This paper investigates the deployment of a cost-efficient fiber-wireless (FiWi) access network with joint utilization of fixed wireless access (FWA) and fiber-to-the-home (FTTH) technologies. In this work, we consider that residential users over a geographical area are to be provided with network services, constrained by the available network resources. We explore the implementation of hybrid FiWi access network that integrates a fiber-based passive optical network (PON) and fifth-generation (5G) wireless access network to provide efficient network services. This paper proposes a methodology for topology optimization of FiWi access networks, considering the usage of three-dimensional (3D) beamforming and 3D resource grid for downlink transmission from the gNB to the users in 5G scenario. We derive the beam codebook and generate multiple beams to simultaneously serve the spatially separated users. We derive the closed-form expression for millimeter-wave channel model incorporating large-scale and small-scale parameters to compute the effective SINR of users. Further, we propose an optimization framework for optimal resource allocation (i.e., beam and resource block) by utilizing the 3D resource grid for downlink transmission. We perform extensive simulations to demonstrate the effectiveness of the proposed methodology for various 3GPP 5G outdoor propagation scenarios, viz., RMa, UMa, and UMi-street canyon.

In this work, we explore a framework for a 5G non-standalone (NSA) heterogeneous network, to meet heterogeneous content requests for users moving in vehicles. We consider that an enhanced NodeB (eNB) acts as a macrocell and next-generation NodeBs (gNBs) act as the small cells. To reduce the downstream latency, entire (or part) of the popular contents are fetched from the core network and cached (stored) at the eNB and gNBs. The computing resources are required at the eNB and gNBs along with the caching resources, for content compression and decompression, leading to a reduced requirement for the caching resources. The eNB and gNBs cooperatively decide on the resources (caching and computing) to be allocated. In this network planning approach, first we compute the optimal coverage radius of the eNB and gNBs. Thereafter, we identify the optimal number of non-overlapping gNBs under the coverage area of the eNB. Finally, we propose a novel deep-Q network (DQN)-based algorithm to train the centralized controller agent so as to identify an optimal policy for caching and computing resource allocation. In case the content popularity and road traffic condition change, the agent can be trained again so as to identify a new optimal policy. We also explore the resource allocation policy using other optimization techniques, such as pattern search, genetic algorithm, and multi-start search. The proposed DQN-based algorithm is scalable and shows an average percentage gain of 66.52%, 76.31%, and 53.64% in terms of computation time to identify an optimal policy for caching and computing resource allocation, over pattern search, genetic algorithm, and multi-start search technique, respectively.