Why Linear Recurrent Memory Works in Partially Observable Reinforcement Learning

The family of linear recurrent neural networks has shown strong performance as recurrent memory units in partially observable reinforcement learning. We provide a theoretical justification for their empirical effectiveness by constructing and studying two linear filters: (i) the first exactly reproduces the pre-softmax logits of the belief vector in a hidden Markov model (HMM) under a deterministic transition matrix, thereby serving as a sufficient statistic for optimal policy learning, (ii) the second achieves vanishing state-decoding error under a nearly deterministic transition matrix, thus reducing state ambiguity to near zero. The results extend to action-controlled HMMs, where the corresponding linear filters become time-varying with action-dependent dynamics. We illustrate our main results through numerical experiments and further show that the constructed linear filter serves as a strong feature extractor in a small reinforcement learning game.

Precoding for High Throughput Satellite Communication Systems: A Survey

With the expanding demand for high data rates and extensive coverage, high throughput satellite (HTS) communication systems are emerging as a key technology for future communication generations. However, current frequency bands are increasingly congested. Until the maturity of communication systems to operate on higher bands, the solution is to exploit the already existing frequency bands more efficiently. In this context, precoding emerges as one of the prolific approaches to increasing spectral efficiency. This survey presents an overview and a classification of the recent precoding techniques for HTS communication systems from two main perspectives: 1) a problem formulation perspective and 2) a system design perspective. From a problem formulation point of view, precoding techniques are classified according to the precoding objective, group, and level. From a system design standpoint, precoding is categorized based on the system architecture, the precoding implementation, and the type of the provided service. Further, practical system impairments are discussed, and robust precoding techniques are presented. Finally, future trends in precoding for satellites are addressed to spur further research.

A New Analytical Approximation of the Fluid Antenna System Channel

Fluid antenna systems (FAS) are an emerging technology that promises a significant diversity gain even in the smallest spaces. Motivated by the groundbreaking potentials of liquid antennas, researchers in the wireless communication community are investigating a novel antenna system where a single antenna can freely switch positions along a small linear space to pick the strongest received signal. However, the FAS positions do not necessarily follow the ever-existing rule separating them by at least half the radiation wavelength. Previous work in the literature parameterized the channels of the FAS ports simply enough to provide a single-integral expression of the probability of outage and various insights on the achievable performance. Nevertheless, this channel model may not accurately capture the correlation between the ports, given by Jake's model. This work builds on the state-of-the-art and accurately approximates the FAS channel while maintaining analytical tractability. The approximation is performed in two stages. The first stage approximation considerably reduces the number of multi-fold integrals in the probability of outage expression, while the second stage approximation provides a single integral representation of the FAS probability of outage. Further, the performance of such innovative technology is investigated under a less-idealized correlation model. Numerical results validate our approximations of the FAS channel model and demonstrate a limited performance gain under realistic assumptions. Further, our work opens the door for future research to investigate scenarios in which the FAS provides a performance gain compared to the current multiple antennas solutions.