Carrier Aggregation (CA) is investigated to address spectrum scarcity in 6G Integrated Sensing and Communication (ISAC) systems. However, inter-band phase offsets impose high complexity on achieving coherent gain across multiple bands. Furthermore, typical CA focuses on multi-frequency-band expansion, while multi-time-duration expansion has received limited attention. To address the above issues, this paper proposes a novel high-accuracy Multi-Band Joint Carrier Phase Sensing (MB-JCPS) framework that utilizes carrier phase information from multiple time-frequency bands to improve range and velocity sensing accuracy. A carrier phase extraction method is introduced based on oversampling Range-Doppler phase spectrum. The impact of non-ideal factors—including time, frequency, and phase offsets, as well as phase noise—on carrier phase is modelled, analyzed, and eliminated via an inter-path difference method. A Real-valued Multi-Carrier Ambiguity Resolution (RMCAR) algorithm is designed to resolve integer ambiguities in the range and velocity carrier phase models. The time-frequency selection problem is formulated as a constrained Rayleigh-quotient optimization problem and solved via successive convex approximation. The Cramér-Rao lower bound (CRLB) for the range and velocity estimation under phase offsets is derived, demonstrating the performance gain of CA in both frequency and time domains. Numerical simulations show that the proposed method achieves higher sensing accuracy with lower complexity than benchmark algorithms in 3GPP Uma-AV environments.

To meet the high-precision positioning demands of Internet of Things (IoT) applications, carrier phase positioning in cellular networks is promising. However, dynamic clock offsets among base stations (BSs) and user equipments (UEs) challenge the accuracy of range-based positioning systems. To achieve centimeter-level positioning in time-varying asynchronous cellular networks, we propose a collaborative framework leveraging the coupled ranging information between UEs and BSs obtained from double-differenced (DD) Time of Arrival (ToA) and Carrier Phase of Arrival (CPoA) measurements. Coarse UE position estimates are first obtained iteratively from DD ToA, then refined via DD CPoA-based integer ambiguity resolution for joint high-accuracy estimation across time. By leveraging coupled measurements and time-invariant integer ambiguities, the method can estimate the UE positions without a dedicated reference node to eliminate clock offsets. Simulations show centimeter-level accuracy in dynamic asynchronous scenarios.

In scenarios where high-precision localization is required, even tiny calibration errors of the base station (BS) can directly and significantly impact the positioning accuracy of the device. Inspired by the excellent precision of carrier phase positioning, this paper proposes a high-precision positioning algorithm based on carrier phase and unscented Kalman filter (UKF) to address BS calibration errors. The state vector of the positioning system is initialized with the initial coordinates of the terminal, which is estimated via Chan's algorithm. The coordinates of the BSs and the terminal are then iteratively refined by combining the double-differential carrier phase and time difference of arrival (TDoA) measurements of multiple moments. Numerical simulations demonstrate that the proposed algorithm achieves centimeter-level positioning accuracy even in the presence of significant BS calibration errors, which effectively overcomes the impact of BS calibration errors on positioning performance.

Fake base stations pose a serious threat to communication security, and accurately locating these stations is essential for effectively managing this issue. To tackle the challenge of efficient and precise positioning of fake base stations over large areas, this paper proposes a unmanned aerial vehicle (UAV) group collaborative passive positioning algorithm that integrates multi-domain information. To initially identify the approximate region of the fake base station and approach it within a large-scale area, this paper introduces a dynamic approach algorithm to adjust the geometric layout of the UAV group based on Horizontal Dilution of Precision (HDOP) and Time Difference of Arrival (TDoA). Once the approximate location of the fake base station is identified, and considering that the UAV group can only capture the weak sidelobe signals leaked by the fake base station at high altitudes, this paper proposes a positioning algorithm that integrates both time-domain and phase-domain information to further enhance positioning accuracy and overcome noise interference.

