# LoRa Resource Allocation Algorithm for Higher Data Rates
## 3 LRA Algorithm Framework
### Resource Allocation Algorithm
MIMO-LoRa [16] enables the reception of multiple signals with different SFs transmitted in parallel by the same EDs, utilizing SNR and transmit power optimization without needing synchronization. In contrast, the proposed algorithm requires synchronization within the same ED, which adds complexity to the algorithm. While MIMO-LoRa is not suitable for low SFs, the proposed LRA algorithm is optimized for these conditions. However, the proposed algorithm may face challenges with higher SFs, due to increased inter-SF interference. Additionally, as a non-LoRaWAN protocol, the LRA may lack compatibility with existing networks built on LoRaWAN. ## 5 Conclusions

In this study, we addressed the challenge of image transmission using LoRa technology, which is typically constrained by its low data rate. To overcome this limitation, we proposed a resource allocation algorithm for LoRa (LRA) that leverages the quasi-orthogonality of spreading factors (SFs) to enable parallel transmissions of larger data sizes using multiple LoRa modules. To achieve this, we developed a specially designed end device (DED) that incorporates two LoRa modules connected to a single controller unit, minimizing hardware complexity. The LRA algorithm is introduced to assign data packets to the transmitters based on their operating SF in a synchronized manner. Furthermore, we examined the transmission time (\(T_{tx}\)) across different SFs of our logical channel (LC) to optimize the number of packets transmitted per parallel transmission round. We performed a two-phase experimental study to demonstrate the feasibility of the parallel transmissions using different parameters and to examine the performance of our proposed algorithm in terms of \(T_{tx}\), BER, data rate, energy consumption, and the quality of the received image. The results indicate that our parallel setup significantly improves data rate and \(T_{tx}\) within the existing LoRa technology. Considering the LRA algorithm in densely deployed LoRa networks to assess scalability would be our next focus to further assess its performance. To achieve this, machine-learning-based approaches for selecting SFs for the DEDs could prove beneficial. Furthermore, Since LoRa signals with identical SFs can significantly interfere with each other, a coding scheme may exist allowing the receiver to distinguish and successfully demodulate these signals. This could open the door to parallel transmissions on the same SF, greatly boosting throughput. Finding such a coding system would be the subject of future research in this area. Conceptualization, G.M.E.R. and H.K. ; Methodology, G.M.E.R. and H.K. ; Software, H.K. ; Validation, G.M.E.R. and H.K. ; Formal analysis, G.M.E.R. and H.K. ; Investigation, G.M.E.R. and H.K. ; Visualization, G.M.E.R. and H.K. ; Supervision, K.A.W. All authors have read and agreed to the published version of the manuscript. This research was funded by Natural Sciences and Engineering Research Council of Canada (NSERC), grant number RGPIN-2018-06274. Not applicable. ## References

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