The representative media access control (MAC) mechanism of IEEE 802.11 is a distributed coordination function (DCF), which operates based on carrier-sense multiple access with collision avoidance (CSMA/CA) with binary exponential backoff. The next amendment of IEEE 802.11 being developed for future Wi-Fi by the task group-be is called IEEE 802.11be, where the multi-link operation is mainly discussed when it comes to MAC layer operation. The multi-link operation discussed in IEEE 802.11be allows multi-link devices to establish multiple links and operate them simultaneously. Since the medium access on a link may affect the other links, and the conventional MAC mechanism has just taken account of a single link, the DCF should be used after careful consideration for multi-link operation. In this paper, we summarize the DCFs being reviewed to support the multi-radio multi-link operation in IEEE 802.11be and analyze their performance using the Markov chain model. Throughout the extensive performance evaluation, we summarize each MAC protocol’s pros and cons and discuss essential findings of the candidate MAC protocols.

Federated learning (FL) is a promising collaborative learning approach in edge computing, reducing communication costs and addressing the data privacy concerns of traditional cloud-based training. Owing to this, diverse studies have been conducted to distribute FL into industry. However, there still remain the practical issues of FL to be solved (e.g., handling non-IID data and stragglers) for an actual implementation of FL. To address these issues, in this paper, we propose a cluster-driven adaptive training approach (CATA-Fed) to enhance the performance of FL training in a practical environment. CATA-Fed employs adaptive training during the local model updates to enhance the efficiency of training, reducing the waste of time and resources due to the presence of the stragglers and also provides a straggler mitigating scheme, which can reduce the workload of straggling clients. In addition to this, CATA-Fed clusters the clients considering the data size and selects the training participants within a cluster to reduce the magnitude differences of local gradients collected in the global model update under a statistical heterogeneous condition (e.g., non-IID data). During this client selection process, a proportional fair scheduling is employed for securing the data diversity as well as balancing the load of clients. We conduct extensive experiments using three benchmark datasets (MNIST, Fashion-MNIST, and CIFAR-10), and the results show that CATA-Fed outperforms the previous FL schemes (FedAVG, FedProx, and TiFL) with regard to the training speed and test accuracy under the diverse FL conditions.

As increasing numbers of access points (APs) are deployed in wireless local area networks (WLANs), they experience severe channel interference. Thus, a well-defined resource-management scheme for APs should be devised. Undoubtedly, a centralized and deterministic resource management is not appropriate, owing to the distributed operation and dynamic channel condition of densely deployed WLANs. To address this problem, an adaptive and distributed radio resource allocation (ADRA) algorithm is proposed in which an optimal mixed strategy for selecting the channel width of APs is derived via a game-theoretic approach. ADRA also fits into the densely deployed WLAN in that it considers partially overlapped channels. Simulation results show that ADRA can provide a comparable throughput and fairness performance to centralized algorithms even though it functions in a distributed manner. Moreover, it is shown that ADRA works adaptively even when network environments change rapidly.

Platooning is considered as a promising technology in the logistics industry since it can provide the benefits of raising fuel-efficiency and increasing road capacity. In platooning, vehicles automatically drive following a leader vehicle keeping a small inter-vehicle distance based on the shared status information via vehicle-to-vehicle (V2V) communications. Therefore, in order to ensure platoon safety and stability, other vehicles’ status information must be timely updated and the platooning messages (i.e., beacons) should be reliably and timely exchanged. In this paper, we propose an age of information (AoI) based beacon transmission (AoIBT) scheme where vehicles request additional beacon transmissions with the consideration of AoI for reducing delay in updating other vehicles’ status caused by beacon loss. Evaluation results demonstrate that AoIBT has significant performance improvement by up to 180% in various platoon environments compared with conventional schemes.