The demand for accurate sensing in the complex environment, such as urban areas and indoor spaces, is critical for future wireless networks, where scattered signals become essential for sensing occluded targets under non-line-of-sight (NLOS) conditions. To reduce the demand for beam-sweeping and geometric assumptions, we propose a bistatic NLOS sensing technique that fully exploits the scattered signals. By modeling the scattering channel responses and leveraging sparsity-driven compressed sensing, our method achieves robust environmental reconstruction to estimate target positions, shapes, and orientations. The proposed algorithm is applicable for estimating the parameters of first-order and second-order scattering targets. Experimental results demonstrate its superiority in occluded target sensing and environmental mapping, thus offering an efficient solution for NLOS sensing in complex scenarios.

Initial access (IA) is a procedure of establishing an initial connection between the base station (BS) and the users. In the fifth generation (5G) mobile communication system, the IA procedure includes beam management, which determines the beam pairs for random access (RA) and data transmission by beam sweeping. The existing beam sweeping method in the 3-rd generation partnership project (3GPP) standard mainly uses a predefined uniform beamforming codebook and sweeps the beams progressively, which is time-consuming and highly inflexible. In this paper, inspired by the fact that the highly non-uniform environment and user distribution mean part of the beam sweeping might be less beneficial, we propose a novel learning-based IA framework for the BS to optimize the beam sweeping patterns. Specifically, we resort to the deep reinforcement learning (DRL) approach to implicitly obtain the unknown environment and user distribution properties by continuously interacting with the environment, and then make decisions based on the rewards achieved by past actions. The simulation results show that our proposed scheme can save much time compared with the new radio (NR) and optimization methods under different datasets and conditions, which greatly improves the beam sweeping efficiency.

Initial random access is a crucial process in wireless communication networks, which sets up reliable connections between the base station (BS) and multiple active users. In this procedure, useful connection information can be naturally obtained to achieve user positioning, and the channel state information (CSI) of multiple users can be further exploited to realize environment sensing. On the other hand, environment sensing is highly related to user positioning as it requires user-specific CSI and benefits from multi-view observations from different user positions. Therefore, in this paper, we propose a joint initial random access, environment sensing, and user positioning framework. Specifically, oversampled cyclic prefixes (CPs) in orthogonal frequency division multiplexing (OFDM) systems, which contain rich environmental information, can be exploited to achieve enhanced channel estimation. Environment sensing and user positioning are further implemented based on the channel estimation results, and the scatter points are then clustered to reconstruct the environment objects. The simulation results show that the proposed framework can achieve a decimeter-level accuracy and a reconstruction ratio of about 89% for user positioning and environment sensing.

High-resolution mmWave radar imaging plays an important role in applications such as autonomous driving. Beam-based imaging methods often require scanning the scene of interest with a sufficiently small angular stepsize to achieve high-resolution, thus leading to a large computational and storage burden. Compressive sensing (CS) is a promising strategy to reconstruct high-dimensional yet sparse signals from low-dimensional measurements with random sampling. Therefore in this paper, we conduct random space sampling by adding random phase shifts on the transmit antennas of a frequency modulated continuous wave (FMCW)-based radar system. We prove that the sensing model can be formulated as a CS problem and solved by Expectation-Maximization Gaussian-Mixture Approximate Message Passing (EMGMAMP)-based approaches. Simulation results show that the model has excellent imaging performance even with very few sensing measurements. To further improve the imaging quality, we consider a multi-view sensing scenario in which sensing results from different positions are fused by proper occlusion processing and coordinate transformation. Finally, appropriate evaluation metrics are proposed for target sensing results to validate the effectiveness of the proposed sensing model and algorithm.

Initial random access is a crucial process in wireless communication networks, which sets up reliable connections between multiple active users and the base station (BS). In this procedure, useful information, including beam pair, timing advance (TA), and channel state information (CSI), can be naturally obtained to achieve environment sensing and user positioning. Environment sensing is highly related to user positioning as it requires user-specific CSI, and more importantly, the multi-view observations from different user locations potentially benefit the fusion of the overall environment information. This makes joint environment sensing and user positioning of great significance. Moreover, initial random access provides observations from different users, avoiding the limited and insufficient observation of a single pair of transceivers, which helps to realize environment sensing in large scenarios. Therefore, in this paper, we propose a joint initial random access, environment sensing, and user positioning framework, exploiting direction, time-delay, reflection, and scattering information brought by beam pair, TA, and CSI. Furthermore, we illustrate the remarkable sensing and positioning performance of the proposed scheme in both light-of-sight (LoS) and non-light-of-sight (NLoS) scenarios.

In mission-critical Industrial Internet of Things (IIoT), multiple sensors make independent observations at different locations and then transmit them to the base station (BS) to obtain a global system state vector. Uploading observation information to the BS by active sensors is a multiple access process. As the task is to complete state estimation instead of maximizing the physical-layer capacity, conventional multiple access schemes cannot be applied directly to mission-critical IIoT applications. Therefore, next generation multiple access (NGMA) techniques are urgently needed to realize the key performance indicators for the design of IIoT networks. Note that, in mission-critical IIoT systems, each sensor can only obtain the observation of a subset of state variables, and the BS only cares about the state information embedded in that observation not the identities of the sensors. This indicates that the whole process of data transmission and state estimation can be totally unsourced, thus resulting in a highly efficient IIoT system implementation. Based on this crucial finding, in this article, we propose an unsourced multiple access (UMA)-based mission-critical IIoT system. Moreover, a decoupled UMA (D-UMA) scheme is proposed to improve transmission efficiency and state estimation performance. We analyse the fundamental aspects of how our design affects and guarantees the controllability, observability, and stability of an IIoT control system. Simulation results verify the remarkable performance of the proposed scheme compared with the conventional orthogonal multiple access (OMA) and nonorthogonal multiple access (NOMA) schemes.

