Achieving accurate and flexible localization in global navigation satellite system (GNSS)-denied underground spaces is critical for Internet of Things (IoT)-enabled logistics and personnel operations. To this end, this paper proposes a scalable multimodal localization framework requiring only a single ultra-wideband (UWB) transmitter connected to a distributed antenna system, avoiding the deployment of multiple additional anchors. An adjacency-masked, change-point-aware hidden Markov model (AC-HMM) is developed for region identification using only two-path UWB measurements, which avoids full channel impulse response (CIR) processing and reduces computational complexity. A multi-scale factor-graph maximum a posteriori inference method (MS-FGM) is then proposed for dynamic localization by fusing UWB and magnetic-field residuals with region and motion constraint factors. Multi-scale temporal aggregation is further introduced to mitigate motion-induced fluctuations and improve localization accuracy and stability. Experiments conducted along the roadways of an underground parking lot demonstrate an average region classification accuracy of 96.81% and a mean positioning error of 0.88 m, outperforming existing methods by up to 42.19%.

To achieve seamless and continuous positioning for smartphones in tunnels, where the Global Navigation Satellite System (GNSS) signals are unavailable, this paper proposes a new method that leverages the existing leaky coaxial cable (LCX) infrastructure from public land mobile networks to introduce the GNSS signals directly from outside the tunnels. This approach introduces three key innovations. First, a continuous hybrid GNSS-LCX channel is proposed which uses a waveguide-to-wireless model to provide continuous GNSS signal coverage via existing 5G LCX without requiring dedicated hardware. Based on this model, a bidirectional clock bias cancellation mechanism is designed for GNSS signals inside the tunnel. This method establishes a quantitative mapping between the position in the tunnel and the pseudorange observations incorporating clock bias, enabling the residual latency from smartphones and wired transmission in the tunnel to be modeled as a function of signal propagation distance and user tracks within the tunnel environment. Furthermore, a 5G-enhanced factor graph optimization (FGO) method is proposed, which integrates 5G measurements and tunnel topology to suppress positioning fluctuations caused by multipath effects in tunnels, while mitigating GNSS positioning latency and ambiguity induced by 5G cell handovers. Field tests in a 150-m tunnel show 1.21 m median accuracy, achieving 46% higher accuracy than fingerprinting and 3.2×faster convergence than conventional methods. This solution reuses 5G-LCX infrastructure for cost-effective and consistent tunnel positioning that bridges the gap between open-sky and underground tunnel environments.

Indoor spaces have already been accounted for a significant proportion of people's lives, and with the advent of the fifth-generation mobile communication technology (5G) era and the rise of the Internet of Things (IoT), the demand for indoor localization is further increasing. Current solutions for indoor positioning, such as Bluetooth, Wi-Fi, ultra-wideband (UWB), and pseudo-satellite technologies, have faced issues in terms of cost, accuracy, and scalability during the promotion and application process. In this regard, distributed antenna systems (DAS) have an absolute advantage in terms of indoor coverage for communication, which shows enormous potential for fusion with current indoor positioning technologies. This paper proposed a universal one-dimensional localization algorithm fusing UWB technology with the indoor DAS system, aimed at narrow indoor environments such as corridors and cable tunnels, which can achieve high-precision localization as evaluated in a real scenario of an indoor corridor in the Beijing University of Posts and Telecommunications (BUPT). Additionally, our algorithm requires only two anchors, effectively reducing positioning costs while improving accuracy. The results indicate that this localization technique effectively meets the requirements defined for indoor scenarios in 3GPP 38.855, with an error of decimeter level.

The global navigation satellite system (GNSS) fails in indoor environments, leading to a growing demand for indoor positioning, ranging from above-ground buildings to under-ground parking lots. Existing indoor positioning technologies face challenges balancing low cost, low latency, and high accuracy, particularly in large underground parking lots (LUPLs). This paper proposes a sparsely deployed Bluetooth and magnetic (SBM) fusion positioning system for LUPL scenarios. Specifically, the spatial characteristics of the received signal strength indicator (RSSI) from sparsely deployed Bluetooth and the magnetic field strength are analyzed. Static and dynamic algorithms are designed for the SBM system by utilizing Bluetooth's area division and coarse positioning, combined with the high precision of magnetic field positioning. Experimental results from a real LUPL deployment demonstrate that, under a sparse beacon distribution (1 beacon per 294 m2), the proposed positioning algorithms achieve static accuracy of 1.32 m, dynamic accuracy of 0.75 m, and low latency on consumer-grade devices.

