Ultra-reliable low-latency communication (uRLLC) is a pivotal enabler for B5G/6G networks, yet it faces severe challenges from rare but critical extreme events, which are characterized by heavy tails in the delay distribution. While the cell-free radio access network (CF-RAN) architecture offers essential spatial diversity to combat these uncertainties, conventional user-centric clustering designs typically focus on average metrics, thereby inadequately addressing such tail behaviors. We propose a novel, tail-risk-aware, user-centric clustering framework operating within the finite blocklength (FBL) regime. Our approach employs extreme value theory (EVT), specifically the peaks-over-threshold (POT) model, to accurately quantify the probability of queue latency violations. This framework is applied to formulate an energy efficiency (EE) maximization problem under strict tail latency constraints. The problem is solved via an efficient online algorithm that integrates Lyapunov optimization with successive convex approximation (SCA). Simulation results demonstrate that the proposed scheme, through its dynamic adaptation of cluster formation to mitigate tail risks, achieves a superior reliability-efficiency trade-off and leads to a significant suppression of extreme latency events.

Network-assisted full-duplex (NAFD) cell-free massive MIMO (CF-mMIMO) systems constitute a promising enabler for supporting dynamic downlink (DL) and uplink (UL) traffic demands in forthcoming sixth-generation (6G) wireless networks. In this paper, we analyze channel aging effects on NAFD CF-mMIMO systems. First, we propose an extended beamforming training scheme to relieve heavy pilot overhead for high-mobility channel estimation, significantly enhancing system performance through cross-link interference (CLI) cancellation. Then, we derive novel closed-form expressions for the UL/DL achievable SEs, enabling a comprehensive analysis of channel aging impacts on spectral efficiency (SE) and energy efficiency (EE) across diverse normalized Doppler shiftfDTsscenarios. Furthermore, we develop a joint optimization framework of transmission interval and power control to balance SE-EE tradeoffs while alleviating channel aging effects. We formulate a mixed-integer multi-objective optimization problem (MOOP), which is subsequently transformed into a tractable single-objective formulation via a weighted ℓpscalarizing method. Based on this analytical foundation, we propose a constrained deep reinforcement learning (DRL) algorithm that integrates a primal-dual optimization strategy with multi-agent deep deterministic policy gradient (MADDPG) for safe policy exploitation. Simulation results validate the accuracy of analytical expressions and demonstrate the superiority of the proposed algorithm over the conventional non-dominated sorting genetic algorithm-II (NSGA-II) in achieving Pareto-optimal SE-EE tradeoffs under channel aging.

Wi-Fi fingerprint-based indoor positioning has been a widely studied research topic. However, the single fingerprint database model fails to effectively establish a complete mapping of dynamic spatial correlations between access points and reference points in complex indoor environments, thereby limiting positioning performance. To address the challenge of adapting to dynamic changes in complex indoor environments, this paper proposes a multi-fingerprint database construction strategy based on Gaussian Mixture Model (GMM) clustering. For each reference point and its associated access points (AP), GMM clustering is applied to identify all possible states within the AP data. By establishing a mapping between different environmental characteristics and fingerprint states, the proposed approach enhances the dynamic representation capability of the database, thereby improving the overall performance of the positioning system. Simulation and experimental results demonstrate that various fingerprint-based positioning algorithms achieve higher accuracy with a multi-fingerprint database compared to a single fingerprint database.

