Reconfigurable intelligent surface (RIS) has emerged as a promising technology to enhance indoor wireless communication and sensing performance. However, the construction of reliable received signal strength (RSS)-based fingerprint databases for RIS-assisted indoor positioning remains an open challenge due to the lack of realistic and spatially consistent channel modeling methods. In this paper, we propose a novel method with open-source code for generating RIS-assisted RSS fingerprint databases. Our method captures the complex RIS-assisted multipath behaviors by extended cluster-based channel modeling and the physical and electromagnetic properties of RIS and transmitter (Tx). And the spatial consistency is incorporated when simulating the fingerprint data collection across neighboring positions. Moreover, an effective sorting algorithm is proposed to solve the online synchronization issue, a closed-form RIS phase configuration strategy is proposed to improve the localization accuracy, and the modeling method of mutual coupling (MC) effect is provided. Extensive simulations are conducted to evaluate the fingerprint database generated by the proposed method. And the positioning performance on the database using different algorithms is analyzed, providing valuable insights for the system design.

Collaborative access points (APs) enabled cell-free networks can provide stable and uniform communication services for all user locations, making them a promising network architecture for the sixth-generation (6G) mobile communication systems. While the performance of pure cell-free networks has been extensively studied, it remains unclear whether deploying large-scale cell-free APs in legacy cellular networks can effectively boost communication performance. Besides, the realization of a cell-free network is considered to be a gradual long-term evolutionary process in which APs will be incrementally introduced and form a hybrid communication network with the existing cellular base stations (BSs). Such a collaboration will bridge the gap between the established cellular network and the innovative cell-free network. Therefore, hybrid cellular and cell-free networks (HCCNs) emerge as a feasible solution for advancing cell-free network development, and it is worthwhile to further explore its performance limits. Different from heterogeneous networks or multipoint coordinated networks, the characterization of HCCNs needs to take both inter- and intra-layer collaboration into account. This paper presents a stochastic geometry-based HCCN model to analyze the distributions of signal and interference and reveal their mutual coupling. Specifically, in order to benefit the user equipments (UEs) from both the cellular BSs and the cell-free APs, a conjugate beamforming design is employed, and the aggregated signal is analyzed using moment matching. Then, the coverage probability of the hybrid network is characterized by deriving the Laplace transforms and their higher-order derivatives of interference components. Furthermore, the average achievable rate of the hybrid network over channel fading is derived based on the interference coupling analysis. Simulation results demonstrate that compared to traditional cellular networks, HCCN effectively narrows communication quality differences between different UEs and improves overall communication performance.

Integrated sensing and communication (ISAC) has been recognized as a key technology in the low-altitude economy. Leveraging the flexibility and high maneuverability of unmanned aerial vehicles (UAVs), we propose a multi-UAV enabled ISAC system to provide communication and tracking services for multiple ground mobile targets (GMTs). By jointly optimizing the communication scheduling and UAV trajectory, we aim to maximize the system rate while guaranteeing tracking demands, subject to anti-collision and energy consumption constraints. Specifically, we decompose the original non-convex optimization problem into two subproblems and develop an efficient approach based on successive convex approximation (SCA) to solve them iteratively. Numerical results demonstrate that the proposed multi-UAV enabled system achieves superior communication performance through the joint optimization of scheduling and trajectories, while fulfilling real-time tracking requirements.

