Integrated sensing and communication (ISAC) plays a crucial role in the development of 6G networks. This study focuses on an ISAC scenario that utilizes a dedicated sensing reference signal (SRS). Similar to the position reference signal (PRS) in 5G, the SRS can be repurposed as other communication reference signals, leading to resource savings. However, it is important to note that sensing is more sensitive to power and is susceptible to severe multi-target interference. To address this issue, the paper proposes the incorporation of matched windows at both the transmitter and receiver ends. This approach aims to enhance energy efficiency by mitigating windowing mismatch loss. To achieve arbitrary window shapes in the frequency-domain while maintaining a constant amplitude waveform, the paper introduces discrete non-linear frequency modulation (DNLFM). DNLFM utilizes a limited number of Newton iterations and a geometrically-equivalent method to reduce waveform generation complexity. This method allows for timely adjustment of parameters based on changing sensing requirements. Additionally, the paper proposes the use of spatial-domain matched windows to minimize sidelobes. Simulation results demonstrate that the proposed methods outperform conventional schemes, highlighting their effectiveness in improving performance.

Insulator faults are an important factor in causing outages and accidents in power transmission lines. In response to problems related to inefficient insulator positioning, limited robustness of insulator defect feature extraction methods, and the scarcity of defective insulator samples leading to poor classifier generalization, a method for insulator defect detection and recognition based on vision big‐model transfer learning and a stochastic configuration network (SCN) is proposed. First, data augmentation methods, such as Mosaic and Mixup, are employed to mitigate overfitting in the YOLOv7 network. Second, StyleGanv3 adversarial generative networks are used to augment the dataset of defective insulators, which enhances dataset diversity. Third, a vision big‐model transfer learning method based on DINOv2 is introduced to extract features from insulator images. Finally, an SCN classifier is used to determine the status of insulators. Experimental results demonstrate that the applied data augmentation methods effectively mitigate overfitting. YOLOv7 accurately detects insulator positions, and the use of the DINOv2 feature extraction method increases the accuracy of insulator defect recognition by 28.6%. Compared with machine learning classification methods, the SCN classifier achieves the highest accuracy improvement of 17.4%. The proposed method effectively detects insulator positions and recognizes insulator defects.

This article presents a new sensing channel modeling approach by jointly considering ray-tracing (RT) and stochastic methods, to accurate and efficient model sensing channels for vehicle-to-everything (V2X) applications. For the former, moving targets are modeled through accurate RT simulations while for the latter a statistical approach is used for generating complex environmental clutter by emphasizing for the first time individual object modeling. This approach is used to form a feature library of objects which ensures space-time consistency while significantly improving the modeling speed. The channel transfer functions generated by RT and stochastic methods are jointly considered through coherent superposition to form a more complete sensing channel which includes both clutters and targets. To verify its effectiveness and accuracy, a comprehensive experimental study has been conducted taking systematic measurements using a 77-GHz mmWave radar as it is the prevalent equipment for sensing used for intelligent driving applications. We have considered a typical V2X scenario, with the radar deployed on vehicles traveling along roads at an urban intersection. The experimental results obtained have demonstrated that the accuracy in target distance detection and velocity estimation has improved leading to errors of less than 0.5 m and less than 0.2 m/s, respectively, while for clutter modeling, the error of power is 3 to 6 dB. Moreover, compared to traditional RT methods, the proposed approach is 20 times faster. Through the proposed approach, realistic sensing channel data can be obtained in a systematic, effective, and accurate manner, facilitating research of sensing-assisted communication applications.

