due to high distortion and attenuation. Lastly, we also noted that BER performance in the AWGN channel is better than Rayleigh and Rician channels with Rayleigh channel exhibiting poor performance than the Rician channel.

Анализ повышения помехоустойчивости цифровых сигналов

This article examines the current state of the science of the causes of digital signal distortion. Cellular and radio relay communications, data transmission over computer networks, digital radio broadcasting and television, presented in various and diverse formats, covered all countries of the world. The high quality of communication, relatively low costs for its organization and, therefore, accessibility to the general population of all countries, ensured this success. Modern digital communication systems use all the mastered frequency ranges, different types of modulation and signal processing methods. A real wireless communication channel has time-frequency scattering, which leads to inter-character (MSI) and inter-channel (MCI) interference. By itself, this phenomenon does not lead to big problems, because there are quite a lot of effective methods of fighting. At the present stage, for channels with time-frequency scattering, multiplexing (compaction) with orthogonal frequency division (OFDM - Orthogonal Frequency Devision Multiplexing) is considered the best. The article presents methods of combating distortion, noise-resistant channel coding, the use of OFDM, FBMC, UFMC.

Wireless-Assisted Key Establishment Leveraging Channel Manipulation

Wireless communication is easily eavesdropped due to the broadcast nature of the wireless medium. This has spurred extensive research into secret key establishment using physical layer characteristics of wireless channels. In all these schemes, the secret keys directly originate from the physical features of the real wireless channel, which is highly dependent on the communication environment nearby. Also, previous schemes require performing information reconciliation, which increases both the costs and the risk of key leakage. In this paper, we exhibit a novel wireless key establishment method allowing the transmitter to specify arbitrary content as the key and cause the receiver to obtain the same key leveraging a channel manipulation technique. We furthermore enable the transmitter to apply error-correction code to the key, so that the receiver can automatically correct any mismatched bits without sending key-related information back to the transmitter over the public channel. Experimental results demonstrate that our key establishment method reaches a success rate as high as 91.0 percent for establishing a 168-bit key between the transmitter and the receiver, and meanwhile the chance that the eavesdropper can infer the key in meter-order range of the receiver is subdued into the range of 0~0.10 percent.

FIT: On-the-Fly, In-Situ Training for SNR-Based Rate Selection

Existing rate adaptation protocols have advocated training to establish the relationship between channel conditions and the optimum modulation and coding scheme. However, innate with in-field operation is encountering scenarios that the rate adaptation mechanism has not yet encountered. Frequently, protocols are optimally tuned for indoor environments but, when taken outdoors, perform poorly. Namely, the decision structure formed by offline training, lacks the ability to adapt to a new situation on the fly . The changing wireless environment calls for a rate adaption scheme that can quickly infer the channel type and adjust accordingly. Typical SNR-based rate adaptation schemes do not capture the nuance of the performance variable in different channel types. In this paper, we propose a novel scheme that allow SNR-based rate selection algorithms to be trained online in the environment in which they are operating. Inspired by the idea that, to do well, an athlete must train for the type of athletic event and environment in which they are competing, we propose FIT, an on-the- f ly, i n-situ t raining mechanism for SNR-based protocols. To do so, we first propose the FIT framework which addresses the challenges of making rate decisions with unpredictable fluctuation and lack of repeatability of real wireless channels. To distinguish between channel types in the training, we then characterize wireless channels according to the link-layer performance and introduce a novel, computationally-efficient, channel performance manifold matching technique to infer the channel type given a sequence of throughput measurements for various link-level parameters. To evaluate our methods, we implement rate selection which uses FIT for training alongside channel performance manifold matching. We then perform extensive experiments on emulated and in-field wireless channels to evaluate the online learning process, showing that the rate decision structure can be updated as channel conditions change using existing traffic flows. The experiments are performed over multiple frequency bands. The proposed FIT framework can achieve large throughput gains compared to traditional SNR-based protocols (8X) and offline-training-based methods (1.3X), particularly in a dynamic wireless propagation environments that lack appropriate training.

A Low Complexity Near-Optimal Iterative Linear Detector for Massive MIMO in Realistic Radio Channels of 5G Communication Systems.

Massive multiple-input multiple-output (M-MIMO) is a substantial pillar in fifth generation (5G) mobile communication systems. Although the maximum likelihood (ML) detector attains the optimum performance, it has an exponential complexity. Linear detectors are one of the substitutions and they are comparatively simple to implement. Unfortunately, they sustain a considerable performance loss in high loaded systems. They also include a matrix inversion which is not hardware-friendly. In addition, if the channel matrix is singular or nearly singular, the system will be classified as an ill-conditioned and hence, the signal cannot be equalized. To defeat the inherent noise enhancement, iterative matrix inversion methods are used in the detectors’ design where approximate matrix inversion is replacing the exact computation. In this paper, we study a linear detector based on iterative matrix inversion methods in realistic radio channels called QUAsi Deterministic RadIo channel GenerAtor (QuaDRiGa) package. Numerical results illustrate that the conjugate-gradient (CG) method is numerically robust and obtains the best performance with lowest number of multiplications. In the QuaDRiGA environment, iterative methods crave large to obtain a pleasurable performance. This paper also shows that when the ratio between the user antennas and base station (BS) antennas () is close to 1, iterative matrix inversion methods are not attaining a good detector’s performance.