Lane-level autonomous driving relies on high-accuracy vehicle positioning. Among different vehicle positioning approaches, direction of arrival (DOA) based solutions are competitive as they avoid measuring delay information. However, a large distance between the vehicle and road side unit (RSU) compared to the antenna array aperture limits the positioning performance of the existing methods. To address this issue, in this paper, we propose an iterative positioning method using two collaborative RSUs and the spatial geometry, where the DOAs and the positions of vehicles are iteratively estimated. Numerical simulations demonstrate that the proposed method can achieve a positioning performance of millimeter-grade, improving the positioning performance by one to two orders of magnitude, compared to state-of-the-art methods.

The limited field of view (FoV) of commercial light field cameras has driven development of various stitching techniques to generate wide-FoV light field images (WLFIs). However, these techniques often introduce local distortions and angular inconsistencies, posing significant challenges for WLFI quality assessment. In this letter, a no-reference WLFI quality assessment (WLFIQA) method based on structured representation and progressive learning is proposed. Specifically, considering the high-dimensional characteristics and distortion properties of WLFIs, a novel joint spatial-angular representation strategy is first designed. For the angular domain, horizontal and vertical sub-aperture images stacks are employed to characterize angular features; for the spatial domain each sub-aperture image is divided into four quadrants, and image blocks containing complementary cues from corresponding positions across different quadrants are used as input for subsequent feature extraction. Furthermore, a global-local feature extraction network is employed to further model multi-scale distortion characteristics. Finally, a progressive learning strategy is designed to enhance the performance of overall perceptual evaluation. Experimental results on a benchmark WLFI dataset show that the proposed method outperforms existing quality methods. The code will be available athttps://github.com/sunyu-iy/WLFIQA.

In this paper, we analyze the role of fluid antenna systems (FAS) in multi-user systems with hardware impairments (HIs). Specifically, we investigate a scenario where a base station (BS) equipped with multiple fluid antennas communicates with multiple communication users (CUs), each equipped with a single fluid antenna. Our objective is to maximize the minimum communication rate among all users by jointly optimizing the BS's transmit beamforming, the positions of its transmit fluid antennas, and the positions of the CUs' receive fluid antennas. To address this non-convex problem, we propose a block coordinate descent (BCD) algorithm integrating semidefinite relaxation (SDR), rank-one constraint relaxation (SRCR), successive convex approximation (SCA), and majorization-minimization (MM). Simulation results demonstrate that FAS significantly enhances system performance and robustness, with notable gains when both the BS and CUs are equipped with fluid antennas. Even under low transmit power conditions, deploying FAS at the BS alone yields substantial performance gains. However, the effectiveness of FAS depends on the availability of sufficient movement space, as space constraints may limit its benefits compared to fixed antenna strategies. Our findings highlight the potential of FAS to mitigate HIs and enhance multi-user system performance, while emphasizing the need for practical deployment considerations.

This work addresses the challenges of communication signal detection and direction of arrival (DOA) estimation in integrated sensing and communications (ISAC) systems with hardware imperfections. Conventional signal processing techniques often fail to effectively manage the complex nonlinearities caused by hardware imperfections, such as those introduced by power amplifiers and local oscillators. Recently, deep neural networks (DNNs) have been employed to mitigate the hardware imperfections, which however require a substantial amount of pilot signals for training, leading to unacceptable overhead and impracticality in fast time-varying channels. In this work, we employ an NN to characterize the nonlinear system, and propose a novel iterative approach to joint NN-based nonlinear system model learning, signal detection and DOA estimation. Instead of relying on pilot signals for NN learning, the proposed approach utilizes communication data signals as virtual training samples, enabling more accurate nonlinear model learning, which subsequently enhances signal detection and DOA estimation. A Bayesian framework is applied to the joint problem, wherein the NN parameters, the communication signals and the DOAs are jointly obtained by developing a message passing based inference algorithm. In particular, we impose sparse priors on the weights of the NN, so that overfitting can be better handled, resulting in significant improvement in system modeling performance. Extensive simulation results show that, compared to the state-of-the-art approaches, the proposed one delivers significantly better performance.

