This paper proposes a novel multi-party key generation method that jointly utilizes channel state information (CSI) and blockchain technology to enhance security in distributed systems. The proposed method starts by extracting CSI from wireless channels, leveraging the channels’ inherent randomness and reciprocity to generate secure key fragments shared among legitimate parties. Then, the key generation process involves several stages, including quantization, reconciliation, and privacy amplification, ensuring that the resulting keys are secure and synchronized across participants. Blockchain technology is then leveraged to securely commit these keys, ensuring that the key agreements are recorded in a decentralized, tamper-resistant ledger. The proposed method effectively combines the physical-layer properties of CSI with the decentralized nature of blockchain, providing robust protection against eavesdropping and tampering attacks. Theoretical analyses and simulation results demonstrate the effectiveness of the proposed method in terms of key mismatch probability and secrecy capacity. Additionally, the randomness of the generated keys by the proposed method is validated using the National Institute of Standards and Technology randomness tests.

Physical layer security (PLS) has garnered increasing attention as a complementary solution to conventional crypto-graphic techniques for addressing the diversity of use cases, deployment scenarios, and device capabilities in today’s wireless networks. Integrating artificial intelligence (AI) into PLS offers a promising solution to various multi-dimensional, heterogeneous, and complex challenges stemming from the growing complexity of networks. However, the opaque nature of AI has raised concerns regarding its trustworthiness and interpretability. This paper addresses these concerns by emphasizing the crucial role of explainability in AI-based PLS in several use cases of next-generation networks. A particular focus is placed on physical layer authentication through an illustrative case study, aiming to enhance AI-empowered PLS’s practicality and trustworthiness. The paper concludes with a discussion of some critical future research directions.

Identifying spectrum opportunities is a crucial element of efficient spectrum utilization for future wireless networks. Spectrum sensing offers a convenient means for revealing such opportunities. Studies showed that usage of the spectrum has a high correlation over multi-dimensions, including time and frequency. However, multi-dimensional spectrum sensing requires high-cost processes. Applying compressive sensing allows for subNyquist sampling. This reduces associated training, feedback, and computation overheads of a spectrum sensing method. However, the accuracy of the signal sparsity assumption and knowledge of the precise sparsity level are necessary for the applicability of compressive sensing. It is common practice to assume a level of known sparsity. On the other hand, in reality, this presumption is incorrect. This paper proposes a method for estimating the multidimensional sparsity for spectrum sensing. By extrapolating it from its counterpart with respect to a compact discrete Fourier basis, the proposed method calculates the sparsity level over a dictionary. A machine learning estimation method achieves this inference. Extensive simulations validate a high-quality sparsity estimation. To validate this observation, real-world measurements are used, where one of the biggest Turkish telecom operators has private uplink bands in the frequency range between 852-856 MHz.

High-quality radio frequency (RF) components are imperative for efficient wireless communication. However, these components can degrade over time and need to be identified so that either they can be replaced or their effects can be compensated. The identification of these components can be done through observation and analysis of constellation diagrams. However, in the presence of multiple distortions, it is very challenging to isolate and identify the RF components responsible for the degradation. This paper highlights the difficulties of distorted RF components’ identification and their importance. Furthermore, a deep multi-task learning algorithm is proposed to identify the distorted components in the challenging scenario. Extensive simulations show that the proposed algorithm can automatically detect multiple distorted RF components with high accuracy in different scenarios.

Radio-frequency (RF) front-end forms a critical part of any radio system, defining its cost as well as communication performance. However, these components frequently exhibit non-ideal behavior, referred to as impairments, due to the imperfections in the manufacturing/design process. Most of the designers rely on simplified closed-form models to estimate these impairments. On the other hand, these models do not holistically or accurately capture the effects of real-world RF front-end components. Recently, machine learning-based algorithms have been proposed to estimate these impairments. However, these algorithms are not capable of estimating multiple RF impairments jointly, which leads to limited estimation accuracy. In this paper, the joint estimation of multiple RF impairments by exploiting the relationship between them is proposed. To do this, a deep multi-task learning-based algorithm is designed. Extensive simulation results reveal that the performance of the proposed joint RF impairments estimation algorithm is superior to the conventional individual estimations in terms of mean-square error. Moreover, the proposed algorithm removes the need of training multiple models for estimating the different impairments.

In cognitive radio systems, predicting spectrum occupancies is a convenient alternative way to continuous spectrum sensing. It can provide information on spectrum usage and so empty spectrum bands can be used by secondary users. The usage of the spectrum bands is highly correlated over both time and frequency. Recently, machine learning algorithms are used to predict spectrum occupancy by exploiting such correlations. However, this approach primarily assumes a supervised learning setting. Despite its outstanding performance, this setting requires the availability of sufficiently large datasets (of labeled data) and is not adaptive to environment changes. In this paper, different from the existing literature, a deep reinforcement learning (RL) algorithm is used to alleviate those shortcomings. In this algorithm, we define the reward functions of the deep RL setting and its state and action spaces such that it is applicable to work dynamically, in an online fashion, in real world settings. Extensive experiments validate the capability of the proposed algorithm in predicting spectrum occupancies as examined over real world spectrum measurements. These are carried out in the 832-862 megahertz frequency bands, which are used by the leading Turkish telecom providers as private uplink bands. This is a significant step towards realizing a standalone spectrum occupancy prediction operation without any control from the operator and minimizing memory requirements while alleviating the need for the labeled dataset.

