The Channel Knowledge Map (CKM) is an emerging technology for enabling future integrated sensing and communication (ISAC) services that have attracted significant research interests in recent years. Current methods for constructing CKM primarily include interpolation-based, model-based, and machine learning algorithms. However, these methods are often limited by their low estimation accuracy or high computational complexity. To address these challenges, we propose a novel approach to construct CKM based on Environment Partitioning and Interpolation Weight Learning (EPIWL). The proposed method leverages channel state information from partially known locations to interpolate and estimate the channel state in the target region, thus completing the CKM construction. To reduce computational complexity, we proposed a Graph Feature Aggregation and Community Detection based Partitioning (GFA-CD-P) algorithm, which selects representative anchor points through environmental partitioning, thereby decreasing the computational load. Furthermore, we propose an interpolation weight learning scheme based on Kolmogorov–Arnold Network (KAN) and Multi-Head Cross Attention (MHCA), namely KM-IWL algorithm, which automatically learn the weight between anchor points and target points, enhancing both the efficiency and accuracy of CKM construction. The experimental results demonstrate that the proposed EPIWL approach achieves a 10 dB improvement in normalized mean squared error (NMSE) and reduces computational complexity by over 60% compared with existing schemes, showcasing robust performance and excellent generalization capability.

Frequency Modulated Continuous Wave (FMCW)-based mmWave radar has attracted widespread attention due to its non-contact and high spatial resolution for multi-target vital signs monitoring. However, current works mostly focus on how to improve detection performance under the steady scenarios with a single target, while unsteady scenarios and physical mutual interference from multiple targets are rarely considered. In this work, we propose an innovative method for multi-target vital signs for unsteady scenarios, such as aerobic exercise. The method automatically distinguishes between steady state and motion state, and completes a best-effort vital signs detection during unsteady state. To differentiate multiple targets, we design a weight vector enhancement method combined with target space localization, and then, apply the variational mode decomposition (VMD) algorithm to extract the respiratory and heart rates of a single target. Moreover, for evaluating the efforts of exercise, we propose Multi-target Motion Recognition (MMR) based on MobileNet-V2 network to recognize the motion states of multiple targets. The experimental results showed that the mean absolute error of respiratory rate and heart rate decreased to 1.37 bpm and 2.56 bpm, respectively. Meanwhile, the MMR algorithm achieves close to 98.1% accuracy in recognizing motion states.

In this work, we investigate a reconfigurable intelligent surface (RIS) aided integrated sensing and communication scenario, where a base station (BS) communicates with multiple devices in a full-duplex mode, and senses the positions of these devices simultaneously. An RIS is assumed to be mounted on each device to enhance the reflected echoes. Meanwhile, the information of each device is passively transferred to the BS via reflection modulation. We aim to tackle the problem of joint localization and information retrieval at the BS. A grid based parametric model is constructed and the joint estimation problem is formulated as a compressive sensing problem. We propose a novel message-passing algorithm to solve the considered problem, and a progressive approximation method to reduce the computational complexity involved in the message passing. Moreover, an expectation-maximization (EM) algorithm is applied for tuning the grid parameters, hence mitigating the model mismatch problem. Finally, we analyze the efficacy of the proposed algorithm through the Bayesian Cramér-Rao bound. Numerical results demonstrate the feasibility of the proposed scheme and the superior performance of the proposed EM-based message-passing algorithm.

In this work, we consider a massive random access (RA) scenario, where massive single-antenna users access a base station (BS) equipped with a large number of antennas. If orthogonal RA protocols are employed, massive collisions will occur due to the limited number of orthogonal preambles given the preamble sequence length. To alleviate this problem, we propose an expectation-maximization-based delay-calibrated user activity detection algorithm, and investigate the benefits of oversampling for accurate delay estimation at the BS. The proposed algorithm alternately estimates the delay and detects active users by noting that the collided users have different transmission delays. The user activity detection problem can be formulated as a compressive sensing (CS) problem due to the sporadic activity patterns. We present the multiple measurement vector (MMV) version of Turbo-CS to solve this problem with considering the noise correlations due to oversampling. Moreover, a greedy search-based delay calibration method is proposed for the estimation of the transmission delay. Numerical results demonstrate the superior performance of the proposed algorithm in terms of the probability of misdetection and the normalized mean square error of time delay.

