Optical interconnects (OIs) based on vertical-cavity surface-emitting lasers (VCSELs) are the main workhorse within data centers, supercomputers, and even vehicles, providing low-cost, high-rate connectivity. VCSELs must operate under extremely harsh and time-varying conditions, thus requiring adaptive and flexible designs of the communication chain. Such designs can be built based on mathematical models (model-based design) or learned from data (machine learning (ML) based design). Various ML techniques have recently come to the forefront, replacing individual components in the transmitters and receivers with deep neural networks. Beyond such component-wise learning, end-to-end (E2E) autoencoder approaches can reach the ultimate performance through co-optimizing entire parameterized transmitters and receivers. This tutorial paper aims to provide an overview of ML for VCSEL-based OIs, with a focus on E2E approaches, dealing specifically with the unique challenges facing VCSELs, such as the wide temperature variations and complex models.

Secure outage probability (SOP) and strictly positive secrecy capacity (SPSC) expressions are derived for single-input multiple-output systems over κ-μ shadowed fading channels. Also, a novel approximation for the SPSC is derived in terms of a single Gauss hypergeometric function. A close match is observed among the results obtained through the derived analytical expressions, the approximation, and Monte-Carlo simulations.

The uplink of multi-user massive multi-input-multioutput systems, in which the base station is equipped with lowresolution analog-to-digital converter architecture is considered in this paper. All the channels are assumed to undergo independent and non-identically distributed Nakagami-m fading. By means of moment matching, the signal-to-interference-plusquantization noise-ratio at the output of maximum ratio combiner is approximated by a gamma random variable. Using this approximate pdf, tight approximations for metrics like outage probability and rate are derived. The gap between the simulation and the approximate result is negligible. Also, for large number of antennas, a decrease in the number of antennas should be accompanied by a proportionate decrease in the threshold to maintain the same outage probability.

This paper derives approximate outage probability (OP) expressions for uplink cell-free massive multiple-input-multiple-output (CF-mMIMO) systems with and without pilot contamination. The system’s access points (APs) are considered to have imperfect channel state information (CSI). The signal-to-interference-plus-noise ratio (SINR) of the CF-mMIMO system is approximated via a Log-normal distribution using a two-step moment matching method. OP and ergodic rate expressions are derived with the help of the approximated Log-normal distribution. For the no-pilot contamination scenario, an exact expression is first derived using conditional expectations in terms of a multi-fold integral. Then, a novel dimension reduction method is used to approximate it by the sum of single-variable integrations. Both the approximations derived for the CF-mMIMO systems are also useful for single-cell collocated massive MIMO (mMIMO) systems and lead to closed-form expression. The derived expressions closely match the simulated numerical values for OP and ergodic rate.

This paper determines the optimum secondary user (SU) power allocation and ergodic multicast rate of point-to-multipoint communication in a cognitive radio network (CRN) in the presence of various quality of service (QoS) constraints for the primary users (PUs). Using tools from extreme value theory (EVT), it is first proved that the limiting distribution of the minimum of independent and identically distributed (i.i.d.) signal-to-interference ratio (SIR) random variables (RVs) is a Weibull distribution, when the user signal and the interferer signals undergo independent and non-identically distributed (i.n.i.d.) κ-μ shadowed fading. Also, the rate of convergence of the actual minimum distribution to the Weibull distribution is derived. This limiting distribution is then used for determining the optimum transmit power of a secondary network in an underlay CRN subject to three different QoS constraints at the primary network in a generalized fading scenario. Furthermore, the optimum transmit power and the asymptotic ergodic multicast rate of SUs is analyzed for varying channel fading parameters.

In this paper, we propose a scheme for the joint optimization of the user transmit power and the antenna selection at the access points (AP)s of a user-centric cell-free massive multiple-input-multiple-output (UC CF-mMIMO) system. We derive an approximate expression for the achievable uplink rate of the users in a UC CF-mMIMO system in the presence of a mixed analog-to-digital converter (ADC) resolution profile at the APs. Using the derived approximation, we propose to maximize the uplink sum-rate of UC CF-mMIMO systems subject to energy constraints at the APs. An alternating-optimization solution is proposed using binary particle swarm optimization (BPSO) and successive convex approximation (SCA). We also propose a complete meta-heuristic-based solution that can be used as an alternative solution for applications where latency is the critical metric. Along with this, we used a genetic algorithm (GA)-based approach to compare the performance of the proposed algorithm. We study the impact of various system parameters on the performance of the system.