AI-driven mobility improvements are attracting growing attention in 5G and future mobile communication networks. To achieve a practically viable solution, it is important to determine the appropriate level of AI control in mobility management under different prediction accuracies. To address this, we propose three handover strategies that represent different levels of AI involvement in mobility control. Each strategy uses reference signal received power (RSRP) prediction based on a compact on-device long short-term memory (LSTM) model tailored for mobility scenarios. We evaluate their performance through system-level simulations under varying prediction accuracies in frequency range 2 (FR2) mobility scenarios. The results show that even assistive use of AI leads to noticeable improvements in handover performance. As prediction accuracy improves, highly AI-dependent strategies outperform assistive approaches. These findings indicate that compact AI models can provide practical benefits, and further improvements in prediction accuracy can enable fully AI-driven handover in future 6G systems.

By placing the computing, storage and networking resources close to the end users, distributed edge computing greatly benefits the performance of 5G communication systems. However, as a tradeoff, resources on the edge are usually limited and imbalanced among the heterogeneous edge nodes. To overcome this drawback, this paper proposes a Transfer Learning based Prediction (TLP) framework that allows the edge nodes to share their resources and data in an efficient manner. In particular, the TLP framework focuses on the prediction of the future traffic load, which is a key reference for many automated network functions. To enhance the efficiency of data and bandwidth, TLP first learns a base model on a data-abundant edge node (the source), and then transfers this model (instead of data) to other data-limited nodes (the targets). To achieve a delicate balance between maintaining common features and learning target-specific features, we develop a new transfer learning technique named Similarity-based Elastic Weight Con-solidation (SEWC), and integrate it into TLP. Experiments on real-world data illustrate that, compared to the state-of-the-art methods, TLP-SEWC reduces the Mean Absolute Error (MAE) of traffic prediction by up to 57.9%.

Within a cellular network, load balancing between different cells is of critical importance to network performance and quality of service. Most existing load balancing algorithms are manually designed and tuned rule-based methods where near-optimality is almost impossible to achieve. These rule-based meth-ods are difficult to adapt quickly to traffic changes in real-world environments. Given the success of Reinforcement Learning (RL) algorithms in many application domains, there have been a number of efforts to tackle load balancing for communication systems using RL-based methods. To our knowledge, none of these efforts have addressed the need for data efficiency within the RL framework, which is one of the main obstacles in applying RL to wireless network load balancing. In this paper, we formulate the communication load balancing problem as a Markov Decision Process and propose a data-efficient transfer deep reinforcement learning algorithm to address it. Experimental results show that the proposed method can significantly improve the system performance over other baselines and is more robust to environmental changes.

Due to the uneven demographic distribution and people’s daily activities, communication systems usually experience highly imbalanced load across different cells. This imbalance leads to unsatisfied users in the congested cells and under-utilized resources in the less-loaded cells. To deal with this issue, existing work migrates the load from heavily loaded cells to lightly loaded cells, by either handing over active mode User Equipment (UEs) to other serving cells, or re-selecting the camping cells for idle mode UEs. In this paper, we further advance the research on Load Balancing (LB) with a hybrid control of both active and idle UEs. This task is challenging, due to the conflicts between Active-UE LB (AULB) and Idle-UE LB (IULB) policies. To overcome this challenge, we propose a Hierarchical Policy Learning (HPL) framework, which coordinates the actions between LB policies with a two-level learning structure. In this way, HPL produces AULB and IULB policies that are better aligned with each other. Extensive simulation results illustrate the efficiency and efficacy of the proposed HPL.

Bluetooth Low Energy (BLE) is one of the widely used low power wireless protocols due to its simplicity and low energy consumption. The BLE standard is being developed further to support a wide range of applications spanning smart homes, wearables, and myriad appliances as part of IoT (Internet of Things). These new applications bring forth an important challenge: connection maintenance with low energy consumption in dynamic channel environments. In this paper, we investigate effective solutions to this technical challenge. First, we show using experiments that the current design of using fixed connection intervals incurs significant performance degradation under dynamic link conditions. To overcome the problem, we mathematically find an optimal connection interval that minimizes energy consumption while maintaining connectivity for a given link condition. Then, we design a simple yet effective connection interval adaptation mechanism for BLE, named CABLE. We implement the proposed solution on real embedded devices, and using extensive testbed experiments and simulation verify that CABLE leads to significant performance improvement while providing resilient connectivity in dynamic link environments.

