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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.

In this paper, we consider a visible light communication (VLC) system with direct currentbiased 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 OFDM 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 wellknown 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.INDEX TERMS Visible light communications, resource allocation, optimization, simulated annealing, heuristics.

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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.

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Route Optimization (RO) is an important feature of Electric Vehicles (EVs) navigation system. This work performs the RO for EVs using the Multi Constrained Optimal Path (MCOP) problem. The proposed MCOP problem aims to minimize the length of the path and meets constraints on travelling time, time delay due to traffic signals, recharging time and recharging cost. The optimization is performed through a design of Simulated Evolution (SimE) which has innovative goodness, allocation and mutation operations for the route optimization problem. The simulations show that the proposed algorithm has performance almost equal to or better than the Genetic Algorithm (GA) and it requires 0.5 ( is the population size and ≥ 2 and generally = 20) times lesser memory than the GA.

In millimeter-wave (MMW) networks, the channel state information (CSI) carries essential information from the user to the base station (BS). The CSI values depend highly on the geometrical and physical features of the environment. Therefore, it is impossible to generate CSI data for computer simulations or analysis through mathematical models. The CSI in MMW networks can only be acquired through physical measurement(s) or with the help of expensive and complicated ray-tracing software. For many users, both these options are infeasible. This work aims to propose a simple and fast method that can generate artificial samples from the real data samples while ensuring that the artificial samples look similar to the real ones. The proposed method helps increase the size of existing CSI datasets and likely to benefit the evolution of deep learning models that need a large amount of training/testing data. The proposed method comprises two parts. (i) The first part applies data clustering and transformations such as principal component analysis (PCA)-based dimensionality reduction and probability integral transform (PIT) to convert the real data into a multivariate normal distribution of a smaller number of variables, and (ii) The second part synthesizes artificial data by learning from the multivariate normal distribution of the first part. The last step in the second part is to apply PIT and inverse PCA transformations to transform the artificial data into the same space as the input data. We compared the proposed method's performance with the well-known Kernel density estimation (KDE)-based methods that use Scott's rule and Silverman's rule to choose the bandwidth parameter value. The results show that the artificial samples generated by the proposed method exhibit very high similarity with the real ones as compared to the KDE-based methods.