This paper addresses contention window optimization for multi-access scenarios. Our investigation into state-of-the-art models revealed that a limited number of nodes dominate the communication channels. Such monopolization issues are critical in networks as they can lead to significant disruptions. To mitigate this monopolization problem, we propose an imitation learning-based backoff mechanism. The proposed model is a reinforcement learning-based contention window optimization method. It imitates the expert's policy to ensure fair policy convergence for the agent and includes opportunities for weight adjustment to boost performance. The proposed model shows a fairness improvement of approximately 20% to 41% across various scenarios.

Machine Learning (ML) is recently considered a key technology for bringing outstanding performance to wireless communications. Conventional research has highlighted the potential of Support Vector Machines (SVMs), which train their model based on optimization theory, to enhance the performance of wireless communications. However, there are practical issues that makes SVM difficult to apply to a wireless communication system. SVM generally entails a heavy training process with high computational complexity, and the model requires a significant amount of time for training. Also, the entire dataset needs to be trained at once, requiring a substantial amount of memory for data storage. To enable SVM in wireless communications, we propose Real-Time Channel Vector Classifier (RTCVC), which employs a light-weight SVM model capable of training and processing incoming data in real-time. A novel input data pre-processing technique is implemented to reduce the computational overhead associated with calculating non-linear functions. The rearranged formulation of the original problem also allows each SVM sub-model to be trained distributively over time based on incremental parameters. For performance evaluation, we implement the RTCVC inter-operating with 5G beam index detection, whose detection probability has been theoretically proven to be significantly enhanced by SVM. The software modules of the RTCVC are based on LibSVM, a well-known open-source library for implementing SVM sub-models. The experimental results confirm that RTCVC significantly reduces training time while maintaining suitable performance for 5G beam index detection.

Artificial intelligence has recently permeated every field, and current research trends in wireless communications naturally involve leveraging machine learning (ML) for communications processing. Numerous previous research studies have theoretically demonstrated that intelligentizing physical layer holds the promise of enhancing performance for the 6G era. In this article, we demonstrate the practical implementation of intelligentizing random access (RA) through real-time channel learning (CL). Real-time CL adapts an ML model to the variations of wireless channel in real time and requires extremely short and low-complexity training. Our research encompasses algorithmic design through the implementation of an off-the-shelf testbed which incorporates real-time CL in softwarized modem. We initially review the fundamental algorithms of communications processing for RA and then proceed to design a proper ML model for real-time CL. The overall intelligentized RA detector is then implemented using the concept of softwarized modem. The implementation achieves real-time CL via efficient interoperation of communications processing and the ML model. Experiments with the implementation confirm that intelligentization through real-time CL is feasible and results in an SNR gain up to 2.7 dB for RA scenarios.