The rapid expansion of marine-related industries has intensified the need for robust maritime data transmission technologies. Traditionally, high-speed millimeter wave (mmWave) systems have been deemed unsuitable for marine environments due to significant propagation path losses (PLs). This study investigates the potential of marine evaporation ducts, a natural phenomena at the air-sea interface, to enhance mmWave frequency propagation by reducing signal attenuation. To examine the large-scale propagation characteristics of mmWave frequencies in environments featuring evaporation ducts, this research employed numerical simulations using ray tracing and parabolic equation methods. Additionally, a detailed field experiment was conducted to measure the PLs for a 22 GHz frequency over a 20 km shore-to-shore, over-the-horizon link with horizontal to horizontal polarization. The findings from both the numerical simulations and field tests reveal substantial multi-path effects caused by the evaporation duct phenomenon. These results indicate the necessity for orthogonal time frequency space technology to support high-speed, low-latency marine communications at mmWave frequencies.

Integrated Sensing, Communication, and Computing (ISCC) combines sensing, communication, and computing functions to improve spectrum efficiency and reduce hardware costs. However, poor link quality in the presence of obstructions leads to high offloading latency. This paper explores the use of intelligent reflecting surface and non-orthogonal multiple access in ISCC systems to enhance link reliability and improve computation offloading efficiency. A latency minimization problem is formulated by jointly optimizing computing, sensing, and communication parameters. Due to the strong coupling of the optimization variables, the problem is decomposed into two modules: computational design and sensing/communication design, which are optimized alternately to achieve a high-quality, stable solution. Simulation results demonstrate that the proposed system significantly reduces task processing latency, and ensures both user sum rate and radar sensing performance, offering a promising approach to enhance ISCC systems under resource-constrained environments.

The early detection of ocular disorders, including Graves’ disease, myasthenia gravis, conjunctival hyperemia, conjunctivitis, and keratitis, which critically impair the vision of millions worldwide, necessitates large-scale screening predicated on ocular appearance measurements as a crucial diagnostic component. The emerging Internet of Medical Things (IoMT) introduces new avenues for local clinics to embrace portable and extensive diagnostics. However, the inherent heterogeneity and blurriness of ocular images, compounded by environmental noise, and the computational resource constraint hinder the high-precision diagnostics on IoMT devices. In response to these challenges, a linguistic-guided generative large vision model (LiGu-LVM) has been formulated to assist and enhance the diagnostic capability of IoMT-enabled ocular scanners, integrating a dynamically allocated high-speed quantization system (DAHSQS), a linguistic-guided generative local-isolation module (LiGu), an oculo visio transformatrix segmentum-analytica modulorum (OVT-SAM), and a multiscale recursive attention segmentation engine (MuRASE). DAHSQS enables the flexible aggregation and transmission of patient imagery to shift heavy diagnostic tasks from IoMT-enabled mobile ocular scanners to computational clusters, facilitating rapid facial measurements and preliminary screening via dynamic task allocation and scalable server clusters. The LiGu module employs natural language guidance to generate key image locations, using extensive prior knowledge embedded within linguistic models for precise semantic isolation. OVT-SAM synthesizes multilevel features from the large vision model, extracting intermediate characteristic information and addressing global features alongside deep semantic understanding in natural images collected from IoMT-enabled ocular scanners. MuRASE achieves high-fidelity segmentation of ocular images by incorporating contextual recursive attention mechanisms and skip connections with layer-wise reverse connectivity. Extensive experiments show proposed method surpassing 80% Intersection Over Union (IoU) in ocular semantic segmentation on the CelebA-HQ dataset, achieving an IoU of 82.9%, thus exceeding the performance of existing models by 4.9%.