Obtaining accurate channel state estimates at reasonable training overheads remains a big challenge for the applicability of multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM). Recently, the exploitation of channel sparsity has led to sub-Nyquist channel sampling thereby reducing the channel training overhead. Still, there is a growing belief in channel sparsity appearance in many dimensions; time, frequency, angle, and space. Accordingly, this paper proposes an algorithm for channel estimation where sparsity in multidimensions is simultaneously exploited. Also, the applicability of sparse coding relies on the validity of a signal sparsity assumption and knowing the exact sparsity level. However, this assumption is not valid in practice, especially when applying learned dictionaries as sparsifying transforms. The problem is more strongly pronounced with multidimensional sparsity. In this paper, we also propose an algorithm for estimating the composite sparsity lying in multiple domains defined by learned dictionaries. Simulations validate a substantial channel estimation quality attained by the proposed algorithm as compared to the existing algorithms. The simulations also validate a high quality of sparsity estimation leading to performances close to the impractical case of assuming known sparsity.

In sparse coding-based channel estimation, atom selection is based on jointly minimizing the sparsity and the error of the representation of the noisy measurement. However, this selection is not necessarily optimal in terms of minimizing the channel estimation error. This calls for better ways of atom selection. Accordingly, we propose an algorithm for improved atom selection in sparse coding for frequency division duplex (FDD) massive multiple-input-multiple-output (MIMO) downlink channel estimation. The proposed algorithm performs iterative atom selection based on two residuals. First is the received signal residual used to guide on a small selection pool of candidate atoms. Second is the residual of an initial channel estimate which is used to pick the best atom within the selection pool. Simulation results show the advantage of the proposed algorithm over standard sparse coding-based channel estimation. Moreover, the proposed algorithm eliminates the need for cell-specific trained dictionaries without sacrificing the performance. Furthermore, the proposed sparse coding can be applied in the process of dictionary learning to train for improved dictionaries achieving further performance enhancement.

The reconfigurable intelligent surface (RIS) technology has attracted interest due to its promising coverage and spectral efficiency features. However, some challenges need to be addressed to realize this technology in practice. One of the main challenges is the configuration of reflecting coefficients without the need for beam training overhead or massive channel estimation. Earlier works used estimated channel information with deep learning algorithms to design RIS reflection matrices. Although these works can reduce the beam training overhead, still they overlook existing correlations in the previously sampled channels. In this paper, different from existing works, we propose to exploit the correlation in the previously sampled channels to estimate RIS interaction more reliably. We use a deep multilayer perceptron for this purpose. Simulation results reveal performance improvements achieved by the proposed algorithm.

Most physical-layer authentication techniques use channel information to prevent spoofing attacks. In such techniques, one must estimate the channel information for each authentication procedure. However, when the number of pilots decreases, authentication accuracy also decreases due to low channel estimation quality. This paper proposes a novel signal relation-based authentication method that relies on the detection of received signal symbols and does not require the estimation of channel information in the testing stage. It is noteworthy that the authentication performance of the proposed scheme remains in a good level. We develop two different solutions for the detection of received signal symbols, namely, minimum mean-square error and long short-term memory. Extensive simulation results show the main insights of the proposed signal relation-based authentication method compared to conventional channel-based authentication method.

Cognitive radio (CR)-based internet of things systems can be considered as an efficient solution for futuristic smart technologies. However, CRs are naturally vulnerable to two major security threats; primary user emulation (PUE) and jamming attacks. Machine learning has been recently applied to the detection of these attacks. Still, the need for feature extraction required by machine learning techniques restrains the full exploitation of raw data. To alleviate this need, this paper proposes one-dimensional deep learning as a framework for identifying such attacks. Simulations show the ability of the proposed algorithm to detect these attacks with high performance.

The identification of spectrum opportunities is a pivotal requirement for efficient spectrum utilization in cognitive radio systems. Spectrum prediction offers a convenient means for revealing such opportunities based on the previously obtained occupancies. As spectrum occupancy states are correlated over time, spectrum prediction is often cast as a predictable time-series process using classical or deep learning-based models. However, this variety of methods exploits time-domain correlation and overlooks the existing correlation over frequency. In this paper, differently from previous works, we investigate a more realistic scenario by exploiting correlation over time and frequency through a 2D-long short-term memory (LSTM) model. Extensive experimental results show a performance improvement over conventional spectrum prediction methods in terms of accuracy and computational complexity. These observations are validated over the real-world spectrum measurements, assuming a frequency range between 832-862 MHz where most of the telecom operators in Turkey have private uplink bands.

Recent research considers the application of a lens antenna array in order to provide efficient beam selection in beamspace massive MIMO. Achieving the advantages of this beam selection paradigm requires efficient channel estimation in the beamspace. Along this line, beamspace sparsity is an efficient regularizer to this problem. In this paper, we propose using a dictionary trained over a set of example beam selection matrices, as a beam selection tool. In this context, a learned dictionary can more effectively guarantee the sparsity of the representation at the specified sparsity level, owing to the dictionary learning process. This means that it gives a better sparse representation, and, consequently, a better channel estimation quality. Simulations validate that using a trained dictionary improves the quality of channel estimation, as tested over two channel models with different operating scenarios.