This paper investigates a design framework for sparse fluid antenna systems (FAS) enabling high-performance direction-of-arrival (DOA) estimation, particularly in challenging millimeter-wave (mmWave) environments. By ingeniously harnessing the mobility of fluid antenna (FA) elements, the proposed architectures achieve an extended range of spatial degrees of freedom (DoFs) compared to conventional fixed-position antenna (FPA) arrays. This innovation not only facilitates the seamless application of super-resolution DOA estimators but also enables robust DOA estimation, accurately localizing more sources than the number of physical antenna elements. We introduce two bespoke FA array structures and mobility strategies tailored to scenarios with aligned and misaligned received signals, respectively, demonstrating a hardware-driven approach to overcoming complexities typically addressed by intricate algorithms. A key contribution is a light-of-sight (LoS)-centric, closed-form DOA estimator, which first employs an eigenvalue-ratio test for precise LoS path number detection, followed by a polynomial root-finding procedure. This method distinctly showcases the unique advantages of FAS by simplifying the estimation process while enhancing accuracy. Numerical results compellingly verify that the proposed FA array designs and estimation techniques yield an extended DoFs range, deliver superior DOA accuracy, and maintain robustness across diverse signal conditions.

In this paper, we investigate reconfigurable intelligent surface (RIS)-assisted communication systems which involve a fixed-antenna base station (BS) and a mobile user (MU) that is equipped with fluid antenna system (FAS). Specifically, the RIS is utilized to enable communication for the user whose direct link from the base station is blocked by obstacles. We propose a comprehensive framework that provides transmission design for both static scenarios with the knowledge of channel state information (CSI) and harsh environments where CSI is hard to acquire. It leads to two approaches: a CSI-based scheme where CSI is available, and a CSI-free scheme when CSI is inaccessible. Given the complex spatial correlations in FAS, we employ block-diagonal matrix approximation and independent antenna equivalent models to simplify the derivation of outage probabilities in both cases. Based on the derived outage probabilities, we then optimize the throughput of the FAS-RIS system. For the CSI-based scheme, we first propose a gradient ascent-based algorithm to obtain a near-optimal solution. Then, to address the possible high computational complexity in the gradient algorithm, we approximate the objective function and confirm a unique optimal solution accessible through a bisection search method. For the CSI-free scheme, we apply the partial gradient ascent algorithm, reducing complexity further than full gradient algorithms. We also approximate the objective function and derive a locally optimal closed-form solution to maximize throughput. Simulation results validate the effectiveness of the proposed framework for the transmission design in FAS-RIS systems.

In this paper, we investigate the question of which technology, fluid antenna systems (FAS) or active reconfigurable intelligent surfaces (ARIS), plays a more crucial role in FAS-ARIS wireless communication systems. To address this, we develop a comprehensive system model and explore the problem from an optimization perspective. We introduce an alternating optimization (AO) algorithm incorporating majorization-minimization (MM), successive convex approximation (SCA), and sequential rank-one constraint relaxation (SRCR) to tackle the non-convex challenges inherent in single-user scenario. Specifically, for the transmit beamforming of the BS optimization, we propose a closed-form rank-one solution with low-complexity. For the optimization the positions of fluid antennas (FAs) of the BS, the Taylor expansions and MM algorithm are utilized to construct the effective lower bounds and upper bounds of the objective function and constraints, transforming the non-convex optimization problem into a convex one. Furthermore, we use the SCA and SRCR to optimize the reflection coefficient matrix of the ARIS and effectively solve the rank-one constraint. To be more general, the proposed AO algorithm is then extended to multi-user scenario. Simulation results reveal that the relative importance of FAS and ARIS varies depending on the scenario: FAS proves more critical in simpler models with fewer reflecting elements or limited transmission paths, while ARIS becomes more significant in complex scenarios with a higher number of reflecting elements or transmission paths. Ultimately, the integration of both FAS and ARIS creates a win-win scenario, resulting in a more robust and efficient communication system. This study underscores the importance of combining FAS with ARIS, as their complementary use provides the most substantial benefits across different communication environments.