Passive distributed antenna systems (DAS) are widely used for indoor mobile communication quality enhancement and the market space continues to rise. However, in the beamforming process, the passive nature of DAS causes repeated beam scanning and beam correlation issues. This paper proposes an environment-sensing hybrid beamforming method for passive DAS, by which multi-distributed antennas use sensed environment information while considering beam correlation for hybrid beamforming design to improve communication performance. As the initial study, a DAS-oriented method based on compressed sensing for extracting environmental information from superimposed signals is proposed to achieve short-time and high-precision sensing. The flexible design of the hybrid beam transmit path based on the reflected environmental information considering antenna correlation improves the communication rate and bypasses the obstacles. In addition, a dynamic adjustment mechanism for power trade-off between sensing and communication is analyzed to achieve reasonable power allocation, which optimizes the performance of the whole system. Finally, simulation results validate the effectiveness of our proposed method to improve the communication performance while maintaining the sensing accuracy significantly.

In the sixth generation (6G) Internet of Things (IoT), integrated sensing and communication (ISAC) emerges as a key technology, which is expected to significantly enhance the perception capabilities and spectrum efficiency of base stations (BSs). It holds potential for applications in unmanned aerial vehicle (UAV) monitoring, vehicle positioning, and crowd detection. However, developing an integrated waveform that efficiently conserves spectrum resources while maintaining lower complexity remains challenging. This paper designs an ISAC system that utilizes orbital angular momentum (OAM) waves generated by a uniform circular array (UCA), which enhances communication and sensing capabilities by allocating distinct modes. This paper proposes a multimode multiplexing communication scheme based on a single UCA and an OAM-circular reception method (OAM-CRM) for the two-dimensional direction-of-arrival (DOA) estimation of targets, encompassing both azimuth and elevation angles. Additionally, an OAM-different modes (OAMDM) algorithm is designed to optimize communication and sensing performance across various mode sets. Simulation results verify the effectiveness of the OAM ISAC system and demonstrate the superior performance of the proposed algorithms compared to conventional methods.

The 4G/5G era in the past decades has witnessed the vigorous development of Hybrid Analog and Digital Beamforming (HBF) technologies in the field of communications under cellular Multiple Input Multiple Output (MIMO) systems. As an evolution, the B5G/6G has strong visions of high-accurate positioning capabilities other than the communication quality, thus a beam alignment method towards positioning enhancement is also urgently desired in cellular systems. To this end, this paper proposed a HBF method for positioning enhancement in cellular MIMO systems. We first derive the Fisher Information for multiple-path assisted positioning as the performance criterion of positioning under a wideband channel with both precoder and combiner considered. Then a HBF strategy is proposed to optimize such criterion over multiple resources involving the transmitting power, beam and frequency dimensions, which is referred to asSensing Beamforming. Furthermore, a Newton based heuristic method is proposed for the estimation of sensing elements (e.g. angle of arrival) from multiple paths, and the positioning results are obtained by a proposed multiple-path assisted positioning method considering the multiple path clutters in the environment. The results indicate that the proposed method can enhance the positioning performance with the accurate estimation of sensing elements.

This paper aims to solve the trajectory tracking control problem for an autonomous vehicle based on reinforcement learning methods. Existing reinforcement learning approaches have found limited successful applications on safety-critical tasks in the real world mainly due to two challenges: 1) sim-to-real transfer; 2) closed-loop stability and safety concern. In this paper, we propose an actor-critic-style framework SRL-TR2, in which the RL-based TRajectory TRackers are trained under the safety constraints and then deployed to a full-size vehicle as the lateral controller. To improve the generalization ability, we adopt a light-weight adapter State and Action Space Alignment (SASA) to establish mapping relations between the simulation and reality. To address the safety concern, we leverage an expert strategy to take over the control when the safety constraints are not satisfied. Hence, we conduct safe explorations during the training process and improve the stability of the policy. The experiments show that our agents can achieve one-shot transfer across simulation scenarios and unseen realistic scenarios, finishing the field tests with average running time less than 10 ms/step and average lateral error less than 0.1 m under the speed ranging from 12 km/h to 18 km/h. A video of the field tests is available athttps://youtu.be/pjWcN_fV24g.

