Phase-time arrays (PTAs), integrating phase shifters and true-time delays, emerge as a cost-effective and energy-efficient architecture for frequency-dependent beamforming in wideband communications. In this work, we investigate a wideband system in which a base station equipped with a PTA and single RF chain serves multiple near-field and far-field users. The goal is to jointly optimize PTA-based beamforming, subband allocation, and power allocation to maximize overall system performance. To this end, we formulate a system utility maximization problem, which includes sum-rate and geometric mean rate maximization as special cases and is highly non-convex. We first develop a three-step alternating optimization (AO) algorithm that iteratively optimizes the beamforming and resource allocations. To further enhance efficiency, we propose an unsupervised learning-based approach that combines a convolutional neural network, a graph attention network (GAT), and a normalization module with a utility-driven loss and a learnable adjacency initialized from hardware couplings. Simulation results confirm that PTAs strike a superior balance between energy efficiency and spectral efficiency compared with fully-digital and phased array architectures. The proposed GAT achieves AO-level performance with orders-of-magnitude lower computational complexity, i.e., only about 0.1% in our simulations.

The success of large foundation models is catalyzing a new paradigm for AI-native 6G network design: wireless foundation models for physical layer design. However, existing models often operate on channel state information (CSI) in the space-time-frequency (STF) domain, where distinct multipath components are inherently superimposed and structurally entangled. This hinders the learning of universal channel representation. Meanwhile, their reliance on global attention mechanisms incurs prohibitive computational overhead. In this paper, we propose AirFM-DDA, an Air-interface Foundation Model operating in the Delay-Doppler-Angle (DDA) domain for physicallayer tasks. Specifically, AirFM-DDA reparameterizes CSI from the STF domain into the DDA domain to explicitly resolve multipath components along physically meaningful axes. It employs a window-based attention module augmented with framestructure-aware positional encoding (FS-PE). This window-based attention aligns with locally clustered multipath dependencies while avoiding quadratic-complexity global attention, and FS-PE injects frame-structure priors into network. Extensive experiments demonstrate that AirFM-DDA achieves superior zero-shot generalization across unseen scenarios and datasets, consistently outperforming the baselines on channel prediction and estimation tasks. Compared to the global attention, its window-based attention reduces training and inference costs by nearly an order of magnitude. Moreover, AirFM-DDA maintains robustness under high mobility, large delay spreads, severe noise, and extreme aliasing conditions.

Integrated sensing and communication (ISAC) systems demand precise and efficient target localization, a task challenged by rich multipath propagation in complex wireless environments. This paper introduces MARBLE-Net (Multipath-Aware Rainbow Beam Learning Network), a deep learning framework that jointly optimizes the analog beamforming parameters of a frequency-dependent rainbow beam and a neural localization network for high-accuracy position estimation. By treating the phase-shifter (PS) and true-time-delay (TTD) parameters as learnable weights, the system adaptively refines its sensing beam to exploit environment-specific multipath characteristics. A structured multi-stage training strategy is proposed to ensure stable convergence and effective end-to-end optimization. Simulation results show that MARBLE-Net outperforms both a fixed-beam deep learning baseline (RaiNet) and a traditional k-nearest neighbors (k-NN) method, reducing localization error by more than 50% in a multipath-rich scene. Moreover, the results reveal a nuanced interaction with multipath propagation: while confined uni-directional multipath degrades accuracy, structured and directional multipath can be effectively exploited to achieve performance surpassing even line-of-sight (LoS) conditions.

Presents corrections to the paper, (SPOT: Single-Shot Positioning via Trainable Near-Field Rainbow Beamforming).