Recently, a two-dimension (2D) modulation waveform of orthogonal time-frequency-space (OTFS) has been a popular 6G candidate to replace existing orthogonal frequency division multiplexing (OFDM). The extensive OTFS researches help to make both the advantages and limitations of OTFS more and more clear. The limitations are not easy to overcome as they come from OTFS on-grid 2D convolution channel model. Instead of solving OTFS inborn challenges, this paper proposes a novel 2D modulation waveform named orthogonal time-frequency division multiplexing (OTFDM). OTFDM uses a 2D dot-product channel model to cope with doubly-selectivity. Compared with OTFS, OTFDM supports grid-free channel delay and Doppler and gains a simple and efficient 2D equalization. The concise dot-division equalization can be easily combined with MIMO. The simulation result shows that OTFDM is able to bear high mobility and greatly outperforms OFDM in doubly-selective channel.

Joint communication and sensing (JCAS) is a very promising 6G technology, which attracts more and more research attention. Compared with communication, radar has many unique features in terms of waveform design criteria, self-interference cancelation (SIC), aperture-dependent resolution, and virtual aperture. This article proposes a novel waveform design named max-aperture radar slicing (MaRS) to gain a large time-frequency aperture, which is generated by orthogonal frequency division multiplexing (OFDM) and occupies only a tiny fraction of OFDM resources. The proposed MaRS keeps the radar advantages of constant modulus, zero auto-correlation sequence, and simple SIC. As MaRS consumes much less resources, conventional processing methods fail, and novel angle-Doppler map-based methods are proposed to obtain the range-velocity-angle information from MaRS echos and strong clutters. To avoid complex full-duplex communication, this article proposes a hybrid-duplex JCAS scheme composed of half-duplex communication and full-duplex radar. The half-duplex communication antenna array is reused, and a small sensing-dedicated antenna array is added. Using these two arrays, a large space-domain sensing aperture is virtually formed to greatly improve the angle resolution. The numerical results show that the proposed MaRS and hybrid duplex can achieve a high sensing resolution with only 0.4% OFDM resources, which reduces the overheads of conventional methods to less than one tenth.

Cell-free massive multi-input multi-output (MIMO) provides ubiquitous connectivity for multiple users. Compared with co-located massive MIMO, the major cost includes fronthaul and access points (AP). This paper assumes an implementation using radio stripes. Maximum ratio combining (MRC) uses a low-loading fronthaul and low-cost APs, but the performance is bad. To solve these problems, this paper first proposes an SVD-based method named quasi-LMMSE (Q-LMMSE). Q-LMMSE is derived from standard linear minimum- mean-square-error (LMMSE) using only the MRC frounthaul signals. Employing the simple MRC fronthaul design, a novel processing method is derived from standard linear minimum- mean-square-error (LMMSE). Simulation results show that the proposed Q-LMMSE gains a higher SE with low fronthaul loading, AP complexity, and latency than existing distributed schemes and performs even better than centralized LMMSE in some cases.

Massive MIMO provides a high spatial degree of freedom (DoF) to support multiple users. To gain the spatial DoF, accurate channel information is crucial, and the pilot is important to obtain it. However, in grant-free transmissions, different users may select the same pilot, which leads to pilot collisions. To solve this problem, this paper proposes an irregular superimposed multi-pilot. The proposed superimposed structure provides a large multi-pilot pool than the conventional cascaded one. Using this structure, the number of single-pilots in multi-pilot can be different, and the pilot number distribution is optimized via a novel Monte-Carlo-based differential evolution. The simulation results verify the analysis and show that the proposed method outperforms existing works.

Due to the limited number of traditional orthogonal pilots, pilot collision will severely degrade the performance of contention-based grant-free transmission. To alleviate the pilot collision and exploit the spatial degree of freedom as much as possible, an extremely sparse orthogonal pilot scheme is proposed for uplink grant-free transmission. The proposed sparse pilot is used to perform active user detection and estimate the spatial channel. Then, inter-user interference suppression is performed by spatially combining the received data symbols using the estimated spatial channel. After that, the estimation and compensation of wireless channel and time/frequency offset are performed utilizing the geometric characteristics of combined data symbols. The task of pilot is much lightened, so that the extremely sparse orthogonal pilot can occupy minimized resources, and the number of orthogonal pilots can be increased significantly, which greatly reduces the probability of pilot collision. The numerical results show that the proposed extremely sparse orthogonal pilot scheme significantly improves the performance in high-overloading grant-free scenario.

