Aiming at the severe performance degradation caused by phase noise in 6G terahertz (THz) communication systems, this paper studies accurate phase noise modeling and efficient compensation for 140 GHz THz systems. An accurate THz local oscillator phase noise model is established based on PDRO phase-locked structure and four-component power-law model with temperature sensitivity and high-frequency roll-off, and power spectrum simulation and time-domain sequence generation are implemented in MATLAB. For SC-FDE systems, two traditional non-iterative compensation methods are investigated, and an MMSE-based optimal interpolation scheme is proposed. Furthermore, an iterative RLS phase noise compensation algorithm based on decision feedback is presented. Finally, engineering implementation and parallel optimization are completed on the UCP4008 processor. Simulation results show that MMSE interpolation outperforms traditional methods, and the RLS algorithm converges rapidly. The hardware module supports parallel compensation of 4 data blocks with a single-block latency of only 621 ns. The proposed model and algorithms provide theoretical and engineering support for phase noise suppression in THz communications

A 60-GHz waveform-sharing joint radar-communication (JRC) transceiver with phase-locked loop (PLL)-based chirp spread spectrum (CSS) modulator is presented for seamless integrated sensing and communication (ISAC). Unlike existing communication-centric JRC transceivers that prioritize high throughput, the proposed sensing-centric transceiver employs unified CSS waveforms to perform uncompromised sensing performance while enabling robust long-range communication. To this end, a PLL-based CSS modulator is implemented, simplifying the transceiver architecture while generating wideband CSS signals with high chirp slope, superior linearity, and low phase noise. Within this modulator, a multi-bank capacitor array is integrated into a two-point modulation (TPM)-based sub-sampling (SS) PLL, enabling fast wideband chirp generation and rapid frequency hopping while achieving excellent phase noise. Moreover, an adaptive binary-search (BS) overlap compensation scheme is proposed to mitigate the discontinuity issues in the multi-bank capacitor array while ensuring robust and orderly convergence. Furthermore, building on the TPM-based sub-sampling PLL (SSPLL), a digital CSS modulation engine is developed to enable wideband CSS signal generation without compromising the chirp linearity. Fabricated in a 65-nm CMOS process, the CSS transceiver core occupies 6.11 mm2 and consumes 568 mW. For the over-the-air (OTA) test, broadband high-gain horn antennas with a gap waveguide (GW)-based transition are integrated with the chip through a low-loss wire-bonding matching scheme, achieving a low-cost, compact, and reliable assembly. OTA measurements demonstrate that the measured range resolution is approximately 5.5 cm, while achieving a communication range exceeding 500 m at a 4.138 Mbps data rate with a bit error rate (BER) below $10^{-5}$ .

Compared with the fifth generation (5G) ultra-reliable and low-latency communication (URLLC), the sixth generation (6G) hyper reliable and low-latency communication (HRLLC) imposes stricter requirements on latency, reliability, and additionally, deterministic latency. The convergence of HRLLC and time-sensitive networking (TSN), termed HRLLC-TSN network, which deeply integrates the wireless network with TSN, thereby enabling cross-layer joint traffic scheduling and guaranteeing deterministic communication, is considered a promising technology to support mission-critical applications such as wireless Industrial Internet of Things (IIoT). This paper considers a wireless IIoT scenario using the HRLLC-TSN network with continuously moving industrial equipments and analyzes the complete data transmission process. Then, the strategy for ensuring deterministic latency is explained, and analytical closed-form expressions for end-to-end latency and reliability are obtained. From these expressions, a dynamic optimization problem is formulated to maximize the number of data frames successfully delivered to industrial equipments and an artificial intelligence method based on multi-agent proximal policy optimization (MAPPO) is proposed to enable rapid and efficient traffic scheduling. Simulation results demonstrate that the proposed MAPPO method outperforms other approaches in both performance and computational complexity, particularly when the number of industrial equipments is large.