The concept of extremely large-scale reconfigurable intelligent surfaces (XL-RIS) holds great promise for enabling sixth-generation (6G) communications. However, the vast number of passive reflection coefficients and the transition from far-field to near-field electromagnetic radiation pose significant challenges for channel estimation, especially under tight pilot overhead constraints. To address these challenges, we propose a novel hybrid integrated sensing and communication architecture and a three-stage channel estimation scheme for XL-RIS-assisted millimeter wave communication systems. The proposed scheme leverages user position data, obtained through a sensing module, to accurately estimate near-field cascaded channels. First, we design an integrated base station architecture that combines a fully-digital sensing module with a hybrid communication module to achieve high-resolution distance and angle estimations using linear frequency modulation signals. Next, we introduce a distance-error-minimization based localization algorithm to effectively estimate user coordinates. To balance channel estimation performance and pilot overhead, we carefully select the appropriate number of position update iterations. Using these estimated coordinates, we calculate the channel fading coefficients for the near-field cascaded channels, facilitating accurate channel estimation. Simulation results validate the effectiveness of our proposed scheme, demonstrating reduced overhead while maintaining superior channel estimation performance.

This study presents an advanced radar-based multitarget human motion recognition (M-HMR) approach using multiple-input-multiple-output (MIMO) frequency-modulated-continuous-wave (FMCW) millimeter-wave (mmWave) radar. First, leveraging the scattering point cloud obtained from radar, a multitarget localization algorithm is proposed, which iteratively filters the scattering points to improve the accuracy of target localization. A signal separation algorithm is then developed to obtain motion signals for each target, wherein an adaptive beamforming algorithm with dynamic beamwidth and empowered by linearly constrained minimum variance (LCMV) is proposed to alleviate the interference among different targets. Based on the results of signal separation, the M-HMR problem is converted into single-target HMR, and a convolutional neural network (CNN) is used to achieve the classification of different motions. Tested on real datasets collected using the mmWave radar system, MMWCAS-RF-EVM, from Texas Instruments, the proposed approach demonstrates high accuracy in isolating target signals and classifying motions, showcasing its potential for complex HMR applications.

Information-centric networking (ICN) can efficiently utilize bandwidth resources and reduce network data transmission latency, but coded multicast gains are not considered. A novel multiple-subsystem nested coding caching (MSNCC) is then proposed in hierarchical multirelay wireless sensor space-air-ground integrated network (SAGIN) with distinct sensor cache capacities and coded caching for potential coded caching gains. The group-based and zero-bit padding coded caching schemes are tightly coupled with two divided subsystems: 1) network layer placement and 2) physical-layer delivery. Through decentralized caching status (DCSs) aware coded caching optimization, the first subsystem achieves caching gains between adjacent layers, and the second subsystem directly exploits coded multicast gains between a server and several sensors. In situation I of the number of sensors larger than that of files, the MSNCC adopts the group-based decentralized caching to transmit coded subfiles between relays and sensors. In situation II of the remaining scenarios, the zero-bit padding coded caching improves link rate and system complexity. The innovation is the MSNCC with distinct sensor cache capacities by combining group-based decentralized, zero-bit padding, and hierarchical caching together. It effectively improves the link load in the worst circumstance by exploiting potential coded multicast opportunities among relays for different transmissions. Simulation results indicate that, in situation I, the MSNCC obtains approximately 38.5% and 26.5% reduction in delivery rate compared with those of uncoded caching and zero-bit padding coded caching, respectively. It also achieves about 33.3% rate gains compared with that of the uncoded caching.

In this paper, we investigate a millimeter wave communication-centric integrated sensing and communication (ISAC) system with hybrid analog and digital (HAD) architecture with the application to vehicle to vehicle (V2V) network. Each vehicle node needs to perform two types of sensing: the panoramic close-range and forward long-range. We adopt multibeam technology for simultaneous forward long-range sensing and communication. Compared to traditional sensing-first-communicating-later (SFCL) scenario, simulation results show that the considered ISAC with HAD scenario can obtain higher communication throughputs while maintaining an acceptable sensing performance.

Millimetre wave (mmWave) beam tracking is a challenging task because tracking algorithms are required to provide consistent high accuracy with low probability of loss of track and minimal overhead. To meet these requirements, we propose in this article a new cost-effective analog beam tracking framework namely Adaptive Tracking with Stochastic Control (ATSC). Under this framework, beam direction updates are made using a novel mechanism based on measurements taken from only two beam directions perturbed from the current data beam. To achieve high tracking accuracy and reliability, we provide a systematic approach to jointly optimise the algorithm parameters. The complete framework includes a method for adapting the tracking rate together with a criterion for realignment (perceived loss of track). ATSC adapts the amount of tracking overhead that matches well to the mobility level, without incurring frequent loss of track, as verified by an extensive set of experiments under both representative statistical channel models as well as realistic urban scenarios simulated by ray-tracing software. In particular, numerical results show that ATSC can track dominant channel directions with high accuracy for vehicles moving at 72 km/hour in complicated urban scenarios, with an overhead of less than 1%.

