This work explores signal transmission for serving multiple communication users (CUs) while simultaneously estimating multiple targets. In this context, the multi-stream signals intended for downlink CUs equipped with multiple antennas are also employed as probing signals for target estimation. We consider precoding design to ensure quality of service, quantified by the CUs’ individual rates and the mean squared error in target estimation. We develop path-following computational procedures that generate a sequence of improved feasible points by iterating closed-form expressions, ensuring convergence. As a byproduct, a computational solution for estimating the targets’ response vectors or their reflection coefficients and angles manifests, addressing long-standing open problems in estimation theory. Computational experiments not only demonstrate their consistency but also reveal that the obtained precoders produce highly directionally selective beampatterns toward the targets, even though this is not a primary objective.

This work investigates the integration of multi-target inference and multiuser communication in an active reconfigurable intelligent surface (aRIS)-assisted network. To infer the targets’ elevation and azimuth pairs and reflection coefficients from signals transmitted by a base station and reflected by the aRIS, which form computationally intractable nonlinear models, we develop a constructive minimum mean square error (MMSE) estimator based on their probability distribution functions. The resulting MSE is expressed analytically as a deterministic function of the probing signal, enabling its optimization. We then formulate the problem of jointly designing a beamformer and the aRISs power-amplified reconfigurable elements to ensure both accurate target inference and fair user rates. A computational program using closed-form updates is developed. Numerical results demonstrate a flexible trade-off between inference accuracy and achieved user rates.

The bistatic Integrated Sensing and Communication (ISAC) is poised to become a key application for next generation communication networks (e.g., B5G/6G), providing simultaneous sensing and communication services with minimal changes to existing network infrastructure and hardware. However, a significant challenge in bistatic cooperative sensing is clock asynchronism, arising from the use of different clocks at far separated transmitters and receivers. This asynchrony leads to Timing Offsets (TOs) and Carrier Frequency Offsets (CFOs), potentially causing sensing ambiguity. Traditional synchronization methods typically rely on static reference links or GNSS-based timing sources, both of which are often unreliable or unavailable in UAVbased bistatic ISAC scenarios. To overcome these limitations, we propose a Time-Varying Offset Estimation (TVOE) framework tailored for clock-asynchronous bistatic ISAC systems, which leverages the geometrically predictable characteristics of the Line-of-Sight (LoS) path to enable robust, infrastructure-free synchronization. The framework treats the LoS delay and the Doppler shift as dynamic observations and models their evolution as a hidden stochastic process. A state-space formulation is developed to jointly estimate TO and CFO via an Extended Kalman Filter (EKF), enabling real-time tracking of clock offsets across successive frames. Furthermore, the estimated offsets are subsequently applied to correct the timing misalignment of all Non-Line-of-Sight (NLoS) components, thereby enhancing the high-resolution target sensing performance. Extensive simulation results demonstrate that the proposed TVOE method improves the estimation accuracy by 60%.

Utilizing communication signals to extract motion parameters has emerged as a key direction in Vehicle-to-Everything (V2X) networks. Accurately modeling the relationship between communication signals and sensing performance is critical for the advancement of such systems. Unlike prior work that relies primarily on qualitative analysis, this paper derives the Cramér-Rao Bound (CRB) for radar parameter estimation in the context of Orthogonal Frequency Division Multiplexing (OFDM) waveforms and Uniform Planar Array (UPA) configurations. Recognizing that vehicles may act as extended targets, we propose two New Radio (NR)-V2X-compatible beamforming schemes tailored to different phases of the communication process. During the initial beam establishment phase, we develop a beamforming approach based on the union of predictive error ellipses, which enhances scatterer localization through temporally assisted beam training. In the beam adjustment phase, we introduce an adaptive narrowest-beam strategy that leverages the positions of scatterers and the communication receiver (CR), enabling effective tracking with reduced complexity. The beam design problem is addressed using the minimum enclosing ellipse algorithm and tailored antenna control methods. Simulation results validate the proposed approach, showing up to a 32.4% improvement in achievable rate with a 32×32 transmit antenna array and a 5.2% gain with an 8×8 array, compared to conventional beam sweeping under identical SNR conditions.

In this paper, we investigate the precoding design for multi-input multi-output (MIMO) integrated sensing and communication (ISAC) systems with the assistance of constructive interference (CI). Considering low-resolution digital-to-analog converter (DAC) and low-resolution phase shifter (PS) as two efficient hardware options, we propose corresponding finite-alphabet precoding schemes based on them. The formulated optimization problem aims at maximizing a weighted objective function consisting of two parts: the minimum CI scaling factor for communications and target illumination power for radar sensing. The cross-entropy optimization (CEO) framework is employed to effectively solve this discrete non-convex optimization problem. Simulation results have been implemented to validate the superiority of the proposed algorithms. When the number of elements in the quantization sets is fixed, the ISAC performance of the precoding scheme based on DAC quantization is superior to that of the precoding scheme based on PS quantization, thanks to the more dispersed level distribution of DAC quantization.