Network-assisted full-duplex (NAFD) cell-free massive multiple-input multiple-output (CF-mMIMO) systems constitute a promising enabler for supporting dynamic downlink (DL) and uplink (UL) traffic demands in forthcoming sixth-generation (6G) wireless networks. In this article, we analyze channel aging effects on NAFD CF-mMIMO systems. First, we propose an extended beamforming training scheme to relieve heavy pilot overhead for high-mobility channel estimation, significantly enhancing system performance through cross-link interference (CLI) cancellation. Then, we derive novel closed-form expressions for the UL/DL achievable SEs, enabling a comprehensive analysis of channel aging impacts on spectral efficiency (SE) and energy efficiency (EE) across diverse normalized Doppler shift f(D)T(s) scenarios. Furthermore, we develop a joint optimization framework of transmission interval and power control to balance SE-EE tradeoffs while alleviating channel aging effects. We formulate a mixed-integer multiobjective optimization problem (MOOP), which is subsequently transformed into a tractable single-objective formulation via a weighted l(p) scalarizing method. Based on this analytical foundation, we propose a constrained deep reinforcement learning (DRL) algorithm that integrates a primal-dual optimization strategy with multiagent deep deterministic policy gradient (MADDPG) for safe policy exploitation. Simulation results validate the accuracy of analytical expressions and demonstrate the superiority of the proposed algorithm over the conventional nondominated sorting genetic algorithm-II (NSGA-II) in achieving Pareto-optimal SE-EE tradeoffs under channel aging.

This paper investigates the optimal local precoding design for cell-free massive MIMO systems, subject to per-user quality-of-service (QoS) constraints and per-access-point (AP) power limits. In this distributed setting, each AP determines its precoding strategy using only locally acquired instantaneous channel state information (CSI) alongside globally available statistical CSI. Since the precoding design based on the useand- then-forget (UatF) bound involves expectation terms, its optimization problem should be formulated as a functional optimization problem. Unlike function optimization problems whose solutions are numerical precoding values, the solution to a functional optimization problem is a precoding function. Consequently, each AP computes its precoding vectors in real-time by applying this derived optimal function to its locally acquired instantaneous CSI. By applying functional optimization theory and the Karush-Kuhn-Tucker (KKT) conditions, we rigorously prove that the optimal local precoder is structurally equivalent to a Local Minimum Mean Square Error (L-MMSE) precoder, with undetermined Lagrange multiplier parameters associated. We then derive an efficient iterative algorithm to compute these multipliers and establish its convergence to a unique fixed point that yields the optimal precoding solution, thereby providing a complete framework for optimal local downlink precoding. Simulation results demonstrate that the proposed method achieves substantial gains over state-of-the-art approaches, validating its effectiveness and robustness across various system configurations.

Network-Assisted Full-Duplex (NAFD) cell-free massive multiple-input multiple-output (CF-mMIMO) systems can improve the spectral efficiency (SE) by dynamically allocating the numbers of uplink (UL) and downlink (DL) remote antenna units (RAUs) to realize simultaneous UL and DL transmission without self-interference (SI). However, with the promotion of the green communication concept and energy efficiency (EE) becoming an important performance metric, the EE of the NAFD system needs further evaluation and optimization under the realistic energy consumption model. In this paper, a practical energy consumption model for the NAFD systems is established, and its advantages in terms of EE are highlighted compared with the existing Co-frequency Co-time Full Duplex (CCFD) systems. A Q-learning-based duplex mode optimization algorithm is proposed to improve system EE. Simulation results show that the proposed mode optimization algorithm can achieve higher EE compared with existing mode selection schemes.

Cell-free is seen as one of the most important technology for the future wireless communications. In this paper, we adopt a wideband cell-free to implement low-altitude coverage to serve multiple uncrewed aerial vehicles (UAVs) in the city playing the core role of low-altitude economy. For practice, distributed computation, asynchronous effects, beam split and imperfect channel state information are considered. We mainly rely on per-beam synchronization (PBS) and discuss different architecture implementations. A wideband asynchronous architecture that reuses the time delay modules exploited in wideband beam split calibration is proposed. In addition, a semi-synchronized path set (SSP-Set) is derived to eliminate asynchronous interference and a geometric scattering graphic convolutional network is used to acquire the (sub)-optimal SSP-Set. Based on these two technologies, a beam-delay alignment transmission (BDAT) scheme is obtained and we implement it with a distributed paradigm. The numerical results demonstrate the proposed BDAT can benefit from the cooperative downlink beamforming and provide a uniformly good service for UAVs.

The cooperative cell-free (CF) multi-static integrated sensing and communication (ISAC) network is a promising technology for sixth-generation (6G) communication, aiming to meet diverse scenarios and service demands. This paper puts forward a deployment framework of multi-static ISAC leveraging network-assisted full-duplex (NAFD) technology, which can perform target sensing and communicate with downlink (DL) and uplink (UL) users simultaneously. Initially, we analyze the communication and sensing performance of the NAFD CF multi-static ISAC system and substantiate the benefits of this system architecture. Then, to handle the challenge posed by the highly coupled DL and UL transmissions and enhance system performance, we delve into the joint optimization of access point (AP) mode selection and joint sensing and communication (JSC) beamforming design (AMJBD) within NAFD CF multi-static ISAC systems. We innovatively design a tailored multi-agent hierarchical reinforcement learning (MAHRL) method to solve this problem, considering the inherent relationship between optimization variables and the collaborative benefits of CF architecture. Simulation experiments confirm the superior performance of the NAFD CF multi-static ISAC system architecture and validate that the proposed MAHRL-based AMJBD method can strike an optimal balance between communication and sensing performance, enhancing system efficiency.

