Wireless Channel Environment Research Articles

A joint vehicular device scheduling and uncertain resource management scheme for Federated Learning in Internet of Vehicles

Federated learning (FL) offers an effective framework for the efficient process in vehicular edge computing. However, FL encompasses the process of distributing and uploading model parameters, which are inevitably transmitted in a wireless network environment. Some challenges in FL-assisted Internet of Vehicles (IoV) sceneries gradually emerging, such as data heterogeneity, concerned device resources, and unstable communication environment, which necessitate intelligent vehicle selection schemes that accelerate training efficiency. Based on these, we consider a new scenario, specifically an FL-assisted IoV system under uncertain communication conditions, and develop an interval many-objective vehicle selection and bandwidth allocation (IMoVSBA) joint optimization scheme. This scheme takes into account computation latency, energy consumption, server utilization, and data quality, while meeting multi-criteria resource optimization requirements. Among these, server utilization is a new objective designed specifically for this joint optimization problem. For the proposed problem, a novel interval many-objective evolutionary algorithm with individual comprehensive indicator to control the evolution direction (IMaOEACI) is designed. Simulation results demonstrate that this method outperforms other schemes in terms of accuracy, training cost, and server utilization, effectively improving training efficiency in wireless channel environments and reasonably utilizing bandwidth resources. It provides significant scientific value and application potential in the field of the IoVs.

Learning efficiency maximization in UAV-and-RIS-aided mobile edge learning system

With the ever-increasing number of Internet of Things devices (IoTDs) and the rapid development of artificial intelligence (AI) technologies, mobile edge learning (MEL) has emerged as a new communication network paradigm that can deploy machine learning on mobile edge computing (MEC) platforms with abundant computational resources. Then how to fully exploit the potential of MEL and enhance its performance is an important issue. To address this issue, the paper proposes an unmanned aerial vehicle (UAV)-and-reconfigurable intelligent surface (RIS)-aided MEL system. In the system, the UAV equipped with a MEL server can fly close to the IoTDs to collect their data for training a deep learning model. And the RIS mounted on the building can improve the wireless channel environment between the UAV and IoTDs to assist the UAV in collecting data. In order to maximize the MEL learning performance while minimizing the system energy consumption, this paper also proposes a new optimization metric called learning efficiency. Then, a learning efficiency maximization problem based on the proposed system is formulated by jointly optimizing the minority class sample size, the transmit resource of the IoTDs, the phase shift of the RIS, and the trajectory of the UAV. Considering the intractability of the problem, we solve it using the alternating optimization (AO) algorithms based on the two types of UAV trajectory design, i.e., a time-division-multiple-access (TDMA) design with higher performance and a Flight-Hover design with lower complexity. The simulation results demonstrate that the proposed optimization metric and algorithms are effective and perform excellently compared to other baselines.

Hybrid Optimized LMMSE-Based Channel Estimation with Low Power Trellis Coded Modulation

Data transfer in a wireless channel environment is plagued by numerous security issues and power transmission loss. Channel state information is crucial to attaining the performance benefit of reconfigurable intelligent surfaces (RISs) in wireless systems. Because of the Doppler effect, mobility makes accurate channel estimate (CE) more difficult. The parameter estimation approach is employed as an alternative to direct estimation in the channel matrix to address the aforementioned problems. Also, to lower the Mean Square Error (MSE) between the estimated and original channel, training-based CE, such as Linear Minimum Mean Square Error (LMMSE), and Least Square Error (LSE), are widely used in some wireless standards. To optimize the channel, certain intelligent optimized strategies are presented. For the Optimal CE, a Trellis Coded Modulation (TCM) with hybrid optimization in LMMSE has been provided. Moth amalgamated Elephant Herding Optimization (MAEHO) algorithm is the proposed hybrid optimization. Finally, the performance of the accepted model is determined in comparison to other existing techniques using a variety of metrics.