This paper presents a novel user-centric sensing framework, where a user equipment (UE) acts as the receiver of a multi-static radar sensing system and utilizes the communication signals emitted by base stations (BSs) and scattered by the targets for joint UE and target tracking. Specifically, we propose to locate the UE using the least squares (LS) estimator with a one-dimensional (1D) search and then develop a two-dimensional (2D) target identification approach using the estimated target location and motion of each path based on mean-shift clustering. After the motion parameters of the UE and targets are estimated, a joint UE and target tracking algorithm is designed based on the analysis of the localization and motion estimation errors. Extensive simulations corroborate the ability of our approach to estimate target parameters and cluster and identify targets. Specifically, the average estimation error of the target number is only 0.18. The speed and heading accuracy of the UE and targets is [0.121 m/s, 3.879°] and [0.199 m/s, 4.669°], respectively. In joint UE and target tracking, our scheme outperforms the benchmarks of the extended Kalman filter (EKF) and belief propagation (BP) by at least 33.16% and 10.29%, respectively, even though the EKF and BP require a-priori knowledge of the motion parameters and target identification.

As the demand for positioning accuracy reaches the centimeter level, Carrier Phase Positioning (CPP) technology has received widespread attention in 5G New Radio (NR) positioning scenarios due to its high ranging accuracy. In this paper, we presents a new single-shot CPP method suitable for 5G NR cellular networks. By performing a search in the coordinate neighborhood space of the Time Difference of Arrival (TDOA) positioning results, the proposed method bypasses the need for resolving the integer ambiguity required for CPP. It also includes a wrapping effect detection and elimination for the carrier phase measurements. The proposed method finally get the positioning result by calculating cost function which considers both TDOA and CPP results. Numerical simulations demonstrate superior performance compared to other positioning solutions.

In time-division duplex (TDD) systems, where uplink and downlink channels alternate during transmission process, the continuity of carrier phase measurements becomes challenging to maintain. This leads to periodic lock-loss in the phase-locked loop (PLL), rendering the integer ambiguity non-constant. To address this issue, a carrier phase-based localization method for TDD systems via extended Kalman filtering is proposed in this study. The proposed method modifies the one-step predicted state within each TDD cycle based on extended Kalman filtering (EKF) to overcome the effects of PLL lock-loss in TDD. Simulation results verificate that the proposed method can overcome the problem of discontinuous measurement in TDD system, accurately solve integer ambiguities and achieve high-precision positioning.

Federated learning has been envisioned as a promising technique to enable the intelligence of Industrial Internet of Things (IIoT). An efficient resource management algorithm is critical yet hard to design in IIoT due to limited power devices and the variety of co-existent services. In this paper, we propose a multi-service joint scheduling algorithm for federated learning on IIoT devices to maximize model accuracy by forming a loss function minimization problem under long-term device energy constraints. The problem is then reformulated into a single-round optimization, which can be solved through a binary search and a greedy algorithm. The transmit power of devices, the CPU frequency for model training, and the selection of devices' services are jointly optimized. Simulations demonstrate that our proposed algorithm outperforms the benchmarks in model accuracy, especially when the energy of the device battery is constrained.

In mobile-edge network, adaptive cache placement and resource allocation are vital for meeting the dynamic requirements of users. The current network cache performance has not been fully implemented, due to the lack of comprehensive knowledge of content popularity dynamics and the impact of network resource deployment on the dynamics. In this article, an epidemic model is applied to characterize the content propagation among users. Based on this model, a joint optimization problem of cache placement and base station resource allocation is formulated, maximizing the satisfaction rate of users. The joint optimization problem is decomposed into two subproblems: 1) caching placement and 2) resource allocation. First, the cache placement strategy is determined based on the dynamic propagation model. Second, considering the impact of network decisions on content propagation, a belief propagation-based iterative algorithm is proposed for dynamic resource allocation. Finally, simulation results reveal that the proposed joint optimization algorithm outperforms the benchmark algorithms.

In addressing the challenges of heterogeneous data and limited communication resources in wireless networks, which often hinder the performance of federated learning, this article introduces a personalized learning approach. This approach not only addresses data heterogeneity but also optimizes resource management in wireless networks. We construct an optimization model aimed at maximizing the decay of the global loss function in a single iteration. The problem is divided into two subproblems: 1) allocation of device-local fine-tuning learning rates and 2) communication resources, tackled through an iterative method. The solutions involve determining near-optimal fine-tuning learning rates and optimizing device resource block and transmission power allocation. Our simulation results demonstrate that, under the constraints of wireless network resources and data heterogeneity, our algorithm outperforms baseline methods in terms of convergence speed and accuracy in personalized federated learning.