During Unmanned Aerial Vehicle (UAV) missions, sudden weather changes like strong winds can destabilize UAV flight, making it essential to quickly transmit weather information to improve safety and efficiency. However, these abrupt data surges can lead to network congestion, affecting transmission timeliness and reliability. Current congestion control schemes often prioritize network efficiency but overlook user fairness. This paper introduces an Adaptive Explicit Congestion Control algorithm based on Fairness (AECCF) for handling Microburst traffic, balancing congestion management with fair resource distribution. The AECCF algorithm works as follows: First, the router node monitors its outgoing packet queue and assesses local link congestion state using a double delay threshold. It then calculates and attaches congestion probability, congestion state, and sojourn time to packets, enabling complete congestion feedback to downstream nodes. In cases of burst traffic, the router labels the traffic as heavily congested and activates flow shaping, transferring some packets to a cache queue for staggered forwarding. The consumer then adjusts its interest packet rate based on congestion markings and fair bandwidth allocation, preventing throughput loss from over-adjustment and maintaining network stability and fairness. Finally, the router forwards interest packets based on the proportion of consumer request prefixes on available interfaces, improving data transmission efficiency under sudden traffic. Simulations on ndnSIM show that AECCF achieves higher throughput and fairness in different scenarios compared to existing methods. For dumbbell networks, AECCF reaches 18.64 Mbps throughput with a fairness index of 0.97, and in complex network scenarios, the throughput is 24.9% higher than that of the PCON protocol, and the fairness index is 0.86.

With the rapid development of computer vision, semantic segmentation as an important part of the technology has made some achievements in different applications. However, in the farmland segmentation scenario of remote sensing images, the capability of common semantic segmentation methods in restoring the farmland edge and identifying narrow farmland ridges needs to be improved. Therefore, in this letter a semantic segmentation method–multiple attention encoder–decoder network (MAENet)–for farmland segmentation is proposed. The design of a dual-pooling efficient channel attention (DPECA) module and its embedment in the backbone to improve the efficiency of feature extraction is described; secondly, a dual-feature attention (DFA) module is proposed to extract contextual information of high-level features; finally, a global-guidance information upsample (GIU) module is added to the decoder to reduce the influence of redundant information on feature fusion. We use three self-made farmland image datasets representing UAV data to train MAENet and compare them with other methods. The results show that the performances of segmentation and generalization of MAENet are improved compared with other methods. The MIoU and Kappa coefficient in the farmland multi-classification test set can reach 93.74% and 96.74%.

Indoor localization based on existing Wi-Fi Received Signal Strength Indicator (RSSI) is attractive since it can reuse the existing Wi-Fi infrastructure. However, it suffers from dramatic performance degradation due to multipath signal attenuation and environmental changes. To improve the localization accuracy under the above-mentioned circumstances, an improved Spearman-distance-based K-Nearest-Neighbor (KNN) scheme is proposed. Simulation results demonstrate that our improved method outperforms the original KNN method under the indoor environment with severe multipath fading and temporal dynamics.

Under the assumption of single bounce channel model, the position and velocity of a mobile station (MS) can be determined by time of arrival (TOA), angle of arrival (AOA) and doppler-shifted frequency (DSF) measurements at three base stations (BSs) when line of sight (LOS) paths between the three BSs and the MS are all blocked. The equations relating the measured TOAs, AOAs and DSFs to the location parameters are nonlinear and under-determined which are hard to solve. A novel grid search method which requires only two-dimensional search in x-y coordinates and achieves good location accuracy is presented. Simulation results verify the effectiveness of the proposed method.

Based on the joint estimations of time of arrival (TOA), angle of arrival (AOA) and angle of departure (AOD) of two or more single-bounced multipaths from a single base station (BS) and an mobile station (MS) in an MIMO system, two schemes, closed-form total least squares solution (CF-TLS) and constraint-based grid-search approach (CB-GSA) are presented to locate an MS. The CF-TLS method is time saving and provides good location accuracy when two or more resolvable single-bounced multipaths are available. However, the CB-GSA method always provides better location accuracy than the CF-TLS method at a cost of large computational burden. When only two single-bounced multipaths can be observed or the range and angle measurement errors are large, the location accuracy of the CB-GSA method outperforms that of the CF-TLS method significantly. By executing the CF-TLS method to give an initial estimation of an MS position, the search area of the CB-GSA method is narrowed, and the location time is significantly reduced. Simulations verify the effectiveness of the two proposed methods.