WiGig networks and 60 GHz frequency communications have a lot of potential for commercial and personal use. They can offer extremely high transmission rates but at the cost of low range and penetration. Due to these issues, WiGig systems are unstable and need to rely on frequent handovers to maintain high-quality connections. However, this solution is problematic as it forces users into bad connections and downtime before they are switched to a better access point. In this work, we use Machine Learning to identify patterns in user behaviors and predict user actions. This prediction is used to do proactive handovers, switching users to access points with better future transmission rates and a more stable environment based on the future state of the user. Results show that not only the proposal is effective at predicting channel data, but the use of such predictions improves system performance and avoids unnecessary handovers.
翻译:WiGig 网络和 60 GHz 频率通信具有广泛的商业和个人用途潜力。它们可以提供非常高的传输速率,但代价是低的范围和穿透力。由于这些问题,WiGig 系统不稳定,需要依靠频繁的交接来保持高质量的连接。然而,这个解决方案有问题,因为它会强制用户接入到不良连接并停机,然后再切换到更好的接入点。在这项工作中,我们使用机器学习来识别用户行为中的模式并预测用户行动。这个预测用来做预测式的交接,将用户切换到未来传输速率更高和更稳定的环境中的接入点,基于用户的未来状态。结果显示,本方案不仅有效地预测了信道数据,而且使用这种预测提高了系统性能并避免了不必要的交接。