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.

With the advent of 5-th generation (5G) and the rising mobile traffic demands, automation in mobility management has become more critical. 3rd Generation Partnership Project (3GPP) has introduced mobility robustness optimization (MRO) function to automate the handover (HO) procedure by optimizing the handover control parameters (HCPs) such as handover margin (HOM) and time-to-trigger (TTT). However, traditional MRO methods require online parameter adjustments, and the performance is subject to initialization. This paper proposes a supervised learning-based parameter configuration mechanism (SL-MRO) for configuring initial cell-pair-specific HOM values using historical network data and geographical information of base stations. Besides, a conventional heuristics-based MRO algorithm (H-MRO) is introduced and implemented in a system-level simulator where the performance evaluation is done. The results demonstrate that SL-MRO achieves similar performance as H-MRO with its best initialization, without the need for online parameter adjustments.

Mobile users frequently face significant interruptions in transmission and reception during handovers from one Base Station (BS) to another, resulting in latencies that are incompatible with the stringent requirements of Ultra-Reliable Low Latency Communications (URLLC). To address this, 3GPP introduced a novel handover procedure, called Layer 1/Layer 2 Triggered Mobility (LTM), in Release 18. LTM uses lower level signaling to respond quicker to mobility events, bypassing the reconfiguration of higher layers while keeping modifications to the lower layers at a minimal level. This drastically reduces service interruptions during handovers, making them practically negligible. However, since LTM uses more frequent L1 measurements, it has a higher handover and ping-pong handover rates, as well as signaling overhead. In this work, we propose to incorporate future channel predictions in LTM to perform cell preparations and handover decisions with the goal of reducing signaling overhead and resource reservation. We focus on a controlled indoor scenario, where future user channel predictions are possible with a high accuracy. Our proactive algorithm reduces the cell preparation rate by 76 % and the handover rate by 72 %, without compromising the network sum throughput. Moreover, the resource reservation time at the target BS is reduced to nearly 0 ms.

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.

With the rapid increase in the amount of exchanged traffic over cellular networks, stemming partly from computation-intensive tasks, and the highly mobile nature of the users, mobility management exhibits considerable challenges in next-generation cellular networks. A way to alleviate these problems is by using Software-defined Radio Access Networks (SD-RAN), where a centralized controller with a complete overview of the network topology (distribution of users across base stations and their channel conditions) can make decisions on the user assignment and resource allocation. To that end, in this paper, we formulate an optimization problem with the objective of maximizing the network utility, where computation-intensive tasks are sent from the users to edge clouds, taking into account the communication constraints (uplink and downlink bandwidth) as well as the finite storage and processing capabilities of edge clouds. Moreover, we provide a user rate guarantee to satisfy an additional application for all users. The problem is NP-hard, therefore, we propose to use Deep Reinforcement Learning (DRL) to solve it. Extensive realistic simulations show that our approach is close to the optimal solution, where the latter is obtained using a solver, while outperforming a benchmark by up to 65%.

The hand blockage effect of the human hand around the user equipment (UE) is too considerable to be ignored in frequency range 2 (FR2). This adds another layer of complexity to the link budget design in FR2 for 5G networks, which already suffer from high path and diffraction loss. More recently, multi-panel UEs (MPUEs) have been proposed as a way to address this problem, whereby multiple distinct antenna panels are integrated into the UE body as a way to leverage gains from antenna directivity. MPUEs also enhance the Rx-beamforming gain because it is now subject to each individual antenna panel. In this paper, the mobility performance of hand blockage induced by three practical hand grips is analyzed in a system-level simulation, where in each grip both the UE orientation and the hand positioning around the UE is different. It is seen that each hand grip has a significant impact on mobility performance of the network, where in the worst case mobility failures increase by 43% compared to the non-hand blockage case. Moreover, a detailed analysis of the tradeoff between the mobility key performance indicators and the panel and Rx beam switching frequency is also studied. Results have shown that both the panel and Rx beam switches can be reduced considerably without compromising on the mobility performance. This is beneficial because it helps in reducing UE power consumption.