In this paper, we propose an adaptive beam search algorithm for the initial alignment of millimetre-Wave beams. The proposed algorithm works by gradually deactivating beams that are unlikely the best beam from a pre-synthesised codebook to save overhead, based on a Bayesian probability criterion with a uniform improper prior. The beam deactivations can be implemented with low-complexity operations that require computing a low-degree polynomial or a search through a look-up table. The proposed algorithm does not require prior knowledge of channel statistics or signal to noise ratios (SNRs) to optimise the amount of searching time, and uses a suitable amount of time to achieve satisfactory beam search accuracy in different SNRs and fading scenarios. Numerical results confirm that the proposed algorithm can adapt to a wide range of channels with a fixed algorithm parameter, and can achieve better balance between beam search overhead and accuracy than non-adaptive approaches with fixed overhead.

Millimeter wave (mmWave) multiple-input multiple-output (MIMO) communication systems with a large number of antennas are power hungry when using conventional high-resolution analog-to-digital/digital-to-analog converters (A/Ds). To reduce the power consumption of mmWave MIMO systems, existing studies have considered hybrid structures with a reduced number of high-resolution or low-resolution A/Ds at either the transmitter or the receiver side. In this paper, we propose and investigate a multi-user hybrid architecture with low-resolution A/Ds equipped at both the transmitter and the receivers. To mitigate the impact of utilizing low-resolution A/Ds at the transceivers, we propose a novel data transmission scheme, which exploits a weighted phased-array to synthesize the beamforming matrix in the analog domain so as to mitigate inter-user interference. Under the scheme proposed, we derive the achievable rate and the energy efficiency to establish guidelines on the optimal resolution choice of A/Ds for hybrid mmWave systems. For a typical total transmit power at the BS, e.g., 30 dBm, the proposed scheme with 5~6-bit A/Ds can significantly improve the energy efficiency by as much as 100% over that of the conventional hybrid MIMO architecture with high-resolution A/Ds (10-bit A/Ds), without significant degradation in data rate performance.

Millimeter Wave (mmWave) communications rely on highly directional beams to combat severe propagation loss. In this paper, an adaptive beam search algorithm based on spatial scanning, called Iterative Deactivation and Beam Shifting (IDBS), is proposed for mmWave beam alignment. IDBS does not require advance information such as the Signal-to-Noise Ratio (SNR) and channel statistics, and matches the training overhead to the unknown SNR to achieve satisfactory performance. The algorithm works by gradually deactivating beams using a Bayesian probability criterion based on a uniform improper prior, where beam deactivation can be implemented with low-complexity operations that require computing a low-degree polynomial or a search through a look-up table. Numerical results confirm that IDBS adapts to different propagation scenarios such as line-of-sight and non-line-of-sight and to different SNRs. It can achieve better tradeoffs between training overhead and beam alignment accuracy than existing non-adaptive algorithms that have fixed training overheads.

This paper proposes a distributed hybrid millimeter wave (mmWave) scheme to exploit the structure of a Densely Deployed Distributed (DDD) small-cell-base-stations (SBSs) system for serving multiple users in a geographic area. Both the SBSs and the users are equipped with full access hybrid architectures with multi-antenna arrays and multiple radio frequency chains. Unlike the conventional cellular networks where users receive data streams from their nearest BSs, the users in our proposed scheme simultaneously receive data streams from different SBSs. With appropriate design of analog beamformers, co-channel multi-data-stream interference can be mitigated and the extra spatial degrees of freedom induced by the geographic distributed SBSs are exploited for data multiplexing. Analytical and simulation results show that the proposed scheme can improve the system sum-rate considerably, especially when the number of scattering components in millimeter wave channels is limited.

