This paper presents MobiFi, a framework addressing the challenges in managing LiFi-WiFi heterogeneous networks focusing on mobility-aware resource allocation. Our contributions include introducing a centralized framework incorporating reactive and proactive strategies for resource management in mobile LiFi-only and LiFi-WiFi networks. This framework reacts to current network conditions and proactively anticipates the future, considering user positions, line-of-sight blockages, and channel quality. Recognizing the importance of long-term network performance, particularly for use cases such as video streaming, we tackle the challenge of optimal proactive resource allocation by formulating an optimization problem that integrates access point assignment and wireless resource allocation using the alpha-fairness objective over time. Our proactive strategy significantly outperforms the reactive resource allocation, ensuring 7.7% higher average rate and 63.3% higher minimum user rate for a 10-user LiFi-WiFi network. We employ sophisticated techniques, including a Branch and Bound-based Mixed-Integer solver and a low-complexity, Evolutionary Game Theory-based algorithm to achieve this. Lastly, we introduce a novel approach to simulate errors in predictive user position modeling to assess the robustness of our proactive allocation strategy against real-world uncertainties. The contributions of MobiFi advance the field of resource management in mobile LiFi-WiFi networks, enabling efficiency and reliability.

Multiple solutions for the coexistence of different radio access technologies operating in the same frequency band have been proposed for 5G and WiFi. Most solutions based on spatial division just consider a small amount of radio access points, one link direction, and/or a single radio access technology. As a consequence, the performance of these solutions on realistic wireless network deployments may be poor and difficult to estimate. This paper investigates the serving of multiple users by multiple radio access technologies with the objective of minimizing the interference among network nodes. This is done by jointly optimizing the beams and link directions as well as the transmission powers, so as to ensure fair and near-optimal throughput allocation over time. For this purpose, a generalized beam-gain model for small-scale antenna arrays is proposed. We evaluate our proposed solution for realistic network scenarios in order to show its effectiveness.

In the next generation of networks, the high data rate and large bandwidth offered by Light-Fidelity (LiFi) are expected to be fully exploited to satisfy the diverse Quality of Service (QoS) requirements of users. LiFi networks are expected to provide full data coverage while also satisfying illumination requirements in indoor environments. Thus, the optimized placement of LiFi Access Points (APs) is of utmost importance. Extending the commonly applied 2D placement, we include the height of the AP, which is an important factor to consider due to the short range of LiFi and limited Field of View (FoV) of the receivers. To this end, we propose a 3D LiFi Access Point placement framework that formulates the placement as a multi-objective optimization problem minimizing the number of APs and maximizing the sum rate while providing a guaranteed minimum rate and illumination level. Since the exact positions of users are unknown during network planning and change dynamically after deployment, the probability distribution of user occurrence is considered in the optimization. This results in the network performance being maximized in areas where users are likely to be present. This optimization problem is solved with the proposed multi-objective genetic algorithm. The framework is evaluated with simulations and the results show that the height of the AP greatly influences the network performance. We conclude that a free choice of the height of each AP results in an average rate that is significantly better than the rate achievable when all APs are placed at the same height.

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.

The ever-growing wireless networks demand high capacity, have strict latency requirements, and must support diverse communication services. A LiFi-WiFi heterogeneous net-work has proven to be a useful tool to satisfy the growing capacity demand. However, to leverage these co-existing, non-interfering technologies, intelligent resource management schemes have to be developed. To support diverse applications with varying delay and data rate requirements, the resource management scheme should consider the Quality of Service (QoS) while allocating wireless resources. In this work, the downlink wireless resources are allocated to users such that the average network packet delay is minimized. Users that are both capable and not capable of multi-homing are considered and a separate optimization problem is formulated for each case. These problems are then solved using a global Branch and Bound-based solver and a genetic algorithm-based Metaheuristic is also proposed. The algorithms are then evaluated with simulations and the results show that the average network packet delay is significantly lowered and each user's strict QoS requirements are satisfied even in a network with heavy traffic flow.

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.

The current cellular standardization for 5G is working towards wireless advances to enable further productivity for industrial automation. However, this development will take several years. Meanwhile, the capabilities of the currently available standards should be pushed to their limits. To this end, in this work, we present results from the first inter-slot successive interference cancellation testbed using commercial off the shelf IEEE 802.15.4 sensors. Through our implementation, we have measured a throughput of 0.72 packets per slot which doubles the currently used contention-based access, Slotted ALOHA, with a limit of 0.36 packets per slot. The hardware effects of the boards, which degrade the successive interference cancellation performance from the theoretical limit of 1 packet per slot, are modeled and validated through measurements. We also propose a model that can be used to calculate the expected successive interference cancellation throughput with the specific hardware available in a factory. Furthermore, our proposed model should replace the perfect physical layer assumptions for researchers to design new MAC algorithms taking practical limitations into account.