The pinching-antenna system (PASS) reconstructs wireless channels through pinching beamforming, i.e., optimizing the activated locations of pinching antennas (PAs) along the waveguide. The aim of this article is to investigate the joint design of baseband beamforming and pinching beamforming. A low-complexity element-wise sequential optimization framework is proposed to address the sum-rate maximization problem in PASS-enabled downlink and uplink channels. i) For the downlink scenario, maximum ratio transmission (MRT), zero-forcing (ZF), and minimum mean square error (MMSE) beamforming schemes are employed as baseband beamformers. For each beamformer, a closed-form expression for the downlink sum-rate is derived as a single-variable function with respect to the pinching beamformer. Based on this, a sequential optimization method is proposed, where the positions of the PAs are updated element-wise using a low-complexity one-dimensional search. ii) For the uplink scenario, signal detection is performed using maximum ratio combining (MRC), ZF, and MMSE combiners. A closed-form sum-rate expression is derived for each linear combiner, and a similar element-wise design is applied to optimize the pinching beamforming. Numerical results are provided to validate the effectiveness of the proposed method and demonstrate that: (i) For all considered linear beamformers, the proposed PASS architecture outperforms conventional fixed-antenna systems in terms of sum-rate performance; (ii) in both downlink and uplink channels, ZF achieves performance close to that of MMSE and significantly outperforms MRT or MRC; and (iii) the proposed element-wise design eliminates the need for alternating updates between the baseband and pinching beamformers, thereby ensuring low computational complexity.

Continuous aperture array (CAPA) is considered a promising technology for 6G networks, offering the potential to fully exploit spatial degrees of freedom (DoFs) and achieve the theoretical limits of channel capacity. This paper investigates the performance gain of a CAPA-based downlink secure transmission system, where multiple legitimate user terminals (LUTs) coexist with multiple eavesdroppers (Eves). The system’s secrecy performance is evaluated using a weighted secrecy sum-rate (WSSR) under a power constraint. We then propose two solutions for the secure current pattern design. The first solution is a block coordinate descent (BCD) optimization method based on fractional programming (FP), which introduces a continuous-function inversion theory corresponding to matrix inversion in the discrete domain. This approach derives a closed-form expression for the optimal source current pattern. Based on this, it can be found that the optimal current pattern is essentially a linear combination of the channel spatial responses, thus eliminating the need for complex integration operations during the algorithm’s optimization process. The second solution is a heuristic algorithm based on zero-forcing (ZF), which constructs a zero-leakage current pattern using the channel correlation matrix. It further employs a water-filling approach to design an optimal power allocation scheme that maximizes the WSSR. In high signal-to-noise ratio regions, this solution gradually approaches the first solution, ensuring zero leakage while offering lower computational complexity. Simulation results demonstrate that: 1) CAPA-based systems achieve better WSSR compared to discrete multiple-input multiple-output (MIMO) systems. 2) The proposed methods, whether optimization-based or heuristic, provide significant performance improvements over existing state-of-the-art Fourier-based discretization methods, while considerably reducing computational complexity.

This paper investigates multi-target position estimation in cell-free massive multiple-input multiple-output (CF mMIMO) architectures, where orthogonal time frequency and space (OTFS) is used as an integrated sensing and communication (ISAC) signal. Closed-form expressions for the Cramér-Rao lower bound and the positioning error bound (PEB) in multi-target position estimation are derived, providing quantitative evaluations of sensing performance. To enhance the overall performance of the ISAC system, a power allocation algorithm is developed to maximize the minimum user communication signal-to-interference-plus-noise ratio while ensuring a specified sensing PEB requirement. The results validate the proposed PEB expression and its approximation, clearly illustrating the coordination gain enabled by ISAC. Further, the superiority of using the multi-static CF mMIMO architecture over traditional cellular ISAC is demonstrated, and the advantages of OTFS signals in high-mobility scenarios are highlighted.

