Fidan Mehmeti

The deployment of 5G networks, which are characterized by high-frequency bands, dense cell deployments, and users with diverse application demands, presents significant challenges for efficient mobility management. Frequent handovers lead to transmission interruptions and reduced network capacity, affecting both the user experience and overall network performance. The current standard handover algorithm selects target base stations solely based on signal strength, without considering the availability of resources, which can result in overloaded cells. To address these challenges, in this paper, we formulate a multi-objective optimization problem that reliably captures the network support for dual connectivity. The objective is to maximize network throughput (of interest to the operator), and to minimize service interruptions (of interest to users). Given the computational complexity of the optimization problem, we develop a Deep Reinforcement Learning (DRL)-based algorithm to perform user-to-BS assignment and wireless resource allocation. We evaluate our approach through realistic simulations in two scenarios: an indoor factory floor that uses Frequency Range (FR) 2 and a two-tier outdoor network that operates in both FR1 and FR2. The proposed algorithm demonstrates significant improvements over state-of-the-art methods, achieving better sum throughput and zero handover rates while operating within 10% of the optimum. Additionally, our algorithm ensures a 100% user rate satisfaction. Instead of performing handovers, it utilizes dual connectivity initiations and keeps their rate 20−30% lower than the handover rate.

The development of 5G networks has enabled diverse applications with stringent communication and computation requirements. Ensuring timely operation for these applications requires proximity to compute and network resources, such as edge servers and User Plane Functions. With control over network resources and partial ownership of compute resources, Mobile Network Operators (MNOs) can assume a larger role in service orchestration within a multi-owner edge infrastructure. However, the increasing demand for edge applications often exceeds MNO capacity. To address this, some user requests can be offloaded to third-party cloud operators, expanding capacity but incurring costs and reducing profitability. Beyond profit, energy consumption is a key concern, particularly as we transition toward 6G. Addressing high energy consumption adds complexity to resource orchestration, as it directly conflicts with the goal of maximizing profit. Moreover, in dynamic edge environments, frequent service migrations increase orchestration overhead and can negatively impact user experience. Navigating these trade-offs requires an effective optimization approach. To this end, we formulate a multi-objective optimization problem for demand placement and routing, aiming to maximize MNO profit while minimizing energy consumption and migration overhead. Given its NP-hard nature, we propose a ranked greedy heuristic to effectively balance these objectives. Simulation results show that our approach outperforms benchmark algorithms in both static and dynamic scenarios, achieving near-optimal performance and deviating less than 7% from the optimal solution.

Signal quality fluctuates significantly due to blockages of Line of Sight, shadowing, and user mobility. This renders mobility management in 5G quite challenging. To improve it, 3GPP introduced Conditional Handover (CHO), which reduces handover failures by preparing target Base Stations (BSs) in advance. CHO adapts to the varying channel conditions and constantly prepares/releases cells, which leads to an increased exchange of control messages between the user and BSs. Connecting to the BS with the strongest signal is not always beneficial because the available resources and other users’ channels should be considered for a successful network operation. Hence, the need to carefully decide when to hand over, and when that happens, to select the best target BS. In this paper, we first formulate an optimization problem that minimizes network signaling by reducing the number of unprepared handovers and wasted cell preparations while providing a minimum rate to everyone. As the problem is NP-hard, we relax it and obtain a lower bound. Then, we propose a Cost-Efficient CHO (CECHO) algorithm with performance guarantees. Using 5G datasets, we compare CECHO with two baselines and show that it outperforms them by at least 45% while being near-optimal. However, reducing the signaling decreases the total throughput, which is an important metric for the network operator. Thus, we expand our initial problem into a Multi-Objective (MO) optimization, where we additionally maximize the network sum throughput. Results show that CECHO-MO increases the sum throughput more than 3× with only a 4% increase in signaling.

Mobility management in 5G is challenging due to the usage of high frequencies and dense cell deployments. As a result, users experience frequent handovers that cause an interruption in transmission/reception and diminish network capacity. In the common handover algorithm, the target Base Station (BS) is selected based solely on the signal strength, while the available resources are not considered, leading to overloaded cells, especially for macro cells with large coverage. Advanced handover techniques are needed in 5G to perform smooth network operation. In this paper, we formulate an optimization problem, whose goal is to provide α-fairness in data rates among users and to reduce handovers. To accomplish that, we jointly perform user assignment and resource allocation while accounting for the interruption due to handovers. This is an integer nonlinear program and, by relaxing it, an upper bound is obtained. Further, because of the time complexity of the original problem, we propose a Deep Reinforcement Learning (DRL)-based algorithm, which finds near-optimal user-to-BS assignments and the amount of resources that should be allocated to a user. Our approach outperforms considerably state of the art in terms of fairness and handover rate while being within at most 12% of the optimum in most cases.

Edge computing has become a very popular service that enables mobile devices to run complex tasks with the help of network-based computing resources. However, edge clouds are often resource-constrained, which makes resource allocation a challenging issue. In addition, edge cloud servers must make allocation decisions with only limited information available, since the arrival of future client tasks might be impossible to predict, and the states and behavior of neighboring servers might be obscured. We focus on a distributed resource allocation method in which servers operate independently and do not communicate with each other, but interact with clients (tasks) to make allocation decisions. We follow a two-round bidding approach to assign tasks to edge cloud servers, and servers are allowed to preempt previous tasks to allocate more useful ones. We evaluate the performance of our system using realistic simulations and real-world trace data from a high-performance computing cluster. Results show that our heuristic improves system-wide performance by 20-25% over previous work when accounting for the time taken by each approach. In this way, an ideal trade-off between performance and speed is achieved.

Massive Machine-Type Communications (mMTC) are one of the three main use cases powered by 5G and beyond networks. These are distinguished by the need to serve a large number of devices which are characterized by non-intensive traffic and low energy consumption. While the sporadic nature of the mMTC traffic does not pose an exertion on the efficient operation of the network, multiplexing the traffic from a large number of these devices within the cell certainly does. This traffic from the Base Station (BS) is then transported further towards the Core Network (CN), where it is combined with the traffic from other BSs. Therefore, planning carefully the network resources, both on the Radio Access Network (RAN) and the CN, for this type of traffic is of paramount importance. To do this, the statistics of the traffic pattern that arrives at the BS and the CN should be known. To this end, in this paper, we derive first the distribution of the inter-arrival times of the traffic at the BS from a general number of mMTC users within the cell, assuming a generic distribution of the traffic pattern by individual users. Then, using the previous result we derive the distribution of the traffic pattern at the CN. Further, we validate our results on traces for channel conditions and by performing measurements in our testbed. Results show that adding more mMTC users in the cell and more BSs in the network in the long term does not increase the variability of the traffic pattern at the BS and at the CN. Furthermore, this arrival process at all points of our interest in the network is shown to be Poisson both for homogeneous and heterogeneous traffic. However, the empirical observations show that a huge number of packets is needed for this process to converge, and this number of packets increases with the number of users and/or BSs.