Radio simultaneous localization and mapping (SLAM) is challenging due to multipath propagation. While line-of-sight (LoS) and first-order non-LoS (NLoS) paths, referred to as NLoS-1 paths, play a critical role in SLAM, no existing techniques can effectively separate them from high-order NLoS paths, i.e., NLoS-npaths (n≥ 2). This paper presents a new framework to accurately identify the LoS/NLoS-1 paths and conduct SLAM. The key idea is to define the virtual user equipment (UE) of a NLoS-npath as then-th order reflection of the UE. We discover that the centers of the circles encompassing the UE, a virtual UE associated with a LoS/NLoS-1 path, and each of some other virtual UEs are aligned in a line, if and only if those virtual UEs are all associated with NLoS-1 paths. Accordingly, we propose to identify the LoS/NLoS-1 paths using Hough transform-based line detection, and estimate the UE’s location and the environments with the identified LoS/NLoS-1 paths using maximum likelihood estimation and mean-shift clustering. We analytically confirm that the localization error asymptotically approaches the Cramér-Rao Lower Bound. Simulations show that our approach outperforms the state of the art in localization accuracy by up to 91.93%, even when the latter assumed all NLoS-1 paths are perfectly identifieda-priori.

Carrier phase positioning has been recently proposed as the potential high-accuracy positioning method in currently ongoing 3GPP 5G New Radio (NR) Release-18. The precise resolution of integer ambiguities is the key to accuracy of carrier phase positioning. In this paper, we propose a new triple-frequency ambiguity resolution in the 5G NR systems, which resolves ambiguities by combining triple-frequency observations. In additional, based on the frequency range of 5G NR systems, the optimal carrier phase combinations are analyzed in this article. Furthermore, we propose a search method based on cost function to optimize the resolution of integer ambiguities. Numerical simulations show that proposed method can achieve a centimeter-level accuracy in wireless cellular networks.

In mobile edge network, dynamically changing content requests can affect network resource deployment. In recent years, epidemic model has been explored to describe the dynamic content popularity. In this article, epidemic model is applied to characterize the content propagation in resource-limited edge networks. The epidemic parameters are analyzed through the data collected by network entities. In order to maximize satisfaction rate of users, we decompose the joint optimization problem and propose a base station caching placement and resource allocation algorithm based on propagation dynamics. Simulation results verify that the proposed strategy offers higher satisfaction rate compared with other benchmark algorithms.

In this paper, we propose a multi-dimensional resource management scheme for Federated Learning with non-independent and identically distributed (non-IID) data on battery-powered IIoT devices. Firstly, we formulate an optimization problem that aims to maximize the learning efficiency given long-term energy and time constraints to balance training accuracy and learning latency. Secondly, based on the derived lower bound of expected convergence rate with non-IID data, we solve the short-term problems by cyclically manage the resources (i.e., radio, computation and resource block (RB) resources, and device energy). Simulation results validate that the proposed scheme outperforms other baseline schemes, especially in energy shortage scenarios.

The mobile feature and the moving positions are important for mobile communications and Internet of Things. Inspired by the potentially excellent precision of carrier phase positioning, this article proposes a mobile feature enhanced high-accuracy positioning algorithm based on the carrier phase and Bayesian estimation. The mobile feature is first estimated using time-differential carrier phase measurements. By combining the estimated position changes with instant ranging and carrier phase measurements, the factorized posterior probability of positioning is established based on the Bayes theorem. In turn, a factor graph-based positioning method is proposed to obtain precise positioning results. The numerical simulation results show that the proposed algorithm can achieve centimeter-level accuracy, and the positioning error can approach the Cramer–Rao lower bound.

Inspired by excellent precision of carrier phase positioning, this paper presents a new carrier phase positioning technique for 5G new radio cellular networks with a focus on clock synchronization and integer ambiguity resolution. A carrier-phase based clock offset estimation method is first proposed to achieve precise clock synchronization among base stations, and proved to achieve the Cramér–Rao Lower Bound (CRLB) asymptotically. A fusion method is developed to fuse the estimated positions of a mobile station (MS) based on time-difference-of-arrival, with the estimated position changes based on the temporal changes of carrier phase measurements. While circumventing the integer ambiguities of the carrier phase measurements, the fusion method provides quality interim estimates of the MS positions, at which the measurements can be linearized to resolve the integer ambiguities. As a result, precise MS positions can be obtained based on the disambiguated carrier phase measurements. Numerical simulations show that the proposed carrier phase positioning can achieve a centimeter-level accuracy in wireless cellular networks.

