Browsing by Author "Siddiqi, U. F."

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Open Access
Deep Q-learning based optimization of VLC systems with dynamic time-division multiplexing
(IEEE, 2020) Siddiqi, U. F.; Sait, S. M.; Uysal, Murat; Electrical & Electronics Engineering; UYSAL, Murat
The traditional method to solve nondeterministic-polynomial-time (NP)-hard optimization problems is to apply meta-heuristic algorithms. In contrast, Deep Q Learning (DQL) uses memory of experience and deep neural network (DNN) to choose steps and progress towards solving the problem. The dynamic time-division multiple access (DTDMA) scheme is a viable transmission method in visible light communication (VLC) systems. In DTDMA systems, the time-slots of the users are adjusted to maximize the spectral efficiency (SE) of the system. The users in a VLC network have different channel gains because of their physical locations, and the use of variable time-slots can improve the system performance. In this work, we propose a Markov decision process (MDP) model of the DTDMA-based VLC system. The MDP model integrates into deep Q learning (DQL) and provides information to it according to the behavior of the VLC system and the objective to maximize the SE. When we use the proposed MDP model in deep Q learning with experienced replay algorithm, we provide the light emitting diode (LED)-based transmitter an autonomy to solve the problem so it can adjust the time-slots of users using the data collected by device in the past. The proposed model includes definitions of the state, actions, and rewards based on the specific characteristics of the problem. Simulations show that the performance of the proposed DQL method can produce results that are competitive to the well-known metaheuristic algorithms, such as Simulated Annealing and Tabu search algorithms.
Open Access
Deep reinforcement based power allocation for the max-min optimization in non-orthogonal multiple access
(IEEE, 2020) Siddiqi, U. F.; Sait, S. M.; Uysal, Murat; Electrical & Electronics Engineering; UYSAL, Murat
NOMA is a radio access technique that multiplexes several users over the frequency resource and provides high throughput and fairness among different users. The maximization of the minimum the data-rate, also known as max-min, is a popular approach to ensure fairness among the users. NOMA optimizes the transmission power (or power-coefficients) of the users to perform max-min. The problem is a constrained non-convex optimization for users greater than two. We propose to solve this problem using the Double Deep Q Learning (DDQL) technique, a popular method of reinforcement learning. The DDQL technique employs a Deep Q- Network to learn to choose optimal actions to optimize users' power-coefficients. The model of the Markov Decision Process (MDP) is critical to the success of the DDQL method, and helps the DQN to learn to take better actions. An MDP model is proposed in which the state consists of the power-coefficients values, data-rate of users, and vectors indicating which of the power-coefficients can be increased or decreased. An action simultaneously increases the power-coefficient of one user and reduces another user's power-coefficient by the same amount. The amount of change can be small or large. The action-space contains all possible ways to alter the values of any two users at a time. DQN consists of a convolutional layer and fully connected layers. We compared the proposed method with the sequential least squares programming and trust-region constrained algorithms and found that the proposed method can produce competitive results.
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Joint bit and power loading for adaptive MIMO OFDM VLC systems
(Wiley, 2020-07) Siddiqi, U. F.; Narmanlıoğlu, Ömer; Uysal, Murat; Sait, S. M.; Electrical & Electronics Engineering; UYSAL, Murat; Narmanlıoğlu, Ömer
Visible light communication (VLC) is a short-range wireless access technology based on the dual use of illumination structure. The frequency-selective characteristics of VLC channels motivate the use of orthogonal frequency division multiplexing (OFDM). In addition, the presence of multiple light sources in most indoor spaces makes multiple-input-multiple-output (MIMO) communication techniques a natural solution for VLC systems. In this paper, we investigate the design of an adaptive MIMO OFDM system. Specifically, we propose adaptive bit and power loading for a direct current-biased OFDM VLC system with MIMO mode switching between repetition coding and spatial multiplexing. We formulate the adaptive algorithm design as an optimization problem where we aim to maximize spectral efficiency through the proper selection of modulation order, power level, and MIMO mode, while satisfying a targeted bit error rate. To solve the underlying NP-hard problems, we use a simulated annealing heuristic. We present Monte Carlo simulation results for a typical indoor setting and illustrate the performance improvements through link adaptation.
Open Access
Resource allocation for visible light communication systems using simulated annealing based on a problem-specific neighbor function
(IEEE, 2019) Siddiqi, U. F.; Sait, S. M.; Demir, Muhammet Selim; Uysal, Murat; Electrical & Electronics Engineering; UYSAL, Murat; Demir, Muhammet Selim
In this paper, we consider a visible light communication (VLC) system with direct current-biased orthogonal frequency division multiplexing (DC-OFDM) and investigate resource allocation for a multi-user environment. Based on the user satisfaction index as a function of data rate, we aim to optimally determine the allocation of the users to different LEDs (acting as access points) and 0I-DM subcarriers. We propose a simulated annealing-based heuristic to maximize the average user satisfaction index. In an effort to make the proposed solution practically feasible, the runtime of the proposed heuristic is kept less than the channel coherence time, whose value is in order of tens of milliseconds. We evaluate the performance of the proposed heuristic algorithm in different scenarios that vary in the number of users, the number of LEDs, and the separation between users. Our results demonstrate that the proposed heuristic outperforms other well-known heuristics (such as standard simulated annealing, iterative greedy, particle swarm optimization, and tabu search) while achieving good quality solutions within a short execution time, i.e., 40-80 ms.