Millimeter-wave (mmWave) communication depends on highly directional beamforming, while fast mobility, blockage, and rapid geometry changes in vehicle-to-everything (V2X) scenarios make beam tracking challenging. In cooperative multi-base-station (BS) systems, conventional hierarchical methods usually separate BS selection and beam selection, which may cause error propagation when beam states change abruptly. To address this issue, this paper proposes Cooperative Radio Sensing with Large Language Models (CRS-LLM), a cooperative beam prediction framework for next-step joint BS-beam prediction. CRS-LLM formulates beam tracking as a single classification problem over the joint BS-beam space, avoiding cascaded decision errors. To adapt channel state information (CSI) to large language models, a dual-view CSI tokenizer extracts frequency-domain and delay-domain channel features through a lightweight CNN front-end and temporal tokenization module. A truncated GPT-style backbone is then used for temporal modeling with parameter-efficient adaptation. In addition, a transition-aware switch-gated predictor combines a stable branch, a residual flip branch, and a low-rank transition prior to capture both smooth evolution and abrupt changes. Simulation results show that CRS-LLM outperforms CSI-Transformer, Hierarchical BS-Beam, and representative CNN- and recurrent-neural-network baselines in Top-1 accuracy and normalized beam gain under different SNR conditions, while also showing strong few-shot performance and promising zero-shot transferability.

Fixed-Position Antennas (FPAs) are constrained by static physical topologies and struggle to adapt to rapidly varying wireless environments. By dynamically reconfiguring the antenna positions, Fluid Antenna Systems (FASs) introduce additional spatial Degrees of Freedom (DoF) for wireless optimization. This paper investigates the joint optimization of Fluid Antenna System (FAS) topology reconfiguration and active beamforming for mobile Integrated Sensing and Communication (ISAC) systems. To enable real-time decision making, an end-to-end optimization framework based on the Soft Actor-Critic (SAC) algorithm is proposed. Simulation results show that the proposed scheme achieves an online inference latency of only 4 ms. Compared to the widely used alternating optimization, it improves communication performance by 42

Integrated sensing and communication (ISAC) has attracted significant attention owing to its dual capability of simultaneous data transmission and environmental sensing. This paper explores the power allocation for multi-static cooperative ISAC in cell-free massive multiple-input multiple-output (mMIMO) systems. Firstly, each transmission block is divided into two phases, the first phase simultaneously accomplishes channel state information (CSI) acquisition and radar target detection, followed by the second phase that provides data transmission services utilizing the acquired CSI and target detection result. Subsequently, we derive the closed-form expressions for both the lower bound of the communication ergodic achievable rate and the Bhattacharyya distance for target detection. Based on these formulations, we then establish an optimization problem aimed at maximizing the communication performance under the constraint of ensuring detection accuracy. Finally, we propose a successive convex approximation (SCA)-based algorithm to jointly optimize the pilot power and payload power. Simulation results demonstrate that the proposed algorithm can significantly enhance system performance.

Integrated sensing and communication (ISAC) aims to design a unified signaling scheme that supports both high-rate data transmission and accurate target sensing. However, joint codebook design is fundamentally constrained by the deterministic–random trade-off (DRT): communication prefers high-entropy, random-like codewords to approach capacity, whereas sensing benefits from structured, deterministic signals for reliable parameter estimation. To address this conflict, we propose an end-to-end cooperative learning framework for joint communication–sensing codebook design, using mutual information (MI) as a unified, unit-consistent metric that bridges information-theoretic communication analysis and estimation-theoretic sensing principles. The framework jointly trains an ISAC encoder with two contrastive-learning-based variational MI estimators (CVMIEs) tailored to the communication and sensing links, respectively. The proposed CVMIEs yield stable variational lower bounds on MI and cooperatively drive the encoder to optimize the codewords by maximizing a weighted sum of the communication and sensing MI estimates, thereby balancing the DRT. Simulation results validate the proposed framework and demonstrate improved communication–sensing trade-offs compared with communication- and sensing-centric baselines.

