# Adaptive Parameters for LoRa-Based Networks Physical-Layer
###### Abstract  
Sub-GHz communication provides long-range coverage with low power consumption and reduced deployment cost. LoRa (Long-Range) has emerged, among existing LPWAN (Low Power Wide Area Networks) technologies, as a promising physical layer alternative to provide ubiquitous connectivity to outdoor IoT devices. LoRa modulation technology supports adapting transmissions based on parameters such as carrier frequency, channel bandwidth, spreading factor, and code rate. In this paper, we propose SlidingChange, a novel cognitive mechanism to support the dynamic analysis and adjustment of LoRa network performance parameters. The proposed mechanism uses a sliding window to smooth out short-term variations and reduce unnecessary network re-configurations. To validate our proposal, we conducted an experimental study to evaluate the performance concerning the Signal-to-Noise Ratio (SNR) parameter of our SlidingChange against InstantChange, an intuitive mechanism that considers immediate performance measurements (parameters) for re-configuring the network. The SlidingChange is compared with LR-ADR too, a state-of-the-art-related technique based on simple linear regression. The experimental results obtained from a testbed scenario demonstrated that the InstantChange mechanism improved the SNR by 4.6%. When using the SlidingChange mechanism, the SNR was around 37%, while the network reconfiguration rate was reduced by approximately 16%. LoRa; IoT; Sub-GHz; wireless network; wireless cognitive radio 20232334597. 23234597.  
the network architecture [13]. Chirp modulation can be illustrated by a pulse, which is a concept used by animals, such as dolphins, or radars, and wireless for communication. CSS technology supports the Shannon-Hartley theorem to enable more robust communication against interference. As communication technology adoption in a given frequency spectrum increases, the transmission medium dispute and the interference intensity also increase. This issue reduces the packet delivery probability, and thus there is a need for using the frequency spectrum to increase the network efficiency as a whole [14]. In the case of LoRa technology, its three operating frequency bands are discontinuous, making the adoption of an aggregation channel or channel shift strategies complicated compared to IEEE 802.11 networks. Furthermore, inter-symbolic interference between channels within one of these frequency bands can be equally harmful to the network as a whole, due to LoRa considering a spread-spectrum modulation scheme. The concept of Cognitive Radio (CR), supported by the concurrent advances in the development of Software Defined Radio (SDR) technologies [15], allows a radio or a system to sense its operational electromagnetic environment and dynamically adjust its operating parameters to maximize throughput, mitigate interference, supporting interoperability, and accessing secondary markets [16]. As a consequence of the potential use of free spectra for opportunistic communications, contributing to the massive adoption of IoT applications, research and development on CR technologies have grown exponentially recently. Cognitive Radio-based technologies support _adaptive and autonomous spectral awareness_ (spectrum sensing (SS)), _detection_ of available channels (spectrum decision making), _dynamic adjustment_ of radio operating parameters (spectrum mobility), and conduct _concurrent communication_ (spectrum sharing). In this context, cognitive networks and adaptive exchanges of network operating ranges become an interesting alternative for improving physical parameters of network quality over time [14; 17; 18]. This raises the following research question (**RQ1**): How can cognitive radio and adaptive exchange of networks be combined to reduce inter-symbolic interference between channels in LoRa networks?