We present a novel maximum likelihood sequence detection (MLSD) receiver structure for nonlinear channels. This scheme is derived by treating the NLC as a multiple input/multiple output system. Then, orthogonal signal components are computed using a special form of space-time whitened matched filter (ST-WMF) obtained by a modified Gram-Schmidt orthogonalization of the Volterra kernels of the NLC. The MLSD receiver consists of the ST-WMF followed by a Viterbi detector (VD) with multidimensional branch metrics. The space orthogonalization and noise whitening achieved by the ST-WMF provide an efficient way to reduce the receiver complexity in the presence of highly dispersive NLC. Complexity reduction is crucial in practical applications such as intensity modulation/direct detection (IM/DD) optical channels. As an example, the number of states of the VD in ST-WMF-MLSD required on a 10 Gb/s, 700 km, IM/DD fiber-optic link is reduced eight times compared with an oversampled MLSD.

In this paper, a Reinforcement Learning (RL) algorithm is presented to speed up the selection process of spatial beams to maximize the mean data rate of a multiantenna wireless system that implements hybrid beamforming in Millimeter Wave (mmWave) frequency bands. In the proposed hybrid beamforming architecture, the analog beamforming layer is codebook-based, and is implemented using a simple array of phase-shifters that delay the RF signal in the different transmit antennas using a fixed number of discrete steps. In contrast, the digital beamforming layer is much more flexible, and implements a fully adaptive (i.e., non-quantized) digital precoding scheme that enables the simultaneous transmission of few independent baseband data streams in the spatial domain. Obtained simulation results show that the use of RL-based techniques reduces the iterations that are needed to find the most convenient analog beamformers and digital precoders to be used in transmission, without affecting notably the upper bound data rate that is achieved when brute-force search is utilized.

The space-time whitened matched filter (ST-WMF) maximum likelihood sequence detection (MLSD) architecture has been recently proposed (Maggio et al., 2014). Its objective is reducing implementation complexity in transmissions over nonlinear dispersive channels. The ST-WMF-MLSD receiver (i) drastically reduces the number of states of the Viterbi decoder (VD) and (ii) offers a smooth trade-off between performance and complexity. In this work the ST-WMF-MLSD receiver is investigated in detail. We show that the space compression of the nonlinear channel is an instrumental property of the ST-WMF-MLSD which results in a major reduction of the implementation complexity in intensity modulation and direct detection (IM/DD) fiber optic systems. Moreover, we assess the performance of ST-WMF-MLSD in IM/DD optical systems with chromatic dispersion (CD) and polarization mode dispersion (PMD). Numerical results for a 10 Gb/s, 700 km, and IM/DD fiber-optic link with 50 ps differential group delay (DGD) show that the number of states of the VD in ST-WMF-MLSD can be reduced ∼4 times compared to an oversampled MLSD. Finally, we analyze the impact of the imperfect channel estimation on the performance of the ST-WMF-MLSD. Our results show that the performance degradation caused by channel estimation inaccuracies is low and similar to that achieved by existing MLSD schemes (∼0.2 dB).