# SCATTER PHY: An Open Source Physical Layer for the DARPA Spectrum Collaboration Challenge
## 2 Related Work  
In [8], the authors proposed the use of deep reinforcement learning to design a universal MAC protocol referred there as Deep-reinforcement Learning Multiple Access (DLMA). They showed through simulations that the proposed DLMA scheme was able to learn the optimal MAC strategy for harmonious co-existence with TDMA and ALOHA networks.  
The authors of [9] employed a Deep Learning Neural Network (DLNN) for the slot prediction task. They demonstrated through simulations that their trained DLNN did online learning and could accurately predict, through spectrum monitoring, the behavior of spectrum usage (i.e., slot transmissions) one second in advance in the context of Multiple Frequency Time Division Multiple Access (MF-TDMA) networks.  
In the work presented in [10], the authors dealt with the problem involving the recognition of different Radio Access Technologies (RATs) employing Deep Autoencoders (DAEs). Their DAE model was used in a Semi-Supervised Learning (SSL) approach for the recognition of wireless technologies using raw IQ samples. The simulation results showed that the proposed DAE approach achieved similar accuracy when compared with Supervised Learning (SL). They also demonstrated that their approach outperformed the SL approach for negative Signal-to-Noise Ratio (SNR) values.  
The authors of [11] modeled a collaborative power control scheme in an interference limited wireless network, where nodes shared the same spectrum band. They employed game theory in order to determine the optimal power allocation for every node. The simulation results showed that by collaborating (i.e., exchange of node information) with other nodes, it was possible to maximize not only a single node's performance, but also other nodes' performances.  
In [12], the authors proposed the use of a Q-learning based dynamic optimal band and channel selection scheme, which took into account the surrounding wireless environments and system demands in order to maximize the available transmission time and capacity at the given time and geographic area. The simulation results showed that their solution dynamically chose a band and channel suitable for the required data rate and operated properly according to the desired system performance.  
All the above described previous works heavily relied on simulations to access the performance of several artificial intelligence algorithms applied to the spectrum sharing problem. However, they lacked experimental validation of their approaches through the use of test-beds or real-world channels. For instance, after the DARPA challenge is over, the Colosseum test-bed, which was developed for the competition, will become a National Wireless Research asset and made available to the public for any kind of wireless experiments. With that in mind, since the inception of our PHY layer, we developed SCATTER PHY to be highly configurable and decoupled from the upper layers, i.e., the layers connected to our PHY layer can access it remotely and change a great number of parameters in real time through well defined control messages. Therefore, we hope, by making the SCATTER PHY layer available as an open-source project, to close the gap between simulation-only validations and real-world experiments.