Presented are Ergodic Spectrum Management (ESM) systems and methods that take advantage of the presence of statistical consistencies (“ergodicity”) and correlations, such as a wireless network's average dimensional consistencies of probability distributions (in time, space, and frequency) of channel gains, to adaptively learn qualitative and quantitative network/user behavior; estimate or predict network performance; and guide locally implemented radio resource management (RRM) decisions of wireless multi-user transmissions in a manner such as to reduce interference and improve latency; connection stability; efficiency; and overall wireless performance. ESM also enhances end-users' Quality of Experience (QoE) by allowing movement across bands and regions as users/devices roam. A remote-cloud-based resource management implementation of ESM's Learn-ed Resource Managers (LRMs) removes the need for heavy edge-computing close to radio cells.