Spread spectrum communication plays a vital role in safeguarding the Internet of Things systems, due to its inherently low probability of interception and anti-jamming capability. However, conventional spread spectrum systems based on pseudorandom sequences are disadvantageous in limited sequence length and deterministic periodicity, making them vulnerable to brute-force attacks. To address these limitations and enhance the randomness of the spread spectrum sequences, a novel wireless channel randomness integrated spread spectrum sequence generation method is proposed in this work. Taking advantages of the intrinsic randomness, temporal variations, and unpredictability of wireless channel fadings, the proposed approach converts the extracted channel features into ordered sequences, which are then used to control the selection of irreducible generating polynomials for spread spectrum sequence generation. The proposed method improves the randomness and secrecy of the integrated spread spectrum sequence. Theoretical analysis and simulation results demonstrate that the proposed sequences not only achieve higher randomness entropy compared to the traditionalm-sequence, but also pass the National Institute of Standards and Technology randomness tests. Furthermore, performance evaluations under various signal-to-interference ratio conditions show improved autocorrelation properties and largely lower bit error rates, validating the effectiveness of the proposed method in improving the anti-jamming capability.

Physical-layer key generation (PLKG) has attracted significant attention due to its lightweight and strong randomness, making it highly suitable for drones with energy and computational constraints. However, when drone communications rely on relay nodes, untrusted relay nodes may launch attacks such as eavesdropping, tampering, replay and man-in-the-middle, leading to key leakage. To address this problem, we propose a drone -physical unclonable functions (PUFs) - PLKG (DPPLKG) scheme that enhances resistance to untrusted relay attacks. In DPPLKG, legitimate drones are equipped with paired PUF hardware, and artificial noise is injected during the PLKG process to reduce the accuracy of untrusted relay channel estimation. The generated physical-layer key serves as the PUFs challenge, and the unique hardware response of PUFs generates the final session key, ensuring consistent and secure key generation among legitimate drones. After analysis, DPPLKG can effectively resist various threats such as relay eavesdropping, tampering, replay, and impersonation attacks. The simulation results show that DPPLKG outperforms traditional PLKG in terms of key generation rate, bit error rate, and key entropy. It can also resist Doppler frequency changes caused by drone mobility and hardware noise caused by temperature changes in PUF devices. In addition, the proposed scheme has an acceptable delay time, making it a practical and efficient solution for achieving drone secure communications in the presence of untrusted relays.

The open nature of wireless media makes networks susceptible to spoofing attacks. In comparison to the conventional cryptographic-based authentication mechanism, Physical Layer Authentication (PLA) employs the intrinsic and distinctive internal security attributes of the physical layer to authenticate the sender, offering the benefits of robust security, minimal complexity, and broad compatibility. In this paper, we propose a hypothesis testing statistic based on channel variance normalization for identity authentication, utilizing two consecutive Channel State Information (CSI). To improve the authentication accuracy, we apply exponential amplification to the statistic mentioned above, aiming to maximize the difference in channel characteristics between legitimate and illegitimate links. Subsequently, we theoretically analyze the performance of the proposed scheme and derive closed-form expressions for four test probabilities: false alarm rate, authentication rate, detection rate, and miss detection rate. Additionally, we evaluate the performance of the scheme under the Neyman-Pearson (NP) criterion and the minimum error (ME) probability criterion, and give the threshold expressions under the two criteria respectively. Finally, we conducted simulation experiments on the proposed scheme in a typical Multiple-Input Multiple-Output (MIMO) scenario. The simulation results demonstrate the effectiveness of the proposed PLA scheme. After appropriate exponential amplification of the channel difference, the overall authentication performance is superior to that without amplification. The simulation results also prove that improving the signal-to-noise ratio, feature dimension, channel correlation coefficient, and reducing the power gain ratio of illegal and legitimate transmitters are helpful to improve the authentication reliability.

The fifth-generation new radio (NR) cellular communication is featured with numerous advancements over Long Term Evolution (LTE) and earlier technologies. It enables more flexible physical-layer resource scheduling across multiple dimensions, and two representative techniques are beamspace transmissions and time-frequency numerology selection. Nevertheless, the lightweight physical-layer secure transmission in NR remains under investigation, especially taking NR beamspace and mobility into consideration. In this work, we propose a physical-layer wireless key generation (KG) efficient beamspace adaptation scheme for NR, where the KG capacity is theoretically characterized by critical NR components including beam direction and beamwidth. In addition, we consider the impacts of user mobility on KG performance. Since NR beamspace plays a key role in deciding the channel probing window in the spatial dimension, the NR beamspace directly affects channel probing results and hence the KG efficiency. To this end, NR beam parameters are obtained to improve the KG performance. Especially, we propose to optimize the NR beamwidth for maximizing the secrecy-delay efficiency, because a tradeoff exists in adapting the beamwidth where smaller beamwidth can improve the channel estimation accuracy but increase the beam sweeping delay. Theoretical analysis and simulation results show that the beam direction adaptation provides spatial degrees of freedom for NR to enhance KG, by enabling beam selection pointing at target areas with richer multipath scatterings. Experimental results demonstrate that the narrow beam is beneficial to enhancing the channel estimation accuracy and the resultant key agreements.