Uplink sensing in cell-free integrated sensing and communication (CF-ISAC) systems provides a promising solution by reusing massive communication signals. This approach offers low system overhead and enables wide-area coverage through densely deployed users and distributed access points (APs). However, due to the tight coupling between communication and sensing, accurate extraction of uplink sensing parameters becomes a critical bottleneck: sensing parameter extraction relies on precise demodulation of uplink communication signals, while high-quality channel estimation for data demodulation, in turn, requires accurate sensing results. To address this challenge, we propose an iterative communication-sensing optimization framework under an uplink CF-ISAC architecture. This framework establishes dynamic information feedback among the three core modules of data detection, channel reconstruction, and target sensing, achieving the collaborative improvement of communication-sensing performance. Specifically, the target sensing module integrates pilot-based sensing and data signal-enhanced sensing to extract target parameters. The channel reconstruction module maps the sensing results to channel state information (CSI). The data detection module recovers data symbols using the reconstructed CSI and feeds back both demodulated symbols and residual errors to the sensing module, enabling iterative correction of target parameter estimation. The simulation results show that the proposed iterative framework achieves simultaneous suppression in communication bit error rate (BER) and enhancement in sensing accuracy through several iterations, thereby effectively breaking through the traditional performance limits.

This paper investigates channel estimation for a 140 GHz terahertz communication system, considering channel characteristics such as near-field spherical wave effects and molecular absorption losses. For hardware implementation, a deployment strategy based on the heterogeneous multi-core architecture of the UCP4008 chip is developed. By jointly optimizing estimation performance and computational complexity, the LS-DFT algorithm is selected as the practical solution, achieving significant computational reduction while maintaining accuracy and ensuring efficient mapping onto the UCP4008 platform. Simulation and MPU-based results demonstrate that the proposed design attains a favorable trade-off between MSE performance and hardware resource utilization, providing an efficient and practical channel estimation solution.

This paper investigates scalable user scheduling in low-altitude, wideband cell-free radio access networks (CF-RANs), where scheduling is decentralized to user-centric distributed units (UCDUs), enabling scalable medium access control (MAC)-layer operation. Firstly, to provide analytical insights into the impact of distributed nature of CF-RAN, a closed-form expression for the uplink spectral efficiency (SE) is derived under zero-forcing (ZF) receiver implemented at edge distributed units (EDUs), revealing the performance degradation due to partial channel state information (CSI) availability. Next, we propose a three-level cooperation framework between the cloud central processing unit (CPU) and UCDUs to strike a balance among scheduling optimality, computational complexity, and fronthaul signaling overhead. This framework allows the system to dynamically adapt to varying network conditions and resource constraints. Furthermore, a low-complexity scheduling scheme based on large-scale fading is developed, enabling real-time operation without requiring matrix inversion. Numerical results validate the accuracy of the SE analysis and demonstrate that the proposed scheduling scheme achieves superior performance while significantly reducing computational complexity.

Repeater-assisted massive MIMO (RA-MIMO) provides a cost-effective solution for distributed macro-diversity without the high-capacity fronthaul of cell-free architectures. However, the distributed deployment of repeaters introduces propagation delay differences and timing misalignments, causing asynchronous reception at the base station. To characterize this effect, we derive a closed-form uplink spectral efficiency (SE) expression for asynchronous RA-MIMO, explicitly accounting for phase offsets, inter-carrier interference (ICI), and inter-symbol interference (ISI). The analysis reveals that the performance degradation is primarily driven by phase offsets disrupting coherent transmission, rather than by ICI/ISI. Motivated by this insight, we propose a delay-aware (DA) combining scheme that compensates for phase offsets at the receiver, and derive its closed-form SE. Simulations confirm the derived expressions and show that asynchronous reception severely degrades SE, whereas the proposed DA combining effectively mitigates this impairment and achieves near-synchronous performance.

To realize the potential of integrated sensing and communication (ISAC) in cell-free (CF) massive MIMO system, an ISAC framework is proposed in this paper. With numbers and locations of scatterers (targets) unknown, based on received downlink data signals from the transmit access point (tAP) antenna, multiple receive access points (rAPs) first estimate delays locally. In each outer iteration, by comparing the estimated delays with the delays of the paths through extracted scatterer locations, selected rAPs search out mismatched estimated delays. Through cooperation, the potential locations of the scatterers forming each candidate location set corresponding to each mismatched delay are obtained, and each set will be evaluated in sequence. Specifically, a joint evaluation algorithm is proposed, where the probability model for the joint evaluation problem is established based on delay and limited angular information. Under the expectation maximization (EM) framework, by fusing information from all the rAPs, the extracted scatterer locations and candidate locations are adjusted, and the evaluation results are estimated, which can be regarded as the global probability of scatterers exist at candidate locations. When the evaluation results of all the locations in a candidate location set are low, the corresponding delay will be discarded, otherwise, the candidate location with the highest evaluation result in the set will be extracted. The proposed framework avoids exhaustive search in different associations of scatterers and estimated delays, and is more flexible than extraction from all the candidate locations based on fixed thresholds. Then, based on a simplified model, the impact of system parameters on delay based location sensing accuracy is revealed with the theoretical analysis. Finally, based on the prototype system with CF radio access network (RAN) architecture, the proposed framework is experimentally validated.

