Carrier Aggregation (CA) is investigated to address spectrum scarcity in 6G Integrated Sensing and Communication (ISAC) systems. However, inter-band phase offsets impose high complexity on achieving coherent gain across multiple bands. Furthermore, typical CA focuses on multi-frequency-band expansion, while multi-time-duration expansion has received limited attention. To address the above issues, this paper proposes a novel high-accuracy Multi-Band Joint Carrier Phase Sensing (MB-JCPS) framework that utilizes carrier phase information from multiple time-frequency bands to improve range and velocity sensing accuracy. A carrier phase extraction method is introduced based on oversampling Range-Doppler phase spectrum. The impact of non-ideal factors—including time, frequency, and phase offsets, as well as phase noise—on carrier phase is modelled, analyzed, and eliminated via an inter-path difference method. A Real-valued Multi-Carrier Ambiguity Resolution (RMCAR) algorithm is designed to resolve integer ambiguities in the range and velocity carrier phase models. The time-frequency selection problem is formulated as a constrained Rayleigh-quotient optimization problem and solved via successive convex approximation. The Cramér-Rao lower bound (CRLB) for the range and velocity estimation under phase offsets is derived, demonstrating the performance gain of CA in both frequency and time domains. Numerical simulations show that the proposed method achieves higher sensing accuracy with lower complexity than benchmark algorithms in 3GPP Uma-AV environments.

Abstract Carrier phase positioning technology is widely used in global navigation satellite systems (GNSS) but not applied to wireless orthogonal frequency division multiplex (OFDM) systems. Carrier phase technology has a high resolution, which can improve the positioning accuracy of wireless cellular networks. Applying the carrier phase to a wireless mobile positioning system has some problems, such as continuous phase tracking, accurate integer ambiguity resolution, positioning errors caused by non‐line‐of‐sight (NLOS) and so forth. This paper offers a positioning technique combining the time‐of‐arrival (TOA) and carrier phase to solve the above problems. Based on Bayesian theory, a two‐step position estimator is introduced in our scheme to solve the terminal position. Besides, an NLOS identification and suppression scheme is proposed to enhance the robustness of the algorithm. Experiments show that even in the NLOS environment, the joint positioning algorithm using TOA and carrier phase can effectively improve the positioning accuracy.

As a novel non‐orthogonal multiple access scheme for fifth generation mobile networks, pattern division multiple access (PDMA) can support more connectivity and significantly improve spectral efficiency compared with conventional orthogonal multiple access (OMA). In order to further to improve the system transmission reliability and spectrum efficiency for PDMA, a novel PDMA framework combined with multiple‐input multiple‐output (MIMO) technology by using space–frequency block coding scheme for downlink transmission is proposed in this study. The performance analysis expressions of system capacity for MIMO‐PDMA and MIMO‐OMA are derived. The detailed MIMO‐PDMA design diagrams for transmitting and receiving end are presented and the equivalent space and frequency channel matrix for each user are derived to facilitate the performance evaluation of the proposed framework. The simulation results are provided to demonstrate that the integration of the two technologies can achieve better performance compared with traditional PDMA and MIMO‐OMA.

In positioning research based on the orthogonal frequency division multiplex (OFDM) system, the observed time difference of arrival positioning method is a widely studied direction. The terminal measures the reference signal sent by the base station to calculate the time of arrival (TOA). Using TOA information, the location of the terminal can be identified. So the accuracy of the positioning is highly dependent on the results of the TOA measurements. Terminals often have difficulty measuring accurate TOA due to multipath channels and occlusion of obstacles. This study proposes two clustering‐based TOA estimation methods. The cluster‐based Max‐peak estimator is used to solve the problem that the energy of the first‐path is not the strongest in the case of multipath. Moreover, a cluster‐based adaptive threshold estimator is used to estimate TOA when the power of the direct path is low. The joint estimator of the two methods is designed to improve estimation accuracy. The proposed method is tested in the OFDM localisation environment, and the results demonstrate that the proposed method has better performance than the conventional way.