In this paper, we consider a protocol architecture that enables IPv6 routing protocol for low power and lossy networks (RPL) to run on top of Bluetooth Low Energy (BLE), aiming to provide a BLE-based multi-hop IoT network. In our approach to RPL over BLE, we propose to use both of advertising and data channels of BLE to create synergistic effects between RPL and BLE to jointly achieve high energy efficiency and reliable multi-hop routing. We design an adaptation layer between BLE and RPL (ALBER) which tightly couples RPL and BLE operations together. Specifically, ALBER provides RPL control message broadcast through BLE, RPL routing metric calculation that reflects BLE link quality, and routing table update that incorporates BLE connection management. We implement ALBER in a Linux kernel to realize RPL over BLE and compare its performance with that of RPL over IEEE 802.15.4 on a multi-hop testbed network. The performance results show that our architecture is not only feasible but also provides almost perfect packet delivery performance (↑100%) and reduces dutycycle up to 32% compared to RPL over IEEE 802.15.4 under varying link dynamics. Our research shows that RPL over BLE is a promising approach which can increase the utility and impactof BLE across different application domains.

Bufferbloat has drawn much attention in the network community for its negative impact on TCP delay performance and user QoE. Recently, it has been more commonly noted in wireless access networks, in part, due to over-provisioned buffer space. Previous works that focused only on bufferbloat prevention have suffered from either deployment or fairness problems when coexisting with conventional TCP flows. In this paper, we address the bufferbloat problem in resource-competitive environments such as Wi-Fi, and design a receiver-side countermeasure for easy deployment that does not require any modification at the sender or intermediate routers. Exploiting TCP and AQM dynamics, our scheme competes for shared resource in a fair manner with conventional TCP flow control methods and prevents bufferbloat. We implement our proposed scheme in commercial smart devices and verify its performance through real experiments in LTE and Wi-Fi networks.

In this demo, we present interoperability between Bluetooth Low Energy (BLE) and IPv6 routing protocol for low power and lossy networks (RPL). To make the operation of RPL over BLE feasible, we design an adaptation layer between BLE and RPL, termed ALBER. Specifically, we develop three technical features in ALBER which enable BLE to be combined with RPL. First, it broadcasts RPL control messages through BLE using advertising channels with low energy consumption. Second, it updates RPL routing table considering connection management of BLE. Lastly, it estimates link quality based on round trip time of link layer ping packets to provide routing metric for RPL. We implement our ALBER on Linux kernel. This demo will present an operation example of RPL over BLE using ALBER in a small scale multi-hop topology, where each node comprises a Raspberry Pi platform and a BLE dongle.

In massive multiple-input multiple-out (M-MIMO) systems, conventional sounding schemes may suffer from pilot contamination of cell edge users or a lowered number of serviced users in a multi-cell scenario. In this paper, we propose a partial sounding resource reuse (PSRR) method which aims to seamlessly guarantee the quality of service (QoS) of mobile users by mitigating the pilot contamination as well as minimize the reduction in the number of serviced users. To this end, the PSRR divides each cell area into center and edge areas, and partially reuses sounding resources among users in neighboring edge areas. We use a Markov chain model to analyze the performance of the PSRR. Then we evaluate the accuracy of our analysis through simulations, and show that the PSRR considerably improves QoS performance over the conventional schemes.

In massive multiple-input multiple-out (M-MIMO) systems, the base station estimates each forward-link channel by using the reverse-link sounding pilot and the channel reciprocity property of time division duplex (TDD) operation. However, conventional non-cooperative sounding schemes, like a sounding sequence assignment scheme with reuse factor-1, cause cell edge users to suffer from pilot contamination in multi-cell scenarios. The pilot contamination problem becomes even worse in an environment where mobile users are travelling through cell edge areas frequently. To alleviate this problem, a cooperative sounding resource reuse method that assigns each cell a set of resources different from other neighboring cells by using a reuse factor-3 scheme, can be considered, but it significantly lowers the number of served users. In this paper, we propose a partial sounding resource reuse (PSRR) method which aims to seamlessly support the quality of service (QoS) of each mobile user by mitigating the pilot contamination and to minimize the reduction in the number of served users. To this end, the PSRR divides each cell area into center and edge areas, and applies a reuse factor-1 scheme for center users while a reuse factor-3 scheme for edge users. We use a Markov chain model to analyze the performance of the PSRR, and evaluate the accuracy of our analysis through simulations. Then we confirm that the PSRR considerably improves QoS performance over the conventional competitive schemes.