Existing Reconfigurable Intelligent Surface (RIS) beamtraining designs often prioritize communication metrics while neglecting geometric factors. This leads to inefficient initial access and poor positioning accuracy, which in turn hinders the fine beam alignment that relies on location priors. To resolve this dilemma, this paper proposes a two-stage beamtraining framework enabled by a novel geometry-aware codebook. First, a multi-RIS Cramér-Rao Lower Bound (CRLB) is derived to quantify positioning limits. Guided by this, a geometry-aware transformer-based network is developed to generate codebooks to optimize spatial beam allocation, by jointly minimizing region-averaged CRLB and maximizing achievable rate. This codebook facilitates the two-stage beamtraining process: an initial scan yields reliable Time-of-Arrival (ToA) and Angle-of-Arrival (AoA) measurements for coarse positioning, which are then utilized to compute precise phase shifts for fine alignment. Extensive simulations under the considered sub-6 GHz multi-RIS setting demonstrate that the proposed framework significantly outperforms state-of-the-art methods in both positioning accuracy and communication rates, with over 80% of realizations achieving sub-meter positioning accuracy after two-stage refinement.

Highly accurate train positioning and safe train operations are essential parts of intelligent transportation systems. Tunnels are frequently encountered in subways and mountainous regions and pose unique challenges due to the unavailability of Global Navigation Satellite System (GNSS). The Leaky CoaXial (LCX) Cable, already a part of the communication infrastructure, serve as antenna within tunnels due to its numerous advantages. The application of LCX positioning shows significant potential for advancing rail technology in the forthcoming BSG (beyond-SG) and 6G eras. To ensure highly accurate and safe train operations, this paper builds upon our previous work on LCX positioning methodology and proposes an integrity monitoring algorithm called 1M - TLP (Integrity Monitoring for Two-stage LCX Positioning) for the two-stage LCX positioning system with redundant infrastructure. A multi-LCX system is introduced and the fault detection and fault exclusion method for the multi-LCX positioning system is proposed. Then, to assess the integrity risks of the multi-LCX positioning system, the Along Track Protection Level (ATPL) for the system is defined. Simulation results show that the proposed 1M - TLP algorithm is capable of detecting random system faults, improving the overall system positioning accuracy and ensuring the safety of train operations.

High precision train positioning is a crucial component of intelligent transportation systems. Tunnels are commonly encountered in subways and mountainous regions. As part of the communication system infrastructure, Leaky CoaXial (LCX) Cable is widely equipped as antenna in tunnels with many advantages. LCX positioning holds great promise as a technology for rail applications in the upcoming B5G (beyond-5G) and 6G eras. This paper focuses on the LCX positioning methodology and proposes two novel algorithms along with a novel communication-positioning integration signal. Firstly, a novel algorithm called Multiple Slot Distinction (MSD) LCX positioning algorithm is proposed. The algorithm utilizes a generated pseudo spectrum to fully utilize the coupled signals radiated from different slots of LCX. This approach offers higher time resolution compared to traditional methods. To further improve the positioning accuracy to centimeter-level and increase the measuring frequency for fast trains, a novel communication-positioning integration signal is designed. It consists of traditional Positioning Reference Signal (PRS) and a significantly low power Fine Ranging Signal (FRS). FRS is configured to be continuous and superposed onto the cellular signal using Non-Orthogonal Multiple Access (NOMA) principle to minimize its interference to communication. A two-stage LCX positioning method is then executed: At the first stage, the closest slot between the receiver and LCX is estimated by the proposed MSD algorithm using PRS; At the second stage, centimeter-level positioning is achieved by tracking the carrier phase of the continuous FRS. This process is assisted by the closest slot estimation, which helps mitigate interference between neighboring slots and eliminate the integer ambiguities. Simulation results show our proposed LCX position methodology outperforms the existing ones and offer great potentials for future implementations.

This paper presents a feasibility study of a novel multiple access technique called Multi-Scale Non-Orthogonal Multiple Access (MS-NOMA) for the next generation communication-positioning integration system. Different from the traditional positioning signals which are mostly Time Division Multiple Access with communication signals and are broadcast to all users, MS-NOMA supports continuous positioning waveform and flexible configurations for different positioning users to obtain higher ranging accuracy, lower positioning latency, less resource consumption and better signal coverage. Our major contributions are: Firstly, we present the MS-NOMA waveform and evaluate its performances by theoretical and simulation analyses. The results show it is feasible to use the MS-NOMA waveform to achieve high positioning accuracy and low Bit Error Rate with little resource consumption simultaneously. Secondly, to achieve optimal positioning accuracy and signal coverage, we model the power allocation problem for MS-NOMA as a convex optimization problem satisfying the Quality of Services requirement and other constraints. Then, we propose a novel Communication and Positioning Performances constrained Positioning Power Allocation (CP4A) algorithm which allocates the power of all P-Users iteratively. The theoretical and numerical results show our proposed MS-NOMA waveform with CP4A algorithm has great improvements of ranging/positioning accuracy than traditional Positioning Reference Signal in cellular network.

Localization plays a key role in Internet of Things. This paper proposes a novel intelligent cooperative multisensor localization method called the edge cloud cooperative localization (ECCL) which has the range and angle observations from the neighbor nodes along with the location observations from an absolute coordinate localization system like global positioning system. The edge cloud structure is proposed which employs several distributed Kalman filters in sensor nodes edge and a centralized cooperative fusion unit in the cloud. For a robust fusion, a cooperative redundancy validation method is proposed to detect the outliers. The proposed ECCL scheme has the advantages of both the distributed and centralized localization, which satisfies the needs of high reliability and high accuracy, especially when sensor nodes have limited computational resources. The simulation and experimental results show that our proposed ECCL algorithm outperforms the other schemes both in outlier detection and localization accuracy.

Global navigation satellite system (GNSS) is not suitable for the dense urban or indoor environments as the satellite signals are very weak. Meanwhile, positioning is an important application of the fifth-generation (5G) communication system. GNSS/5G integrated positioning system becomes a promising research topic with the development of 5G standard. This paper focuses on the integrated methodology of GNSS and device to device (D2D) measurements in 5G communication system. We analyze the characteristics of this type of integrated system and propose a high-efficiency D2D positioning measure protocol, named crossover multiple-way ranging, which consumes less communication resources. Then, to deal with the high-dimensional state space in the integrated system, a state dimension reduction method is proposed to overcome the particle degeneracy problem of particle filter which is used to fusion GNSS and 5G D2D measurements. Three integrated algorithms in different scenarios have been proposed: the first one is the integrated algorithm when the range measurements can be measured directly. The second one is the integrated algorithm with unknown time skew and offset of each mobile terminal. The third one is the integrated algorithm in GNSS-denied environment which is prevalent in urban and indoor applications. The simulation and experimental results show that our proposed integrated methodology outperforms the nonintegrated one.