In physical-layer secret key generation, key generation performance is susceptible to hardware impairments (HIs), which can degrade channel reciprocity. Additionally, the security of loop-back mechanism based schemes in frequency division duplex (FDD) systems requires further enhancement. To address these challenges, this paper proposes a secure key generation scheme based on a loop-back mechanism for FDD systems. By transmitting signals across different frequency bands in a loop-back fashion, the proposed scheme mitigates the adverse effects of HI variations across frequency bands and enhances system security. Theoretical analyses are conducted on the normalized mean square error and the secret key rate of the loop-back mechanism in both FDD and time division duplex (TDD) systems, providing a clear security assessment of the proposed scheme. Simulation and experimental results demonstrate that by accounting for HI differences in the frequency domain, the proposed scheme improves channel reciprocity, enhances the secret key rate, achieves a higher key generation ratio (KGR), and reduces the key disagreement rate (KDR) compared to state-of-the-art methods.

In physical-layer security schemes, radio frequency fingerprint (RFF) identification is vulnerable to the channel variations, and the identification of the long term evolution (LTE) mobile devices deserves further investigation. In this paper, we propose an RFF extraction method based on the LTE demodulation reference signal (DMRS) processing which can mitigate the channel impairments during the RFF extraction process. First, we analyze the impacts of RFF and the multipath channel on the DMRS in the LTE physical uplink shared channel and take the flexible time-frequency resource block allocations into account. Then, we propose an RFF extraction method based on the wavelet decomposition and reconstruction of the DMRS signals. It is found that the in-phase/quadrature (IQ) direct current offset and the frequency-dependent IQ imbalance mainly affect high-frequency components of the frequency domain DMRS signals, while the power amplifier memory nonlinearity and the channel effects mainly influence the low-frequency components. By removing the low-frequency components of the frequency domain DMRS signals, the proposed method is robust to the channel impairments. Finally, the simulation and experimental results show that our method can effectively reduce the channel impacts and retain the device RFF. The effectiveness of this method is verified via different classification tasks. The classification accuracy can reach 98.5% and 93.9% in the stationary and mobile scenarios, respectively. Meanwhile, by combining the training sets collected in the static and the moving scenarios together in the training process, the model can achieve better classification performance.

In physical-layer security schemes, radio frequency fingerprint (RFF) identification is vulnerable to the channel variations, and the identification performance of mobile devices using long term evolution (LTE) signals remains to be validated. In this paper, we propose an RFF extraction method based on LTE demodulation reference signal (DMRS) signal processing for LTE mobile devices. First, we analyze the impacts of the RFF and channel fading on DMRS in the LTE uplink channel. Then, we propose an RFF extraction method based on the wavelet decomposition and reconstruction of DMRS. By removing the low-frequency components of DMRS, which are mainly affected by the channel effects, our proposed method is robust to the channel impairments. Finally, our simulation and experimental results show that our method can effectively reduce the channel impacts and retain the RFF of devices. The effectiveness of this method is verified via different classification tasks. The classification accuracy can reach 98.5% and 93.9% in the stationary and mobile scenarios, respectively.

The promising physical-layer authentication (PLA) scheme enjoys low computational complexity and provides a lightweight solution for the wireless transmission security problem. However, the ideal but impractical assumptions are made on the priori knowledge in the traditional PLA schemes, which fail because the priori knowledge can be interfered by the impersonation attacks. Hence, a blind PLA scheme featured by the composite radio sample characteristics, active monitoring slots, and blind hypotheses tests is proposed in this paper. In particular, the intrinsic location-specific channel response integrated with the transmitter-specific signal knowledge is sampled during the active monitoring slots and is used to conduct minimum error (ME) and Neyman-Pearson (NP) hypothesis tests. The authentication reliability is enhanced significantly by jointly using the ME and NP criteria. For both the stationary and the non-stationary signal cases, our theoretical analysis shows that the temporal channel variations, the spatial channel correlations and the spectral bandwidth contribute to improve the authentication reliability. Our simulation results not only demonstrate the effectiveness of the proposed PLA scheme, but also indicate the possible scenarios in which ME outperforms NP, and NP outperforms ME.

The problem of full-duplex primary user (PU) signal space extraction is studied for cognitive radios, and polarization Jones vector distance (PJVD) under generalized likelihood ratio test (GLRT) paradigm is used as the extraction metric. Based on the orientation and magnitude difference of the signal samples from different PUs, the GLRT-based PJVD (GLRT-PJVD) method can efficiently detect each PU's signal space by calculating the PJVD between any two samples. It is found that the samples from the same PU experience smaller PJVD in comparison with those from different PUs. Due to the Gaussian distributed noise, the resulting PJVD is non-central chi-squared distributed. Based on the distribution, the extraction metric is constructed as the likelihood ratio of PJVD. A closed-form expression of the optimal threshold is derived under the large argument assumption for amplitude modulated primary signals. Theoretical analysis shows that the proposed GLRT-PJVD method outperforms the polarization similarity-based method in terms of both error probability and computational complexity. It is revealed that the performance gain is proportional to the SUs' antenna number, the PUs' polarization angle, and the signal amplitude difference. The analytical results are verified by the Monte Carlo simulation, and the performance of GLRT-PJVD is validated.

In traditional cognitive radio networks, the spectrum sharing schemes are spectrum-efficient in specific circumstances. In order to improve the robustness of spectrum sharing schemes in a dynamic environment from the perspective of energy efficiency, an adaptive spectrum sharing scheme is proposed for full duplex cognitive radios in this work. The proposed schemes allow secondary users (SUs) to adapt their way of accessing the licensed spectrum according to the behaviors of primary users (PUs). The SUs adaptively decide to access the licensed spectrum in the transmit phase, provided that PUs' traffic arrives at a low rate and energy efficiency gains exist. Based on the intermittent nature of the arrival and departure processes of PUs' traffic, the closed-form expressions of energy efficiency for the traditional and the proposed schemes are investigated. By defining energy efficiency gain and energy efficiency loss, the impacts of the spectrum sensing error and channel estimation error are analyzed to evaluate the energy efficiency of the proposed scheme. It shows that the proposed adaptive spectrum sharing scheme improves the overall energy efficiency. The numerical results validate the energy efficiency improvements and the robustness of the proposed scheme.

The problem of full-duplex primary users (PUs) signal extraction is studied for cognitive radio networks, and the polarization Jones vector distance (PJVD) is used as the extraction metric. Based on the orientation and magnitude difference of the signal samples from different PUs, the PJVD based signal extraction (PJVD-SE) can efficiently detect each PU's signal polarization by calculating the PJVD between any two samples. Due to the Gaussian distributed noise, the resultant PJVD for primary samples is noncentral chi-squared distributed. Based on the distribution, the extraction error probability is investigated. Theoretical analysis shows that the proposed PJVD-SE technique outperforms the polarization similarity based technique in terms of both error probability and computation complexity. It is found that the performance gain is proportional to the SU's antenna number, PUs' polarization angle and PUs' signal amplitude difference. In addition, the presented analytical studies are verified through extensive simulation results and the performance of PJVD-SE is validated.

In traditional cognitive radio networks (CRNs), the interweave, underlay, overlay, and hybrid spectrum sharing schemes are only spectrum-efficient in specific circumstances. In order to improve the robustness of spectrum sharing schemes in a dynamic traffic environment from the perspective of energy efficiency, adaptive spectrum sharing schemes are proposed in this paper for both half-duplex (HD) and full-duplex (FD) CRNs. The proposed schemes allow the secondary users to adaptively access the licensed spectrum, provided that PUs' traffic arrives at a low rate and energy efficiency gains exist. Based on the intermittent nature of the arrival and departure processes for PUs' traffic, closed-form expressions of energy efficiency for the traditional interweave, underlay, overlay, hybrid and the proposed schemes are investigated for both HD and FD modes. By defining energy efficiency loss, the impacts of the spectrum sensing error and channel estimation error are analyzed to evaluate the energy efficiency of the proposed schemes. Theoretical analysis shows that the proposed adaptive spectrum sharing schemes improve the minimum energy efficiency in HD mode and improve the overall energy efficiency in FD mode. The numerical results validate the energy efficiency improvements and the robustness of the proposed schemes.

Traditionally, primary users (PUs) in cognitive radio networks typically work in frequency division duplex or time division duplex mode, and secondary users (SUs) can protect PUs by blindly learning every PU's signal space and transmitting in the orthogonal space. However, the fifth-generation communication promotes the full-duplex technique and PUs are likely to work in FD mode in the future, i.e., two PUs transmit simultaneously at the same frequency. The emerged problem is that the sample of signal may be a compound signal from multiple PUs, and each PU's signal space cannot be directly derived, thus the available degree of freedom (DoF) for SUs' transmission may be limited and strong interference will be incurred to PUs. In this work, cognitive radio communications with full-duplex PUs is studied. Based on the distinctive polarization features of samples from different PUs, we propose a novel blind polarization adaption technique, which allows SUs to extract the signal space of each PU from the hybrid samples by studying polarization similarity of the sampled vectors. In order to reduce the polarization similarity inaccuracy induced by noise depolarization effect, a subspace based noise suppression method is also proposed. Numerical results show that the noise can be largely suppressed even in the low signal-to-noise-ratio region, which allows the proposed polarization similarity based polarization adaption technique to guarantee satisfactory interference level on PUs.