Millimeter wave (MmWave) communications is capable of supporting multi-gigabit wireless access thanks to its abundant spectrum resource. However, severe path loss and high directivity make it vulnerable blockage events, which can be frequent in indoor dense urban environments. To address this issue, paper, we introduce intelligent reflecting surface (IRS) as a new technology provide effective reflected paths enhance the coverage mmWave signals. In framework, study joint active passive...

In this paper, we consider channel estimation for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) systems, where an IRS is deployed to assist the data transmission from base station (BS) a user. It shown that purpose of joint active and passive beamforming, knowledge large-size cascade matrix needs be acquired. To reduce training overhead, inherent sparsity in mmWave channels exploited. By utilizing properties Katri-Rao Kronecker products, find sparse representation...

In this paper, we consider the problem of detecting a primary user in cognitive radio network by employing multiple antennas at receiver. vehicular applications, radios typically transit regions with differing densities users. Therefore, speed detection is key, and so, based on small number samples particularly advantageous for applications. Assuming no prior knowledge user's signaling scheme, channels between user, variance noise seen generalized likelihood ratio test (GLRT) developed to...

We consider the problem of downlink channel estimation for millimeter wave (mmWave) MIMO-OFDM systems, where both base station (BS) and mobile (MS) employ large antenna arrays directional precoding/beamforming. Hybrid analog digital beamforming structures are employed in order to offer a compromise between hardware complexity system performance. Different from most existing studies that concerned with narrowband channels, we wideband mmWave channels frequency selectivity, which is more...

Mobile relaying has aroused great interest in wireless communications recently, thanks to the rapid development and evolvement of unmanned aerial vehicles. This letter establishes utility mobile facilitating secure communications. In particular, we consider transmit optimization a four-node (source, destination, buffer-aided relay, eavesdropper) channel setup, wherein aim at maximizing secrecy rate this system. However, maximization problem is nonconvex intractable solve. To circumvent...

We consider the problem of recovering block-sparse signals whose cluster patterns are unknown a priori. Block-sparse with nonzero coefficients occurring in clusters arise naturally many practical scenarios. However, knowledge block partition is usually unavailable practice. In this paper, we develop new sparse Bayesian learning method for recovery patterns. A pattern-coupled hierarchical Gaussian prior introduced to characterize pattern dependencies among neighboring coefficients, where set...

Terahertz (THz) communications open a new frontier for the wireless network thanks to their dramatically wider available bandwidth compared current micro-wave and forthcoming millimeter-wave communications. However, due short length of THz waves, they also suffer from severe path attenuation poor diffraction. To compensate THz-induced propagation loss, this paper proposes combine two promising techniques, viz., massive multiple input output (MIMO) intelligent reflecting surface (IRS), in...

We consider the problem of spectrum sharing in a cognitive radio system consisting primary user and secondary user. The work non-cooperative manner. Specifically, is assumed to update its transmit power based on pre-defined control policy. does not have any knowledge about user's power, or strategy. objective this paper develop learning-based method for order share common with To assist user, set sensor nodes are spatially deployed collect received signal strength information at different...

In this paper, we propose a new channel estimation scheme for TDD/FDD massive MIMO systems by reconstructing (sometimes also referred to as covariance computing or fitting) uplink/downlink matrices (CCMs) with the aid of array signal processing techniques. Specifically, angle parameters and power angular spectrum (PAS) are extracted from instantaneous uplink state information (CSI). Then, CCM is reconstructed can be used improve without any additional training cost. By virtue reciprocity...

For most existing spectrum sensing detectors, the design of their test statistics relies on certain signal-noise model assumptions and hence, detection performance heavily depends accuracy assumed models. Therefore, recently, much attention in research is focused deep learning which free from assumptions. Note that, learning, convolutional neural networks (CNNs) long-short term memory (LSTM) have powerful capabilities extracting spatial temporal features input, respectively. In this letter,...

Recently, intelligent reflecting surface (IRS) has emerged as an appealing technique that enables wireless communications with low hardware cost and power consumption. In this letter, we consider IRS-assisted point-to-point multi-input multi-output (MIMO) system, where a source communicates its destination the help of IRS. Our goal is to maximize spectral efficiency system by jointly optimizing (active) precoding at (passive) phase shifters (PSs) However, turns out be intractable mixed...