Secure Device-to-Device Communication in IoT: Fuzzy Identity from Wireless Channel State Information for Identity-Based Encryption

With the rapid development of the Internet of Things (IoT), ensuring secure communication between devices has become a crucial challenge. This paper proposes a novel secure communication solution by extracting wireless channel state information (CSI) features from IoT devices to generate a device identity. Due to the instability of the wireless channel, the CSI features are fuzzy and time-varying; thus, we a employ locally sensitive hashing (LSH) algorithm to ensure the stability of the generated identity in a dynamically changing wireless channel environment. Furthermore, zero-knowledge proofs are utilized to guarantee the authenticity and effectiveness of the generated identity. Finally, the identity generated using the aforementioned approach is integrated into an IBE communication scheme, which involves the fuzzy extraction of channel state information from IoT devices, stable identity extraction for fuzzy IoT devices using LSH, and the use of zero-knowledge proofs to ensure the authenticity of the generated identity. This identity is then employed as the identity information in identity-based encryption (IBE), constructing the device’s public key for achieving confidential communication between devices.

Hybrid Precoding Design for Wireless Fading Channels

Precoding techniques are widely used to eliminate interference between multiple user signals in a wireless fading channel environment. The linear MMSE precoding technique, one of the most widely used techniques so far, has low computational complexity but has the disadvantage of relatively poor symbol error rate (SER) performance. The symbol-level precoding (SLP) technique, on which much research has been conducted recently, has excellent SER performance but has the disadvantage of being too computationally complex. In this paper, we propose a hybrid precoding technique that simultaneously applies SLP and MMSE precoding to appropriately adjust SER performance and computational complexity performance. If two different types of precoding techniques are applied simultaneously, interference may occur between signals to which different types of precoding are applied, which can significantly deteriorate SER performance. Therefore, in this paper, precoding is designed to prevent interference between the two signals using the null space of the channel matrices. Through computer simulation, the proposed scheme showed that its SER performance was superior to that of the linear MMSE scheme, and the computational complexity was much lower than that of the SLP scheme.

FEC-Aided Decision Feedback Blind Mismatch Calibration of TIADCs in Wireless Time-Varying Channel Environments

FEC-Aided Decision Feedback Blind Mismatch Calibration of TIADCs in Wireless Time-Varying Channel Environments

Toward Adaptive Semantic Communications: Efficient Data Transmission via Online Learned Nonlinear Transform Source-Channel Coding

The emerging field semantic communication is driving the research of end-to-end data transmission. By utilizing the powerful representation ability of deep learning models, learned data transmission schemes have exhibited superior performance than the established source and channel coding methods. While, so far, research efforts mainly concentrated on architecture and model improvements toward a static target domain. Despite their successes, such learned models are still suboptimal due to the limitations in model capacity and imperfect optimization and generalization, particularly when the testing data distribution or channel response is different from that adopted for model training, as is likely to be the case in real-world. To tackle this, in this paper, we propose a novel online learned joint source and channel coding approach that leverages the deep learning model’s overfitting property. Specifically, we update the off-the-shelf pre-trained models after deployment in a lightweight online fashion to adapt to the distribution shifts in source data and environment domain. We take the overfitting concept to the extreme, proposing a series of implementation-friendly methods to adapt the codec model or representations to an individual data or channel state instance, which can further lead to substantial gains in terms of the end-to-end rate-distortion performance. Accordingly, the streaming ingredients include both the semantic representations of source data and the online updated decoder model parameters. The system design is formulated as a joint optimization problem whose goal is to minimize the loss function, a tripartite trade-off among the data stream bandwidth cost, model stream bandwidth cost, and end-to-end distortion. The proposed methods enable the communication-efficient adaptation for all parameters in the network without sacrificing decoding speed. Extensive experiments, including user study, on continually changing target source data and wireless channel environments, demonstrate the effectiveness and efficiency of our approach, on which we outperform existing state-of-the-art engineered transmission scheme (VVC combined with 5G LDPC coded transmission).

