Approximate computing in B5G and 6G wireless systems: A survey and future outlook

As modern 5G systems are being deployed, researchers question whether they sufficient for the oncoming decades of technological evolution. Growing numbers interconnected intelligent devices put these networks under tremendous pressure, demanding their development. Paving way beyond and 6G systems, commonly denoted by B5G herein, therefore means seeking enablers to increase efficiency from different perspectives. One novel look on this is application inexact computations where nine 9s reliability not needed, example, in non-critical mobile broadband traffic. The paradigm Approximate Computing (AxC) focuses such areas constrained quality degradation results savings that benefit users operators. This paper surveys state-of-the-art publications intersection AxC identifying emphasizing trends tendencies existing work directions future research. highlights resource allocation algorithms as particularly mesmerizing former, while research related Intelligent Reflective Surfaces appears most prominent latter. In both, problems often NP-hard and, thus, only solvable using heuristics or approximations, Successive Convex Approximation Reinforcement Learning frequently applied.