In this paper, we investigate a full-duplex (FD) UAV-assisted multi-user system under multiple malicious jammers and propose a robust online scheme for secure communication with mobile downlink and uplink users. A random mobility model is adopted to simulate user movement, and the problem is formulated as a two-stage online optimization framework comprising a present-point and a prediction-point problem. To tackle the non-convexity, we develop inner-approximation algorithms using successive convex approximation (SCA) and the S-procedure. Simulation results verify the effectiveness of the proposed method.

With the rapid development of Internet of Things (IoT) technology, interconnectivity between devices has become increasingly widespread. However, traditional IoT security measures struggle to cope with increasingly complex security threats and cannot fully exploit the advantages of interconnectivity due to the limited computational resources of the devices. To address this, we propose a blockchain-based IoT security framework comprising wallet component, smart contract component, multilayer security component, and common component. This framework, designed for resource-constrained environments and embedded into cellular communication modules, enables multiend offloading of computational tasks and secure transmission for IoT devices. Experimental results show that the data processing capability of the decentralized network architecture based on this framework is improved by 115.06% compared to traditional methods, enhances the security and autonomy of IoT devices, and significantly strengthens the degree of IoT decentralization. This provides a valuable reference for designing next-generation IoT security architectures.

The rapid development of low altitude Unmanned Aerial Vehicles (UAVs) as a new mode of transportation has injected a new driving force into the market development, but at the same time, unreported “black flight” UAVs have also created new risks in civil aviation safety, citizen privacy protection and other social security areas. In this regard, the Integrated Sensing And Communication (ISAC) capability of Base Station (BS) can provide an effective means of communication and supervision of low-altitude UAVs. For example, by demarcating the electronic fence area, the ISAC BS can realize automatic detection of illegal invasion of UAVs, effectively guaranteeing low-altitude safety in the context of low-altitude economy. By leveraging the high mobility of UAVs and their strong air-ground Line-of-Sight (LoS) channels, UAV-enabled ISAC is anticipated to provide superior sensing and communication coverage, and enhanced sensing and communication performance compared to terrestrial ISAC. However, existing work mainly focus on single BS sensing with the assistance of communication, which may not fully activate ISAC’s potential and achieve high-precision long-range sensing. Given the above considerations, this paper provides a cellular-connected UAV system, where the BS and connected UAV are employed to perform cooperative detection tasks for precise detection. To unleash the potential of ISAC in cellular-connected UAV systems, on the one hand, we propose an Extended Kalman Filtering (EKF) based data fusion algorithm to provide precise environment information and achieve beyond LoS sensing. On the other hand, according to the fusion results, we optimize the communication rate performance by jointly designing the transmit beamforming and trajectory subject to the power and practical fight constraints to combat the effect of mobility, while ensuring the sensing requirements, which can achieve a positive feedback loop. Extensive simulation results demonstrate that the proposed data fusion algorithm improves the estimation accuracy by 67% and the joint design of beamforming and trajectory algorithm improves the communication data rate by more than 31%.

Millimeter wave (MmWave) has become the most promising candidate to enable multi-Gbps transmission and high-precision detection due to the large available bandwidth. Because of the serious propagation attenuation, it is necessary for a vehicle-to-infrastructure (V2I) system communicating in the mmWave band to overcome severe path loss by utilizing beamforming technology. However, swift and accurate beam alignment at the transceivers is challenging when considering user mobility and fast-varying wireless environment. In this paper, we propose an Adaptive Discounted Thompson Sampling (ADTS) based beam alignment algorithm without any prior information such as coarse user location information, which ignores historical observations made beyond the past time periods and avoids misleading for the current state. Besides, we conduct performance analysis to determine the achievable performance bound of the proposed algorithm. Using simulation results, we show that our proposed algorithm achieves good performance in terms of the average effective achievable rate and the beam alignment accuracy without prior knowledge.

Millimeter-wave Integrated Sensing and Communications (ISAC) with multi-beam design holds significant promise for vehicular networks, offering multi-target omnidirectional sensing and high-capacity communication services concurrently. Nonetheless, the considerable challenge of potential mutual interference arises due to the high mobility and density of transmitters in such networks. To address this challenge effectively, we propose leveraging inter-vehicle communication to schedule communication and sensing signals for vehicles, thereby enhancing networked sensing capabilities. We first introduce an analytical framework to characterize the mutual interference among multiple vehicles. Subsequently, we evaluate the effectiveness of our proposed interference mitigation method in terms of interference probability, duration, and the achievable detectable density. Additionally, recognizing the different performance requirements of communication and sensing functions, we investigate a joint resource allocation problem catering to both aspects. Simulation results demonstrate a notable enhancement in the proposed ISAC-based interference mitigation, with a 58% reduction in interference probability compared to benchmarking schemes.

