Multi-player online games depict the interaction of multiple players with each other over
time. Strongly monotone games are of particular interest since they have benign properties …

In this paper, we study the problem of learning in quantum games-and other classes of
semidefinite games-with scalar, payoff-based feedback. For concreteness, we focus on the …

In this paper, we examine the maximization of energy efficiency (EE) in next-generation
multiuser MIMO-OFDM networks that vary dynamically over time-eg, due to user mobility …

The areas of machine learning and communication technology are converging. Today's
communications systems generate a huge amount of traffic data, which can help to …

In this paper, we investigate a distributed learning scheme for a broad class of stochastic
optimization problems and games that arise in signal processing and wireless …

We examine the long-run behavior of multiagent online learning in games that evolve over
time. Specifically, we focus on a wide class of policies based on mirror descent, and we …

A wide array of modern machine learning applications–from adversarial models to multi-
agent reinforcement learning–can be formulated as non-cooperative games whose Nash …

This paper presents new learning-based, energy-delay-aware power control strategies for
the uplink of dynamic cell-free (CF) massive multiple-input multiple-output (MIMO) networks …

In this paper, we investigate a power control of uplink connection in the user-centric ultra-
dense heterogeneous networks (HetNets), which are studied as different types of access …

This paper studies the energy efficiency (EE) oriented precoding design in multi-cell
massive multiple-input multiple-output (MIMO) systems, with only statistical channel state …