Cell-free architectures with distributed access points (APs) offer great potential for joint communication and sensing, yet they suffer from stringent synchronization requirements for coherent operation. To address this limitation, this paper proposes a cell-free millimeter-wave (mmWave) integrated sensing and communication (ISAC) framework specifically tailored for drone detection. The design leverages a reconfigurable intelligent surface (RIS) to enable a novel delay-synchronized scheme: instead of fully synchronizing every AP, this study only estimates and compensates the propagation delays from each AP to the RIS via an RIS-based closed-loop calibration. This partial synchronization ensures that multi-AP signals arrive with phase aligned at the RIS, enabling coherent sensing beams even for a moving drone. The RIS reflects these combined beams toward the drone, and a generalized likelihood ratio test (GLRT) detector at the receiver processes the returned echoes for drone presence. Simulation results confirm that the proposed delay-synchronized, RIS-assisted approach achieves significantly improved detection accuracy compared to baseline architectures that are RIS-free or unsynchronized, thereby providing a promising solution for robust drone detection in next-generation cell-free networks.

Coexistence of device-to-device (D2D) communication and cell-free massive multiple-input multiple-output (CF-mMIM O) is a promising technology for future wireless systems. It is expected to enhance the spectral efficiency (SE), as well as reduce the transmission delay for user equipments. In this paper, we propose a joint access point (AP) selection and power control scheme to maximize the sum SE, oppositing to the existing paper which performs the power control based on the AP selection and the AP selection is performed by the heuristic algorithm, leading to the performance is limited. To address the non-convex optimization problem, the original problem is decomposed into two subproblems: AP selection and power control, both are all resolved by sequential convex approximation (SCA) method. Furthermore, the penalty method is applied in the AP selection, alleviating the narrowing of feasible region brought by the SCA which results in the feasible solution of the original problem unattainable. At the same time, the convergence prove of corresponding algorithm is presented. Numerical results demonstrate the superior performance compared to the existing scheme in terms of improving the performance of the system.

In this paper, we develop a scalable framework for asynchronous cell-free (CF) massive multiple-input multiple-output (mMIMO) systems with partially coherent transmission. To address the scalability issue, we exploit the dynamic cooperation cluster concept and propose a novel scalable clustering algorithm. Specifically, in the first stage, the users are connected to a subset of access points (APs), aiming to make the users and APs establish the best connection in terms of large-scale fading. Next, the connected APs with the same quantized phase shift are grouped into a set of coherent clusters. By AP clustering, the APs in the same coherent cluster transmit data coherently, while the APs in different clusters transmit data in a non-coherent fashion. This can effectively improve the spectral efficiency of the asynchronous CF mMIMO system while ensuring the requirement of scalability. Finally, numerical results verified the effectiveness of the proposed method.

Applying integrated sensing and communication (ISAC) to a cell-free massive multiple-input-multiple-output (CF mMIMO) architecture has attracted increasing attention. This approach equips CF mMIMO networks with sensing capabilities and resolves the problem of unreliable service at cell edges in conventional cellular networks. However, existing studies on cell-free ISAC (CF-ISAC) systems have focused on the application of traditional integrated signals. To address this limitation, this study explores the employment of the orthogonal time frequency space (OTFS) signal as a representative of innovative signals in the CF-ISAC system, and the system’s overall performance is optimized and evaluated. A universal downlink spectral efficiency (SE) expression is derived regarding multiantenna access points (APs) and optional sensing beams. To streamline the analysis and optimization of the CF-ISAC system with the OTFS signal, we introduce a widely applicable approximation on the achievable SE. Based on this, an AP mode selection algorithm is developed to dynamically adapt to network requirements, and a power allocation algorithm is proposed to maximize the minimum communication signal-to-interference-plus-noise ratio (SINR) of users while ensuring a specified sensing SINR value. The results demonstrate the tightness of the proposed approximation and the efficiency of the proposed algorithms. Finally, the superiority of using the OTFS signals is verified by a 13-fold expansion of the SE performance gap over the application of orthogonal frequency division multiplexing signals. These findings could guide the future deployment of the CF-ISAC systems, particularly in the field of millimeter waves with a large bandwidth.