This work investigates communication signal detection and direction of arrival (DOA) estimation for an integrated sensing and communications (ISAC) system with multiple hardware imperfections, including power amplifier nonlinearity, in-phase and quadrature phase imbalance, and phase-gain error (PGE). Conventional signal processing techniques struggle with the complex nonlinearities arising from these imperfections. Recently, deep neural networks (DNNs) have been employed to mitigate hardware impairments; however, they require a substantial number of pilot signals for training, leading to significant overhead, making them impractical in many applications. In this work, we design a signal flow inspired neural network (NN) to characterize the nonlinear ISAC system. Then, we propose a Bayesian method to jointly estimate the parameters of the PGE, the communication signals, and the DOAs by developing a message-passing inference algorithm based on the NN. Extensive simulation results demonstrate that the proposed method provides efficient and robust signal detection and DOA estimation performance under PGE, and significantly outperforms state-of-the-art ones.

Cognitive radio (CR) networks face significant challenges in spectrum sensing, especially under spectrum scarcity. Fluid antenna systems (FASs) can offer an unorthodox solution due to their ability to dynamically adjust antenna positions for improved channel gain. In this letter, we study an FAS-driven CR setup where a secondary user (SU) adjusts the positions of fluid antennas to detect signals from the primary user (PU). We aim to maximize the detection probability under the constraints of the false alarm probability and the received beamforming of the SU. To address this problem, we first derive a closed-form expression for the optimal detection threshold and reformulate the problem to find its solution. Then, an alternating optimization (AO) scheme is proposed to decompose the problem into several subproblems, addressing both the received beamforming and the antenna positions at the SU. The beamforming subproblem is addressed using a closed-form solution, while the fluid antenna positions are solved by successive convex approximation (SCA). Simulation results reveal that the proposed algorithm provides significant improvements over traditional fixed-position antenna (FPA) schemes in terms of spectrum sensing performance.

This letter considers a fluid antenna system (FAS)-aided secure and covert communication system, where the transmitter adjusts multiple fluid antennas’ positions to achieve secure and covert transmission under the threat of an eavesdropper and the detection of a warden. This letter aims to maximize the secrecy rate while satisfying the covertness constraint. Unfortunately, the optimization problem is nonconvex due to the coupled variables. To tackle this, we propose an alternating optimization (AO) algorithm to alternatively optimize the optimization variables in an iterative manner. In particular, we use a penalty-based method and the majorization-minimization (MM) algorithm to optimize the transmit beamforming and fluid antennas’ positions, respectively. Simulation results show that FAS can significantly improve the performance of secrecy and covertness compared to the fixed-position antenna (FPA)-based schemes.

This paper characterizes the physical layer security (PLS) in a network utilizing massive multiple-input multiple-output (MIMO) for over-the-air computation (AirComp). When the direct links between the access point (AP) and the sensors are blocked, an intelligent reflecting surface (IRS) is employed to establish communication. Furthermore, the AP sends artificial noise (AN) to the eavesdropper to prevent wiretapping. We study the problem of minimizing the mean-square-error (MSE) between the original and intercepted signals subject to the transmit power constraints at the AP and the sensors, as well as how the MSE threshold hinders the eavesdropper under both perfect and imperfect channel state information (CSI). In the case of perfect CSI, obtaining a globally optimal solution for the investigated non-convex problem is challenging due to the optimization variables’ couple nature. Hence, we convert the problem into two sub-problems to obtain locally optimal solutions. One sub-problem can be solved by an exact penalty-based algorithm, while the other has a closed-form solution using the popular majorization-minimization (MM) algorithm. For the imperfect CSI, the robust beamforming optimization problem formulated is still non-convex. To address this, we harness the block coordinate descent (BCD) algorithm for alternately optimizing the variables to solve it. The results of our simulations demonstrate that the superior MSE performance exhibited by the proposed scheme.