In this paper, we propose a multi-beam non-orthogonal multiple access (NOMA) scheme for hybrid millimeter wave (mmWave) systems and study its resource allocation. A beam splitting technique is designed to generate multiple analog beams to serve multiple NOMA users on each radio frequency chain. In contrast to the recently proposed single-beam mmWave-NOMA scheme which can only serve multiple NOMA users within the same analog beam, the proposed scheme can perform NOMA transmission for the users with an arbitrary angle-of-departure distribution. This provides a higher flexibility for applying NOMA in mmWave communications and thus can efficiently exploit the potential multi-user diversity. Then, we design a suboptimal two-stage resource allocation for maximizing the system sum-rate. In the first stage, assuming that only analog beamforming is available, a user grouping and antenna allocation algorithm is proposed to maximize the conditional system sum-rate based on the coalition formation game theory. In the second stage, with the zero-forcing digital precoder, a suboptimal solution is devised to solve a non-convex power allocation optimization problem for the maximization of the system sum-rate which takes into account the quality of service constraints. Simulation results show that our designed resource allocation can achieve a close-to-optimal performance in each stage. In addition, we demonstrate that the proposed multi-beam mmWave-NOMA scheme offers a substantial spectral efficiency improvement compared to that of the single-beam mmWave-NOMA and the mmWave orthogonal multiple access schemes.

In this paper, we propose a multi-beam non- orthogonal multiple access (NOMA) framework for hybrid millimeter wave (mmWave) systems. The proposed framework enables the use of a limited number of radio frequency (RF) chains in hybrid mmWave systems to accommodate multiple users with various angles of departures (AODs). A beam splitting technique is introduced to generate multiple analog beams to facilitate NOMA transmission. We analyze the performance of a system when there are sufficient numbers of antennas driven by a single RF chain at each transceiver. Furthermore, we derive the sufficient and necessary conditions of antenna allocation, which guarantees that the proposed multi-beam NOMA scheme outperforms the conventional time division multiple access (TDMA) scheme in terms of system sum-rate. The numerical results confirm the accuracy of the developed analysis and unveil the performance gain achieved by the proposed multi- beam NOMA scheme over the single-beam NOMA scheme.

In this paper, we investigate the system performance of a multi-cell multi-user (MU) hybrid millimeter wave (mmWave) multiple-input multiple- output (MIMO) network adopting the channel estimation algorithm proposed in [1] for channel estimation. Due to the reuse of orthogonal pilot symbols among different cells, the channel estimation is expected to be affected by pilot contamination, which is considered as a fundamental performance bottleneck of conventional multicell MU massive MIMO networks. To analyze the impact of pilot contamination on the system performance, we derive the closed-form approximation expression of the normalized mean squared error (MSE) of the channel estimation performance. Our analytical and simulation results show that the channel estimation error incurred by the impact of pilot contamination and noise vanishes asymptotically with an increasing number of antennas equipped at each radio frequency (RF) chain deployed at the desired BS. Thus, pilot contamination is no longer the fundamental problem for multi-cell hybrid mmWave systems.

In this paper, we investigate the system performance of a multi-cell multi-user (MU) hybrid millimeter wave communications in a multiple-input multiple-output (MIMO) network. Due to the reuse of pilot symbols among different cells, the performance of channel estimation is expected to be degraded by pilot contamination, which is considered as a fundamental performance bottleneck of conventional multi-cell MU massive MIMO networks. To analyze the impact of pilot contamination to the system performance, we first derive the closed-form approximation of the normalized mean-squared error of the channel estimation algorithm proposed by Zhao et al. over Rician fading channels. Our analytical and simulation results show that the channel estimation error incurred by the impact of pilot contamination and noise vanishes asymptotically with an increasing number of antennas equipped at each radio frequency chain at the desired BS. Furthermore, by adopting zero-forcing precoding in each cell for downlink transmission, we derive a tight closed-form approximation of the average achievable rate per user. Our results unveil that the intra-cell interference and inter-cell interference caused by pilot contamination over Rician fading channels can be mitigated effectively by simply increasing the number of antennas equipped at the desired BS.