Detecting vehicles from satellite videos presents several significant challenges: 1) Satellite video frames typically possess extremely high resolutions, often containing millions or even hundreds of millions of pixels, whereas onboard computational resources remain constrained; 2) The non-uniform spatial distribution of vehicles results in inefficient allocation of computational resources; 3) Vehicles are typically small with limited distinguishing features, further complicating the detection task. In this article, we propose LSCNet (Large Scenes to Clusters), an efficient and lightweight satellite video vehicle detection network specifically designed to address these challenges. To alleviate the difficulties associated with large-scale imagery and uneven vehicle distributions, we introduce a plug-and-play Object Cluster Module (OCM). The OCM leverages inter-frame information from satellite video to adaptively identify and prioritize clustered regions, thereby enhancing detection precision. Furthermore, to improve the extraction of discriminative features from small-sized vehicles, we propose a lightweight Multi-frame Feature Aggregation Module (MFAG), which effectively captures the spatiotemporal characteristics of vehicles while maintaining computationally efficiency. Additionally, we refine the regression loss function by integrating the Kullback-Leibler Divergence (KLD), enabling the generation of higher-quality bounding boxes and significantly boosting the detection performance for small objects. Experimental evaluations on the Jilin-1 satellite video dataset demonstrate that the proposed method achieves improved detection accuracy while maintaining real-time performance, thereby validating its robustness and practical effectiveness.

Cell-free (CF) massive multiple-input multiple-output (mMIMO) is a promising technology for enhancing the spectrum efficiency in future wireless systems. It leverages distributed access points (APs) to jointly serve multiple users over the same time-frequency resource. In this paper, we study the uplink performance of CF mMIMO orthogonal frequency division multiplexing (OFDM) wireless systems under asynchronous signal reception. First, we provide a complete and detailed frequency-domain uplink asynchronous signal reception model for CF mMIMO-OFDM systems, which shows that asynchronous reception introduces phase shift, inter-carrier interference (ICI), and inter-symbol interference (ISI) at the receivers. Then, we design the minimum mean square error channel estimation under asynchronous reception. The results indicate that the phase shift, ICI, and ISI destroy the orthogonality of pilot sequences at APs, leading to significant channel estimation error. Furthermore, the FDM pilot sequences prove to be more effective in mitigating the impact of asynchronous reception on channel estimation compared to the CDM pilot sequences. Given the imperfect channel state information caused by pilot contamination and asynchronous reception, we characterize the uplink achievable spectral efficiency under asynchronous reception. The results indicate that severe uplink rate loss mainly arises from phase shift caused by asynchronous reception. Numerical simulation results corroborate our analyses.

Cell-free massive multiple-input–multiple-output (CF mMIMO) is a promising technology to enhance the spectrum efficiency in future wireless systems by leveraging distributed access points (APs) to jointly serve the users over the same time-frequency resource. However, for uplink communications under orthogonal frequency division multiplexing (OFDM), the signals received at different APs from different users are generally asynchronous, due to the varying distances between the APs and users. In this article, we study the performance of CF mMIMO-OFDM wireless system under asynchronous signal reception in terms of uplink channel estimation and achievable spectral efficiency. In particular, we devise the minimum mean square error (MMSE) channel estimation under asynchronous reception and reveal how the phase shift, intersymbol interference, and intercarrier interference introduced by asynchronous reception destroy the orthogonality between different pilot sequences at the APs. In addition, given the imperfect channel state information caused by pilot contamination and asynchronous reception, we obtain closed-form expressions for the uplink achievable rate for both centralized and distributed operations. We show that for both operations, the uplink achievable rates under asynchronous reception are much smaller than those under synchronous reception due to the phase shift caused by asynchronous reception. Last, numerical results demonstrate that under asynchronous reception, the maximum sum-rate loss with MMSE and maximum ratio (MR) combining in centralized and distributed operations reach 29.5%, 4%, 45.4%, and 8%, respectively. This indicates that while MMSE combining is much more sensitive to asynchronous reception than MR combining, it still achieves a higher achievable rate.

