Multi-input multi-output (MIMO) technique is attractive for visible light communication (VLC), which exploits the high signal-to-noise ratio (SNR) of a single channel to overcome the capacity limitation due to the small modulation bandwidth of the light emitting diode. This paper establishes a MIMO VLC system under the non-negativity, peak power and dimmable average power constraints. Assume that perfect channel state information at the transmitter is known, the MIMO channel is changed to parallel, non-interfering sub-channels by using the singular value decomposition (SVD). Based on the SVD, the lower bound on the channel capacity for MIMO VLC is derived by employing entropy power inequality and variational method. Moreover, by maximizing the derived lower bound on the capacity under the given constraints, the receiver deployment optimization problem is formulated. The problem is solved by employing the principle of particle swarm optimization. Numerical results verify the derived capacity bound and the proposed deployment optimization scheme.
In this paper, the capacity of the point-to-point VLC system is investigated by means of functional analysis subject to amplitude constraint (and average intensity constraint). It is proved that the capacity can be reached by a unique probability density function (PDF). Two sets of necessary and sufficient conditions for the optimum PDF are derived. Moreover, the capacity-achieving PDF are proved to be discrete and finite. Given that the capacity can be achieved by a set of discrete and finite constellations, the capacity-achieving constellation optimization problems under amplitude constraint (and average intensity constraint) are formulated and algorithms are proposed to solve the corresponding problem. Since digital implementation is applied in most practical VLC systems, constellation optimization problem maximizing the mutual information subject to an additional equal probability constraint are put forward and analyzed.
In secure spatial modulation (SM) networks, power allocation (PA) strategies are investigated in this paper under the total power constraint. Considering that there is no closed-form expression for secrecy rate (SR), an approximate closed-form expression of SR is presented, which is used as an efficient metric to optimize PA factor and can greatly reduce the computation complexity. Based on this expression, a convex optimization (CO) method of maximizing SR (Max-SR) is proposed accordingly. Furthermore, a method of maximizing the product of signal-toleakage and noise ratio (SLNR) and artificial noise-to-leakageand noise ratio (ANLNR) (Max-P-SAN) is proposed to provide an analytic solution to PA with extremely low-complexity. Simulation results demonstrate that the SR performance of the proposed CO method is close to that of the optimal PA strategy of Max-SR with exhaustive search and better than that of Max-P-SAN in the high signal-to-noise ratio (SNR) region. However, in the low and medium SNR regions, the SR performance of the proposed Max-P-SAN slightly exceeds that of the proposed CO.
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