Multistatic collaborative sensing eliminates self-interference, achieves spatial diversity gains, and enables wide-range seamless integrated sensing and communication (ISAC). However, conventional data fusion methods suffer from severe error amplification in geometry-sensitive regions. In addition, the conventional analog phased array solution introduces large beam sweeping overhead, whereas the fully digital arrays request high hardware cost. We propose a multistatic sensing framework enabled by a phase-time array (PTA). The rainbow beamforming maps spatial directions to orthogonal frequency division multiplexing (OFDM) subcarriers, achieving wide-angle coverage with a single radio frequency (RF) chain. We develop two parameter-level schemes-a geometry-aware analytical estimator (GDOP-WLS) and a lightweight multilayer perceptron (PF-MLP)-to mitigate the effects of topological singularities. Additionally, an end-to-end signal-level convolutional neural network (SF-CNN) directly estimates target coordinates from raw signals, avoiding cascaded estimation errors. The results demonstrate that the parameter-level schemes ensure robust convergence under adverse geometric conditions with minimal computational latency. Conversely, the signal-level scheme achieves sub-meter precision but requires an increased computational load. Consequently, the proposed framework establishes a scalable solution for collaborative surveillance of unmanned aerial vehicles (UAVs), providing flexible trade-offs among hardware complexity, latency, and accuracy.

Phase-time arrays, which integrate phase shifters (PSs) and true-time delays (TTDs), have emerged as a cost-effective architecture for generating frequency-dependent rainbow beams in wideband sensing and localization. This letter proposes an end-to-end deep learning-based scheme that simultaneously designs the rainbow beams and estimates user positions. Treating PS and TTD coefficients as trainable variables allows the network to synthesize task-oriented beams that maximize localization accuracy. A lightweight fully connected module then recovers the user's angle-range coordinates from its feedback of the maximum quantized received power and its corresponding subcarrier index after a single downlink transmission. Compared with existing analytical and learning-based schemes, the proposed method reduces overhead by an order of magnitude and delivers consistently lower two-dimensional positioning error.

Most existing semantic communication systems employ analog modulation, which is incompatible with modern digital communication systems. Although several digital transmission approaches have been proposed to address this issue, an end-to-end bit-level method that is compatible with arbitrary modulation formats, robust to channel noise, and free from quantization errors remains lacking. To this end, we propose BitSemCom, a novel bit-level semantic communication framework that realizes true joint source-channel coding (JSCC) at the bit level. Specifically, we introduce a modular learnable bit mapper that establishes a probabilistic mapping between continuous semantic features and discrete bits, utilizing the Gumbel-Softmax trick to enable differentiable bit generation. Simulation results on image transmission demonstrate that BitSemCom achieves both competitive performance and superior robustness compared to traditional separate source-channel coding (SSCC) schemes, and outperforms deep learning based JSCC with uniform 1-bit quantization, validating the effectiveness of the learnable bit mapper. Despite these improvements, the bit mapper adds only 0.42

Millimeter-wave (mmWave) OFDM radar equipped with rainbow beamforming, enabled by phase-time arrays (PTAs), provides wide-angle coverage and is well-suited for fast real-time target detection and tracking. However, accurate detection of multiple closely spaced targets remains a key challenge for conventional signal processing pipelines, particularly those relying on constant false alarm rate (CFAR) detectors. This paper presents CFARNet, a learning-based processing framework that replaces CFAR with a convolutional neural network (CNN) for peak detection in the angle-Doppler domain. The network predicts target subcarrier indices, which guide angle estimation via a known frequency-angle mapping and enable high-resolution range and velocity estimation using the MUSIC algorithm. Extensive simulations demonstrate that CFARNet significantly outperforms a baseline combining CFAR and MUSIC, especially under low transmit power and dense multi-target conditions. The proposed method offers superior angular resolution, enhanced robustness in low-SNR scenarios, and improved computational efficiency, highlighting the potential of data-driven approaches for high-resolution mmWave radar sensing.