Use of Logarithmic Rates in Multi-Armed Bandit-Based Transmission Rate Control Embracing Frame Aggregations in Wireless Networks

Herein, we propose the use of the logarithmic values of data transmission rates for multi-armed bandit (MAB) algorithms that adjust the modulation and coding scheme (MCS) levels of data packets in carrier-sensing multiple access/collision avoidance (CSMA/CA) wireless networks. We argue that the utilities of the data transmission rates of the MCS levels may not be proportional to their nominal values and suggest using their logarithmic values instead of directly using their data transmission rates when MAB algorithms compute the expected throughputs of the MCS levels. To demonstrate the effectiveness of the proposal, we introduce two MAB algorithms that adopt the logarithmic rates of the transmission rates. The proposed MAB algorithms also support frame aggregations available in wireless network standards that aim for a high throughput. In addition, the proposed MAB algorithms use a sliding window over time to adapt to rapidly changing wireless channel environments. To evaluate the performance of the proposed MAB algorithms, we used the event-driven network simulator, ns-3. We evaluated their performance using various scenarios of stationary and non-stationary wireless network environments including multiple spatial streams and frame aggregations. The experiment results show that the proposed MAB algorithms outperform the MAB algorithms that do not adopt the logarithmic transmission rates in both the stationary and non-stationary scenarios.

Disentangled Representation Learning for RF Fingerprint Extraction Under Unknown Channel Statistics

Deep learning (DL) applied to a device’s radio-frequency fingerprint (RFF) has attracted significant attention in physical-layer authentication due to its extraordinary classification performance. Conventional DL-RFF techniques are trained by adopting maximum likelihood estimation (MLE). Although their discriminability has recently been extended to unknown devices in open-set scenarios, they still tend to overfit the channel statistics embedded in the training dataset. This restricts their practical applications as it is challenging to collect sufficient training data capturing the characteristics of all possible wireless channel environments. To address this challenge, we propose a DL framework of disentangled representation (DR) learning that first learns to factor the signals into a device-relevant component and a device-irrelevant component via adversarial learning. Then, it shuffles these two parts within a dataset for implicit data augmentation, which imposes a strong regularization on RFF extractor learning to avoid the possible overfitting of device-irrelevant channel statistics, without collecting additional data from unknown channels. Experiments validate that the proposed approach, referred to as DR-based RFF, outperforms conventional methods in terms of generalizability to unknown devices under unknown complicated propagation environments, e.g., dispersive multipath fading channels, even though all the training data are collected in a simple environment with dominated direct line-of-sight (LoS) propagation paths.

Design of a Channel Robust Radio Frequency Fingerprint Identification Scheme

Radio frequency fingerprint (RFF) identification is an emerging device authentication technique that exploits the hardware imperfections resulting from the manufacturing process. Due to the varying impact of the wireless channel during RFF training and test stages, it is challenging to design channel-independent RFF techniques. This article designs a channel robust RFF identification scheme by leveraging the different spectrum of adjacent signal symbols, named the Difference of the Logarithm of the Spectrum (DoLoS), which does not rely on a single RFF feature or requires additional manipulation of the devices under test. Specifically, DoLoS exploits the fact that two different symbols in a packet exhibit different RFF features but have a similar channel response during the channel coherence time. We implemented the DoLoS with the IEEE 802.11 orthogonal frequency division multiplexing (OFDM) system as a case study. We carried out extensive experiments using seven Wi-Fi devices of the same model in different wireless channel environments, including 12 data collection positions in two completely different environments. Compared with conventional RFF identification schemes that do not eliminate channel effects, our scheme is robust to channel variations and the highest identification accuracy is 99.02% in the single-environment evaluation and 97.05% in the cross-environment evaluation.

Collaborative Cognitive Wireless Network Optimization Model and Network Parameter Optimization Algorithm

In recent years, the combination of cognitive radio and collaborative communication has been widely studied and applied because of its ability to increase user throughput and improve spectrum utilization in a flat-fading wireless channel environment. Such cognitive radio networks that use user collaboration to improve channel capacity and spectrum utilization are called collaborative cognitive radio networks. A Nash equilibrium game-based relay node selection algorithm is investigated, which aims to maximize the utility function of primary and cognitive users. Secondly, a Stackelberg game is introduced, which aims to select the better set of nodes to achieve spectrum sharing. Simulation results show that the algorithm proposed in the study maximizes the utility functions of both primary and cognitive users and enables the selection of a better set of nodes for spectrum sharing. Specifically, the Nash equilibrium game-based relay node selection algorithm at c = 0.3 ∗ 10−6 results in better utility values for both PU and CU, and the algorithm enables more CU to access the spectrum so that users can get longer access time. The relay node selection algorithm based on the Stackelberg game demonstrates high feasibility. Under the condition of parameter α = α ∗ , the algorithm can achieve high-quality cooperation, and CU in better positions can be used as relay cooperation nodes. The algorithm can improve the main user utility function by 20%–35%.

