## 2 Related Work  
The use of camera technologies other than traditional CMOS-based ones has experienced a significant surge in recent years. For example, [PERSON] et al. suggested employing multispectral sensors to enhance the link capacity and multiplexing capabilities of OCC links [13]. Additionally, [PERSON] et al. investigated the application of thermographic cameras for communications in specific niche applications [14]. In this context, event cameras have been proposed as optical receivers, capitalizing on the a priori higher achievable data rate. This is primarily attributed to the different paradigm used for data gathering, which operates asynchronously rather than in a frame-based manner. Accordingly, event cameras offer advantages for use in real-time interaction systems, such as wearable electronics [15], robotics [16], and unmanned aerial vehicles (UAVs), where low latency and energy efficiency are required. However, the operational characteristics of these cameras introduce new challenges, such as stream data processing, contrasting with the traditional frame-based processing utilized in OCC. In [17], a method was proposed for the low-latency pose tracking of a drone in movement. This approach utilizes a DVS camera as the receiver and several LEDs as markers. Differentiation of markers on the aerial vehicle is achieved by employing different tags blinking frequencies (above 1 kHz and below 2 kHz). Tracking the position of a set of markers involves computing the time interval between successive transitions of events of the same polarity. Then, individual \"evidence maps\" are constructed, one for each active marker, corresponding to the probability that the marker is located at a given pixel. Finally, after detection, a particle filter and a multi-hypothesis tracker are used to reconstruct the object's pose. Experiments showed that DVS performance was not affected by fast motion, unlike the CMOS camera, which suffered from motion blur. This initial study confirmed that event camera technology is a promising candidate for detecting and identifying light transmitters, even in motion. Later, a camera communication system utilizing a neuromorphic vision sensor (NVS) and algorithms for detecting and demodulating light sources was proposed and implemented in [18]. The system employed LED clusters operating within the infrared (IR) spectrum (940 nm) and a single RGB LED as transmitters. Data encoding was achieved through pulse position modulation (PPM) with a duty cycle set at 10%. The detection process begins by generating artificial synchronous frames using events captured by the NVS camera, ensuring a frame rate at least twice the transmitted blinking frequency. In practice, a frame rate of forty times the transmitter frequency (40\(\cdot\)T\({}_{f}\)) is employed. Subsequently, clusters of pixels potentially associated with modulated light sources are extracted from the artificial frames. This involves computing the union of a subset of the artificial frames and detecting pixel clusters of sizes equal to or greater than S \(\times\) S pixels. The \(q\) pixel sets are then utilized to generate waveforms, combining positive and negative events separately. To detect the beginning of a packet, a matched filter employing a start frame delimiter (SFD) as a template is utilized. Data extraction involves identifying peaks in the filtered waveform, enabling accurate demodulation. Subsequently, the waveforms are sampled by subtracting consecutive pairs of samples, with each pair representing a symbol. Decisions are made based on the sign (positive or negative) resulting from this operation, with a positive outcome denoting a logical '0' and a negative outcome indicating a logical '1'. Due to the refractory period effect, the use of a pixel cluster was found to be essential for effective data decoding.