Collaborative processing and radio resource management for cloud-based radio access networks

Next-generation wireless communication systems are expected to deliver extreme improvements in throughput and energy efficiency, in an era of pervasive machine learning applications. Meeting the ambitious technical objectives set out in beyond-5G visions requires a collaborative effort among a large number of wireless access points. Leveraging advances in optimisation theory, this thesis studies collaborative radio resource management for the two most promising cloud-based wireless network architectures: Cloud Radio Access Networks (C-RANs) and Cell-Free Massive Multiple-Input-Multiple-Output (CFmMIMO) networks. In these networks, the significant advantages achieved through centralised baseband processing, spatial multiplexing and large antenna arrays make it possible to support distributed machine learning algorithms over the air. However, on top of the traditional issue of limited available radio resources, C-RANs and CFmMIMO architectures must resolve new issues arising from the limited-capacity fronthaul links that connect the access points with the central processing unit in the cloud. Furthermore, executing distributed machine learning algorithms over dynamic and unpredictable wireless links remains a major obstacle. The first contribution of this thesis is proposing novel resource allocation solutions to improve throughput and energy efficiency for C-RANs via edge baseband processing. Here, we propose that baseband signals be partially processed at the access points instead of being fully processed at the central processing unit. We show that in the best-case scenarios, our optimisation-based edge processing approach provides up to 50% energy efficiency gain over the existing centralised compression-based solutions. We further extend our results to full-duplex transmissions, and show that full-duplexing can improve the network throughput by almost 1.5 times while tripling the energy-efficiency gain, compared with half-duplex transmissions. In the second contribution, we consider a C-RAN content delivery network where each access point stores the frequently requested content in its local cache. Local caching has been shown to substantially reduce data traffic on the fronthaul links. We propose new transmission and resource allocation algorithms that are applicable to any caching scheme. Here, both multicasting and unicasting cases are analysed in detail. Simulation results show that for a given caching scheme, our proposals can deliver up to 20% network throughput gain and 14% energy efficiency gain. In the third contribution, we propose a novel communication scheme for CFmMIMO networks to support any federated learning (FL) framework. This scheme exploits the channel hardening property of massive MIMO, where the effective channel gains remain relatively unchanged during the large-scale channel coherence time. We propose that within this coherence time, one (instead of all) iteration of an FL process is executed, thus guaranteeing the stable operation of the whole FL process. In this communication scheme, we develop successive convex approximation algorithms that allocate radio resources to minimise the FL training time. Simulation results show that our proposals reduce the FL training time by more than half compared to the heuristic approaches. In the fourth contribution of this thesis, we extend the results in the third contribution to mitigate the `straggler effect' in wireless federated learning. Here, a user with unfavorable wireless link conditions may significantly slow down the entire FL process. We study new user selection methods for CFmMIMO networks and develop algorithms that minimise the FL training time. Significantly, a reduction of up to 63% in training time has been observed in our numerical results with practical parameter settings.