Mobility management in 5G, especially at higher frequencies, is challenging because the signal quality fluctuates significantly due to blockages of Line of Sight (LoS), shadowing and user mobility. As a result, users experience frequent handovers, which reduce the network capacity. In order to perform smooth network operation, the decisions when to handover and to which Base Station (BS) a user is to be assigned should be considered jointly. Another important goal is to strive for fairness in data rates among the users. To this end, in this paper, we formulate an optimization problem whose solution provides proportional fairness and reduces the handover rate significantly. To solve the problem, we propose a Deep Reinforcement Learning (DRL) algorithm, specifically a Deep Q Network (DQN), which turns out to find a near-optimal user-to-BS assignment. We compare our approach with other state-of-the-art baselines and show that it outperforms them considerably in terms of fairness, handover, ping-pong and radio link failure rates while being within 96% of the optimal solution. Our DQN algorithm also reduces the handover rate by 86% and avoids ping-pong handovers.

Frequency range 2 (FR2) has become an integral part of 5G networks to fulfill the ever-increasing demand for data hungry-applications. However, radio signals in FR2 experience high path and diffraction loss, which also pronounces the problem of inter and intra-cell interference. As a result, both the serving and target links are affected, leading to radio link failures (RLFs) and handover failures (HOFs), respectively. To address this issue, multi-panel user equipment (MPUE) is proposed for 5G-Advanced whereby multiple spatially distinct antenna panels are integrated into the UE to leverage gains from antenna directivity. It also opens the possibility of using UE-side Rx-beamforming for each panel. In this paper, three different Rx-beamforming approaches are proposed to improve the serving link, the target link, and the handover process for an MPUE equipped with three directional panels. Thereafter, the mobility performance is analyzed in a system-level simulation for a multi-beam FR2 network. Results have shown that the proposed schemes can help reduce RLFs by 53% and HOFs by 90%.

Mobility management in 5G is challenging, and at higher frequencies, a larger number of cells is needed to provide similar coverage to that in 4G. Consequently, Base Stations (BSs) are placed much more densely and users experience frequent handovers, reducing network capacity. Advanced handover techniques are needed in 5G to perform smooth network operation. In this paper, we formulate an optimization problem, whose goal is to strive for fairness in data rates among users and to reduce handovers. To accomplish that, we consider jointly the decisions when to handover and to which BS a user is to be assigned. This is an integer nonlinear program, and by relaxing it, we obtain an upper bound. Further, due to its NP-hardness, we propose a centralized and a multi-agent Deep Q Network (DQN)-based algorithm, which both find near-optimal user-to-BS assignments. We evaluate our Reinforcement Learning-based solutions for networks of different sizes and users with different velocities. We compare our approaches with baselines and show that they outperform them considerably in terms of fairness and radio link failures while being within 95% of the optimum. Our DQN algorithms also reduce the handover rate by up to 93% and avoid ping-pong handovers almost completely.

Conditional handover (CHO) has been introduced in 5G to improve mobility robustness, namely, to reduce the number of handover failures by preparing target Base Stations (BSs) in advance and allowing the user to decide when to make a handover. This algorithm constantly prepares and releases BSs, thereby adapting to the fast changing radio condition. A user might make a handover to a distant BS that has a favorable channel only for a short time due to signal fluctuations. This increases the handover rate and might result in a Radio Link Failure (RLF) afterwards. Moreover, the constant preparation and release of BSs leads to an increased exchange of control messages between the user, the serving BS and all target BSs. Hence, there is a need to carefully select the target BSs. Therefore, we propose the Enhanced CHO (ECHO) scheme that uses trajectory prediction to prepare the BSs along the user's path. To achieve this, we also propose a Sequence to Sequence (Seq2Seq) mobility prediction model. ECHO with only one prepared BS (ECHO-1) outperforms CHO with three prepared BSs. ECHO-1 reduces the handover rate by 23 percent and the RLF rate by 77 percent, while also reducing the number of control messages in the network by 69 percent.

A LiFi-RF heterogeneous network can provide additional capacity to standalone wireless technologies due to their non-interfering nature. However, due to the properties of the short-range LiFi channel, the network is prone to transient channel variations that result in frequent, unnecessary handovers. This handover process creates an overhead and can result in the loss of connection. To ensure a stable connection for all users, a low complexity resource allocation algorithm, that considers the loss due to handovers, is proposed to minimize the number of handovers. This algorithmic approach is evaluated with simulations. For scenarios with unavoidable handovers, a system approach to manage vertical handovers is proposed to minimize the vertical handoff overhead and to offer a seamless interface switch, thereby resulting in a stable network. This protocol is implemented in hardware and the results show a negligible overhead.