In this paper, we present a deep reinforcement learning (DRL) framework for vehicular optical camera communication (OCC) systems that ensures ultra-reliable and low-latency communication (uRLLC). We first formulate a throughput maximization problem that aims at optimizing speed of vehicles, channel code rate, and modulation order while respecting the uRLLC requirements. We model reliability by satisfying a target bit error rate and latency as transmission latency. To improve the transmission rate and provide high reliability and low latency, our scheme uses low-density parity-check codes and adaptive modulation. We then solve the optimization problem using the actor-critic-based DRL scheme with Wolpertinger framework. We employ a deep deterministic policy gradient algorithm to operate over continuous action spaces. The evaluation confirms that our proposed DRL-based optimization scheme achieves superior performance compared to radio frequency-based communication systems as well as variants of the proposed scheme. Finally, we verify through simulations that our proposed solution can maximize the communication rate while meeting the uRLLC constraints.

In dynamic and time-varying vehicular networks, existing vehicular communication systems cannot guarantee ultra-reliable and low latency communication (uRLLC). To address this, we propose a novel deep reinforcement learning-based vehicular optical camera communication (OCC) system with an aim to maximize the throughput and ensure uRLLC. To achieve this, our scheme chooses the optimal code rate, modulation scheme and speed of vehicles for multiple vehicular links. We use OCC, which offers interference-free communication as an alternative to radio frequency systems. Moreover, we employ 5G New Radio low-density parity-check codes and an adaptive modulation scheme to support variable rates and ultra-reliability. The proposed large-scale and continuous problem is solved through an actor-critic algorithm based on Wolpertinger architecture. We extendedly evaluate the system performance and compare it with several other schemes from the literature as well as with variants of our scheme. We observe from the results that the proposed method achieves higher average throughput and lower latency than all the other schemes under comparison. Further, the proposed scheme can meet the uRLLC constraints, whereas other schemes under comparison fail to respect these constraints most of the time.

In this paper, we propose a vehicular optical camera communication system that can meet low bit error rate (BER) and ultra-low latency constraints. First, we formulate a sum spectral efficiency optimization problem that aims at finding the speed of vehicles and the modulation order that maximizes the sum spectral efficiency subject to reliability and latency constraints. This problem is mixed-integer programming with nonlinear constraints, and even for a small set of modulation orders, is NP-hard. To overcome the entailed high computational and time complexity which prevents its solution with traditional methods, we first model the optimization problem as a partially observable Markov decision process. We then solve it using an independent Q-learning framework, where each vehicle acts as an independent agent. Since the state-action space is large we then adopt deep reinforcement learning (DRL) to solve it efficiently. As the problem is constrained, we employ the Lagrange relaxation approach prior to solving it using the DRL framework. Simulation results demonstrate that the proposed DRL-based optimization scheme can effectively learn how to maximize the sum spectral efficiency while satisfying the BER and ultra-low latency constraints. The evaluation further shows that our scheme can achieve superior performance compared to radio frequency-based vehicular communication systems and other vehicular OCC variants of our scheme.

Achieving ultra-reliable and low latency communication (uRLLC) in vehicular networks is challenging because of their time-varying and dynamic nature. In this paper, we propose a deep reinforcement learning (DRL) based vehicular optical camera communications (OCC) system that aims at maximizing the transmission rate. In doing so, we optimize the speed of vehicles, the channel code rate, and the modulation order while respecting the uRLLC requirements. We define reliability by satisfying a predefined bit error rate and latency as transmission latency. To improve the transmission rate and ensure reliability and low latency, we use low-density parity-check codes and adaptive modulation. We then solve the optimization problem using the actor-critic DRL framework with Wolpertinger architecture. We deal with the continuous action spaces by employing a deep deterministic policy gradient algorithm. The evaluation verifies that our proposed optimization scheme can achieve superior performance than the comparison schemes. Finally, the results further confirm that the proposed solution can maximize the communication rate while guaranteeing the uRLLC requirements.

Optical camera communications (OCC) has emerged as a key enabling technology for the seamless operation of future autonomous vehicles. In this paper, we introduce a spectral efficiency optimization approach in vehicular OCC. Specifically, we aim at optimally adapting the modulation order and the relative speed while respecting bit error rate and latency constraints. As the optimization problem is NP-hard problem, we model the optimization problem as a Markov decision process (MDP) to enable the use of solutions that can be applied online. We then relaxed the constrained problem by employing Lagrange relaxation approach before solving it by multi-agent deep reinforcement learning (DRL). We verify the performance of our proposed scheme through extensive simulations and compare it with various variants of our approach and a random method. The evaluation shows that our system achieves significantly higher sum spectral efficiency compared to schemes under comparison.

In this paper, we analyze the performance of vehicular optical camera communication (OCC) towards ultra-reliable and low latency communications (uRLLC). The employed vehicular OCC model uses light-emitting diodes (LED) as transmitter and camera as receiver. In particular, we investigate the performance of the proposed system in terms of bit error rate (BER), spectral efficiency, and transmission latency at different inter-vehicular distances and angle of incidences (AoI). Further, we investigate the use of adaptive modulation to improve the spectral efficiency. From our analysis, we note that by satisfying a given target BER, higher spectral efficiency and lower latency can be achieved through adjusting the AoI towards the smaller degrees and switching into the suitable modulation order. Finally, we verify the results through simulations, which show that OCC can ensure ultra-low latency as well as satisfy the reliability requirements in automotive vehicles.

Localizing smartphones in indoor environments offers excellent opportunities for e‐commence. In this paper, we propose a localization technique for smartphones in indoor environments. This technique can calculate the coordinates of a smartphone using existing illumination infrastructure with light‐emitting diodes (LEDs). The system can locate smartphones without further modification of the existing LED light infrastructure. Smartphones do not have fixed position and they may move frequently anywhere in an environment. Our algorithm uses multiple (i.e., more than two) LED lights simultaneously. The smartphone gets the LED‐IDs from the LED lights that are within the field of view (FOV) of the smartphone’s camera. These LED‐IDs contain the coordinate information (e.g., x‐ and y‐coordinate) of the LED lights. Concurrently, the pixel area on the image sensor (IS) of projected image changes with the relative motion between the smartphone and each LED light which allows the algorithm to calculate the distance from the smartphone to that LED. At the end of this paper, we present simulated results for predicting the next possible location of the smartphone using Kalman filter to minimize the time delay for coordinate calculation. These simulated results demonstrate that the position resolution can be maintained within 10 cm.