The Open Radio Access Network (O-RAN) promises unprecedented flexibility through its reconfigurable architecture and AI-driven control. However, this agility exposes a critical fragility: AI models trained on one network configuration suffer significant performance degradation after an upgrade due to dramatic data drift. The standard solution, reactive retraining, is unacceptably slow, leaving the network in a suboptimal state for tens of minutes and undermining the core benefits of O-RAN's dynamism. This paper introduces RANPilot, the first framework to address this challenge through proactive AI adaptation. RANPilot constructs a lightweight "virtual O-RAN" (a trace-driven emulator) to synthesize high-fidelity training data representing the post-reconfiguration state before the physical change occurs, allowing AI models to be adapted in advance. Extensive experiments on a real-world 5G testbed demonstrate that RANPilot achieves near interruption-free AI services upon reconfiguration, reducing AI downtime by 85% to 94% against reactive baselines. By shifting the AI evolution paradigm from reactive redevelopment to proactive preparation, RANPilot explores a digital-leadoff approach to enable robust AI in reconfigurable O-RAN deployments.

LoRa technology has been extensively implemented in various IoT applications, offering widespread low-power connectivity for millions of nodes across thousands of logical channels. However, current LoRa networks lack an efficient mechanism for monitoring channel activity across these numerous channels, which prevents network operators from effectively detecting physical layer activities and implementing additional functionalities (e.g., channel access control). Existing solutions either involve complex iterations over each logical channel or fail to detect extremely weak packets in low SNR conditions. These limitations affect their scalability and robustness in monitoring the vast number of logical channels available in the LoRa spectrum. To address this issue, this paper introduces SlideLoRa, an innovative packet detection method that enables detection across all logical channels under various channel conditions. SlideLoRa consolidates the complete energy of LoRa symbols using an expanded demodulation window combined with a fine-grained sliding window, effectively reconstructing the distorted frequency-domain information of LoRa packets. To achieve this, SlideLoRa incorporates a series of novel solutions, including peak tracking in low SNR, peak sequence matching, peak extraction, and packet parameter retrieval. Experimental results demonstrate that SlideLoRa enhances packet detection capability by 1.7× compared to the state-of-the-art.

This paper addresses the interference challenges, aiming to improve spatial reuse and optimize spectrum efficiency in LPWANs. We reveal that existing strategies such as interference cancellation and MIMO are ill-suited to the low-cost low-rate characteristics of LPWANs. Our work introduces a novel framework, HydraNet, which leverages the capture effect of LPWAN radios to enable robust concurrent transmissions. HydraNet exempts from strict clock synchronization or accurate channel estimation as required by conventional spatial reuse strategies for interference nulling. We conduct in-depth studies with LoRa radios to uncover their underlying packet reception mechanisms and for the first time characterize their unique capture effect. Based on the new findings, we devise novel strategies to jointly control the timing and power of concurrent LPWAN transmissions. These strategies ensure sufficient power differences between packets and interference at their intended receivers. We prototype HydraNet and integrate with operational LoRaWANs and comprehensively evaluate its performance. Results show that HydraNet achieves higher spectrum utilization with up to 3.6 × throughput improvements over the state-of-the-art.

LoRaWANs are envisioned to connect billions of IoT devices through thousands of physically overlapping yet logically orthogonal channels (termed logical channels). These logical channels hold significant potential for enabling highly concurrent scalable IoT connectivity. Large-scale deployments however face strong interference between logical channels. This practical issue has been largely overlooked by existing works but becomes increasingly prominent as LoRaWAN scales up. To address this issue, we introduce Canas, an innovative gateway design that is poised to orthogonalize the logical channels by eliminating mutual interference. To this end, Canas develops a series of novel solutions to accurately extract the meta-information of individual ultra-weak LoRa signals from the received overlapping channels. The meta-information is then leveraged to accurately reconstruct and subtract the LoRa signals over thousands of logical channels iteratively. Real-world evaluations demonstrate that Canas can enhance concurrent transmissions across overlapping logical channels by 2.3× compared to the best known related works.

LoRa technology promises to enable Internet of Things applications over large geographical areas. However, its performance is often hampered by poor channel quality in urban environments, where blockage and multipath effects are prevalent. Our study uncovers that a slight shift in the position or attitude of the receiving antenna can substantially improve the received signal quality. This phenomenon can be attributed to the rich multipath characteristics of wireless signal propagation in urban environments, wherein even small antenna movement can alter the dominant signal path or reduce the polarization angular difference between transceivers. Leveraging these key observations, we propose and implement MoLoRa, an intelligent mobile antenna system designed to enhance LoRa packet reception. At its core, MoLoRa represents the position and attitude of an antenna as a state and employs a statistical optimization method to search for states that offer optimal signal quality efficiently. Through extensive evaluation, we demonstrate that MoLoRa achieves a maximum Signal-to-Noise Ratio (SNR) gain of 13 dB in a few attempts, enabling formerly problematic blind spots to reconnect and strengthening links for other nodes.