Massive multiple-input multiple-output (MIMO) technology is a key enabler of modern wireless communication systems, which demand accurate downlink channel state information (CSI) for optimal performance. Although deep learning (DL) has shown great potential in improving CSI feedback, most existing approaches fail to exploit the semantic relationship between CSI and other related channel metrics. In this paper, we propose SemCSINet, a semantic-aware Transformer-based framework that incorporates Channel Quality Indicator (CQI) into the CSI feedback process. By embedding CQI information and leveraging a joint coding-modulation (JCM) scheme, SemCSINet enables efficient, digital-friendly CSI feedback under noisy feedback channels. Experimental results on DeepMIMO datasets show that SemCSINet significantly outperforms conventional methods, particularly in scenarios with low signal-to-noise ratio (SNR) and low compression ratios (CRs), highlighting the effectiveness of semantic embedding in enhancing CSI reconstruction accuracy and system robustness.

Joint phase-time arrays (JPTA) emerge as a cost-effective and energy-efficient architecture for frequency-dependent beamforming in wideband communications by utilizing both true-time delay units and phase shifters. This paper exploits the potential of JPTA to simultaneously serve multiple users in both near- and far-field regions with a single radio frequency chain. The goal is to jointly optimize JPTA-based beamforming and subband allocation to maximize overall system performance. To this end, we formulate a system utility maximization problem, including sum-rate maximization and proportional fairness as special cases. We develop a 3-step alternating optimization (AO) algorithm and an efficient deep learning (DL) method for this problem. The DL approach includes a 2-layer convolutional neural network, a 3-layer graph attention network (GAT), and a normalization module for resource and beamforming optimization. The GAT efficiently captures the interactions between resource allocation and analog beamformers. Simulation results confirm that JPTA outperforms conventional phased arrays (PA) in enhancing user rate and strikes a good balance between PA and fully-digital approach in energy efficiency. Employing a logarithmic utility function for user rates ensures greater fairness than maximizing sum-rates. Furthermore, the DL network achieves comparable performance to the AO approach, while having orders of magnitude lower computational complexity.

Existing communication systems follow the Shannon separated source and channel coding (SSCC) paradigm. However, the SSCC framework inherently suffers from a mismatch between source and channel coding rates, leading to the well-known “cliff effect” in performance. Deep learningbased joint source-channel coding (DeepJSCC) could mitigate this issue and prior work has demonstrated great potential of DeepJSCC over traditional SSCC. Yet, it still remains unclear if DeepJSCC could outperform the latest deep learning based source compression together with state-of-the art channel coding, i.e., DeepSSCC. To this end, using video transmission as a case study, this paper proposes a new DeepJSCC framework for wireless video transmission based on feature-space, i.e., FVCJSCC, and compares DeepJSCC and DeepSSCC under varying channel conditions. Simulation results show that FVC-JSCC outperforms benchmark methods, and further ablation study validates that the performance gain of FVC-JSCC originates from the joint training of source and channel coding. This system provides a robust solution for video semantic communication in challenging channel conditions and band-limited scenarios.

This article presents a novel approach to designing millimeter-wave (mmWave) cellular communication systems, based on joint phase time array (JPTA) radio frequency (RF) frontend architecture. JPTA architecture comprises time-delay components appended to conventional phase shifters, which offer extra degrees of freedom to be exploited for designing frequency-selective analog beams. Hence, a mmWave device equipped with JPTA can receive and transmit signals in multiple directions in a single time slot per RF chain, one direction per frequency subband, which alleviates the traditional constraint of one analog beam per transceiver chain per time slot. The utilization of subband-specific analog beams offers a new opportunity in designing mmWave systems, allowing for enhanced cell capacity and reduced pilot overhead. To understand the practical feasibility of JPTA, a few challenges and system design considerations are discussed in relation to the performance and complexity of the JPTA systems. For example, frequency-selective beam gain losses are present for the subband analog beams, e.g., up to 1 dB losses for 2 subband cases, even with the state-of-the-art JPTA delay and phase optimization methods. Despite these side effects, system-level analysis reveals that the JPTA system is capable of improving cell capacity: 5%-tile cell throughput by up to 65%. To the best of the authors' knowledge, this article is the first to explain the system-level benefits and system-design challenges of JPTA, with an analysis of the performance tradeoff based on an intuitive metric of beam gain losses.