Based on the Richardson and Urbanke (RU) algorithm, the widely used quasi-cyclic low-density parity-check (QC-LDPC) code encodes the parity check matrix (PCM) in blocks, making hardware implementation possible. However, in a QC-LDPC encoding system with multiple expansion factors, storing information bits in excessive register bit widths would reduce the flexibility and throughput of the encoder when the RU algorithm is used. Therefore, this article proposes a novel architecture to reduce the complexity of the encoder by decreasing the number of expansion factors. This architecture optimizes the storage structure of the PCM and the pipeline structure of the encoding core, which obviously improves the flexibility and throughput of the encoder. In addition, this article presents two algorithms to optimize the pipeline structure, decreasing the latency for information bits to enter the encoder and ensuring that the pipeline of the encoding core would not stall. Moreover, the proposed architecture can be applied to encoding systems with multiple expansion factors, such as 5G and IEEE 802.11, and it has been verified on field-programmable gate array (FPGA). Compared with the most advanced solution, the proposed encoder achieves a 77% increase in resource utilization. As a case study, this encoder improves the performance of the soft base station by 2.59 times.

Multi-target detection and tracking based on fusion of millimeter-wave (MMW) radar and camera play an important role in intelligent transportation system (ITS). However, most previous studies rely heavily on the information from one sensor and assisted by another, or require some additional measurement work. To address this issue, in this paper, we propose a radar-camera fusion method based on position deducing, where the camera and radar serve as mutual reference to deduce the position of the object. Since the azimuth accuracy and target detection rate of visual positioning algorithm are higher than those of radar, the proposed method improve the accuracy of the lateral positioning and reduce the missed detection. Experiments illustrate that the proposed method achieves highprecision positioning with an accuracy of 0. 110m. In addition, when there is at least one reference target, the detection rate and the false alarm rate are approximately 99.15% and 0.03%, respectively.

This paper addresses high-resolution vehicle positioning and tracking. In recent work, it was shown that a fleet of independent but neighboring vehicles can cooperate for the task of localization by capitalizing on the existence of common surrounding reflectors, using the concept of Team Channel-SLAM. This approach exploits an initial (e.g. GPS-based) vehicle position information and allows subsequent tracking of vehicles by exploiting the shared nature of virtual transmitters associated to the reflecting surfaces. In this paper, we show that the localization can be greatly enhanced by joint sensing and mapping of reflecting surfaces. To this end, we propose a combined approach coined Team Channel-SLAM Evolution (TCSE) which exploits the intertwined relation between (i) the position of virtual transmitters, (ii) the shape of reflecting surfaces, and (iii) the paths described by the radio propagation rays, in order to achieve high-resolution vehicle localization. Overall, TCSE yields a complete picture of the trajectories followed by dominant paths together with a mapping of reflecting surfaces. While joint localization and mapping is a well researched topic within robotics using inputs such as radar and vision, this paper is first to demonstrate such an approach within mobile networking framework based on radio data.

With the development of the 5G mobile communication system, the network infrastructure is becoming more widely deployed. Ubiquitous 5G wireless signals bring new opportunities to the positioning of connected autonomous vehicles. Radio-based positioning can utilize the existing 5G network infrastructure to locate connected vehicles without additional sensor equipment installed in the vehicle. Moreover, location-related information of vehicles can be obtained from V2V communication supported by 3GPP, which provides a new way to increase positioning accuracy. In this paper, we develop a V2V communication assisted cooperative localization algorithm that exploits the location-related information in V2V communication and the multipath radio signals received by vehicles to further improve localization accuracy. A Bayesian model is derived for feature-based simultaneous localization and mapping (SLAM) according to the information exchanged between vehicles and the characteristics of the multipath components, and a cooperative belief propagation algorithm is used to locate vehicles on a factor graph. Simulation results show that this algorithm has better positioning performance than non-V2V-cooperative scenarios.