Automatic Modulation Recognition (AMR) is critical in identifying various modulation types in wireless communication systems. Recent advancements in deep learning have facilitated the integration of algorithms into AMR techniques. However, this integration typically follows a centralized approach that necessitates collecting and processing all training data on high-powered computing devices, which may prove impractical for bandwidth-limited wireless networks. In response to this challenge, this study introduces two methods for distributed learning-based AMR on the collaboration of multiple receivers to perform AMR tasks. The TeMuRAMRD 2023 dataset is employed to support this investigation, uniquely suited for multi-receiver AMR tasks. Within this distributed sensing environment, multiple receivers collaborate in identifying modulation types from the same RF signal, each possessing a partial perspective of the overall environment. Experimental results demonstrate that the centralized-based AMR, with six receivers, attains an impressive accuracy rate of 91%, while individual receivers exhibit a notably lower accuracy, at around 41%. Nonetheless, the two proposed decentralized learning-based AMR methods exhibit noteworthy enhancements. Based on consensus voting among six receivers, the initial method achieves a marginally lower accuracy. It achieves this while substantially reducing the bandwidth demands to a 1/256th of the centralized model. With the second distributed method, each receiver shares its feature map, subsequently aggregated by a central node. This approach also accompanies a substantial bandwidth reduction of 1/8 compared to the centralized approach. These findings highlight the capacity of distributed AMR to significantly enhance accuracy while effectively addressing the constraints of bandwidth-limited wireless networks.
With the advent of deep learning, there has been an ever-growing list of applications to which Deep Convolutional Neural Networks (DCNNs) can be applied. The field of Multi-Task Learning (MTL) attempts to provide optimizations to many-task systems, improving performance by optimization algorithms and structural changes to these networks. However, we have found that current MTL optimization algorithms often impose burdensome computation overheads, require meticulously labeled datasets, and do not adapt to tasks with significantly different loss distributions. We propose a new MTL optimization algorithm: Batch Swapping with Multiple Optimizers (BSMO). We utilize single-task labeled data to train on a multi-task hard parameter sharing (HPS) network through swapping tasks at the batch level. This dramatically increases the flexibility and scalability of training on an HPS network by allowing for per-task datasets and augmentation pipelines. We demonstrate the efficacy of BSMO versus current SOTA algorithms by benchmarking across contemporary benchmarks & networks.
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