Initial access (IA) is a procedure of establishing an initial connection between the base station (BS) and the user. In the fifth generation (5G) millimeter wave (mmWave) communication system, the IA procedure includes beam management, which determines the beam pair for random access and data transmission by beam sweeping. The existing beam sweeping method in the 3-rd generation partnership project (3GPP) standard mainly uses a predefined uniform beamforming codebook and sweeps the beams progressively, which is time-consuming and highly inflexible. Note that the non-uniform and quasi-stationary environment and user cluster distribution information can be exploited for the BS to optimize the beam sweeping patterns. Therefore, this paper proposes a novel reinforcement learning (RL) framework for IA to efficiently acquire beam sweeping patterns. Specifically, a dimension-reduced beamforming codebook is designed to solve the problem of large search space and a comprehensive RL environment is constructed for the BS to capture the properties of environment layout and user distributions. Simulation results verify the remarkable performance of our proposed schemes in terms of beam sweeping efficiency.

In this paper, we consider the problem of integrated sensing and communication (ISAC) system design over wireless networks. Specifically, the mobile station (MS) in the ISAC system sends uplink communication signal beams to the base station (BS), and the BS accomplishes the environment sensing by processing the propagation gain from received beams. Since the uniform discretization of the environment scenario has inaccurate descriptions of the BS/MS position and object occlusion relationship, we propose a non-uniform pixel discretization method. According to the location of the transceiver and the direction of beams, we discretize the environment into layered non-uniform pixels, which reflect the occlusion relationship between objects in the environment. Based on the sparse features of environmental scatterers, we propose an environment sensing algorithm based on compressed sensing and approximate message passing. The proposed algorithm achieves accurate environment sensing by iteratively removing occlusion interference. At the same time, with the continuous sweeping of the transmitting and receiving beams, the environment sensing results gradually improve. Finally, simulation results demonstrate the effectiveness of the proposed ISAC system design and algorithm.

A large number of new applications are emerging in the future sixth-generation (6G) communication systems. Federated learning (FL) enables massive user equipments (UEs), such as mobile phones and Internet of Things (IoT) devices, to cooperatively learn a shared model for prediction in various applications, while keeping the training data local. However, in practical scenarios, there are still some problems in deploying FL systems, including serving a large number of active UEs, longtime delay, and the risk of UEs’ privacy leakage. To tackle these issues, we introduce unsourced random access (URA) into the FL systems. URA can support massive connectivity and its unsourced property can protect the UEs’ identity privacy. Moreover, considering the trade-off between communication and computation performance and the various importance of different UEs’ local models in training epochs, two importance metrics are designed. The UEs can decide their own active probability according to the metrics among the communication rounds, which avoids the additional cost of being scheduled by the base station (BS) and maximums the use of the limited communication resources to ensure UEs with higher priority can upload trained models, thus improving the training efficiency. Simulation results verify the remarkable communication and computation performance of the proposed schemes.

Massive Machine-Type Communication (mMTC) is expected to support massive connectivity for a large number of machine-type devices (MTDs). In many practical applications, the base station (BS) only needs to recover the list of received messages instead of the identities of active users, which is called unsourced random access (URA). In this paper, we propose a rateless URA scheme, which builds a bridge between rateless code and URA. Specifically, every active user divides the message into several sub-blocks, selects certain sub-blocks and linearly combines them in each time slot according to the transmission pattern, which is randomly chosen from a common pattern matrix by the user before transmission. Then the index of the transmission pattern and the coded sub-block are stitched together, mapped into a codeword in a common codebook and transmitted. At the receiver, the decoder creates decoding trees to group the coded sub-blocks by transmission patterns and recover the original messages. Simulation results verify the remarkable performance of the proposed URA scheme.

The core requirement of massive Machine-Type Communication (mMTC) is to support reliable and fast access for an enormous number of machine-type devices (MTDs). In many practical applications, the base station (BS) only concerns the list of received messages instead of the source information, introducing the emerging concept of unsourced random access (URA). Although some massive multiple-input multiple-output (MIMO) URA schemes have been proposed recently, the unique propagation properties of millimeter-wave (mmWave) massive MIMO systems are not fully exploited in conventional URA schemes. In grant-free random access, the BS cannot perform receive beamforming independently as the identities of active users are unknown to the BS. Therefore, only the intrinsic beam division property can be exploited to improve the decoding performance. In this paper, a URA scheme based on beam-space tree decoding is proposed for mmWave massive MIMO system. Specifically, two beam-space tree decoders are designed based on hard decision and soft decision, respectively, to utilize the beam division property. They both leverage the beam division property to assist in discriminating the sub-blocks transmitted from different users. Besides, the first decoder can reduce the searching space, enjoying a low complexity. The second decoder exploits the advantage of list decoding to recover the miss-detected packets. Simulation results verify the superiority of the proposed URA schemes compared to the conventional URA schemes in terms of error probability.