Low-resolution analog-to-digital converters (ADCs) are promising for reducing energy consumption and costs of multiuser multiple-input multiple-output (MIMO) systems with many antennas. We propose low-resolution multiuser MIMO receivers where the signals are simultaneously processed by 1-bit ADCs and a comparator network, which can be interpreted as additional virtual channels with binary outputs. We distinguish the proposed comparator networks in fully and partially connected. For such receivers, we develop the low-resolution aware linear minimum mean-squared error (LRA-LMMSE) channel estimator and detector according to the Bussgang theorem. We also develop a robust detector which takes into account the channel state information (CSI) mismatch statistics. By exploiting knowledge of the channel coefficients we devise a mean-square error (MSE) greedy search and a sequential signal-to-interference-plus-noise ratio (SINR) search for optimization of partially connected networks. Numerical results show that a system with extra virtual channels can outperform a system with additional receive antennas, in terms of bit error rate (BER). Furthermore, by employing the proposed channel estimation with its error statistics, we construct a lower bound on the ergodic sum rate for a linear receiver. Simulation results confirm that the proposed approach outperforms the conventional 1-bit MIMO system in terms of BER, MSE and sum rate.

In this work, we investigate a reconfigurable intelligent surface (RIS) aided integrated sensing and communication (ISAC) scenario, where a base station (BS) communicates with multiple devices in a full-duplex mode, and senses the positions of these devices simultaneously. An RIS is assumed to be mounted on each device to enhance the reflected echoes. Meanwhile, the information of each device is passively transferred to the BS via reflection modulation. We aim to tackle the problem of joint localization and information retrieval at the BS. A grid based parametric model is constructed and the joint estimation problem is formulated as a compressive sensing (CS) problem. Moreover, an expectation-maximization (EM) algorithm is applied for tuning the grid parameters to mitigate the model mismatch problem. Finally, we analyze the efficacy of various CS algorithms through the Bayesian Cramér-Rao bound (BCRB). Numerical results demonstrate the feasibility of the proposed scenario and the superior performance of the proposed EM-tuning method.

In this work, large-scale multiple-antenna systems are investigated, where the base station employs a large antenna array with low-cost and low-power 1-bit analog-to-digital converters. To compensate for the performance loss caused by the coarse quantization, oversampling is applied at the receiver. Unlike existing works that use uniform oversampling, which samples the signal at a constant rate, a novel dynamic oversampling scheme is proposed. The basic idea is to perform time-varying nonuniform oversampling, which selects samples with nonuniform patterns that vary over time. We consider two system design criteria: a design that maximizes the achievable sum rate and another design that minimizes the mean square error of detected symbols. Dynamic oversampling is carried out using a dimension reduction matrix Δ, which can be computed by the generalized eigenvalue decomposition or by novel submatrix-level feature selection algorithms. Moreover, the proposed scheme is analyzed in terms of convergence, computational complexity and power consumption at the receiver. Simulations show that systems with the proposed dynamic oversampling outperform those with uniform oversampling in terms of computational cost, achievable sum rate and symbol error rate performance.

In this paper, we propose a dynamic oversampling technique for asynchronous large-scale multiple-antenna systems with 1-bit analog-to-digital converters at the base station that is suitable for sustainable internet of things and cellular networks. To the best of our knowledge, this is the first paper to introduce a dynamic oversampling technique for such systems. The main idea is to sample the received signal at a higher rate and only few weighted samples are chosen for further signal processing. We apply the generalized eigenvalue decomposition algorithm for linearly combining the samples and performing dimension reduction. We investigate the proposed technique in terms of the Bussgang theorem based sum rate capacity. Numerical results show that with the proposed dynamic oversampling technique the system can use small number of processing samples to achieve the same sum rates as the standard uniform oversampling technique while maintaining the same power consumption.

Large-scale multiple-antenna systems have been identified as a promising technology for the next generation of wireless systems. However, by scaling up the number of receive antennas the energy consumption will also increase. One possible solution is to use low-resolution analog-to-digital converters at the receiver. This paper considers large-scale multiple-antenna uplink systems with 1-bit analog-to-digital converters on each receive antenna. Since oversampling can partially compensate for the information loss caused by the coarse quantization, the received signals are firstly oversampled by a factor M. We then propose a low-resolution aware linear minimum mean-squared error channel estimator for 1-bit oversampled systems. Moreover, we characterize analytically the performance of the proposed channel estimator by deriving an upper bound on the Bayesian Cramér-Rao bound. Numerical results are provided to illustrate the performance of the proposed channel estimator.

Low‐density parity‐check (LDPC) codes have excellent performance for a wide range of applications at reasonable complexity. LDPC codes with short blocks avoid the high latency of codes with large block lengths, making them potential candidates for ultra reliable low‐latency applications of future wireless standards. In this work, a novel informed dynamic scheduling (IDS) strategy for decoding LDPC codes, denoted reliability‐based residual belief propagation (Rel‐RBP), is developed by exploiting the reliability of the message and the residuals of the possible updates to choose the messages to be used by the decoding algorithm. A different measure for each iteration of the IDS schemes is also presented, which underlies the high cost of those algorithms in terms of computational complexity and motivates the development of the proposed strategy. Simulations show that Rel‐RBP speeds up the decoding at reduced complexity and results in error rate performance gains over prior work.