Cell-Free Massive MIMO (CF-mMIMO) is regarded as one of the most promising wireless technologies for future applications. It is expected to tackle the inter-cell interference problem with traditional cellular networks in order to achieve high quality of service (QoS). The coexistence of Human-Type Communication (HTC) and Machine-Type Communication (MTC) poses a challenge to today’s and future communication technologies, including CF-mMIMO. However, there have been few studies on the support of CF-mMIMO for HTC/MTC coexistence. In this paper, we present a comprehensive resource allocation mechanism for CF-mMIMO with HTC/MTC coexistence, which consists of two components. First, to handle the device activity detection issue caused by the absence of orthogonal pilot sequence, we propose a pilot assignment scheme based on the continuous method and a device activity detection scheme based on the spatial separation characteristics of CF-mMIMO, aiming at reducing the activity detection error probability. Second, we formulate a multi-objective optimization problem (MOOP) and devise a power control method based on MOOP to deal with the conflict caused by the different service requirements of HTC and MTC. Numerical results show that the proposed pilot assignment scheme outperforms the comparable pilot assignment schemes in terms of activity detection error probability. In addition, combined with the proposed pilot assignment scheme, the devised MOOP power control method leads to higher HTC normalized sum rate and MTC energy efficiency than the comparable power control methods.

Satellite video single object tracking (SVSOT) stands as a pivotal research area. However, it faces significant challenges in low-light environments, particularly when dealing with dark and weak vehicles. Previous studies have predominantly focused on tracking methods under favorable lighting conditions, neglecting the complexities introduced by inadequate illumination. The difficulty in extracting features from targets in low-light environments, coupled with the susceptibility of dark and weak targets to background noise, exacerbates these challenges. In low-light environments, dark and weak vehicles exhibit less distinctive features and are more susceptible to background interference due to the reduced contrast. To tackle the above challenges, this work proposes an innovative correlation filter (CF)-based tracker (RETrack) that incorporates a retinex-inspired target enhancement. This enhancer integrates an effective low-light enhancement within the CF-based tracker, enhancing target visibility by reallocating target energy based on the characteristics of target motion. Moreover, to mitigate background interference and leverage background information efficiently, an adaptive label update mechanism is developed to suppress background disturbance. Furthermore, this work constructs a satellite video low-light tracking benchmark SATDark, which comprises 120 sequences of dark and weak vehicles. Comprehensive experiments show that RETrack surpasses current leading trackers on the SATDark, providing innovative insights and advancing the field of satellite video object tracking. Additionally, RETrack achieves real-time processing speeds exceeding 30 frames/s on a single CPU, underscoring its practical applicability and efficiency.

The newly emerging federated learning (FL) framework offers a new way to train machine learning models in a privacy-preserving manner. However, traditional FL algorithms are based on an event-triggered aggregation, which suffers from stragglers and communication overhead issues. To address these issues, in this paper, we present a time-triggered FL algorithm (TT-Fed) over wireless networks, which is a generalization of classic synchronous and asynchronous FL. Taking the resource-constrained and unreliable nature of wireless networks into account, we jointly consider the user selection and bandwidth optimization problem to minimize the FL training loss. The optimization problem is decomposed into tractable sub-problems with respect to each global aggregation round, and finally solved by our proposed greedy search algorithm. Simulation results show that compared to asynchronous FL (FedAsync) and FL with asynchronous tiers (FedAT) benchmarks, our proposed TT-Fed algorithm improves the converged test accuracy by up to 12.5% and 5%, respectively, under highly imbalanced and non-IID data, while substantially reducing the communication overhead.

User-Centric Cell-Free Massive MIMO (UC CF-mMIMO) is a promising technology to improve the performance of mobile networks. With UC CF-mMIMO, each access point (AP) only provides services for a limited number of users. However, which AP should be selected for a user is still a challenging problem. In this paper, we propose an AP selection strategy for UC CF -mMIMO, which utilizes congestion games to balance user data rate and required AP fronthaul capacity. Our numerical results indicate that the proposed AP selection strategy outperforms the existing approaches in terms of required AP fronthaul capacity while guarantees user data rate.

Satellite videos dynamically monitor the Earth’s surface by using staring imaging, which has gained increased attention and enabled target tracking applications. While various tracking methods are processed on the original video, the rapid light changes due to staring imaging are not considered and have a negative effect on tracking. To reduce the effects of illumination and improve the performance of satellite video target tracking, an intrinsic satellite video decomposition model with motion target energy constraint, called MTE-ISVD, is proposed in this paper. The proposed algorithm introduces two main constraints: The first is a temporal constraint of reflectance, which can solve the flicker problem by preserving reflectance coherence in the time domain with the property that the background pixels in satellite videos are nearly consistent between adjacent frames. The second is a motion target energy constraint, which can concentrate the signal energy of the motion targets in the reflectance by representing them with the surrounding background in the shading. The decomposition problem is reformulated as a quadratic function minimization, which can be addressed using the standard conjugate gradient in closed form. For visual and quantitative comparisons, we perform experiments on five Jilin-1 satellite videos and analyze the results in terms of visual comparison, target tracking improvement, stability evaluation and processing time comparison. The experimental results demonstrate that our proposed method outperforms the other representative intrinsic decomposition methods in terms of processing speed, stability, and motion target representation.

