Joint bit and power loading for adaptive MIMO OFDM VLC systems

Abstract

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.

On the Achievable Max-Min User Rates in Multi-Carrier Centralized NOMA-VLC Networks

“…The pseudo-code outlines an iterative method whose termination criterion could be the maximum number of iterations (e.g., 1000 iterations). The iterative method makes a random change in the current binding of users and only lets the new solution replace the current one if the new binding is better than or equal to the current one using Equations ( 14) and (15). The simulation results, shown in Section V-A, illustrate that the above-described method is efficient in preventing the LEDs from having an odd number of users with a relatively low number of iterations.…”

Deep Reinforcement Based Power Allocation for the Max-Min Optimization in Non-Orthogonal Multiple Access

“…A non-convex optimization problem has many local minima and it is hard to determine the globally optimal solution [14], [24], [2]. Different methods such as evolutionary algorithm (EA) [25], [26], [27], [28], monotonic optimization [2], DRL [2], etc., can be applied to solve them.…”

Deep Q-Learning Based Optimization of VLC Systems With Dynamic Time-Division Multiplexing

“…We also implemented two well-known optimization metaheuristic algorithms: (i) SA algorithm; and, (ii) TS algorithm [20] to benchmark the performance of the proposed model. Both of these metaheuristic algorithms have successfully solved different optimization problems in the VLC systems [13], [12], [10]. The results of the TS algorithm are significantly better than the SA algorithm and are used as target values, (∆ Q ), to be reached by the proposed model.…”

“…4) We performed simulations and demonstrate that the solution quality of the DQL approach with the proposed MDP model is competitive to two well-known metaheuristic algorithms: simulated annealing (SA) and tabu search (TS). Both these algorithms have already been applied to solve different optimization problems in the realm of VLC systems [12], [13]. The organization of this article is as follows.…”