Ultra-Reliable Risk-Aggregated Sum Rate Maximization via Model-Aided Deep Learning

Traditionally, the WSR problem is addressed either deterministically using well-known methodologies such as Zero-Forcing [1], WMMSE [2], or fractional programming [3], or stochastically through the design of ergodic-optimal resource allocation policies, maximizing expected WSR under stochastic resource constraints [4], under either perfect or imperfect channel state information (CSI) [5]. While resource policies (e.g., beamforming) optimizing ergodic (i.e., expected) or even deterministic QoS metrics (e.g., WSR) may be proven to perform optimally "on average" or "in the long term", they often result in inferior user-perceived system performance. In particular, such optimal policies do not respond adequately to the presence of relatively (in)frequent, albeit operationally significant deep-fade events or, more generally, fading channel adversities, causing severe and abrupt drops in (perceived) service. This is a real and practical issue, especially considering that, in many actual scenarios, statistical dispersion of channel fading typically exhibits heavy-tailed characteristics. In fact, it is well-known that ergodic-optimal policies often behave in a channel-opportunistic manner [6, 7], completely discontinuing service to certain users in case of adverse channel conditions and low signal-to-noise ratio. This is not only inefficient in terms of communications, but also leads to substantial spectrum under-utilization. To mitigate those issues, contemporary works have considered formulations based on outage probabilities, or explicit minimum user rate constraints [8-12].