The allocation of secure resources in non-orthogonal multiple access (NOMA) systems has gained significant recognition as a vital research focus in the realm of the Internet of Things (IoT). Previous studies have overlooked the security challenges associated with integrating NOMA with finite blocklength (FBL) transmission. Therefore, this paper examines a secure downlink NOMA system utilizing FBL transmission, which includes a base station (BS), a near user, a far user, and an external eavesdropper. We develop an optimization problem with the objective of maximizing the near user’s effective secrecy throughput, considering the secrecy rates, decoding error probabilities (DEPs), and effective secrecy throughput for both users. Notably, by meticulously defining the DEPs of the users as optimization variables, the monotonicity and concavity of these DEPs in relation to the blocklength, transmission power, and transmission rate can be established effectively. The problem is divided into two sub-problems focusing on the essential conditions for the secrecy rate of the near user, especially in scenarios where successive interference cancellation (SIC) is unsuccessful. These sub-problems are addressed using the block coordinate descent (BCD) algorithm and an exact penalty method. For comparison, the BCD algorithm is also applied to solve the optimization problem using the orthogonal multiple access (OMA) scheme. Numerical simulations confirm the effectiveness of our proposed approaches in improving secure resource allocation when NOMA is combined with FBL transmission.

This paper addresses the fairness issue within fluid antenna system (FAS)-assisted non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) systems, where a single fixed-antenna base station (BS) transmits superposition-coded signals to two users, each with a single fluid antenna. We define fairness through the minimization of the maximum outage probability for the two users, under total resource constraints for both FAS-assisted NOMA and OMA systems. Specifically, in the FAS-assisted NOMA systems, we study both a special case and the general case, deriving a closed-form solution for the former and applying a bisection search method to find the optimal solution for the latter. Moreover, for the general case, we derive a locally optimal closed-form solution to achieve fairness. In the FAS-assisted OMA systems, to deal with the non-convex optimization problem with coupling of the variables in the objective function, we employ an approximation strategy to facilitate a successive convex approximation (SCA)-based algorithm, achieving locally optimal solutions for both cases. Besides, we address a more general scenario involving interference and channel estimation overheads, deriving exact users’ outage probabilities and employing a combination of bisection, one-dimensional (1D) search, and SCA algorithms to efficiently and effectively solve max-min optimization problems in both NOMA and OMA systems, significantly enhancing system fairness and computational efficiency. Our numerical results demonstrate that the proposed schemes significantly enhance outage performance over conventional OMA and NOMA benchmarks, even in the presence of interference, confirming their effectiveness in realistic scenarios. The performance of our closed-form and SCA algorithm-based solutions in FAS-assisted NOMA and OMA systems closely approaches that of the optimal solutions, further validated by the effective approximation of users’ outage probabilities in simulations.

Holographic multiple-input and multiple-output (HMIMO) is a promising technology with the potential to achieve high energy and spectral efficiencies, enhance system capacity and diversity, etc. In this work, we address the challenge of HMIMO near field (NF) channel estimation, which is complicated by the intricate model introduced by the dyadic Green’s function. Despite its complexity, the channel model is governed by a limited set of parameters. This makes parametric channel estimation highly attractive, offering substantial performance enhancements and enabling the extraction of valuable sensing parameters, such as user locations, which are particularly beneficial in mobile networks. However, the relationship between these parameters and channel gains is nonlinear and compounded by integration, making the estimation a formidable task. To tackle this problem, we propose a novel neural network (NN) assisted hybrid method. With the assistance of NNs, we first develop a novel hybrid channel model with a significantly simplified expression compared to the original one, thereby enabling parametric channel estimation. Using the readily available training data derived from the original channel model, the NNs in the hybrid channel model can be effectively trained offline. Then, building upon this hybrid channel model, we formulate the parametric channel estimation problem with a probabilistic framework and design a factor graph representation for Bayesian estimation. Leveraging the factor graph representation and unitary approximate message passing (UAMP), we develop an effective message passing-based Bayesian channel estimation algorithm. Extensive simulations demonstrate the superior performance of the proposed method.