Multipolarization Superposition Beamforming: Novel Scheme of Transmit Power Allocation and Subcarrier Assignment

The 5th generation (5G) new radio (NR) access technology and the beyond-5G future wireless communication require extremely high data rate and spectrum efficiency. Energy-efficient transmission/reception schemes are also regarded as an important component. The polarization domain has attracted substantial attention in this aspect. This paper is the first to propose multi-polarization superposition beamforming (MPS-Beamforming) with cross-polarization discrimination (XPD) and cross-polarization ratio (XPR)-aware transmit power allocation utilizing the 5G NR antenna panel structure. The appropriate orthogonal frequency division multiplexing (OFDM) subcarrier assignment algorithm is also proposed to verify the theoretical schemes via simulations. The detailed theoretical derivation along with comprehensive simulation results illustrate that the proposed novel scheme of MPS-Beamforming is significantly beneficial to the improvement of the performance in terms of the symbol error rate (SER) and signal-to-noise ratio (SNR) gain at the user equipment (UE). For instance, a provided practical wireless channel environment in the simulations exhibits 8 dB SNR gain for 10−4 SER in a deterministic channel, and 4 dB SNR gain for 10−5 SER in abundant statistical channel realizations.

Radio Signal Modulation Recognition Method Based on Deep Learning Model Pruning

With the development of communication technology and the increasingly complex wireless communication channel environment, the requirements for radio modulation recognition are also increased to avoid interference and improve the efficiency of radio spectrum resources. To achieve high recognition accuracy with less computational overload, we propose a radio signal modulation recognition method based on deep learning, which uses a pruning strategy to reduce computational overload, based on the original model, CNN-LSTM-DNN (CLDNN), and the double-layer long short-term memory (LSTM). Effect factors are analyzed in terms of recognition accuracy by adjusting the parameters of each network layer. The results of the experiments show that the model not only has a greater precision improvement than some existing models, but also reduces the computational resources necessary.

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.

Delay Analysis of Mobile Edge Computing Using Poisson Cluster Process Modeling: A Stochastic Network Calculus Perspective

Wireless networks in next generation will provide users ubiquitous computing services with low delay by devices at the network edge, namely mobile edge computing (MEC). The intensive computation tasks can be partially offloaded to the MEC server via the wireless link and then processed through the MEC computation resources to cater for the delay demand. A parallel computation process is formed in the MEC network consists of local computation at MEC users (MUs) and MEC computation at MEC servers. However, the fluctuating wireless channel environment, changeable spatial distribution of MUs and the randomness of MEC servers’ locations make it hard to characterize and guarantee the end-to-end quality of service requirements. In this work, we are devoted to analyze and optimize the overall delay bound for MEC networks under two orthogonal frequency division multiple access (OFDMA) strategies via stochastic network calculus (SNC). Specifically, Poisson cluster process is utilized to capture the randomness of MEC servers’ and users’ spatial locations and to derive the Laplace transform of interference suffered by an MU of interest. The upper bounds for the delay violation probability of two OFDMA strategies are established by exploiting SNC with the Mellin transform of signal-to-interference ratio. Furthermore, we propose an optimal task offloading scheme by minimizing the overall delay, which balances the local computation delay and MEC delay.

Energy efficiency maximization for IRS-assisted NOMA networks

In this paper, we consider a non-orthogonal multiple access (NOMA) system assisted by intelligent reflecting surface (IRS). As an emerging technology that has received widespread attention, IRS can reconfigure the wireless channel environment by adjusting the relationship between the incident angle and the exit angle, thereby improving system performance. Our goal is to use the flexible assistance of IRS to achieve the maximum energy efficiency of the NOMA system. Our design objects are the beam vector design of the base station and the phase matrix design of the IRS. The original problem is highly non-convex. We consider using the block coordinate descent method to design the phase matrix and beam vector separately. Simulation results show that our proposed scheme has better performance than traditional OMA and systems without IRS assistance.

