Performance Limits of Compressive Sensing Channel Estimation in Dense Cloud RAN

Towards reducing the training signaling overhead in large scale and dense cloud radio access networks (CRAN), various approaches have been proposed based on the channel sparsification assumption, namely, only a small subset of the deployed remote radio heads (RRHs) are of significance to any user in the system. Motivated by the potential of compressive sensing (CS) techniques in this setting, this paper provides a rigorous description of the performance limits of many practical CS algorithms by considering the performance of the, so called, oracle estimator, which knows a priori which RRHs are of significance but not their corresponding channel values. By using tools from stochastic geometry, a closed form analytical expression of the oracle estimator performance is obtained, averaged over distribution of RRH positions and channel statistics. Apart from a bound on practical CS algorithms, the analysis provides important design insights, e.g., on how the training sequence length affects performance, and identifies the operational conditions where the channel sparsification assumption is valid. It is shown that the latter is true only in operational conditions with sufficiently large path loss exponents.Comment: 6 pages, two-column format; ICC 201

Learning Optimal Fronthauling and Decentralized Edge Computation in Fog Radio Access Networks

Fog radio access networks (F-RANs), which consist of a cloud and multiple edge nodes (ENs) connected via fronthaul links, have been regarded as promising network architectures. The F-RAN entails a joint optimization of cloud and edge computing as well as fronthaul interactions, which is challenging for traditional optimization techniques. This paper proposes a Cloud-Enabled Cooperation-Inspired Learning (CECIL) framework, a structural deep learning mechanism for handling a generic F-RAN optimization problem. The proposed solution mimics cloud-aided cooperative optimization policies by including centralized computing at the cloud, distributed decision at the ENs, and their uplink-downlink fronthaul interactions. A group of deep neural networks (DNNs) are employed for characterizing computations of the cloud and ENs. The forwardpass of the DNNs is carefully designed such that the impacts of the practical fronthaul links, such as channel noise and signling overheads, can be included in a training step. As a result, operations of the cloud and ENs can be jointly trained in an end-to-end manner, whereas their real-time inferences are carried out in a decentralized manner by means of the fronthaul coordination. To facilitate fronthaul cooperation among multiple ENs, the optimal fronthaul multiple access schemes are designed. Training algorithms robust to practical fronthaul impairments are also presented. Numerical results validate the effectiveness of the proposed approaches.Comment: to appear in IEEE Transactions on Wireless Communication