This paper proposes a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) segmented symbiotic backscatter non-orthogonal multiple access (NOMA) system, where the STAR-RIS is composed of an enhancing primary signal (EP) zone and a backscatter device (BD) zone. To evaluate the overall system transmission reliability, we derive a tight lower bound of the coexistence outage probability (COP), where an imperfect/realistic successive interference cancellation (SIC) is considered. It is shown analytically that the error floor of the COP in both the near field and far field coverages would appear as long as the residual interference due to SIC is non-negligible, resulting in a diversity order of zero. Such an error floor is mainly dominated by the residual interference parameters, the decoding thresholds, and the power allocation ratios. More importantly, unlike the Gamma approximation approach, the adopted Laplace approach can capture the true diversity order with perfect SIC in the near-field/far-field coverage, which is dominated by the bottleneck number of the STAR-RIS elements belonging to the EP and BD zones, regardless of the dual-hop channel statistics. In addition, it is shown that the COP performance improves with either the quantification order or the concentration parameter of the imperfect channel state information.

This paper proposes a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) segmented symbiotic ambient backscatter system. The elements of the STAR-RIS are segmented into an enhancing primary-signal (EP) zone and a backscatter-device (BD) zone, which are adjusted to enhance receivers’ reception of the primary signal from S and to convey STAR-RIS’s information, respectively. With the aid of the proposed Max-Min rule, the optimal reflection coefficients to achieve the maximum sum ergodic capacity are derived. A simplified yet realistic setup of the reflection coefficients is then developed to balance the decoding performance of the two receivers, where the optimal reflection coefficients are shown to counteract the channel gain variation adaptively. Moreover, it is demonstrated that the ergodic capacity (EC) of the backscatter channel, under high signal-to-noise ratio (SNR) conditions, is primarily influenced by the number of STAR-RIS elements in the BD zone. Whereas the EC of the primary channel is determined by the ratio of STAR-RIS elements in the EP region to those in the BD region.

Integrated communication and navigation (ICAN) has emerged as a crucial technology for embedding navigation functions into 6G satellite Internet waveforms. However, the effective ICAN poses challenges for ICAN-enabled low-Earth orbit (LEO) satellites. Existing methods struggle to balance the requirements of communication and navigation within a single waveform and fail to adequately address the high-dynamic scenarios of LEO satellites. To overcome these challenges, this article proposes an orthogonal frequency-division multiplexing (OFDM)-based ICAN signal scheme. Leveraging the symmetry of the pilot cross-ambiguity function enables precise joint time–frequency channel estimation. First, by directly utilizing the intermediate results of communication, it achieves the delay and Doppler estimation limits while ensuring communication performance. Second, an analytical solution for the delay and Doppler estimation error is derived, allowing the direct determination of the minimum pilot allocation under any given accuracy requirements, effectively balancing the tradeoff between communication and navigation. Third, field results from an ICAN-enabled LEO satellite (altitude 1100 km) show that the delay and Doppler estimation accuracy can be controlled within 10 cm and 12 Hz under a pilot ratio of 0.089, respectively, with an approximate 58.34% accuracy and 10–15-dB carrier-to-noise ratio improvement over that of traditional Global Navigation Satellite Systems (GNSS). These findings offer valuable insights and practical References for the design and implementation of ICAN-enabled LEO satellites.

Global Navigation Satellite System signals are often affected by multipath errors, which impact the accuracy of positioning measurements. Traditional methods frequently fail to effectively mitigate multipath errors across different environments, primarily due to their inherent sensitivity to varying conditions. Here, we propose a multipath error cancellation method that utilizes antenna jitter, which mitigates multipath errors by rapidly changing the relative phases of direct and multipath signals without requiring changes to the receiver structure. The model that combines theoretical analysis with experimental verification is conducted to identify the minimum jitter amplitude required for effective error reduction in straight-line jitter scenarios. Moreover, extensive satellite data collection and verification were performed in Changsha, China, from December 2023 to August 2024. The results indicate that the proposed method enhances robustness and applicability across various environments compared to traditional approaches. Notably, it enables a vehicle-mounted antenna, priced at just a few dollars, to achieve positioning accuracy comparable to that of high-precision antennas costing thousands of dollars, making advanced positioning technology more accessible.

Integrated Communication and Navigation (ICAN) has emerged as crucial technology for embedding navigation into communication waveforms for 6G satellite internet. The effective integration of communication and navigation poses the challenge in ICAN-enabled Low Earth orbit (LEO) satellites. Existing methods struggle to conduct accurate channel estimation when faced with the LEO high-dynamic channels. To address this issue, a joint time-frequency channel estimation method is proposed, which leverages the symmetry of the 2D cross-ambiguity function to perform precise and robust Doppler and delay discrimination. Due to direct utilization of communication intermediate results, it achieves the measurement limits while ensuring communication performance. In addition, a closed expression of the estimation mean square error is derived, effectively balancing the trade-off between communication and navigation within a single frame. Results show that the proposed method can control a delay and Doppler accuracy within 10 cm and 12 Hz, respectively, when the pilot ratio is 0.089 and Signal-to-Noise Ratio (SNR) is -10 dB, with an approximate 58.34% improvement over traditional Global Navigation Satellite Systems.

Navigation resource allocation for low-earth-orbit (LEO) constellations refers to the optimal allocation of navigational assets when the number and allocation of satellites in the LEO constellation have been determined. LEO constellations can not only transmit navigation enhancement signals but also enable space-based monitoring (SBM) for real-time assessment of GNSS signal quality. However, proximity in the frequencies of LEO navigation signals and SBM can lead to significant interference, necessitating isolated transmission and reception. This separation requires that SBM and navigation signal transmission be carried out by different satellites within the constellation, thus demanding a strategic allocation of satellite resources. Given the vast number of satellites and their rapid movement, the visibility among LEO, medium-earth-orbit (MEO), and geostationary orbit (GEO) satellites is highly dynamic, presenting substantial challenges in resource allocation due to the computational intensity involved. Therefore, this paper proposes an optimal allocation algorithm for LEO constellation navigation resources based on dynamic programming. In this algorithm, a network model for the allocation of navigation resources in LEO constellations is initially established. Under the constraints of visibility time windows and onboard transmission and reception isolation, the objective is set to minimize the number of LEO satellites used while achieving effective navigation signal transmission and SBM. The constraints of resource allocation and the mathematical expression of the optimization objective are derived. A dynamic programming approach is then employed to determine the optimal resource allocation scheme. Analytical results demonstrate that compared to Greedy and Divide-and-Conquer algorithms, this algorithm achieves the highest resource utilization rate and the lowest computational complexity, making it highly valuable for future resource allocation in LEO constellations.