Dynamic spectrum access (DSA) is a key technology in cognitive radios, where secondary users (SUs) opportunistically access spectral holes of primary users (PUs) (i.e., channels unoccupied by PUs). The existing DSA schemes often use the listen-before-talk (LBT) mechanism to avoid transmission collisions with PUs. However, LBT-based schemes may not be able to achieve high utilization efficiency of spectral holes as SUs need to perform spectrum sensing over multiple spectrum holes heavily. To address this issue, in this work, we propose a new mechanism called listen-after-collision (LAC), where an SU accesses a channel of PUs without spectrum sensing, and it performs spectrum sensing only after a transmission collision occurs. Moreover, a deep$Q$-network with an improved Thompson sampling algorithm (DQN-iTSA) is proposed to predict both the availabilities and the time lengths of spectral holes to avoid unacceptable transmission collisions and also to determine the order of the channels for sensing by jointly considering the characteristics of spectral holes and the channel qualities of SU transmissions. Extensive simulation results are provided to demonstrate the superior performance of DQN-iTSA, which shows that DQN-iTSA achieves the highest throughput among the compared methods.

Conventional auto-correlation based detectors often require the knowledge of a covariance matrix, which is usually replaced with its corresponding sample covariance matrix by dividing the received signal vector into a number of sub-vectors. This can lead to performance loss due to the deviation of the sample covariance matrix from the population covariance matrix. In this work, the received signal is used as a single signal vector and the use of sample covariance matrices is avoided. Taking advantage of oversampling, we obtain a truncated approximate covariance matrix of primary signals, which leads to a new approximate log-likelihood-ratio-test (aLLRT) detector with low complexity. In addition, a noise power estimator is also proposed by exploiting oversampling, which is incorporated into the new detector for practical implementation. Theoretical analyses for the false-alarm and detection probabilities of the proposed detector are conducted, and their accurate expressions are obtained via performing a nonlinear transformation to the test-statistic of the proposed detector. Numerical results show that, compared to state-of-the-art detectors, the proposed detector improves the detection probability by at least 10%.

Unmanned aerial vehicles (UAVs) offer exceptional maneuverability and mobility, making them valuable for data collection in the Internet of Things (IoT). However, to ensure sustainable data services, UAVs with limited battery capacity require energy replenishment during their operational period. In this study, we investigate the joint design of charging station (CS) placement and UAV trajectory to enable continuous and timely data gathering in IoT networks. We formulate a mixed combinatorial optimization problem aimed at minimizing the network’s peak age of information (AoI) by deploying a specific number of CSs from a set of potential sites and designing the UAV trajectory for data gathering and energy recharging. Convex optimization techniques are employed to find the optimal UAV trajectory, given any feasible CS placement solution. Furthermore, we demonstrate that, with the optimized UAV trajectory, the optimal CS placement problem becomes a maximization problem of a non-submodular, non-decreasing set function under a cardinality constraint, known to be NP-hard. To tackle this challenge, we propose a greedy CS deployment algorithm that provides an approximate optimal solution within a constant factor of 1α1-(1-αγK)K, where α ϵ [0,1] represents the generalized curvature, γ ϵ [0,1] denotes the submodularity ratio, and K represents the number of CSs. Additionally, we introduce a low-complexity CS placement algorithm based on path allocation, which is particularly useful in scenarios involving UAVs with very limited battery capacity. Through simulation results, we demonstrate that our proposed approaches, which jointly optimize CS placement and UAV trajectory, achieve significantly smaller AoI values compared to distance-based strategies, both with and without UAV trajectory optimization.

