LoRa technology has garnered significant attention for its exceptional performance in city-wide applications. LoRa encodes data across multiple samples to enable long-range communication, with the level of redundancy controlled by the Spreading Factor (SF). However, practical limitations restrict how high the SF can be set. To overcome communication challenges at the highest allowable SF settings, we introduce SF-any, a software-based coding paradigm that extends an SFk packet to a quasi-SF(k + m) packet. SF-any encodes a quasi-SF(k + m) symbol with 2mSFk symbols. Hardware imperfections introduce time-varying frequency drifts and phase offsets, resulting in frequency leakage during quasi-SF(k +m) packet decoding. To mitigate this, we strategically insert pilots into the packet for imperfection estimation and compensation. Additionally, to maintain and exploit the coding structure in LoRa PHY, we employ a grouped repetition code at the transmitter and a joint demodulation and decoding scheme at the receiver. Comprehensive evaluations demonstrate that SF-any’s performance seamlessly scales with increasing SF, achieving up to a 14dB improvement over SF12 packets (the highest SF in LoRa PHY), and up to a 12dB improvement compared with state-of-the-art approaches.

Unmanned aerial vehicles (UAVs) can be utilized as aerial base stations (ABSs) to provide wireless connectivity for ground users (GUs) in various emergency scenarios. However, it is a NP-hard problem with exponential complexity in M and N, in order to maximize the coverage rate of M GUs by jointly placing N ABSs with limited coverage range. The problem is further complicated when the coverage range becomes irregular due to site-specific blockages (e.g., buildings) on the air-ground channel, and/or when the GUs are moving. To address the above challenges, we study a multi-ABS movement optimization problem to maximize the average coverage rate of mobile GUs in a site-specific environment. The Spatial Deep Learning with Multi-dimensional Archive of Phenotypic Elites (SDL-ME) algorithm is proposed to tackle this challenging problem by 1) partitioning the complicated ABS movement problem into ABS placement sub-problems each spanning finite time horizon; 2) using an encoder-decoder deep neural network (DNN) as the emulator to capture the spatial correlation of ABSs/GUs and thereby reducing the cost of interaction with the actual environment; 3) employing the emulator to speed up a quality-diversity search for the optimal placement solution; and 4) proposing a planning-exploration-serving scheme for multi-ABS movement coordination. In particular, the locations of ABSs/GUs are converted into grid pattern representations, whose dimension and associated DNN complexity are invariant with arbitrarily large M and/or N. Moreover, the virtual emulator-planning combined with the actual site-deployment effectively compensates for the prediction errors due to model approximation. Numerical results demonstrate that the proposed approach significantly outperforms the benchmark Deep Reinforcement Learning (DRL)-based method and other two baselines in terms of average coverage rate, training time and/or sample efficiency. Moreover, with one-time training, our proposed method can be applied in scenarios where the number of ABSs/GUs dynamically changes on site and/or with different/varying GU speeds, which is thus more robust and flexible compared with conventional DRL-based methods.