Uplink integrated sensing and communication (ISAC) systems have recently emerged as a promising research direction, enabling simultaneous uplink signal detection and target sensing. In this paper, we propose the flexible projection (FP)-type receiver that unifies the projection-type receiver and the successive interference cancellation (SIC)-type receiver by using a flexible tradeoff factor to adapt to dynamically changing uplink ISAC scenarios. The FP-type receiver addresses the joint signal detection and target response estimation problem through two coordinated phases: 1) Communication signal detection using a reconstructed signal whose composition is controlled by the tradeoff factor, followed by 2) Target response estimation performed through subtraction of the detected communication signal from the received signal. With adjustable tradeoff factors, the FP-type receiver can balance the enhancement of the signal-to-interference-plus-noise ratio (SINR) with the reduction of correlation in the reconstructed signal for communication signal detection. The pairwise error probability (PEP) expressions are analyzed for both the maximum likelihood (ML) and the zero-forcing (ZF) detectors, revealing that the optimal tradeoff factor should be determined based on the adopted detection algorithm and the relative power of the sensing and communication (S&C) signals. A homotopy optimization framework is first applied for the FP-type receiver with a fixed tradeoff factor. This framework is then extended to develop the dynamic flexible projection (DFP)-type receiver, which iteratively adjusts the tradeoff factor for improved algorithm performance and environmental adaptability. Finally, we show that the length of the jointly processed signal should scale with the antenna size to fully unleash the potential of the uplink ISAC receiver.

Cell-free massive multiple-input-multiple-output (CF-mMIMO) is regarded as one of the promising technologies for next-generation wireless networks. However, due to its distributed architecture, geographically separated access points (APs) jointly serve a large number of user-equipments (UEs), and there are inevitably discrepancies in the arrival time of transmitted signals. In this paper, we investigate millimeter-wave (mmWave) scalable CF-mMIMO orthogonal frequency division multiplexing (OFDM) systems with asynchronous reception in a wide area coverage scenario, where asynchronous timing offsets may exceed far beyond the cyclic prefix (CP) duration. To address the issue, we propose a novel per-beam timing advance (PBTA) hybrid precoding architecture and derive closed-form expressions of the spectral efficiency (SE) for downlink asynchronous transmission. Both scalable centralized and distributed implementations are taken into account. Furthermore, we formulate the sum rate maximization problem and develop two low-complexity joint beam selection and UE association (BSUA) algorithms considering the impact of asynchronous timing offset. Simulation results demonstrate that asynchronous interference can severely degrade performance in wide-area scenarios, and our proposed PBTA scheme exploits beam-domain synchronization to align signal arrivals, effectively suppressing asynchronous interference and delivering notable performance gains. Additionally, the proposed BSUA algorithms achieve superior SE performance with low computational complexity.

This paper investigates the two-timescale design for access point (AP) mode selection and power allocation to realize the full potential of the cooperative bi-static ISAC network with low system overhead, where the beamforming at the APs is adapted to the rapidly-changing instantaneous channel state information (CSI), while the AP mode selection and power allocation are adapted to the slowly-changing statistical CSI. Firstly, the minimum mean square error (MMSE) estimator is applied to estimate the channels between the APs and the channels between the APs and the user equipments (UEs). Then we adopt the low-complexity maximum ratio transmission (MRT) beamforming and maximum ratio combining (MRC) detector, and derive the closed-form expressions of the ergodic rate of the UEs and the sensing signal-to-interference-plus-noise-ratio (SINR). A non-convex mix integer optimization problem is formulated to maximize the minimum sensing SINR under the communication quality of service (QoS) constraints. McCormick envelope relaxation and successive convex approximation (SCA) techniques are applied to solve the challenging non-convex mix integer optimization problem. Extensive simulation results demonstrate the analytical accuracy of the closed-form expressions and validate the convergence and effectiveness of the proposed AP mode selection and power allocation scheme.