Edge computing provides crucial support to next-generation mobile services that demand high data rates, low latency, and high reliability. The development of high data rate communication has lead to the emergence of cell densification as a key enabler for 5G. Unfortunately, the cost of fiber for backhaul and fronthaul poses hindrance to wide-scale adoption of small cells. Free Space Optics (FSO) offers a cost-effective alternative, though its performance is vulnerable to adverse weather, particularly fog. Recent advances in adaptive optics and automatic steering/tracking have improved FSO reliability by reducing pointing errors and enabling dynamic link reconfiguration, which further supports intelligent traffic diversion and load balancing across edge servers. In this paper, we propose a multi-hop mesh optical access network that integrates fiber-based Passive Optical Networks (PONs) with steerable FSO links to provide reliable and scalable backhaul for dense Radio Access Networks (RANs). A joint optimization framework for dynamic routing and edge server load balancing is developed. Extensive simulations demonstrate that the proposed design ensures fairness, reduces the number of required FSO transceivers, and delivers data rates comparable to established benchmarks.

The development of high data rate communication technologies have resulted in cell densification, which in turn has led to the development of centralized radio access networks (C-RANs) followed by open radio access networks (O-RANs). The O-RAN segregates the base station into three logical entitities; the central unit (CU), the distributed unit (DU) and the radio unit (RU). The CU, DU and RU require low latency, low jitter and high data rate connections for seamless operation, which is known as X-haul. A passive optical network (PON) is a potential solution for X-haul design. Unfortunately, a PON along with its original uplink protocol is not readily suitable for the purpose. The packetization procedure of PON introduces jitter to the Xhaul bit stream. Further, the delay requirements of the X-haul limit the number of sources that can be connected to the Xhaul. Advanced features like coordinated multipoint requires synchronisation among the different X-haul bit streams as well. Therefore, in this paper, we develop an optimal uplink system that allows PON to be used as an X-haul connection technology. The proposal maximizes the throughput of the PON while conforming to the delay and synchronisation requirements. Moreover, the proposal nullifies the jitter introduced by the PON scheduler. We have performed extensive simulations for verifying our results.

The development of Multi-access edge computing (MEC) has resulted from the requirement for supporting next generation mobile services, which need high capacity, high reliability and low latency. The key issue in such MEC architectures is to decide which edge nodes will be employed for serving the needs of the different end users. Here, we take a fresh look into this problem by focusing on the minimization of migration events rather than focusing on maximizing usage of resources. This is important because service migrations can create significant service downtime to applications that need low latency and high reliability, in addition to increasing traffic congestion in the underlying network. This paper introduces a priority induced service migration minimization (PrISMM) algorithm, which aims at minimizing service migration for both high and low priority services, through the use of Markov decision process, learning automata and combinatorial optimization. We carry out extensive simulations and produce results showing its effectiveness in reducing the mean service downtime of lower priority services and the mean admission time of the higher priority services.

This paper focusses on Service Level Agreement (SLA) based end-to-end Quality of Service (QoS) maintenance across a wireless optical integrated network. We use long term evolution/5G based spectrum access system (SAS) in the wireless network and the optical network is comprised of an Ethernet Passive Optical Network (EPON). The proposal targets a learning-based intelligent SAS where opportunistic allocation of any available bandwidth is done after meeting the SLA requirements. Such an opportunistic allocation is particularly beneficial for nomadic users with varying QoS requirements. The opportunistic allocation is carried out with the help of Vickrey-Clarke-Groves (VCG) auction. The proposal allows the users of the integrated network to decide the payment they want to make in order to opportunistically avail bandwidth. Learning automata is used for the users to intelligently converge to the optimal payment value based on the network load. The payment made by the users is later used by the optical network units of the EPON to prepare the bids for the auction. The proposal has been verified through extensive simulations.

Multi-access edge computing nodes are being developed in support of next generation applications, which require high capacity, high reliability and low latency. One important problem that the research community has recently focused on is the allocation strategy of applications to the different MEC server nodes. In our approach, rather than focusing on maximizing usage of resources, we focus on the minimization of migration events, which can create significant service downtime to applications that need low latency and high reliability, in addition to increasing traffic congestion in the underlying network. This paper introduces a priority induced service migration minimization (PrISMM) algorithm, which aims at minimizing service migration for both high and low priority services, through the use of learning automata. We carry out extensive simulations and produce results showing its effectiveness in reducing the mean service downtime of lower priority services and the mean admission time of the higher priority services.

Long term evolution uplink-scheduling is one of the major research area nowadays; since third generation partnership project standard has not specified any particular algorithm for uplink scheduling. In general, the existing algorithms ensure to maximize the achievable physical layer throughput by considering only the channel gains of the user equipments (UE). However, they do not guarantee the actual media access control (MAC) layer throughput or the fairness of the achievable throughput among users as they do not take the traffic of individual UEs into account. Therefore, in this paper, we propose a novel scheduling algorithm using both the traffic and the channel gains of the individual UEs. Our algorithm uses weighted bipartite matching and achieves an optimal MAC throughput with minimal computational complexity. We further enhance the algorithm to increase the fairness with a little sacrifice in the MAC throughput.

To achieve high capacity in cellular networks, frequency reuse factor of unity is used. However, it suffers from heavy co-channel interference at cell edge regions. This leads to poor Signal to Interference plus Noise Ratio (SINR) and hence poor performance. Fractional Frequency Reuse (FFR) is one of the methods being considered to improve cell edge performance. In this work we present the impact of SINR threshold and bandwidth partitioning on the successful deployment of FFR scheme. We have considered both Real Time and Best Effort traffic.