Wireless powered mobile-edge computing has been envisioned as a promising paradigm to enhance the computation capability of low-power wireless devices in industrial Internet of things. An efficient resource scheduling method is critical yet challenging to design in such a scenario due to stochastic traffic arrival, time-coupling uplink/downlink decision, and incomplete system state knowledge. To tackle these challenges, an online optimization algorithm is proposed in this article to maximize long-term system utility balancing throughput and fairness, subject to data age and stability constraints. A set of virtual queues is designed to transform the scheduling task, which is hard to solve due to time-dependent data age constraints, into a stochastic optimization problem. Leveraging Lyapunov and convex optimization techniques, the proposed approach can achieve asymptotically near-optimal online decisions without any prior statistical knowledge, and maintain the asymptotic optimality in the presence of partial and outdated network state information. Numerical simulations corroborate the theoretical analysis and demonstrate the effectiveness of the proposed approach.

In recent years, data traffic has grown at a drastic speed. Caching popular contents proactively at the edge of networks has become one of the effective methods to offload the huge burden on backhaul links. The reinforcement learning (RL) based edge caching method is capable to fit into the changeable environments and transfer its parameters to other caching cells. In order to converge to the optimal policy quickly and avert the cold-start problem, we propose a transferable edge caching method based on reinforcement learning. The method relies on Asynchronous Advantage Actor-Critic (A3C) algorithm and is applied to dense small cell networks (DSCNs) under content popularity diversity. Compared with Q-Learning based and other caching methods, simulation results verifies that the proposed approach offers faster convergence and efficiently avoids coldstart problem.

Due to their high mobility, flexible deployment and stable maneuverability, unmanned aerial vehicles (UAVs) have been deemed as a promising and indispensable role for various emerging applications (e.g., dangerous area detection, dynamic target tracking, and map remote sensing). Compared to the static monitoring equipments, UAV-mounted high-definition camera and signal transceiver can be used cost-effectively as an on-demand aerial platform to detect the unknown region and send the real-time data back at the same time. However, these highlighted limitations of battery capacity and communication resource extremely affect the UAV’s performance such as flight endurance and data transmission. Motivated by the above conflicts, this paper aims to minimize the total energy consumed by the UAV during the region detection mission through jointly optimizing the collected data size, transmission time, and flying trajectory. Toward this end, we derive the optimal data collection and transmission time in closed forms via convex optimization, and propose a model-free reinforcement learning-based algorithm for training the UAV to plan its trajectory without knowing the environment information in advance. Simulation results validate the performance of our designs in terms of convergence, energy consumption, and energy efficiency.

The cache-delivery market is generally composed of Content Provider (CP), users, and Mobile Network Operator (MNO) equipped with Base Stations (BSs). In order to deal with the dishonest problems of different parties, we build a caching-chain network based on blockchain. This network is a pure peer-to-peer system, which allows file acquisition transactions without going through a centralized issuer or controller, but attains a reliable and tamper-proof value transfer. The utilization of smart contracts protects the interests of all parties in the untrusted caching market. By reasonably distributing the reward of generating new blocks, we can motivate the MNO to allocate more resources for offloading the traffic of the CP. In addition, we use the linear regression model to predict user mobility and design the cache placement policy accordingly. Furthermore, we compare the performance of three caching algorithms through simulation. And the simulation results show that an appropriate choice of parameters can raise the CP’s profit.