The newly emerging federated learning (FL) framework offers a new way to train machine learning models in a privacy-preserving manner. However, traditional FL algorithms are based on an event-triggered aggregation, which suffers from stragglers and communication overhead issues. To address these issues, in this paper, we present a time-triggered FL algorithm (TT-Fed) over wireless networks, which is a generalized form of classic synchronous and asynchronous FL. Taking the constrained resource and unreliable nature of wireless communication into account, we jointly study the user selection and bandwidth optimization problem to minimize the FL training loss. To solve this joint optimization problem, we provide a thorough convergence analysis for TT-Fed. Based on the obtained analytical convergence upper bound, the optimization problem is decomposed into tractable sub-problems with respect to each global aggregation round, and finally solved by our proposed online search algorithm. Simulation results show that compared to asynchronous FL (FedAsync) and FL with asynchronous user tiers (FedAT) benchmarks, our proposed TT-Fed algorithm improves the converged test accuracy by up to 12.5% and 5%, respectively, under highly imbalanced and non-IID data, while substantially reducing the communication overhead.

At present, sparse Bayesian learning (SBL) is introduced into direction of arrival (DOA) estimation for both coherent and incoherent signals. Instead of directly extending DOA estimation to DOA tracking, we construct an array data model including DOA change of adjacent time to decrease computational complexity and avoid grid effect. Thus, we regard DOA tracking as a parameter estimation problem in terms of Taylor expansion and Bayesian rule. Owing to the existence of hidden variables, we adopt the Expectation Maximization (EM) algorithm to calculate the DOA change. More importantly, we realize DOA tracking by utilizing the estimated signal power and noise power when the number of signal sources varies with time. The proposed method is numerically evaluated with an assumption of uniform linear array. The results show that the proposed algorithm has higher tracking accuracy over conventional methods.

In the cognitive radio scenario, the case of extremely low SNR is often encountered, which incurs a deceasing of detection performance. To circumvent this difficulty, we propose a promising spectrum sensing algorithm by exploiting dictionary learning. In the proposed algorithm, the energy of maximum sparse component acquired by dictionary learning is utilized as a test statistic to improve detection performance as much as possible. More importantly, we extract the maximum sparse component of the received signal via a learned dictionary and the forced sparsity 1 to maximize the SNR of maximum sparse component. Some simulations are carried out to verify theoretical results. Simulation results show that the proposed algorithm overwhelmingly outperforms the sparse-representation-denosing-based algorithm and the conventional energy-based algorithm in the extremely low SNR case.

Since offset QAM-based filter bank multicarrier (FBMC/OQAM) systems has inherent imaginary interference, most channel estimation techniques for FBMC are now scattered auxiliary pilots on slow-fading channels. However, in more demanding scenarios, auxiliary pilots can prove to cause serious mistakes. In this paper, an algorithm of Kalman filter channel estimation based on feedback interference calculation pilot in fast fading channels is proposed. Simulation results show that the proposed method has good performance in time-varying multipath channels. Compared with the traditional channel estimation methods, the proposed method has greatly improved its performance.

As the blind zone phenomenon becoming more and more common in the next generation communication system, the blind zone recognition technique has gain much attention. One big challenge in blind zone recognition is the fluctuation of received signal strength (RSS) especially in indoor environment where multipath effect is severe. Previous methods using median filter are under the improper hypothesis which results in unsatisfying results. Moreover, traditional blind zone recognition methods mainly focus on outdoor environment and are with the characteristics of low efficiency, inflexibility, and high cost. In this paper, we propose a new algorithm based on fractional Fourier transform to address the RSS fluctuation and solve the problem of blind zone recognition in indoor environment. The comparison to our method and median filter method shows that our method provides much more accurate results.

