To overcome the limitations of traditional satellite navigation technologies in complex and signal-obstructed industrial environments, this paper presents MSM-Pnet, a novel semi-supervised FM-based positioning framework leveraging FM signals of opportunity. By integrating wavelet packet decomposition with a multi-scale Vision Transformer and a hybrid masking strategy that combines random and time–frequency-aware masking, MSM-Pnet introduces a masked autoencoder architecture capable of robust positioning with limited labeled data. Experimental results demonstrate that MSM-Pnet consistently outperforms conventional supervised learning methods in both indoor and outdoor environments, while also significantly reducing model complexity. These results highlight the method’s potential as a cost-effective and scalable solution for seamless indoor–outdoor positioning for Internet of Things systems.

Accurate and efficient positioning in complex environments remains a critical challenge where satellite-based systems (e.g., GNSS) suffer from signal attenuation and multipath interference. This paper proposes WK-Pnet, a lightweight positioning framework that utilizes frequency modulation (FM) signals and integrates Wavelet Packet Decomposition (WPD) with knowledge distillation. WK-Pnet first decomposes raw FM IQ signals using WPD to extract fine-grained multi-scale time-frequency features, preserving both spectral and phase information. These features are then fed into a deep neural network for location estimation. To reduce computational complexity, we employ a knowledge distillation strategy that transfers knowledge from a large-capacity ResNeXt-based teacher model—enhanced with a spatial attention mechanism—to a compact student network with significantly fewer parameters and FLOPs. The proposed method is validated on publicly available indoor and outdoor datasets, showing that WK-Pnet achieves comparable positioning accuracy to the teacher model while reducing FLOPs by 95.9%, model parameters by 99.3%, and inference latency by 90.5% on edge devices. Experimental comparisons also reveal that WPD outperforms STFT and EMD in positioning stability and accuracy, especially in outdoor scenarios. WK-Pnet demonstrates strong robustness, low-latency inference, and high accuracy, making it highly suitable for real-time, resource-constrained mobile and IoT applications.

Frequency Modulation (FM) broadcast signals, regarded as opportunistic signals, hold significant potential for indoor and outdoor positioning applications. The existing FM-based positioning methods primarily rely on Received Signal Strength (RSS) for positioning, the accuracy of which needs improvement. In this paper, we introduce FM-Pnet, an end-to-end FM-based positioning method that leverages deep learning. This method utilizes the time-frequency representation of FM signals as network input, enabling automatically learning of deep features for positioning. We also propose two strategies, noise injection and enriching training samples, to enhance the model’s generalization performance over long time spans. We construct datasets for both indoor and outdoor scenarios and conduct extensive experiments to validate the performance of our proposed method. Experimental results demonstrate that FM-Pnet significantly outperforms traditional RSS-based positioning methods in terms of both positioning accuracy and stability.

Abstract Accurate acquisition of channel state information (CSI) is crucial but difficult in frequency division duplex (FDD) massive multiple‐input multiple‐output (MIMO) systems. To improve the estimation accuracy and to minimize the training consumption, an adaptive training‐feedback scheme based on spatial reciprocity in FDD is proposed. The main idea of this scheme is to construct a reference frame that matches the channel structure. Reference vectors with the strongest correlation with the uplink channel are selected to design the pilots. The pilots can adapt to channel changes in this proposed scheme, which lead to substantial reduction of the training and feedback overheads. The simulation results show that under full feedback, the throughput of proposed adaptive training‐feedback scheme can approach to the optimal performance, and under finite‐bit feedback, the channel utilization is also significantly high with less training.

Although deep learning methods have made significant advancements across various domains, recent research has shown that carefully crafted adversarial samples can lead to a significant degradation in the performance of deep learning models. Such adversarial examples raise concerns about the reliability and safety of deep learning-based models. Currently, there is a lack of research on the robustness of deep learning based DOA methods against adversarial samples. This letter aims to fill this research gap by leveraging the differentiability of the transformation process from the original signal to the covariance matrix. By utilizing this differentiability, the robustness of the DOA estimation model, which takes the covariance matrix as input, is investigated. Four different white-box attack methods are considered to generate adversarial samples to evaluate the resilience of the model. The experimental results demonstrate that all four methods employed significantly increase the estimation error of the DOA estimation model, posing a serious threat to the model's security.

The matrix decomposition (MD) based FIR filter design technique can synthesize a traditional FIR filter with much less implementation complexity. A scheme for obtaining more sparse coefficients of a MD-FIR filter is proposed, which consists of two parts. The first part is the previous procedure of designing a MD-FIR filter (i.e., design an initial MD-FIR filter using a certain MD method and optimize its coefficients). The second part is the proposed procedure of obtaining more sparse coefficients, where the MD-FIR filters' coefficients also need to be optimized. The performances of the various initial MD-FIR filters, which are obtained based on the various MD methods, in the implementation of this scheme are experimentally compared. The MD-FIR filter's coefficients can be effectively optimized by the trust-region iterative-gradient-searching (TR-IGS) algorithm. We present further results on the TR-IGS. The error bound for each iteration of TR-IGS is analyzed. A convergent online implementation scheme of the TR-IGS is presented and analyzed theoretically and experimentally. The proof of the convergence is provided. A sufficient condition for determining whether a theoretical termination point of the scheme is a strict local optimum point is provided. The step-size optimization problem for the TR-IGS is analyzed theoretically and experimentally.