Non-terrestrial network (NTN) enabled low-altitude economy (LAE) has emerged as a promising economic paradigm that leverages advanced air mobility (AAM) vehicles to revolutionize connectivity in the six-generation (6G) era. By deploying unmanned aerial vehicles (UAVs) as flying edge nodes, wireless caching can significantly alleviate network congestion and reduce latency, enabling the efficient handling of massive terrestrial user requests in LAE applications. However, the limited energy and storage capacity of UAVs pose significant challenges to provide persistent and diverse content delivery services. To address such limitations, this paper proposes a multi-UAV-enabled coded caching scheme for energy-efficient data delivery, in which both the communication coverage and cache hit are satisfied. Taking into account the dynamics of user mobility and user preferences, we design an energy minimization problem with the joint optimization of coding vectors, caching variables, user grouping, and updated UAV locations. We initially deploy UAVs using a constrained K-means clustering algorithm based on user locations, and evaluate the clustering effectiveness with the silhouette coefficient. Then, we solve this problem by proposing a multi-UAV enabled coded caching optimization (MUCCO) scheme, embedded with a novel projected distance-based user grouping method, semidefinite programming (SDP), and matching theory. The simulation results demonstrate that the proposed MUCCO scheme can achieve low energy consumption compared to other schemes, with scalable user density and file library size.

The dynamic production environments and stringent quality of service (QoS) requirements in discrete manufacturing smart factories pose significant challenges to deploying edge intelligence networks. These networks must simultaneously satisfy critical QoS metrics while maintaining adaptability to fluctuations in resource availability and task priorities. To address the industrial demands for real-time responsiveness, lightweight design, and flexible deployment, this paper proposes an adaptive deployment optimization strategy for edge intelligent networks based on an improved K-means particle swarm optimization (IK-PSO) algorithm. The strategy incorporates dynamic clustering and weight adjustment mechanisms to optimize multiple performance metrics, including latency, throughput, reliability, and interference mitigation. Experimental results validate that the IK-PSO-based deployment optimization strategy rapidly converges to high-quality solutions across different scenarios and various factory complexities, significantly improving network performance. This study provides a practical and efficient solution for network deployment in smart factories, contributing to the ongoing development of intelligent production and resource management.

Cell-free wireless communication is envisioned as one of the most promising network architectures, which can achieve stable and uniform communication performance while improving the system energy and spectrum efficiency. The deployment of cell-free networks is envisioned to be a long-term evolutionary process, in which cell-free access points (APs) will be gradually introduced into the communication network and collaborate with the existing cellular base stations (BSs). To further explore the performance limits of hybrid cellular and cell-free networks, this paper develops a hybrid network model based on stochastic geometric toolkits, which reveals the coupling of the signal and interference from both the cellular and cell-free networks. Specifically, the conjugate beamforming is applied in hybrid cellular and cell-free networks, which enables user equipment (UE) to benefit from both cellular BSs and cell-free APs. The aggregate signal received from the hybrid network is approximated via moment matching, and coverage probability is characterized by deriving the Laplace transform of the interference. The analysis of signal strength and coverage probability is verified by extensive simulations.

Integrated sensing and communication (ISAC) has emerged as a new paradigm for the sixth generation (6G) mobile communication systems. However, embedding ISAC into a conventional communication system may degrade the mutual benefit of radar sensing and communication due to the low-spectral efficiency and weak interference management. Therefore, in this article, we propose a full-duplex (FD) non-orthogonal multiple access (NOMA)-enabled ISAC framework in which a dual functional base station (BS) operates simultaneous target detection and uplink/downlink communication with the same temporal and spectral resources. To exploit some insights into the benefit of such a framework, we investigate the sensing signal processing procedure and communication model. Towards this end, a joint transmit and receive beamforming design is studied in the cases where single-target detection with perfect channel state information (CSI) and multitarget detection with imperfect CSI are considered, respectively. The corresponding optimization problems aim to maximize the sensing signal-to-interference-plus-noise ratio (SINR), subject to uplink communication SINR requirement for each uplink user equipment (UUE) and downlink communication SINR requirement for each downlink user equipment (DUE). We propose an alternating-optimization algorithm to solve the formulated non-convex optimization problems efficiently. Specifically, at each iteration, the closed forms of the optimal sensing and uplink communication receive beamforming vectors are obtained, respectively. Then, the sub-optimal transmit beamforming vector is solved by equivalent transformation and semi-definite relaxation (SDR) method. Numerical results demonstrate that the proposed FD-NOMA ISAC system outperforms the orthogonal multiple access (OMA)-based ISAC in terms of both sensing and communication performances. Notably, the efficacy of the proposed scheme is heavily dependent on factors, such as self cancelation (self interference) and CSI uncertainty.

