Efficient power allocation for downlink MIMO-NOMA-based visible light communication systems

Visible light communication (VLC) networks are emerging as a viable option to meet the ever-growing wireless data demand, primarily for indoor environments in recent years. However, developing a high data rate VLC system using off-the-shelf light emitting diodes (LEDs) is difficult due to the narrow modulation bandwidth of the white light emitting diodes. The existing studies have integrated Multiple-Input Multiple-Output (MIMO) and Non-Orthogonal Multiple Access (NOMA) schemes into the downlink VLC system so as improve its performance. Despite its apparent advantages, the NOMA scheme suffers from multi-user interference problem. For an efficient NOMA scheme, which uses successive interference cancellation (SIC) technique to decode the superimposed received signal of multipleusers, adaptive and fair power allocation methods are required. The existing power allocation schemes are inefficient when the number of users increases. In this work, a low complexity and efficient power allocation strategy is proposed for MIMO-NOMA based VLC systems. The proposed parametric power allocation strategy is based on the order of channel gain among end-users and overcomes the limitations of the existing schemes. The performance of the proposed power allocation scheme in an indoor 4×4 MIMO-NOMA-based multi-user VLC was analyzed through simulation. A zero-forcing detection mechanism is used in the analysis. Simulation results using MATLAB depict the effectiveness of the proposed power allocation strategy. It achieves higher performance than the existing power allocation schemes, gain ratio power allocation (GRPA) and normalized gain difference power allocation (NGDPA) when the number of users are greater than two. Specifically, the proposed power allocation scheme improves the achievable sum rate of the NGDPA and GRPA schemes by 18.36% and 65.98%, respectively, in 4× 4 MIMO-based visible light communication networks with eight uniformly distributed users.