Cooperating Spectrum Sensing(CSS) is the cornerstone of dynamically shared spectrum access in CRN. But it’s hampered by subpar conditions like noise that’s not right, a lack of data, and hardware issues too. These defects distort sensing data and cause the “SNR wall” effect, seriously degrade the performance of traditional sensing algorithms. To solve those issues this article gives a comprehensive review on CSS, the survey is started with creating a common analysis platform which will isolate the key problems then we will be taking a look at them from 3 points of view: noisiness, Data Integrity, Hardware Impairment Based on the aforementioned framework, we do a comprehensive overview and comparison of 3 major categories of mainstream solutions - statistical learning method based on the generalized Gaussian mixture model and meta-heuristic optimization; deep learning approach integrated with Convolutional Neural Networks (CNN) and Transformer architecture; and Deep Reinforcement Learning-based communication-sensing co-design strategy. The evaluations cover several areas like the kind of deformities it addresses, use of prior knowledge, complexity in processing, and whether it can work in actual situations. This complete examination leads to a good plan that covers finding things, missing them accidentally, and doing well even if there’s some trouble. our analysis tells us how good each method is, what trade-offs they have, and when they work best. This gives us simple rules about which methods to use and how to make them. In the end, we give potential directions for future research, which are new paradigms for adaptive and privacy-preserving CSS in the dynamic spectrum-sharing, heterogeneous network integration, and increasing privacy-security environment.

Massive Multiple-Input Multiple-Output (Massive MIMO) serves as a foundational enabling technology for 5G and future communication systems, markedly boosting spectral and energy efficiency through the deployment of large-scale antenna arrays. However, the scaling-up of antenna arrays has led to a substantial increase in system power consumption and hardware costs, with high-precision analog-to-digital converters (ADCs) emerging as the dominant power consumption bottleneck in the radio frequency chain. To alleviate system complexity and power consumption, low-resolution ADCs (1–3 bits) have attracted extensive research interest in recent years. Such schemes can substantially curtail hardware costs and energy consumption while retaining satisfactory system performance. Nevertheless, the introduction of severe nonlinear distortion due to low-precision quantization disrupts the linear Gaussian model assumption upon which traditional receiver algorithms rely, resulting in compromised channel estimation and signal detection performance. Quantization errors demonstrate non-Gaussian and input-dependent characteristics, leading to the degradation of amplitude information and thus constraining the applicability of technologies such as high-order modulation and high-precision sensing. This paper presents a systematic review of low-precision quantization techniques for Massive MIMO. It first investigates the impacts of low-bit quantization on system models and signal statistical properties. Subsequently, it elaborates on transceiver architectures and key design challenges pertaining to low-precision ADCs/DACs. The paper highlights signal processing and algorithmic strategies to overcome quantization distortion, including Bussgang decomposition linearization methods, statistical inference techniques such as approximate message passing (AMP), model-driven deep learning frameworks, and Σ–Δ quantization architectures endowed with noise-shaping capabilities. Finally, it discusses the challenges and future directions of this technology in emerging scenarios, including terahertz communications, intelligent reflecting surfaces, and integrated sensing and communication. This paper seeks to provide researchers with a systematic technical overview, clarifying the intrinsic connections and trade-offs among different methods, and offering valuable insights for the realization of high-energy-efficiency and low-cost Massive MIMO systems.

Dynamic Time Warping (DTW) is employed to a great extent for comparing time series in machine learning, but is computationally expensive, especially with sparse data containing many zeros. To address this, faster DTW variants have been developed, including Sparse DTW (SDTW), Constrained Sparse DTW (CSDTW), and Binary Sparse DTW (BSDTW). This paper presents Constrained Binary Sparse DTW (CBSDTW), which adds a warping path constraint to BSDTW and significantly reduces computational complexity compared to Constrained DTW (CDTW), offering an efficient way to leverage sparsity in time series analysis.