Attaining centimeter-level vehicle positioning is a fundamental requirement for lane-level autonomous driving. Pursuing this objective from the perspective of direction-of-arrival (DOA) estimation is promising, which has emerged as a prominent research topic. In order to simultaneously meet the demands of low complexity and high accuracy, it is crucial for DOA-based solutions to address pressing challenges, including the model mismatch problem and reliable positioning in scenarios with limited samples. This article explores a novel vehicle positioning scheme employing DOAs obtained from collaborative base stations (BSs) or roadside unit (RSU). To cope with the actual propagation scenarios and avoid nonrandom systematic error, the exact spatial geometry (ESG) for DOA estimation is adopted. Under the ESG model, a two-stage unitary approximate message passing (UAMP)-based DOA estimation method is proposed. With DOAs estimated at multiple collaborative BSs/RSUs, the locations of vehicles are finally obtained with cross-localization criterion. Numerical simulations are provided to show that the proposed method is effective and delivers competitive performance. Furthermore, inspired by intriguing simulation results, we design a DOA subset selection mechanism that enhances the reliability of positioning performance.

Spatiotemporal spectrum sensing of multiple primary users (PUs) sharing the same channels with unknown and irregular coverage presents significant challenges. The receive signal strength (RSS) levels at secondary users (SUs) vary greatly due to path propagation loss and shadowing. Moreover, due to security concerns and limited energy at SUs, the reported RSS measurements to a fusion center are noisy and incomplete. These challenges seriously impact the performance of existing cooperative spectrum sensing (CSS) techniques. In this work, to address these issues, we propose a robust graph learning based CSS (RoGL-CSS) detector, after revealing the low rank property of the expectation of RSS matrix and formulating a graph learning problem. By solving the graph learning problem with the alternating direction method of multipliers (ADMM), a probability matrix (graph) representing the correlations among SUs is acquired, which is applied to recover the expectation of RSSs from corrupted measurements and select SUs for CSS. Specifically, a robust RSS recovery algorithm with a learned probability matrix is adopted, and the SUs with recovered RSSs of high correlations are collected for implementing CSS. Numerical results are provided to demonstrate the superiority of the proposed detector compared to state-of-the-art detectors. At a false-alarm probability of 10%, with measurement missing rate 20% and outlier rate 30%, RoGL-CSS achieves performance improvement of at least 16% and 9% in detection probability, compared to other detectors for scenarios of two and five PUs, respectively.

As an important branch of Internet of Vehicles (IoV) systems, autonomous vehicle (AV) positioning based on direction-of-arrival (DOA) estimation has received extensive attention in recent years. In this article, an AV positioning method under unknown mutual coupling is proposed within the framework of a large-dimensional asymptotic theory (LAT). First, enhanced and closed-form DOA estimation is achieved by jointly exploiting large-scale uniform linear arrays (ULAs), Toeplitz rectification and the phase transformation result associated with the sample covariance matrix; second, a more reliable subset/set of DOAs is constructed according to the signal-to-noise at receivers; finally, robust AV positioning is achieved with the reliable subset/set. Motivated by satisfactory DOA estimation performance, an AV contour extraction scheme is developed with the aid of two antennas installed on an AV. The proposed method shows several salient advantages compared with existing methods, including improved resolution and accuracy, reduced computational complexity, robustness to mutual coupling and unreasonable DOA estimates, as well as the ability to effectively extract AV contour information.

Tailor-made for massive connectivity and sporadic access, grant-free random access has become a promising candidate access protocol for massive machine-type communications (mMTC). Compared with conventional grant-based protocols, grant-free random access skips the exchange of scheduling information to reduce the signaling overhead, and facilitates the sharing of access resources to enhance access efficiency. However, some challenges remain to be addressed in the receiver design, such as the unknown identity of active users and multiuser interference (MUI) on shared access resources. In this work, we deal with the problem of joint user activity and data detection for grant-free random access. Specifically, the approximate message passing (AMP) algorithm is first employed to mitigate MUI and decouple the signals of different users. Then, we extend the data symbol alphabet to incorporate the null symbols from inactive users. In this way, the joint user activity and data detection problem is formulated as a clustering problem under the Gaussian mixture model. Furthermore, in conjunction with the AMP algorithm, a variational Bayesian inference-based clustering (VBIC) algorithm is developed to solve this clustering problem. Simulation results show that, compared with state-of-art solutions, the proposed AMP-combined VBIC (AMP-VBIC) algorithm achieves a significant performance gain in detection accuracy.