This paper addresses the critical challenges of communication overhead, data heterogeneity, and privacy in deep learning for channel state information (CSI) feedback in massive MIMO systems. To this end, we propose Fed-PELAD, a novel federated learning framework that incorporates personalized encoders and a LoRA-adapted shared decoder. Specifically, personalized encoders are trained locally on each user equipment (UE) to capture device-specific channel characteristics, while a shared decoder is updated globally via the coordination of the base station (BS) by using Low-Rank Adaptation (LoRA). This design ensures that only compact LoRA adapter parameters instead of full model updates are transmitted for aggregation. To further enhance convergence stability, we introduce an alternating freezing strategy with calibrated learning-rate ratio during LoRA aggregation. Extensive simulations on 3GPP-standard channel models demonstrate that Fed-PELAD requires only 42.97% of the uplink communication cost compared to conventional methods while achieving a performance gain of 1.2 dB in CSI feedback accuracy under heterogeneous conditions.

High-frequency wideband cellular communications over mmWave and sub-THz offer the opportunity for high data rates. However, it also presents high path loss, resulting in limited coverage. High-gain beamforming from the antenna array is essential to mitigate the coverage limitations. The conventional phased antenna arrays (PAA) cause high scheduling latency owing to analog beam constraints, i.e., only one frequency-flat beam is generated. Recently introduced joint phase-time array (JPTA) architecture, which utilizes both true-time-delay (TTD) units and phase shifters (PSs), alleviates analog beam constraints by creating multiple frequency-dependent beams for scheduling multiple users at different directions in a frequency-division manner. One class of previous studies offered solutions with "rainbow" beams, which tend to allocate a small bandwidth per beam direction. Another class focused on uniform linear array (ULA) antenna architecture, whose frequency-dependent beams were designed along a single axis of either azimuth or elevation direction. This paper presents a novel 3D beamforming design that maximizes beamforming gain toward desired azimuth and elevation directions and across sub-bands partitioned according to scheduled users’ bandwidth requirements. We provide analytical solutions and iterative algorithms to design the PSs and TTD units for a desired subband beam pattern. Through simulations of the beamforming gain, we observe that our proposed solutions outperform the state-of-the-art solutions reported elsewhere.

Joint phase-time arrays (JPTA) is a new mmWave radio frequency front-end architecture constructed with appending time-delay elements to phase shifters for analog beamforming. JPTA allows the mmWave base station (BS) to form multiple frequency-dependent beams with a single RF chain, exploiting the extra degrees of freedom the time-delay elements offer. Without requiring extra power-hungry RF chains, a BS with JPTA can schedule multiple users in different directions in a frequency-division multiplexing (FDM) manner. A BS with JPTA achieves various advantages over the traditional analog beamforming system. Simulation results show that JPTA can bring significant system-level benefits, e.g., extending uplink throughput coverage by 100%. To realize these system benefits of JPTA, high-resolution delay elements with a wide delay dynamic range are essential. With newly developed delay elements, we demonstrate that a single TRX RF chain can serve four users in four different directions in the mmWave band.

In this paper, we establish the potential of joint phase-time arrays (JPTAs) for uplink coverage extension in cellular systems. JPTA allows the base station (BS) to create frequency-dependent wide-beams without sacrificing the array gain. In this work, we propose a novel use case of JPTA where the BS exploits the frequency-dependent beams to serve multiple users’ equipment (UEs) at different directions simultaneously with the full array gain and with no inter-UE interference. This is achieved by assigning each UE a corresponding bundle of sub-carriers that benefit from the full array-gain. A key feature of this scheme is the prolonged channel access for uplink communication for each user, due to the BS’s ability to serve multiple UEs at the same time. We focus on two performance metrics: uplink coverage and uplink throughput. Our results show that using JPTA can extend the uplink coverage range by $3\times $ while boosting the uplink throughput by providing more flexibility for the BS in resource allocation. These results are based on both theoretical analysis and 3GPP spec-compliant simulations with a sub-terahertz transceiver prototype.