In this paper, we develop a low-complexity channel estimation for hybrid millimeter wave (mmWave) systems, where the number of radio frequency (RF) chains is much less than the number of antennas equipped at each transceiver. The proposed channel estimation algorithm aims to estimate the strongest angle-of-arrivals (AoAs) at both the base station (BS) and the users. Then all the users transmit orthogonal pilot symbols to the BS via these estimated strongest AoAs to facilitate the channel estimation. The algorithm does not require any explicit channel state information (CSI) feedback from the users and the associated signalling overhead of the algorithm is only proportional to the number of users, which is significantly less compared to various existing schemes. Besides, the proposed algorithm is applicable to both non-sparse and sparse mmWave channel environments. Based on the estimated CSI, zero-forcing (ZF) precoding is adopted for multiuser downlink transmission. In addition, we derive a tight achievable rate upper bound of the system. Our analytical and simulation results show that the proposed scheme offer a considerable achievable rate gain compared to fully digital systems, where the number of RF chains equipped at each transceiver is equal to the number of antennas. Furthermore, the achievable rate performance gap between the considered hybrid mmWave systems and the fully digital system is characterized, which provides useful system design insights.

In this paper, we develop a low-complexity channel estimation for hybrid millimeter wave (mmWave) systems, where the number of radio frequency (RF) chains is much less than the number of antennas equipped at each transceiver. The proposed mmWave channel estimation algorithm first exploits multiple frequency tones to estimate the strongest angle-of-arrivals (AoAs) at both base station (BS) and user sides for the design of analog beamforming matrices. Then, all the users transmit orthogonal pilot symbols to the BS along the directions of the estimated strongest AoAs in order to estimate the channel. The estimated channel will be adopted to design the digital zero-forcing (ZF) precoder at the BS for the multi-user downlink transmission. The proposed channel estimation algorithm is applicable to both the non-sparse and sparse mmWave channel environments. Furthermore, we derive a tight achievable rate upper bound of the digital ZF precoding with the proposed channel estimation algorithm scheme. Our analytical and simulation results show that the proposed scheme obtains a considerable achievable rate of fully digital systems, where the number of RF chains equipped at each transceiver is equal to the number of antennas. Besides, considering the effect of various types of errors, i.e., random phase errors, transceiver analog beamforming errors, and equivalent channel estimation errors, we derive a closed-form approximation for the achievable rate of the considered scheme. We illustrate the robustness of the proposed channel estimation and multi-user downlink precoding scheme against the system imperfection.

In this paper, we investigate channel estimation and multiuser downlink transmission of a time division duplex massive multiple-input multiple-output (MIMO) system in millimeter wave (mmWave) channels. We propose a tone-based linear search algorithm to facilitate the estimation of angle-of-arrivals (AoAs) of the strongest line-of-sight (SLOS) channel component as well as the scattering components of the users at the base station. Based on the estimated AoAs, we reconstruct the SLOS component and scattering components of the users for downlink transmission. We then derive the achievable rates of maximum-ratio transmission (MRT) and zero-forcing (ZF) precoding based on the SLOS component and the SLOS-plus-scattering components (SLPS), respectively. Taking into account the impact of pilot contamination, our analysis and simulation results show that the SLOS-based MRT can achieve higher data rate than that of the traditional pilot-aided-CSI-based (PAC-based) MRT, under the same mean square errors of channel estimation. As for ZF precoding, the achievable rates of the SLPS-based and the PAC-based are identical. Furthermore, we quantify the achievable rate degradation of the SLOS-based MRT precoding caused by phase quantization errors in the large number of antennas regime. We show that the impact of phase quantization errors on the considered systems cannot be mitigated by increasing the number of antennas and therefore the resolutions of radio frequency phase shifters is critical for the design of efficient mmWave massive MIMO systems.

In this paper, we investigate uplink channel estimation and multiuser downlink transmission of a massive MIMO time-division duplex system in the presence of pilot contamination, where the base station (BS) with M antennas communicates with N single-antenna users in a cell. We assume that all channels are affected by Rician fading. We also assume that angles of arrival from users to the BS are different. We first analyze the impact of pilot contamination on the channel estimation, based on which we derive a tight sum-rate approximation. We also obtain the asymptotic sum-rate for large Rician K-factor in the large signal to noise ratio regime. Furthermore, we examine the impact of the Rician K-factor on the sum-rate of the system, showing that the sum-rate increases as K-factor increases.