This work deals with directional of arrival (DOA) estimation with a large antenna array. We first develop a novel signal model with a sparse system transfer matrix using an inverse discrete Fourier transform (DFT) operation, which leads to the formulation of a structured block sparse signal recovery problem with a sparse sensing matrix. This enables the development of a low complexity message passing based Bayesian algorithm with a factor graph representation. Simulation results demonstrate the superior performance of the proposed method.

Cognitive radio (CR) is a promising technology to address the challenge of spectrum scarcity due to the massive number of objects in the Internet of Things (IoT). Equipping IoT objects with CR capability can also alleviate interference situations and achieve seamless connectivity in IoT. This paper deals with CR spectrum sensing and proposes a new eigenvalue-based detector by exploiting the summation of multiple large eigenvalues of the covariance matrix of received signals. By analyzing the distribution of the sum of the dependent large eigenvalues, we derive an approximate but explicit expression for the theoretical performance of the proposed detector. The theoretical analysis of the proposed detector is validated and its superior performance is demonstrated with real world signals. It is shown that the proposed detector outperforms the existing eigenvalue-based detectors and is more robust against noise uncertainty.

This paper deals with spectrum sensing for cognitive radio-based Internet of Things (IoT) systems and their coexistence with Long Term Evolution (LTE) systems. Due to the sparsity of the covariance matrix of IoT/LTE signals, we reveal that the likelihood ratio test approximates to energy detection (ED) at low signal to noise ratio. However, the noise (power) uncertainty can degrade the performance of ED severely, especially when low-cost IoT devices are employed for spectrum sensing. To tackle this issue, we derive the relationship among noise power, total power, and autocorrelation coefficient of received signals, and propose an unbiased estimator of noise power without the knowledge of the presence/absence of IoT/LTE signals. We then design a new ED with multiple estimates of noise power from historical and current sensing data, and analyze its theoretical performance. Numerical results are provided to verify the theoretical results and demonstrate the superior performance of the proposed detector. It is shown that, by exploiting sufficient historical sensing data, the performance of the proposed ED can closely approach that of the ideal ED.

This work presents a blind detector for sensing orthogonal frequency division multiplexing (OFDM) signals under multiple fading channels. The detector exploits the fact that the AWGN at secondary receivers has a flat power spectral density, while the received OFDM signal under multipath fading channels does not. Closed form expression of the decision threshold is provided. It is shown that the decision threshold is independent of the number of samples. In addition, the proposed detector is robust against frequency offsets. Numerical results demonstrate the superior performance of the proposed detector.

Cooperative spectrum sensing has been studied to combat the hidden terminal problem by exploiting the spatial diversity in cognitive radio (CR) networks. This paper concerns blind cooperative spectrum sensing with soft fusion, where thea prioriknowledge of channels and primary signals is unavailable, and soft information is transmitted from each secondary user (SU) to a fusion center for detection. We first introduce the Quade test to design a blind detector. Then, a new detector with both lower computational complexity and lower overhead is derived, where only the estimated power and the variance of the instantaneous power at each SU are required at the fusion center. The analytical expressions for the detection performance, in terms of false-alarm probability and detection probability, are derived for the proposed detector. Simulation results are provided to validate the theoretical analyses and demonstrate the superior performance of proposed detector compared to the state-of-the-art detectors. It is also shown that, with the increase of the number of hidden terminals in the CR, the proposed detector can maintain high detection performance while the conventional detectors exhibit rapid performance degradation.