Downlink CSI Feedback Algorithm With Deep Transfer Learning for FDD Massive MIMO Systems

In this paper, a channel state information (CSI) feedback method is proposed based on deep transfer learning (DTL). The proposed method addresses the problem of high training cost of downlink CSI feedback network in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. In particular, we obtain the models of different wireless channel environments at low training cost by fine-tuning the pre-trained model with a relatively small number of samples. In addition, the effects of different layers on training cost and model performance are discussed. Furthermore, a model-agnostic meta-learning (MAML)-based method is proposed to solve the problem associated with large number of samples of a wireless channel environment required to train a deep neural network (DNN) as a pre-trained model. Our results show that the performance of the DTL-based method is comparable with that of the DNN trained with a large number of samples, which demonstrates the effectiveness and superiority of the proposed method. At the same time, although there is a certain performance loss compared with the DTL-based method, the MAML-based method shows good performance in terms of the normalized mean square error (NMSE).

PATH LOSS MODELING FOR URBAN WIRLESS NETWORKS IN BAGHDAD

An accurate propagation modeling of radio waves propagation is very important task in cellular network design as it provides the detailed useful knowledge about the wireless channel environment characteristics. Theoretical or empirical RF propagation models provide the required useful information about the signal path loss and fading to evaluate the received signal level, the coverage area, and the outage probability in specific regions. This paper aimed to develop an empirical radio wave propagation model based on observations and sets of measurement data collected from different sites through drive test. These measurements are used to determine the received signal power at some locations to create an empirical radio wave propagation model that is suitable to be appropriate in cellular network accurate design and link budget prediction at the city of Baghdad.

Research on anti-jamming and anti-interception performance of MC-CDMA system based on Code-Hopping

In the wireless channel environment, there are all kinds of jamming. With the wide application of OFDM Technology in wireless communication system, the technology of signal analysis is becoming more and more mature. Pure OFDM signal exposed in the wireless channel is no longer safe, facing the risk of jamming and interception, especially in the military field. Multi-carrier Code Division Multiple Access (MC-CDMA) technology is what combines the characteristics of OFDM and CDMA. It has the advantages of OFDM system and CDMA system at the same time. It reduces the transmission rate and improves the reliability. Compared with OFDM system, MC-CDMA has better BER performance in the same SNR environment. Based on the single user MC-CDMA system, the system model and simulation analysis are carried out, and the performance comparison with OFDM system is carried out. Based on the above work, the theory of code hopping spread spectrum is introduced to design a new code hopping spread spectrum system based on MC-CDMA, and the anti-jamming and anti interception performance of the system is analyzed.

Intelligent Outage Probability Prediction for Mobile IoT Networks Based on an IGWO-Elman Neural Network

Advances in sensor technology have accelerated the development of mobile internet applications and contributed to the tremendous growth of the Internet of Things (IoT). Mobile IoT networks typically operate in complex and highly variable urban wireless channel environments. This makes effective and reliable communications challenging. To ensure efficient and stable communications, mobile IoT networks must adapt to the complex environment. This can be achieved by predicting the outage probability (OP). This paper investigates OP analysis and prediction for these networks. Exact closed-form OP expressions are derived and Monte-Carlo simulation is used to verify the analysis and evaluate the OP performance. Then, an OP prediction approach based on an improved grey wolf optimization (IGWO) algorithm and Elman neural network (IGWO-Elman) is proposed. The IGWO algorithm employs improved opposition-based learning to optimize the population initialization and ensure sufficient population diversity. The Elman neural network uses the IGWO algorithm to obtain good network parameters. Simulation results are presented which demonstrate that the prediction accuracy of IGWO-Elman is better than SVM, ELM, and BP algorithms. In terms of prediction accuracy, the IGWO-Elman algorithm is increased by 44%.