In this paper, we consider the problem of joint waveform and passive beamforming design for MIMO integrated sensing communication (ISAC) systems, where a reconfigurable intelligent surface (RIS) is deployed to assist downlink communication. The objective maximize signal-to-interference-and-noise-ratio (SINR) radar meanwhile minimizing multi-user interference To address problem, block coordinate descent (BCD) method proposed. Specifically, given fixed waveform, an Element-wise Closed-Form...

Conventional compressed sensing theory assumes signals have sparse representations in a known dictionary. Nevertheless, many practical applications such as line spectral estimation, the sparsifying dictionary is usually characterized by set of unknown parameters continuous domain. To apply conventional technique to applications, parameter space has be discretized finite grid points, based on which “nominal dictionary” constructed for signal recovery. Discretization, however, inevitably...

We consider the problem of channel estimation for millimeter wave (mmWave) systems, where, to minimize hardware complexity and power consumption, an analog transmit beamforming receive combining structure with only one radio frequency chain at base station mobile is employed. Most existing works mmWave exploit sparse scattering characteristics channel. In addition sparsity, channels may exhibit angular spreads over angle arrival, departure, elevation domains. this paper, we show that give...

Channel estimation is challenging for millimeter-wave massive MIMO with hybrid precoding, since the number of radio frequency chains much smaller than that antennas. Conventional compressive sensing based channel schemes suffer from severe resolution loss due to angle quantization. To improve accuracy, we propose an iterative reweight-based superresolution scheme in this paper. By optimizing objective function through gradient descent method, proposed can iteratively move estimated...

We propose DGG: Deep clustering via a Gaussian-mixture variational autoencoder (VAE) with Graph embedding. To facilitate clustering, we apply Gaussian mixture model (GMM) as the prior in VAE. handle data complex spread, graph Our idea is that information which captures local structures an excellent complement to deep GMM. Combining them facilitates network learn powerful representations follow global and structural constraints. Therefore, our method unifies model-based similarity-based...

Hybrid beamforming is a promising low-cost solution for large multiple-input multiple-output systems, where the base station equipped with fewer radio frequency chains. In these selection of codewords analog essential to optimize uplink sum rate. this paper, based on machine learning, we propose data-driven method beam achieve near-optimal rate low complexity, which highly dependent training data. Specifically, take problem as multiclass-classification problem, dataset consists number...

Grant-free non-orthogonal multiple access has recently gained significant attention for reducing signaling overhead in machine-type communications. In this context, compressed sensing (CS) been identified as a good candidate joint activity and data detection due to the inherent sparsity nature of user activity. This paper augments frame-based multi-user uplink scenarios where users are (in)-active duration frame, namely, frame-wise model. First, we formulate block CS (BCS)-based sparse...

Grant-free non-orthogonal multiple access is an emerging research topic in machine-type communications, which used to reduce signaling overhead. In this context, letter introduces a novel joint channel estimation (CE) and multiuser detection (MUD) framework for the frame based multi-user transmission scenario where users are (in)active duration of frame. First, considering inherent frame-wise sparsity pilot data phases entire frame, we formulate measurement vector-compressive sensing...

We consider the problem of uplink channel estimation for millimeter wave (mmWave) systems, where base station (BS) and mobile stations (MSs) are equipped with large antenna arrays to provide sufficient beamforming gain outdoor wireless communications. Hybrid analog digital structures employed by both BS MS due hardware constraints. propose a layered pilot transmission scheme CANDECOMP/PARAFAC (CP) decomposition-based method joint channels from multiple users (i.e., MSs) BS. The proposed...

We consider the problem of downlink training and channel estimation in frequency division duplex (FDD) massive MIMO systems, where base station (BS) equipped with a large number antennas serves single-antenna users simultaneously. To obtain state information (CSI) at BS FDD has to be estimated by via then fed back BS. For large-scale overhead for CSI uplink feedback could prohibitively high, which presents significant challenge. In this paper, we study behavior minimum mean-squared error...

In cognitive radio, most spectrum sensing algorithms are model-based and their detection performance relies heavily on the accuracy of assumed statistical model. this letter, we propose a convolutional neural network-based deep learning algorithm for sensing. Compared with algorithms, our proposed approach is data-driven requires neither signal-noise probability model nor primary user (PU) activity pattern The simultaneously takes in present data historical data, which inherent PU can be...

Abstract Objective This study aims to examine the efficiency and consistency of ChatGPT in identifying intimate partner violence (IPV) frequency emotional informational support provided. Background The integration artificial intelligence–based conversational large language models, such as ChatGPT, understanding relationship dynamics has sparked both interest debate within scientific community. tool could be valuable offering immediate, personalized responses questions about relationships,...