The sixth generation (6G) mobile communication networks are expected to offer a new paradigm of cellular integrated sensing and communication (ISAC). However, due to the intrinsic difference between wireless sensing and communication in terms of coverage requirement, current cellular networks that are deliberately planned mainly for communication coverage are difficult to achieve seamless sensing coverage. Therefore, this paper studies the coverage issue for cellular ISAC systems, which aims to concurrently sense a prescribed region while serving a group of communication users equipment (UEs). Towards this end, the radar sensing signal processing procedures and communication signal models are presented in a general multi-static cellular ISAC system with coordinated multi-point joint transmission (CoMP-JT), and an optimization problem is formulated to maximize the worst-case sensing signal-to-noise ratio (SNR) in the prescribed sensing coverage region, subject to the signal-to-interference-plus-noise ratio (SINR) requirement for each communication UE. To gain useful insights, we first investigate the basic bi-static ISAC system, for which a closed form expression of the optimal beamforming is obtained for the special case with one UE and one sensing point. Then, the general case with multiple communication UEs and contiguous regional sensing coverage is further studied, in which the mesh grid approach and direction discretization approach are proposed for ease of solving the optimization problem. Afterward, we further investigate the beamforming optimization in the multi-static ISAC system to maximize the probability of detection of the prescribed sensing coverage region. We show that the problem is equivalent to maximize the sum of sensing SNR from the different transmit BSs. The formulated problems are non-convex, and we propose an efficient algorithm based on successive convex approximation (SCA) technique. Numerical results demonstrate that the proposed ISAC design is able to achieve seamless sensing coverage in the prescribed region while guaranteeing the communication requirements of the UEs.

Cell-free massive MIMO and symbiotic radio communication have been recently proposed as the promising beyond fifth-generation (B5G) networking architecture and transmission technology, respectively. To reap the benefits of both, this paper studies cell-free symbiotic radio communication systems, where a number of cell-free access points (APs) cooperatively send primary information to a receiver, and simultaneously support the passive backscattering communication of the secondary backscatter device (BD). We first derive the achievable communication rates of the active primary user and passive secondary user under the assumption of perfect channel state information (CSI), based on which the transmit beamforming of the cell-free APs is optimized to characterize the achievable rate-region of cell-free symbiotic communication systems. Furthermore, to practically acquire the CSI of the active and passive channels, we propose an efficient channel estimation method based on two-phase uplink-training, and the achievable rate-region taking into account CSI estimation errors is further characterized. Simulation results are provided to show the effectiveness of our proposed beamforming and channel estimation methods.

Computation offloading over fog computing has the potential to improve reliability and reduce latency in future networks. This paper considers a scenario where roadside units (RSUs) are installed for offloading tasks to the computation nodes including nearby fog nodes and a cloud center. To guarantee the reliable communication, we formulate the first subproblem of power allocation, and leverage the conditional value-at-risk approach to analyze the successful transmission probability in the worse-case channel condition. To complete computation tasks with low latency, we formulate the second subproblem of task allocation into a multi-period generalized assignment problem (MPGAP), which aims at minimizing the total delay by offloading tasks to the `right' fog nodes at `right' period. Then, we propose a modified branch-and-bound algorithm to derive the optimal solution and a heuristic greedy algorithm to obtain approximate performance. In addition, the master problem is proposed as a non-convex optimization problem, which considers both the reliability-guaranteed and delay-sensitive requirements. We design the Lagreedy algorithm by combining the subgradient algorithm and the heuristic algorithm. Comprehensive evaluations demonstrate that the Lagreedy is able to obtain the shortest delay with a high power consumption, while the branch-and-bound algorithm can achieve both shorter delay and lower power consumption with reliability guarantees.