Scattered pilot-aided channel estimation in offset QAM-based filter bank multicarrier (FBMC/OQAM) systems has so far been only considered for slow fading channels. In more demanding scenarios, the classical auxiliary pilot (AP) idea has been shown to result a severe error. This paper proposes novel channel estimation techniques for FBMC/OQAM systems for fast fading channels. Unlike the conventional auxiliary pilot, the FBMC/OQAM systems' transmitter adopts the proposed comb pilot arrangement mode based on auxiliary pilot. Meanwhile feedback interference estimation algorithm and feedback interference calculation algorithm are proposed at receiver, which can eliminate the imaginary interference of all the pilot symbols. Numerical results show that the FBMC/OQAM systems with the proposed channel estimation algorithms based on the proposed pilot have a better bit error rate (BER) performance compared to that with the auxiliary pilot algorithm. And compared with AP, the greater the Doppler frequency is, the greater performance superiority the proposed channel estmator achieves.

Ultra dense network (UDN) can satisfy the explosive data rate demand in 5G with the high frequency reuse and dense deployment of small-cell base stations (BSs). However, the co-channel interference arised from the aforementioned properties influences the performance of the communication system severely. In this paper, a gossip based distributed power control (GBDPC) algorithm is proposed to overcome the co-channel interference in UDNs in a distributed manner. The proposed algorithm utilizes a broadcast gossip method to realize a consensus with the received SINR values from all the BSs in a digraph communication topology distributedly. The stability and convergence rate of the GBDPC algorithm are investigated by quadratic Lyapunov functions. The simulation results verify the theoretical analysis, and the further investigation shows the promotion on the rate of convergence when the connection weight factor is set to approximate the maximum out-degree of the digraph.

Wireless sensor network (WSN) technology provides support infrastructure to solve blind zone coverage. Previous studies mainly focus on deployment and management of sensor nodes for enhancing signal coverage on a specific zone. But few of them are on coordinating several nodes to recognize blind zone. Moreover, it is a great challenge how to extract, transmit and process information energy efficiently in a decentralized way. In this paper, we propose a distributed algorithm for convenient and efficient blind zone recognition based on the early work on the unbiased broadcast gossip algorithm (UBGA) and the compressed sensing. Using UBGA, a faster consensus algorithm, the system can simultaneously compute the compressed data based on compressed sensing technique and disseminate them throughout the network. From these compressed data one can reconstruct the original data with approximate accuracy by querying any sensor values using the gradient projection strategy. Simulation results illustrate that our solution, proposed in this paper, can effectively recognize blind zone.

Supporting trunking multimedia services in TD-LTE public networks is becoming a research innovation to provide effective multimedia services for trunking mobile users. In previous research, we have designed a downlink packet scheduler named discrete-time linear control with priority (DTLCP) algorithm, considering both QoS requirements and priority of user equipment (UE). DTLCP could guarantee QoS performance of trunking multimedia services for high-priority users. However, DTLCP is significantly unfair to low-priority users. It is definitely possible to occur that low-priority users failing of obtaining radio resources for a long time and they starve to death, especially when the cellular networks overload or the traffic is massive. To overcome the shortcoming of DTLCP algorithm, an innovative optimized algorithm is proposed in this paper. Additionally, an intelligent priority proportional fair (IP/PF) scheduler has been properly designed. The performance of the proposed scheme has been evaluated by theory and simulations. A comparison with recently proposed scheduling strategies has also been presented. Results have clearly shown that the proposed approach is able to improve the performance of DTLCP algorithm in fairness.

In this letter, we present an eavesdropping-based gossip algorithm (EBGA). In the novel algorithm, when a node unicasts its values to a randomly selected neighboring node, all other nodes, which eavesdrop these values, simultaneously update their state values. By exploiting the broadcast nature of wireless communications, this novel algorithm has similar performance to broadcast gossip algorithms. Although broadcast gossip algorithms have the fastest rate of convergence among all gossip algorithms, they either converge to a random value rather than the average consensus, or need out-degree information available for each node to guarantee convergence to the average consensus. Utilizing non-negative matrix theory and ergodicity coefficient, we have proved that this novel algorithm can converge to the average consensus without any assumption which is difficult to be realized in real networks.