Beam management (BM), i.e., the process of finding and maintaining a suitable transmit and receive beam pair, can be challenging, particularly in highly dynamic scenarios. Side-information, e.g., orientation, from on-board sensors can assist the user equipment (UE) BM. In this work, we use the orientation information coming from the inertial measurement unit (IMU) for effective BM. We use a data-driven strategy that fuses the reference signal received power (RSRP) with orientation information using a recurrent neural network (RNN). Simulation results show that the proposed strategy performs much better than the conventional BM and an orientation-assisted BM strategy that utilizes particle filter in another study. Specifically, the proposed data-driven strategy improves the beam-prediction accuracy up to 34% and increases mean RSRP by up to 4.2 dB when the UE orientation changes quickly.

Beam alignment - the process of finding an optimal directional beam pair - is a challenging procedure crucial to millimeter wave (mmWave) communication systems. In this work, we propose a beam alignment method that learns a site-specific probing codebook and uses the probing codebook measurements to predict the optimal narrow beam. A novel neural network (NN) architecture is designed to jointly learn the probing codebook and the beam predictor in an end-to-end fashion. The learned codebook consists of site-specific probing beams that can capture particular characteristics of the propagation environment. The proposed method relies on beam sweeping of the learned probing codebook, does not require additional context information and is compatible with the beam sweeping-based beam alignment framework in 5G. We demonstrate using realistic ray-tracing data that the proposed method can achieve high beam alignment accuracy and signal-to-noise ratio (SNR) while significantly reducing the beam sweeping complexity and latency.

Hybrid beamforming is an attractive solution to build cost-effective and energy-efficient transceivers for millimeter-wave and terahertz systems.However, conventional hybrid beamforming techniques rely on analog components that generate a frequency flat response such as phase-shifters and switches, which limits the flexibility of the achievable beam patterns.As a novel alternative, this paper proposes a new class of hybrid beamforming called Joint phase-time arrays (JPTA), that additionally use true-time delay elements in the analog beamforming to create frequency-dependent analog beams.Using as an example two important frequency-dependent beam behaviors, the numerous benefits of such flexibility are exemplified.Subsequently, the JPTA beamformer design problem to generate any desired beam behavior is formulated and nearoptimal algorithms to the problem are proposed.Simulations show that the proposed algorithms can outperform heuristics solutions for JPTA beamformer update.Furthermore, it is shown that JPTA can achieve the two exemplified beam behaviors with one radio-frequency chain, while conventional hybrid beamforming requires the radio-frequency chains to scale with the number of antennas to achieve similar performance.Finally, a wide range of problems to further tap into the potential of JPTA are also listed as future directions.

Beam alignment - the process of finding an optimal directional beam pair - is a challenging procedure crucial to millimeter wave (mmWave) communication systems. In this work, we propose a beam alignment method that learns a site-specific probing codebook and uses the probing codebook measurements to predict the optimal narrow beam. A novel neural network (NN) architecture is designed to jointly learn the probing codebook and the beam predictor in an end-to-end fashion. The learned codebook consists of site-specific probing beams that can capture particular characteristics of the propagation environment. The proposed method relies on beam sweeping of the learned probing codebook, does not require additional context information and is compatible with the beam sweeping-based beam alignment framework in 5G. We demonstrate using realistic ray-tracing data that the proposed method can achieve high beam alignment accuracy and signal-to-noise ratio (SNR) while significantly reducing the beam sweeping complexity and latency.