# LaRaCog: A Protocol for Cognitive Radio-Based LoRa Network
## 2 Related Works  
CR and IoT domains have been actively researched in the last decades. Only a few researchers spoke about CR with a low-power wide-area network (LPWAN or LPWA network) but remained generic about cognitive IoT. [PERSON] and [PERSON] [10] proposed a solution to mitigate radio collisions in IoT unlicensed bands. It is based on learning algorithms implemented on the IoT device side, at a very low cost of implementation and no protocol overhead. The work was demonstrated on LoRa devices in a real LoRaWAN network and named IoTligent. The results show that the system is able to use the available channels by learning on the spot when ISM sub-bands are less occupied or jammed than others. [PERSON] et al. [11] discuss the limitations of LoRaWAN and raised an open question about the CR. From the author's point of view, the inclusion of CR into the LoRaWAN standard could significantly reduce the energy consumption. In [12], the authors proposed an adaptable protocol for LoRa networks with low overhead and complexity. The work introduces an adaptive algorithm for LoRa in two different frequencies. The results obtained by a real scenario show an SNR improvement of 4.68%. [PERSON] et al. [13] introduce a cognitive low-power wide-area network (Cognitive-LPWAN) architecture to safeguard stable and efficient communications in a heterogeneous IoT. The work proposes the usage of artificial intelligence (AI) to balance the demand between heterogeneous IoT devices with communication delays and energy consumption. The experimental results show that this scheme can meet the communication's demand in applications where appropriate communication technologies are chosen to achieve a better interaction experience. [PERSON] et al. [14] presented different LoRa demodulation and decoding strategies, Software-Defined Radio (SDR) implementations, as well as performance assessment under various channel conditions. The simulations show that LoRa systems have good properties for time and/or frequency selective channels, thanks to the robustness of its underlying Chirp-Spread Spectrum (CSS) modulation. Although CR is a promising solution to solve spectrum inefficiency, it brings challenges especially on the hardware design and cost levels as highlighted in [15] or at the complexity of managing such networks in LPWAN systems where the signal is going to propagate for long distances that add to hardware and software challenges, which is a security concern that will require heavy encryption techniques and thus more energy consumption, as stated in [16]. Other researchers worked on LoRaWAN performance optimization without considering CR, such as [PERSON] et al. [17], who showed that a GW can maximize the network capacity by optimizing the spreading factor (SF) distribution to EDs. The work presents combinations of bandwidths and spreading factors to enhance the capacity of LoRa networks and compute the packet success ratio in each combination. [PERSON] et al. [18] suggest an optimization algorithm of SF and transmission power while avoiding near-far perturbations. This is achieved by allocating distant users to different channels. The near-far perturbations mean that a packet of an ED far from the gateway suffers destructive collisions more than a packet of an ED close to the GW. Simulations show that the packet error rate can be decreased down to 50% for distant EDs in a moderate contention scenario. [PERSON] et al. [19] propose EXPLoRa, which improves the data rate and the network's robustness compared to the adaptive data rate (ADR) mechanism. It allocates proper SFs to EDsbased on distance, Received Signal Strength Indicator (RSSI), and the number of connected EDs in the network. [PERSON] et al. [20] evaluated collisions in high-density LoRa networks. The work shows imperfect orthogonality due to different SFs. Larger SFs in congested networks are most likely supposed to have collisions. [PERSON] et al. [9] suggested LoRa+, a new mechanism that improves the QoS of LoRaWAN.