In this paper, we investigate the downlink transmission for cache-enabled fog radio access networks aiming at maximizing the delivery rate under the constraints of fronthaul capacity, maximum transmit power, and size of files. To reduce the delivery latency and the burden on fronthaul links and make full use of the local cache and baseband signal processing capabilities of enhanced remote radio heads (eRRHs), a two-level transmission scheme including cache-level and network-level transmission is proposed. In cache-level transmission, only requested files cached at the local cache are transmitted to the corresponding users. The duration of cache-level transmission is the delay caused by the transfer between the baseband unit (BBU) and eRRHs as well as the signal processing at the BBU. The remaining requested files are jointly transmitted to the corresponding users at network-level transmission. For cache-level transmission, a centralized optimization algorithm is first presented and then a decentralized optimization algorithm is provided to avoid the exchange of signaling among eRRHs. Meanwhile, another centralized optimization algorithm is presented to tackle the optimization problem for network-level transmission. All presented algorithms are proved to converge to the Karush-Kuhn-Tucker solutions of the problems. Numerical results are provided to validate the effectiveness of the proposed transmission scheme as well as evaluating the system performance.

In cloud radio access network, a baseband unit (BBU) performs the baseband processing for a cluster of low‐power low‐cost remote radio heads (RRHs) that are connected to the BBU through low‐latency fronthaul links. In this study, the authors study the optimisation of two energy‐efficient compression and precoding strategies which take transmit power constraint, fronthaul capacity constraint and user specific rate constraint into account. To overcome the non‐convexity nature of the original problem, they first transform the objective of the original problem into a parameterised subtractive form and obtain an approximate convex problem via the successive convex approximation. Then, an effective optimisation algorithm with provable convergence is designed to solve the effective problem. Numerical results reveal that the proposed scheme outperforms the conventional maximum sum rate and minimum total power consumption schemes in terms of the energy‐efficiency criterion. In particular, compression after precoding strategy outperforms compression before precoding strategy when both of their RRHs perform the same user scheduling, while the opposite conclusion can be drawn otherwise.