A New Class of Structured Beamforming for Content-Centric Fog Radio Access Networks

Abstract

A multi-user fog radio access network (F-RAN) is designed for supporting content-centric services. The requested contents are partitioned into sub-contents, which are then 'beamformed' by the remote radio heads (RRHs) for transmission to the users. Since a large number of beamformers must be designed, this poses a computational challenge. We tackle this challenge by proposing a new class of regularized zero forcing beamforming (RZFB) for directly mitigating the inter-content interferences, while the 'intra-content interference' is mitigated by successive interference cancellation at the user end. Thus each beamformer is decided by a single real variable (for proper Gaussian signaling) or by a pair of complex variables (for improper Gaussian signaling). Hence the total number of decision variables is substantially reduced to facilitate tractable computation. To address the problem of energy efficiency optimization subject to multiple constraints, such as individual user-rate requirement and the fronthauling constraint of the links between the RRHs and the centralized baseband signal processing unit, as well as the total transmit power budget, we develop low-complexity pathfollowing algorithms. Finally, we confirm their performance by simulations.Index Terms-Fog radio access network (F-RAN), soft-transfer fronthauling, regularized zero-forcing beamforming, content service, Gaussian signaling.

Collaborative Beamforming Aided Fog Radio Access Networks

“…which represents a scalable complexity. This is in contrast to the computational complexity order O(( (k, )∈K×L N k, ) 3 ) of the convex-solver based algorithms [28].…”

“…Then the concerted F-RAN is only deemed successful, if the throughput of the users is above the minimum threshold subject to the constraints on both the fronthaul capacity and the transmit power. Since the throughput is a complex nonlinear function of all the beamformer weights and there are numerous beamformers, determining the CBF weights constitutes a large-dimensional nonlinearly constrained optimization problem [21], [22], [25]- [28]. The family of advanced optimization algorithms such as those proposed in [26], [28], which iteratively solve convex problems were only capable of handling scenarios of up to five four-antenna based RRHs serving ten users.…”