Mobile edge computing (MEC) supporting localized context awareness creates a new technological frontier for 5G and beyond. Due to very asymmetric traffic related to MEC and the time division duplexing (TDD) system, we efficiently exploit the networking and computing functionalities for TDD orthogonal frequency division multiple access (TDD-OFDMA) technology supporting multiple services. The primary technical challenge of TDD-OFDMA systems lies in dynamic configuring based on the unknown characteristics of future traffic, i.e., the information lag. Therefore, a model-free online TDD configuration scheme is proposed based on context analysis and multi-armed bandit (MAB) optimization. The characteristics of future traffic are predicted by the context-aware MEC computing, so that TDD configuration is novelly modeled as a contextual MAB problem. Solving MAB by the contextual upper-confidence-bound, TDD configuration can be dynamically adjusted according to network traffic. To simultaneously reduce the energy consumption and makespan of mobile devices (MDs), a greedy resource allocation (GRA) embedded in the TDD configuration is further developed to select MDs and allocate resources. GRA algorithm decomposes the complex multi-factor coupling non-convex problem into a series of convex sub-problems, thereby asymptotically obtaining the selection and allocation with polynomial time complexity. Simulations justify significant performance gain in mobile networking and MEC.

With tremendous computing power in the radio access network, mobile edge computing (MEC) that can support localized context awareness creates a new technological frontier for 5G and beyond. To efficiently exploit the networking and computing functionalities, Time Division Duplex Orthogonal Frequency Division Multiple Access (TDD-OFDMA) type has been considered in this paper. To take advantage of dynamic features in TDD, a model-free online TDD configuration scheme is proposed based on context analysis and Multi-Armed Bandit (MAB) optimization. The TDD configuration problem is therefore novelly modeled as a contextual MAB problem, and is solved by the contextual Upper-Confidence-Bound (C-UCB), which dynamically adjusts TDD configuration to network traffic since that the system cost can be reduced. To further reduce the energy consumption and makespan of mobile devices (MDs), a greedy resource allocation (GRA) embedded in the TDD configuration is developed to select MDs and allocate resources. The simulations demonstrate that proper TDD configuration successfully reduces the system cost, and C-UCB technique approaches the ideal TDD configuration, with significant performance gain when the GRA effectively select and allocate, to strike simultaneous efficiency for mobile networking and MEC.

In complex scenarios where line-of-sight (LOS) and non-line-of-sight (NLOS) paths both exist, a LOS/NLOS identifi-cation method is necessary. In this paper, we propose a millimeter wave (mmWave) LOS/NLOS identification scheme utilizing mean-shift (MS) clustering algorithm and a 3D angle-of-arrival (AOA) localization algorithm using both LOS and one-bound reflection NLOS paths. In order to separate LOS and one-bound NLOS paths from multiple-bound NLOS paths, we first make all possible reflection condition assumptions for all paths to give all possible user equipment (UE) locations. Each path's assumption corresponds to one possible UE location. Then by applying mean-shift clustering to the calculated locations, we find the cluster with the most points as the set of correct hypothetical points. For the points in this cluster, the corresponding LOS/NLOS assumptions are considered to be correct, which means the LOS/NLOS conditions are successfully identified. Given known reflection conditions and original AOA measurements, the position estimate is then solved by the proposed AOA localization algorithm. Simulation results demonstrate that our scheme is capable of achieving high identification accuracy and localization precision.

Mobile edge computing (MEC), which enables the wireless devices to offload their computation tasks to the edge servers, has been considered as a promising technology to offer low-latency computing services and prolong lifetime for the Internet of Things (IoT) devices. To further improve the system efficiency, non-orthogonal multiple access (NOMA) is exploited to the MEC system. In this paper, we investigate a NOMAbased MEC system in ultra dense network and pursue an energy efficient offloading strategy for resource-limited devices, while meeting the execution latency constraint Due to the non-convexity of the optimization problem, we propose a low complexity energy efficient task offloading algorithm based on alternating direction method of multipliers (ADMM) decomposition technique to transform it into multiple parallel convex subproblems and obtain the optimal solution. Numerical results show that the proposed scheme can significantly decrease energy consumption for user and achieve lower complexity.

Edge caching can improve the efficiency of content delivery by making full use of the limited storage capacity. Exploiting cooperation between base stations (BSs) could promote the diversity of contents in the whole network and alleviate backhaul traffic. However, prior works mostly overlook the effect of cooperation during caching placement period. In this paper, firstly we propose a local content cloud cooperation scheme to utilize the cache capacity of adjacent BSs. Considering the impact of the capacity of cooperative link and backhaul link, then we formulate an optimizing problem to minimize the average consumption, which consists of content downloading delay and transmission cost. To reduce the complexity and the expenditure of information exchange, we solve the NP-hard problem by the belief propagation method. Meanwhile, simulation shows that the proposed cooperation-based caching placement decreases the average consumption and enhance the hit ratio compared with other strategies.

