5 research outputs found
Modeling and Predictability Analysis on Channel Spectrum Status Over Heavy Wireless LAN Traffic Environment
Using the real wireless spectrum occupancy status in 2.4 and 5 GHz bands collected at a railway station as representative of a heavy wireless LAN (WLAN) traffic environment, this paper studies the modeling of durations of busy/idle (B/I) status and its predictability based on predictability theory. We first measure and model the channel status in the heavy traffic environment over almost all of the WLAN channels at 2.4 GHz and 5 GHz bands in a busy (rush hour) period and non-busy period. Then, using two selected channels at 2.4 GHz and 5 GHz bands, we analyze the upper bound (UB) and lower bound (LB) of predictability of the busy/idle durations based on predictability theory. The analysis shows that the LB predictability of durations can be easily increased by changing their probability distribution. Based on this property, we introduce the data categorization (DC) method. By categorizing the busy/idle durations into different streams, the proposed data categorization can improve the prediction performance of some streams with large LB predictability, even if it employs a simple low-complexity auto-regressive (AR) predictor
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig