The vehicle to everything (V2X) serves as a crucial foundation for future intelligent transportation systems. Security concerns within the V2X have garnered significant attention and key generation from wireless channels have emerged as a promising technique. However, applying key generation to V2X is quite challenging because the fast moving vehicles result in very small coherence time and impact channel measurements correlation. This paper designed a practical V2X key generation by enhancing channel state information (CSI) reciprocity and carried out extensive experimental evaluation. In particular, the designed key generation consists of channel probing, CSI preprocessing, CSI compensation and key establishment. In the channel probing, we deliberately reduced the time delay between uplink and downlink transmissions, to allow almost simultaneous measurements. We then carefully designed CSI preprocessing to remove hardware carrier leakage and eliminate noise effects. Furthermore, we devised CSI compensation by using interpolation or deep learning prediction to further improve the reciprocity. Finally, key establishment converted the measured CSI into binary sequences and reconcile on a common key via low-density parity-check (LDPC) code. We adopted universal software radio peripheral (USRP) X310 platforms for channel measurements and implemented the above algorithms. We carried out extensive experiments in real-road environments with various vehicle speeds. These carefully designed algorithms enabled our system working robustly even in high mobility scenarios, e.g., 40 km/h. Experimental results demonstrated common and random key can be generated with a key block error rate (BER) less than 0.1.

Radio frequency fingerprint identification (RFFI) is a promising authentication mechanism for physical layer security. In this paper, we thoroughly validate the feasibility of using RFFI for cellular long-term evolution (LTE) devices. Firstly, we conduct simulations to examine the subtle impacts of hardware impairments on LTE signals. The simulated results reveal that I/Q imbalance and power amplifier non-linearity introduce significant distortions within in-band spectrum, forming unique hardware fingerprints. We then leverage the strong channel correlation between adjacent subcarriers and separate the channel-robust radio frequency fingerprints (RFF) from uplink demodulation reference signal (DMRS) in Msg3. Subsequently, we construct a hybrid feature matrix to serve as input for a shallow long short-term memory (LSTM) network. Due to the more effective channel mitigation strategy, our method outperforms three benchmarks in terms of classification accuracy under cross-scenario testing. Additionally, we explore the impacts of bandwidth configuration on RFFI, and experimental findings demonstrate that LTE terminals will exhibit more distinct RFF when occupying a larger number of physical resource blocks (RB) during transmission. We also investigate the stability of RFF towards frequency band variations. The results suggest that there will be a significant accuracy loss under training with one band but testing with another, indicating the importance of frequency band-independent feature extraction in practical environments. Lastly, we expose four key implications to pave the way for exploring corresponding solutions. To the best of our knowledge, it is the first performance evaluation of the RFFI system on different frequency bands and with multiple bandwidth configurations.

Backscatter communications (BCs), allowing passive devices to transmit information by reflecting incident RF signals, have emerged as an attractive solution for the green Internet of Things (IoT). In the practical implementation of BC systems, we observe two common and interesting phenomena: wavy backscatter signals and striped-shape constellation clusters. These phenomena differ significantly from the traditional point-to-point communication and the theoretical BC systems, substantially degrading the system performance. Unfortunately, their causes and potential solutions remain unexplored. Motivated by this, this paper investigates the origins and designs of the corresponding solving methods. Specifically, we first reveal the causes of these phenomena: the time-varying interference stemming from the phase-locked loop (PLL) non-ideality. Then, we introduce our solutions: the dynamic self-interference cancellation (DSIC) and the data-aided decision boundary (DDB) algorithms. Finally, we implement and evaluate our solutions on a practical BC platform. Experimental results show that our solutions can reduce the bit error rate (BER) by up to two orders of magnitude, extend the communication range by over three times, and maintain linear runtime complexity, demonstrating their effectiveness and applicability in practical BC systems.

Recently, semantic communication (SC) has been regarded as one of the most potential paradigms of 6G. Current SC frameworks require the physical layer channel state information (CSI) in order to handle the severe signal distortion induced by channel fading. Since practical CSI cannot be obtained accurately and the overhead of channel estimation cannot be neglected, we therefore propose a generative adversarial network (GAN) based SC framework (Ti-GSC) that doesn’t require CSI. In Ti-GSC, there are two main modules, i.e., an autoencoder-based encoder-decoder module (AEDM) and a GAN-based non-CSI signal distortion suppression (SDS) module (GSDSM), where SDS only relies on learning the syntactic distribution and the semantics of the transmitted data, so no prior information such as CSI is needed by GSDSM. In order to measure signal distortion, a novel loss function is proposed where two terms, i.e., a syntactic distortion loss term and a semantic distortion loss term, are newly added, and a differentiable semantic measurement method is designed based on the intermediate layers of the AEDM decoder. To achieve better training results of Ti-GSC, two training schemes, i.e., the joint optimization based training (JOT) and the alternating optimization based training (AOT) are designed for the proposed Ti-GSC. Experimental results show that JOT is more efficient for Ti-GSC, and Ti-GSC outperforms conventional communication frameworks in terms of bilingual evaluation understudy (BLEU) score in both Rician and Rayleigh fading channels. Moreover, without CSI, the BLEU score achieved by Ti-GSC is about 40% and 62% higher than that achieved by existing SC frameworks in Rician and Rayleigh fading, respectively. Besides, each term of the presented loss function has a great impact on the BLEU performance of Ti-GSC, where in Rician fading syntactic learning has the greatest impact, and in Rayleigh fading, the adversarial learning becomes important.

Radio frequency fingerprint (RFF)-based wireless device authentication is an emerging technique to prevent potential spoofing attacks in wireless communications. The random access preamble of the physical random access channel (PRACH) in Long Term Evolution (LTE) systems is the first message sent from a user equipment (UE). However, PRACH preambles change under different evolved Node B (eNB), which will affect the RFF extraction. In this paper, a signal-independent RFF extraction method is first proposed to extract varying LTE PRACH preambles under different LTE eNBs. Residual transient segment (RTS) features from the varying PRACH preambles are extracted for RFF identification. A convolutional neural network (CNN) based ensemble deep learning scheme is proposed to integrate benefits from different RFF features. An experimental system under real operator LTE eNB is designed to capture and identify real UE signals. Experimental results show that the classification accuracy of five UEs can reach more than 95% under the same eNB and 85% under different eNBs. Furthermore, longtime evaluations show that the UE RTS feature is robust over time.

Radio frequency fingerprint (RFF) is an effective way to improve the security of wireless communications. Existing research mainly focused on the classification capability and the robustness of RFFs but overlooked malicious attacks. In this paper, a colluding impersonation attack framework is proposed to emulate the RFF of legitimate users. A colluding attacker is introduced to observe the signal features of the impersonation attacker and the legitimate user and compare their difference. The difference is fed back to the impersonation attacker to help improve its RFF impersonation method. With this idea, the impersonation attack is realized by the Generative Adversarial Network (GAN) structure. The RFF impersonation is formulated as the generator whose objective is to output the signal with RFF similar to the legitimate user, viewed from the colluding attacker’s perspective. Simulation results show that the proposed method can effectively impersonate the legitimate user’s RFF under the dynamic block fading channel.

The wide popularity of 4G/5G mobile terminals increase the requirements of wireless security. Radio frequency fingerprint (RFF) technology can strengthen 4G/5G air interface accessing security at the physical layer. In this paper, a wavelet transform (WT) coefficient graphs RFF extraction with auto-encoder (AE) based rogue terminal detection scheme is proposed. At first, WT coefficients at 48 scales are extracted from the transient-power-off part of LTE physical random access channel (PRACH) preamble. Then, an AE network structure aimed for 2D WT coefficient graph is designed for rogue terminal detection. We successfully distinguish 7 mobile phones and 1 USRP under the proposed mechanism, where the authorized terminals from the same manufacturer can be identified with an accuracy of 90.08%. In addition, extensive experiments are carried out at LOS and NOLS scenarios, respectively, the proposed LTE identification scheme has demonstrated robustness in dynamic environments.

This paper investigates the user clustering and power control in the uplink multiple-input single-output non-orthogonal multiple access (MISO-NOMA) networks. A joint optimization problem is formulated to minimize the system transmit power. The formulated optimization problem is prohibitively complicated, especially when the number of users is large. Alternatively, a two-step user clustering and power control algorithm is proposed. First, a K-means-based algorithm is proposed for user clustering, where both channel gain and channel correlation among users are taken into account for the distance measurement to reduce the intra- and inter-cluster interference. Then, a semi-orthogonal user selection (SUS) algorithm is designed, with which the optimal cluster number and cluster centers can be dynamically obtained. Further, the closed-form expression of the optimal intra-cluster power control is derived, and the resulting inter-cluster power control problem is solved by designing an efficient iterative algorithm. Simulation results show that the proposed K-means-based iterative power control scheme outperforms other reference methods, and can approach the optimal performance in terms of power consumption and energy efficiency at a much lower computational complexity.

Radio frequency fingerprint (RFF) identification technique has drawn great attention to wireless terminal authentication. Long-Term Evolution (LTE) has been widely deployed all over the world. RFF-based LTE terminal identifications can prevent the potential impersonation or denial of service (DoS) attacks in the physical layer. This paper proposes a novel multi-channel convolutional neural network (MCCNN) for LTE terminal identification. Differential constellation trace figure (DCTF) is extracted from the random access preamble of the physical random access channel (PRACH). To the best knowledge of the authors, this is the first work dedicated to RFF-based LTE terminal identification. The proposed scheme is evaluated in the hardware experimental system consisting of the LTE eNodeB implemented on the software-defined radio (SDR) platform and six LTE mobile phones. Experimental results show that the classification accuracy can reach 98.96% at the SNR level of 30 dB with the line-of-sight (LOS) scenarios. Furthermore, long-time evaluations show that the proposed DCTF-MCCNN scheme is robust over time.

This paper investigates the user clustering and power control in the uplink multiple-input single-output non-orthogonal multiple access (MISO-NOMA) networks. A joint optimization problem is formulated to minimize the system transmit power. The original optimization problem is prohibitively complicated when the number of users is large. Alternatively, a two-step user clustering and power control algorithm is proposed. First, an im-proved K-means algorithm is proposed for user clustering, where the clustering metric considers both channel gain and channel correlation among users to reduce the intra- and inter-cluster interference. With this basis, the optimal cluster number and cluster centers are dynamically obtained by the semi-orthogonal user selection (SUS) algorithm. Further, the closed-form expression of the optimal intra-cluster power control is derived, and the resulting inter-cluster power control problem is solved iteratively. Simulation results show that the proposed scheme achieves the near-optimal performance in terms of power consumption and energy efficiency with low computational complexity.

Existing research on Radio Frequency Fingerprint (RFF) mainly focus on unilateral device identification in one communication direction. In practice, it is difficult for IoT devices to identify the base station due to their hardware insufficiencies. In this paper, a bidirectional device identification method is proposed for IoT scenarios. The inherent reciprocity of the communication pair’s RFFs is exploited to offload the learning process, which is supposed to be proceeded by the IoT device, to the base station. An autoencoder-based RFF reciprocal conversion network is proposed to predict the downlink RFF based on the data samples acquired in the uplink, so that the training process of the downlink identification network can be accomplished by the base station and the computational complexity of IoT devices is reduced. Evaluations with real-world data show that, the IoT devices can achieve a high accuracy to identify the base station using the identification network trained by the base station.

Physical layer security key generation exploits unpredictable features from wireless channels to achieve high security, which requires high reciprocity in order to set up symmetric keys between two users. This paper investigates enhancing the channel reciprocity using a loop-back scheme with multiple frequency bands in time-division duplex (TDD) communication systems, in order to mitigate the effect of hardware fingerprint interference and synchronization offset. The scheme is evaluated to be robust to passive eavesdropping and active Man-in-the-Middle attack through both theoretical analyses and practical measurements. A secret key generation protocol is subsequently designed. The performance of the proposed secret key generation method is then evaluated through both numerical simulation and experiments. Results demonstrate that the proposed scheme can effectively mitigate non-reciprocity and outperforms the classical TDD scheme in both key disagreement rate and key generation rate.

To cope with an enormous number of concurring uplink connections in the future network, a novel non-orthogonal multiple access scheme is proposed in this paper, where a composite channel is formed by allowing different users to share the same pilot. The composite channel estimate is then used to equalize the superposed signal from multiple users. With the help of channel hardening feature of massive MIMO, the superposed signal is transformed into a power-domain NOMA form, from which data of different users can be restored successively with interference cancellation. The optimal decoding order as well as power selection scheme is first derived with global channel information. Then distributed power selection schemes are evaluated to reveal the overloading performance of the proposed scheme when other concurring user's channel information is not available. Simulation results show that the proposed scheme can achieve satisfactory overloading performance. Among the distributed power selection methods, the path loss partial compensation based one approaches the optimal performance, making it a promising solution in reality.

The fifth generation (5G) wireless network faces unprecedented massive concurrent random access requests in uplink (UL) contention-based (CB) transmission. In this paper, a non-orthogonal coded access (NOCA) UL transmission in combination with parallel interference cancellation (PIC) based reception scheme at base station is investigated. Non-orthonormal code generated by Zadoff-Chu (ZC) sequences is used to spread the data bits over orthogonal frequency division multiple (OFDM) symbols. It is shown through both link level and system level simulations that in dense urban scenario NOCA-PIC scheme is capable of working in highly overloaded system conditions. It can support more than 4 times concurrent random access number compared with OFDM based CB transmission.

Time-Reversal (TR) precoding schemes are regarded as promising candidates for robust and energy efficient wireless systems. These schemes are characterized by their low computational complexity at the receiver side. In multipath Rayleigh fading channel, this paper presents analytical expressions of the average bit error rate (BER) of TR technique and the Equal Gain Transmission (EGT) scheme in MISO-OFDM systems. It further provides simple and tight upper bounds on the ergodic capacity of these schemes. Hence, these theoretical analyses might be very useful to avoid lengthy simulations. It also provides an interesting conclusion: for large number of transmit antennas, i.e. in Massive MIMO, TR technique outperforms the EGT scheme and its performance approaches that of Maximum Ratio Transmission (MRT) scheme. Hence, it confirms the applicability of TR scheme in the future Massive MIMO systems. Results with LTE-A system parameters are provided in order to corroborate our analysis.

In this work, a new fast-decodable space-time block code (STBC) is proposed. The code is full-rate and full-diversity for 4 × 2 multiple-input multiple-output (MIMO) transmission. Due to the unique structure of the codeword, the proposed code requires a much lower computational complexity to provide maximum-likelihood (ML) decoding performance. It is shown that the ML decoding complexity is only O(M4,5) when M-ary square QAM constellation is used. Finally, the proposed code has highest minimum determinant among the fast-decodable STBCs known in the literature. Simulation results prove that the proposed code provides the best bit error rate (BER) performance among the state-of-the-art STBCs.

The 3D MIMO code is a robust and efficient spacetime coding scheme for the distributed MIMO broadcasting. However, it suffers from the high computational complexity if the optimal maximum-likelihood (ML) decoding is used. In this paper we first investigate the unique properties of the 3D MIMO code and consequently propose a simplified decoding algorithm without sacrificing the ML optimality. Analysis shows that the decoding complexity is reduced from O(M8) to O(M4.5) in quasi-static channels when M-ary square QAM constellation is used. Moreover, we propose an efficient implementation of the simplified ML decoder which achieves a much lower decoding time delay compared to the classical sphere decoder with Schnorr-Euchner enumeration.

This paper investigates the distributed space-time (ST) coding proposals for the future Digital Video Broadcasting-Next Generation Handheld (DVB-NGH) standard. We first theoretically show that the distributed MIMO scheme is the best broadcasting scenario in terms of channel capacity. Consequently we evaluate the performance of several ST coding proposals for DVB-NGH with practical system specifications and channel conditions. Simulation results demonstrate that the 3D code is the best ST coding solution for broadcasting in the distributed MIMO scenario.

In contrast to the classical cyclic prefix (CP)-OFDM, the time domain synchronous (TDS)-OFDM employs a known pseudo noise (PN) sequence as guard interval (GI). Conventional channel estimation methods for TDS-OFDM are only based on the PN sequence and consequently suffer from intersymbol interference. This paper proposes a novel two-stage channel estimation method which combines the estimation results from the PN sequence and, most importantly, the estimation results obtained from the OFDM data symbols. A simple feedback loop that excludes the channel decoder is employed for the OFDM data based estimation. The MMSE criteria is used to obtain the best combination results and an iterative process is proposed to progressively refine the estimation. Both MSE and BER simulations are carried out to prove the effectiveness of the proposed algorithm in the DTMB system which is based on TDS-OFDM signalling.