Millimeter automotive radar has great potential in advanced driver assistance systems (ADASs) to enable safety features, such as adaptive cruise control and collision avoidance. However, with widely deployment of millimeter radars on vehicles, the risk of radar mutual interference becomes a major factor limiting the high performance of radar detection. In this article, we analyze the mutual interference among multiple frequency modulated continuous wave (FMCW) radars. On the one hand, we study the interference in detail by considering co-channel interference (CCI) and adjacent channel interference (ACI) simultaneously. Besides, the CCI is analyzed by employing stochastic geometry model while the ACI is assessed by the deterministic analysis method. On the other hand, we propose a time-frequency division multiple access (TFDMA) scheme to mitigate the interference in a coordinated manner and evaluate it in terms of mitigation delay, the probability of interference, effective detectable density, maximum number of interference-free radar, and control signaling overhead. Finally, we study the power allocation strategy to enable the effectiveness of the coordinated interference mitigation approach based on the interference analysis. Simulation results verify the proposed framework for interference analysis by employing Monte Carlo method, and the performance improvement of the coordinated interference mitigation approach is 3.5 dB.

We consider the degrees of freedom (DoF) of the 2 × 2 × 2 multiple-input multiple-output (MIMO) interference network with arbitrary antenna configuration and practical rank-deficiency. Different from existing researches on the 2 × 2 × 2 MIMO interference model, the antenna number can be arbitrarily different at the transmitters, relays and receivers simultaneously, and the ranks of the channel matrices can be arbitrarily different. The exact DoF sum of this scenario is obtained. For different configurations of antennas and ranks, the outer bound is proved mainly by the min-cut outer bound and the achievability is presented with effective schemes, which combine zero-forcing (ZF) scheme, ZF over broadcast channel, interference alignment in X channel, and aligned interference neutralization (AIN) to match the outer bound. The traditional AIN scheme is modified in this paper to maximize the utilization of signal space overlap. Some related earlier works can be seen as special cases of our research.

Adaptive modulation and coding (AMC) scheme suffers performance degradation with the signaling feedback delay, as channel state information (CSI) is outdated with time variant channel. In this paper, we propose a robust AMC scheme to mitigate the effect of signaling feedback delay, especially in vehicular environments. Channel information estimated in current time interval is utilized to obtain the distribution of channel fading in upcoming time interval. Average block error rate (BLER) for given modulation and coding scheme (MCS) is derived. Simulation results indicate that the proposed scheme guarantees reliable performance even in a vehicular environment with maximum Doppler frequency shift of 300 Hz.

In this paper the optimal threshold is proposed to derive channel impulse response on the multi-path taps and eliminate noise on the other taps. The choice of threshold, which is crucial for the accuracy of the estimator, is analyzed theoretically and the optimal threshold introducing the least MSE is derived. Simulation results show that the performance of our proposed channel estimation with the optimal threshold has significant improvement over the conventional channel estimation algorithm.

In this paper, the performance of discrete Fourier transform (DFT) based channel estimation (CE) for orthogonal frequency division multiplexing (OFDM) systems with transmit antenna diversity is analyzed. Conventional DFT-based CE suffers from the dispersive distortion of an estimated channel impulse response (CIR) due to the existence of virtual carriers (VCs). We analyze the mean squared error (MSE) performance and provide a concise expression which reveals the leakage effect due to VCs. Further, analytical expression for bit error rate (BER) with the effect of VCs is also derived. Simulation results illustrate the accuracy of the theoretical analysis.

In this contribution, an optimal inter-antenna and subblock shifting and inversion (IASSI) scheme exploiting additional degrees of freedom brought by multiple antennas and subblocks is proposed to alleviate peak-to-average power ratio (PAPR) problem of MIMO-OFDM systems. In order to reduce the complexity of proposed scheme, two suboptimal schemes based on sequential search and transformation are presented. Besides, combination of IASSI and matrix transform is proposed, which takes advantage of low autocorrelation characteristic of constant amplitude zero autocorrelation (CAZAC) sequence to further reduce PAPR. Simulation results show that IASSI improves PAPR reduction performance compared with existing PAPR reduction scheme. And when combined with orthogonal matrix transform utilizing CAZAC sequence, further gain can be obtained. All these proposed methods provide flexible tradeoff between satisfied PAPR reduction performance and cost of implementation.

A new noise variance estimation algorithm is presented for a multiuser OFDM system. In this estimator , the noise variance is derived from the traditional channel estimation results and no additional system cost is needed. Its performance is analyzed theoretically. The computational complexity is small and its performance is robust to noise power. Simulation results show that under multi-path fading channel the proposed estimator can obtain accurate real time measurements of the noise variance.

In this paper, we propose a low complexity scheme for adaptive MIMO-OFDM system. A new method for calculating the process gain is developed to reduce the complexity and avoid pseudo inverse operations in the transmitter. The computation method is derived for the adaptive V-BLAST architecture which is based on the ZF-SIC detection algorithm and adaptive modulation and bit loading algorithm. The proposed process gain algorithm is proved equal to the ZF-SIC based process gain computation algorithm. So the proposed algorithm can achieve the same result as the ZF-SIC algorithm, which is proved through simulation methods in the paper. Also the complexity analysis is presented.