For providing highly demanding services with powerful computational ability and ultra low-latency communication, mobile edge computing (MEC) has been recognized as a bright rising star among key technologies for the next-generation networking. Generally, jointly optimizing offloading decision and resource allocation in one multi-variable problem is complicated. To decrease computational scale and develop practicable strategy by splitting problems, we divide the workflow of MEC-enabled base station into two stages. First, through formulating a task offloading problem, we propose a low-complexity improved simulated annealing-based heuristic offloading decision (SAHOD) algorithm to maximize network revenue from the perspective of mobile network operator. Then, the optimal fine-grained resource allocation solution is obtained in closed forms via Lagrange duality decomposition method. Furthermore, an effective realtime sub-gradient-based resource allocation (SGRA) algorithm is presented to converge to a specific optimal allocation strategy within the adjustable accuracy. For given users, simulation results show that our SAHOD algorithm can earn about 20.5% more revenue than value-based greedy algorithm. Besides, our SGRA algorithm can converge within 4 iterations and obtain approximately 19.3% more sum rates than static scheduling method.

The emerging fifth generation (5G) wireless system will have different requirements in terms of rate, latency and reliability. Network slicing which addresses the deployment of multiple logical networks as independent business operations on a common physical infrastructure has attracted more attentions. However, problems such as isolation and resource allocation of wireless network slices still exist. The paper focuses on the allocation of spectrum resources for 5G RAN slices. Firstly, The hierarchical auction model can satisfy both inter-slice isolation and intra-slice customization. Then, we propose a Hierarchical Auction and Dynamic Programming based Resource Allocation (HA&DP-RA) algorithm to allocate resources for the slices. The dynamic programming algorithm is adopted to obtain a stable solution. Finally, simulation results show the effectiveness of the proposed scheme.

In this paper, we propose an indoor positioning model using the principle of Bayesian estimation. By analyzing the mobile features of the target and combining these features with state transition probability, we narrow down the belief region of the location distribution. To make our system model more intuitive, some basic concepts about the factor graph and the corresponding sum-product algorithm are introduced. Then, we map our system model onto the factor graph and make basic rules for message propagation. Based on the probability graphic model, mobile features enhanced sum-product algorithm is implemented to calculate the posterior probability of the target given range measurement observations. Simulation results show that the positioning precision of the proposed algorithm is higher than that of the existing algorithms at different speed levels, and the proposed algorithm also performs more robust in noisy indoor environment.

As location information becomes vitally important, positioning has been a highly desirable feature of 5G system which enables a huge amount of location-based applications and services. Millimeter wave (mmWave) is the promising technology for both offering better spectrum resource and positioning performance. A virtualized indoor office scenario with only one mmWave base station (BS) is considered in this paper. User equipment (UE) motion feature, mmWave line-of-sight (LoS) and first order reflection paths' AoA-ToA are fused for indoor positioning. Firstly, an improved least mean square (LMS) algorithm that combines motion message is proposed to refine the multi-path AoA estimation. Furthermore, a modified multi-path unscented Kalman filter (UKF) is proposed to track UE's position in the scenario. The information exchanges of the two stages not only consist of estimates(position, AoA) but also variance of position. Based on the simulation results, the proposed methods provide 2 times LoS-AoA estimation gains and centimeter 3D positioning accuracy respectively. Besides, this strategy is capable of positioning task with insufficient anchor nodes (AN).

Recently, network slicing which addresses the deployment of multiple logical networks as independent business operations on a common physical infrastructure has attracted more and more attentions. Despite the advances of network slicing, there are still many open issues regarding the isolation of the wireless slices and resources allocation. The paper focus on spectrum resource allocation for Cloud-RAN slices. We develop a bankruptcy game based algorithm to allocate resource for the Cloud-RAN slices. Cloud and slices are modeled to the bankrupt company and debtors in the game respectively, where Shapley value is adopted to obtain a stable solution. Simulation results show that the bankruptcy game based algorithm significantly improve resource utilization and guarantee the fairness of allocation.