Unlike traditional data collection applications which primarily rely on uplink transmissions, emerging applications (e.g., device actuation, firmware update, packet reception acknowledgment) increasingly demand robust downlink transmission capabilities. Current LoRaWAN systems struggle to support these applications due to the inherent asymmetry between downlink and uplink capabilities. While uplink transmissions can handle multiple packets simultaneously, downlink transmissions are restricted to a single logical channel at a time, significantly limiting the deployment of applications that require substantial downlink capacity. To address this challenge,FDLoRaintroduces an innovative in-band full-duplex LoRa gateway design, featuring novel solutions to mitigate self-interference (i.e., the strong downlink interference to ultra-weak uplink reception). This approach enables full-spectrum in-band downlink transmissions without compromising the reception of weak uplink packets. Building on the capabilities of full-duplex gateways,FDLoRapresents a new downlink framework that supports concurrent downlink transmissions across multiple logical channels of available gateways. Evaluation results show thatFDLoRaenhances downlink capacity by 5.7× compared to LoRaWAN in a three-gateway testbed and achieves 2.58× higher downlink concurrency per gateway than the current leading solutions.

LoRa is a promising technology that provides widespread low-power IoT connectivity. With its capabilities for multi-channel communication, orthogonal transmission, and spectrum sharing, LoRaWAN is poised to connect millions of IoT devices across thousands of logical channels. However, current LoRa gateways rely on hardwired Rx chains that cover less than 1% of these channels, restricting the potential for large-scale LoRa communications. This paper introduces XGate, a groundbreaking gateway design that uses a single Rx chain to simultaneously receive packets from all logical channels, enabling scalable LoRa transmission and flexible network access. Unlike the hardwired Rx chains in existing gateway designs, XGate dynamically allocates resources, including software-controlled Rx chains and demodulators, based on the extracted meta-information of incoming packets. XGate overcomes several challenges to efficiently detect incoming packets without prior knowledge of their parameter configurations. Evaluations demonstrate that XGate enhances LoRa concurrent transmissions by$8.4\times $compared to state-of-the-art solutions.

LoRa is a promising technology that offers ubiquitous low-power IoT connectivity. With the features of multi-channel communication, orthogonal transmission, and spectrum sharing, LoRaWAN is poised to connect millions of IoT devices across thousands of logical channels. However, current LoRa gateways utilize hardwired Rx chains that cover only a small fraction (<1%) of the logical channels, limiting the potential for massive LoRa communications. This paper presents XGate, a novel gateway design that uses a single Rx chain to concurrently receive packets from all logical channels, fundamentally enabling scalable LoRa transmission and flexible network access. Unlike hardwired Rx chains in the current gateway design, XGate allocates resources including software-controlled Rx chains and demodulators based on the extracted meta information of incoming packets. XGate addresses a series of challenges to efficiently detect incoming packets without prior knowledge of their parameter configurations. Evaluations show that XGate boosts LoRa concurrent transmissions by 8.4× than state-of-the-art.

Unlike traditional data collection applications (e.g., environment monitoring) that are dominated by uplink transmissions, the newly emerging applications (e.g., device actuation, firmware update, packet reception acknowledgement) also pose ever-increasing demands on downlink transmission capabilities. However, current LoRaWAN falls short in supporting such applications primarily due to downlink-uplink asymmetry. While the uplink can concurrently receive multiple packets, downlink transmission is limited to a single logical channel at a time, which fundamentally hinders the deployment of downlink-hungry applications. To tackle this practical challenge, FDLoRa develops the first-of-its-kind in-band full-duplex LoRa gateway design with novel solutions to mitigate the impact of self-interference (i.e., strong downlink interference to ultra-weak uplink reception), which unleashes the full spectrum for in-band downlink transmissions without compromising the reception of weak uplink packets. Built upon the full-duplex gateways, FDLoRa introduces a new downlink framework to support concurrent downlink transmissions over multiple logical channels of available gateways. Evaluation results demonstrate that FDLoRa boosts downlink capacity by 5.7x compared to LoRaWAN on a three-gateway testbed and achieves 2.58x higher downlink concurrency per gateway than the state-of-the-art.

Unmanned aerial vehicles (UAVs) with integrated computing platforms can be used to provide computing offloading services for ground user equipments (UEs) with limited local computing capabilities, especially in remote areas. In this paper, we focus on the task offloading in an aerial edge network (AEN) assisted by a UAV. We aim at minimizing the sum energy consumption of all UEs by the joint optimization of the task offloading decisions and the UAV position under the constraints of the latency and the total energy of UAV. The formulated optimization problem is a mixed-integer nonconvex problem and involves coupling of many optimization variables. To address this challenge, we first transform the original optimization problem into a linear convex optimization problem via reformulation linearization technology, and then the alternating direction method of multipliers (ADMM) algorithm is proposed to achieve the approximate optimal solution. Numerical results confirm that the proposed ADMM algorithm can effectively reduce the total of energy consumption of UEs and ensure the continuous operation of the UEs.

This paper presents a novel model predictive control (MPC) strategy to overcome the adverse influences of stochastic time delay on a network control system (NCS). A controller node determines the predictive values of a manipulated variable and transmits them to the corresponding smart actuator node. If the present value of a manipulated variable is unavailable due to time delay, the actuator implements a predictive one. The switch of a manipulated variable between the present value and predictive may one cause the unsmooth operation of a system. Therefore, a simple low-pass filter is used to solve the problem. Similarly, when the present value of a controlled variable cannot reach the corresponding controller node in time from a smart sensor node, its predictive value is also employed by the controller node. A simulation study demonstrates the effectiveness of the proposed approach in an NCS with stochastic time delay.