An estimation method of pseudo‐random (PN) codes in the periodic long code direct sequence spread spectrum signals using a pair of spreading code and scrambling code [i.e. long scrambling code direct sequence spread spectrum (LSC‐DSSS)] is investigated in this study. Via the investigation of properties of triple correlation function (TCF) of m ‐sequences, the existence of common peaks in the TCFs of different m ‐sequences is proved, and the corresponding relationship between common peaks and primitive polynomials is further investigated. Four theorems are proposed as supplements of triple correlation theory and a novel estimation algorithm of the PN codes in LSC‐DSSS signals is put forward on the basis of the theorems. With certain carrier frequency and chip rate of spreading code, this algorithm first eliminates the influence of information codes through delay‐and‐multiply operation. Then the TCF of signal is calculated, and the two PN codes in signal are successfully estimated finally by searching and using the common peak coordinates in the TCF. Simulation results show that the proposed algorithm exhibits excellent performance in estimating PN codes in LSC‐DSSS signals.

By introducing amplify‐and‐forward relaying into a cognitive radio system, typical cognitive relay networks are studied for the optimisation problems of the spectrum and power allocation. By applying the mixed spectrum access of overlay and underlay approaches, an interference evaluation strategy is proposed to use different spectrum and power allocation methods while the secondary users (SUs) are located in different service regions of the primary users (PUs). In the interference evaluation strategy, the service area of the PUs is divided according to the possible interference strength of the PUs from the SUs compared with the location‐aware strategy in which the service area is divided only by the location information. An optimal power allocation algorithm is developed to maximise the throughput of the SUs under the condition of anti‐interference performance of the PUs and the total power constraints of the SUs. A type of computing algorithm that joins the subgradient and the Newton's method is used in order to resolve the complex optimisation problem. Numerical results show that the performance using the interference evaluation strategy, such as the throughput, the power consumption, and the energy efficiency, outperforms that using the location‐aware strategy.

In this paper, a multi-cell multiple-input multiple-output (MIMO) uplink system is considered. For uplink transmission, to implement decoding, the base station (BS) generally needs to estimate channels with the aid of training sequences. Obviously, for full frequency reuse systems, training also suffers from inter-cell interference, namely pilot contamination. In this paper, the impact of pilot contamination on the performance of the considered system is studied based on the maximum likelihood (ML) decoder. Specifically, an exact analytical expression of pair-wise error probability (PEP) is derived, and then, a lower bound and an upper bound of PEP are given, to explicitly show the impact of pilot contamination. With the detailed analysis of these bounds, we can discover: (1) pilot contamination can result in error floor, provided that the number of BSs in the considered system is larger than the length of a frame; and (2) if the length of a frame is larger than or equal to the number of BSs, and an appropriate coding scheme is applied, error floor can be removed. Finally, a coding design criterion is proposed. Based on this criterion, it can be shown that the considered system can achieve the same diversity to what can be achieved by a single isolated cell system.

In this letter, a blind spectrum sensing method is proposed which does not need any information of primary users and the noise power. For a given number of observation samples of primary channels, the spectrum sensing is reformulated into a Student's t-distribution testing problem. The analytical results of the blind spectrum sensing are given. It is shown that over flat fading channels in noise of uncertainty power the blind spectrum sensing greatly outperforms the energy detection at about 4 dB gain.

In this paper, design of a distributed differential space time code is considered. To satisfy the commuting property required in the design, Weyl reciprocity is hired. Introducing methods and illustrating examples show that the proposed methods have more flexible, and more easy to get codes than existing methods, especially in a case with high dimensional unitary matrices and a large number of relays.

One of the most important challenges in cognitive radio is how to measure or sense the existence of a signal transmission in a specific channel, that is, how to conduct spectrum sensing. In this letter, we first formulate spectrum sensing as a goodness of fit testing problem, and then apply the Anderson-Darling test, one of goodness of fit tests, to derive a sensing method called Anderson-Darling sensing. It is shown by both analysis and numerical results that under the same sensing conditions and channel environments, Anderson-Darling sensing has much higher sensitivity to detect an existing signal than energy detector-based sensing, especially in a case where the received signal has a low signal-to-noise ratio (SNR) without prior knowledge of primary user signals.

Cognitive radio has been regarded as a promising technology to improve spectrum utilization significantly. In this letter, spectrum allocation model is presented firstly, and then spectrum allocation methods based on genetic algorithm (GA), quantum genetic algorithm (QGA), and particle swarm optimization (PSO), are proposed. To decrease the search space we propose a mapping process between the channel assignment matrix and the chromosome of GA, QGA, and the position of the particle of PSO, respectively, based on the characteristics of the channel availability matrix and the interference constraints. Results show that our proposed methods greatly outperform the commonly used color sensitive graph coloring algorithm.

Abstract One of the basic capabilities of cognitive radio is to adapt the radio parameters according to the changing environment and user needs. This paper proposes a new adaptation method which uses particle swarm optimization (PSO) to optimize cognitive radio parameters given a set of objectives. The procedure of the proposed method is presented and multicarrier system is used for simulation analysis. Experimental results show that the proposed method performs far better than genetic algorithm (GA)‐based adaptation method in terms of convergence speed, converged fitness values, and stability. The proposed method can also provide the tradeoffs of the objective functions, and the resulting parameter configuration is consistent with the weights of the objective functions. Copyright © 2008 John Wiley & Sons, Ltd.