Server and Network Assisted Dynamic adaptive streaming over HTTP (SAND) is a promising technology to cope with the dramatic increase in video streaming traffic. The new emerging Mobile Edge Computing (MEC) paradigm may further facilitate bitrate adaptation and video transcoding in a SAND system with the help of local edge servers. A critical issue in MEC-SAND framework is to guarantee Quality of Experience (QoE) for clients while achieving efficient utilization of edging network resources. In this paper, we aim to develop an adaptive video delivery scheme to minimize the delay in MEC assisted ultra-dense networks. In our scheme, each client is mapped to a server that better fits its requirements and transmission condition, and a bitrate selection mechanism is exploited to decide the best video version for the client. Besides, time-consuming transcoding tasks are carefully scheduled considering edge computing capacity. We formulate a Mix-Integer Non-Linear Programming (MINLP) problem to jointly determine cell association, bitrate adaption, and computing resource allocation. We exploit the generalized Benders decomposition method to reduce the solving complexity of the formulated problem. Numerical results validate the effectiveness and efficiency of the proposed scheme.

In this paper, we investigate the issue of resource allocation for secure energy efficient communication in a multiuser orthogonal frequency division multiplexing (OFDM) based full-duplex (FD) relaying network in the presence of a passive eavesdropper whose channel state information (CSI) is not perfectly known. Our goal is to maximize the overall secure energy efficiency (SEE), which presents the relationship between energy consumption and secrecy performance. In the context of multiuser communications, such a resource allocation strategy jointly combines subcarrier permutation, subcarrier pair allocation, as well as power allocation altogether. The considered optimization problem is formulated as a mixed integer nonconvex programming problem, which is generally NP hard. Analyzing the property of such a problem, we first use the Dinkelbach's method to eliminate the fractional form and then exploit Generalized Benders decomposition to decouple the original problem into a master problem for pure integer programming and a primal problem for nonlinear programming. More specific, given the nonconvexity of the primal problem, we accordingly transform it into an equivalent relaxed convex problem by applying dual decomposition, alternative convex search, and difference of convex function programming. The numerical results are provided to validate the theoretical analysis and to demonstrate the effectiveness of the proposed algorithm.

The emerging mobile crowd sensing has become a new paradigm where a crowd of mobile users utilize their smart devices to conduct complex computation and sensing tasks in mobile social networks, in which the content sharing plays a significant role. In this work, we propose a novel many-to-many content sharing framework by enabling users to exchange information through separate connections with different partners of multiple relationships simultaneously. Under the statistical channel statement information condition, the many-to-many pairing problem is formulated as a Stable Multiple Activity (SMA) matching game and solved by a distributive stable b-matching algorithm in multigraphs. Based on the matching model, we further perform the power allocation scheme to improve system performance. Simulation results demonstrate the superiority of our proposed SMA method with lower complexity.

This paper investigates secrecy efficiency (SE) optimization in a friendly full-duplex (FD) decode- and-forward (DF) relay network with a passive eavesdropper. Targeting on SE maximization, the power allocation is optimized subject to the total power and minimum secrecy rate constraints adapting to the assumption of unknown eavesdropper's channel state information (CSI). By exploiting the properties of fractional programming (FP) and Difference of Convex functions (DC) programming, the resulting nonconvex optimization problem can be relaxed into a more tractable equivalent problem. Simulation results demonstrate that our proposed scheme can reach a better trade-off between security performance and energy consumption.

This paper analyzes the effect of distributed time-reversal (DTR) transmission scheme on the signal-to-noise ratio (SNR) at its intended and unintended receivers. By focusing the temporal and spatial signal energy on the intended receiver, DTR can effectively maintain a satisfactory SNR level while lowering received signal level at passive eavesdroppers or unintended co-channel users. The DTR performance is analyzed in terms of a SNR gap between the desired and unintended receivers. The SNR gain of DTR is also analyzed over traditional distributed direct transmission and several cooperative beamforming transmission schemes without time-reversal. Numerical results demonstrate the performance improvement of the time-reversal transmission and the validity of our analytical results.