A 10-Gigabit Capable Symmetrical Passive Optical Network (XGS-PON) is considered as a cost-efficient fronthaul network solution for the Fifth Generation (5G) Centralized Radio Access Network (C-RAN). However, meeting the stringent latency requirements of C-RAN fronthaul with XGS-PON is challenging, as its upstream capacity is shared in the time-domain, and Dynamic Bandwidth Allocation (DBA) mechanism is employed to manage upstream traffic. The major issue with conventional DBA algorithms is that data arriving in the Optical Network Unit (ONU) buffer must wait for at least one DBA cycle before being scheduled, leading to poor delay performance. To address this, we propose a novel DBA algorithm named Traffic Prediction-based Enhanced Residual Bandwidth Utilization (TP-ERBU) that integrates a traffic prediction mechanism with enhanced residual bandwidth utilization to optimize delay performance in Option 7.x functional split-based C-RAN fronthaul over XGS-PON. The algorithm predicts future traffic to reduce delays in ONUs and reallocates residual bandwidth from lightly loaded ONUs to heavily loaded ones. Additionally, we develop an XGS-PON-based C-RAN simulation module named xCRAN-SimModule, using the OMNeT++ network simulator. Simulation results demonstrate that TP-ERBU improves packet delay by 20.59%, upstream channel utilization by 38.33%, packet loss by 25.00%, jitter by 5.71%, and throughput by 15.56% compared to existing algorithms.

The future social metaverse traffic demands over 5G and beyond (B5G) networks can be supported by efficiently placing the user plane function (UPF). Third-generation partnership project (3GPP) standard mandates that all application traffic in B5G core network must be routed through a UPF before it can reach its destination. In this regard, we propose an optimal UPF placement algorithm, referred to as Decomposed Minimum Power UPF Placement (DeMPUP), which selects the minimum power consumption path from the next-generation node B (gNB) to the edge server on the overlay backbone network. Coupled with the optimal path selection, the proposed algorithm also identifies the optimal node over which the UPF would be placed. We provide a power consumption model of the core network with a corresponding joint non-linear programming (NLP) minimization problem for UPF placement. Owing to the high complexity of the formulated NLP, for enhanced scalability over large B5G cores, we decompose the problem into two sub-problems. The first sub-problem identifies a set of feasible paths commensurate with UPF power requirement, and the subsequent sub-problem selects the optimum path among the feasible paths that minimizes the power consumption. The selected path also uniquely identifies the node to place the UPF for minimum power consumption. Analysis of DeMPUP shows that it executes in linear time and scales linearly with the number of nodes. Experimental results reveal significant power reduction for different network sizes.

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.

The advancements in the field of the Internet of Things (IoT) have fueled technological advancements in remotely handling agricultural operations, as well as the successful implementation of Precision Agriculture (PA) around the world. In this paper, we present Cluster-based Routing using an Optimized framework for Precision agricultural monitoring (CROP) that uses the recently developed Sooty Tern Optimization Algorithm (STOA). CROP specifically aims to detect unauthenticated entry into the agricultural field and various other factors related to PA. We perform a simulation analysis of CROP, and the performance of CROP is overwhelming, as it not only enhances stability period and network longevity by 58.9% and 65.5% respectively, but also proves to be scalable as compared to the Fuzzy-C-Means (FCM) algorithm and other routing protocols pertaining to PA.

During network planning phase, optimal network planning implemented through efficient resource allocation and static traffic demand provisioning in IP-over-elastic optical network (IP-over-EON) is significantly challenging compared with the fixed-grid wavelength division multiplexing (WDM) network due to increased flexibility in IP-over-EON. Mathematical programming based optimization models used for this purpose may not provide solution for large networks due to large computational complexity. In this regard, a greedy heuristic may be used that intuitively selects traffic elements in sequence from static traffic demand matrix and provisions the traffic elements after necessary resource allocation. However, in general, such greedy heuristics offer suboptimal solutions, since appropriate traffic sequence offering the optimal performance is rarely selected. In this regard, we propose a reinforcement learning technique (in particular a Q-learning method), combined with an auxiliary graph (AG)-based energy efficient greedy method to be used for large network planning. The Q-learning method is used to decide the suitable sequence of traffic allocation such that the overall power consumption in the network reduces. In the proposed heuristic, each traffic from the given static traffic demand matrix is successively selected using the Q-learning method and provisioned using the AG-based greedy method.

Wireless-optical broadband access networks (WOBANs) have gained extreme importance due to their ability to provide ubiquitous and high-speed Internet access. Since in long-reach WOBAN, wireless front-end is fed by a long-reach optical back-end, provisioning protection against failure of a WOBAN segment (due to failure of its feeder fiber and/or optical line terminal) is a primary concern. The failure causes simultaneous disconnection of a large number of users, resulting to poor connection availability and service dissatisfaction for users and high revenue loss for network operators. Focusing on the problem of a WOBAN segment failure, we propose a novel multi-cast multi-hop protection architecture (MCMHPA) to design a survivable WOBAN wherein traffic of a failed WOBAN segment is recovered by employing the residual bandwidth of other neighboring WOBAN segments. In MCMHPA, we provide a novel architecture of backup optical network units (optical devices used to transmit/receive traffic in condition of the segment failure) to implement multi-cast and multi-hop mechanisms. The proposed architecture offers multi-hop backup optical paths by using the minimum number of optical devices and fibers, thereby incurring low expenditure to design a survivable WOBAN.