Modern random access avoids scheduling overheads and greatly improves the transmission efficiency of small packets. The major challenge is the random collision, and slotted ALOHA (SA)-based methods were proposed to cancel collisions via the receiving time slot diversity. Based on power-domain multiple access (PDMA), some nonorthogonal multiple access (NOMA) SA methods increased the active user number. This article refines PDMA-based methods at the first step, including: 1) the existing NOMA irregular repetition SA is enhanced and 2) NOMA coded SA is proposed. However, PDMA-based methods require accurate sensing and power control to separate multiple users in the power domain, which is not friendly to low-cost devices. This article proposes a NOMA SA method named spreading SA (SSA), which does not rely on any sensing or power control. Both physical and medium access control layers are considered, and the outage model, instead of the collision model, is proposed to be used for NOMA SA. SSA uses random nonorthogonal code spreading to reduce collisions as a relatively large number of codes can be employed. Compared with the power domain, the code domain gains a much higher degree of freedom. It can be utilized without any coordination with the help of blind detection technologies. Moreover, collision resolution diversity SSA (CRDSSA) is proposed to further improve the user loading. The simulation results validate the analysis and show that the proposed SSA-based methods support a higher user loading and much higher flexibility than existing NOMA SA works in most cases.

In truly grant-free (TGF) transmissions, pilot is crucial to fully exploit the user separation capability of spatial domain. Based on pilots, joint active user detection and channel estimation can be done. The state-of-art work suggests to use the recovered data to improve the channel estimation accuracy. In this paper, a novel data-assisted algorithm is proposed. This algorithm jointly utilizes the recovered and unrecovered data to further improve the performance. The constant modulus (CM) feature is used as the optimization target. To obtain an efficient convergence, a fixed modulus version of CM is employed, and multiple relatively good spatial combining vectors are normalized and used as the initial values of the quasi-Newton iterations. Furthermore, a target function of post signal to interference plus noise ratio is proposed to gain a better performance and faster convergence. The simulation results show that the proposed method achieves a tremendous performance gain, especially when more receiving antennas are employed.

Autonomous grant-free data-only transmissions greatly simplify machine type communications, but present many challenges. One of them is the compressed sensing multiple measurement vector (MMV) problem of user detection. The sensing matrix is very underdetermined, and the amount of measurement vectors is large, which affect performance and complexity, respectively. In this paper, a fast power reconstruction (FPR) algorithm is proposed. FPR simplifies MMV to single measurement vector, and no iterations is required, which reduce the complexity. The row dimension of sensing matrix is expanded, and thus the performance is improved. The simulation results validate its good performance and ultra-low computational complexity.

Contention-based grant-free transmission is very promising for future massive machine-type communication (mMTC). In contention-based transmission, the random pilot collision becomes a challenge. To solve it, multi-pilot scheme is proposed. However, the existing work relies on the low correlation of spatial channels, and cannot be applied when the number of antennas is relatively small. This paper proposes a novel independent multi-pilot scheme, which utilizes the diversity of multiple pilots, instead of the spatial correlation. The interference cancellation of both data symbols and multiple pilots is utilized to improve the performance. The simulation results show that the proposed scheme significantly improves the performance in high-overloading mMTC and outperforms the existing work in a massive antenna array case.

Multi-user shared access (MUSA) is an autonomous grant- free and high overloading non-orthogonal transmission technology. However, the performance degrades when combining MUSA with discrete Fourier transform spreading OFDM (DFT-s-OFDM) directly. To solve this problem, the real Fourier-related transform spreading methods are researched which are able to reduce the peak-to-average power ratio (PAPR) and increase the stability against channel fading. Furthermore, it enables the receiver to blindly estimate time offset (TO) and frequency offset (FO). The simulation results show that the proposed system performs very well in a frequency selective channel model containing multipath, TO and FO.