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

Evaluating the Probability of Channel Availability for Spectrum Sharing using Cognitive Radio

Spectrum sharing between service providers improves the spectral efficiency, probability efficiency of sensing and reduces the call blockage. If the number of service providers is increased to share the spectrum, this may reduce the high traffic patterns of the calls. In this paper, we present an approach for channel availability and call arrival rate. The results are used to evaluate the probability of the channel availability of a frequency band within a time period. In this work, we propose an algorithm for predicting the call arrival rate which used to predict the traffic process

Spectrum Sharing between Cooperative Relay and Ad-hoc Networks: Dynamic Transmissions under Computation and Signaling Limitations

This paper studies a spectrum sharing scenario between a cooperative relay network (CRN) and a nearby ad-hoc network. In particular, we consider a dynamic spectrum access and resource allocation problem of the CRN. Based on sensing and predicting the ad-hoc transmission behaviors, the ergodic traffic collision time between the CRN and ad-hoc network is minimized subject to an ergodic uplink throughput requirement for the CRN. We focus on real-time implementation of spectrum sharing policy under practical computation and signaling limitations. In our spectrum sharing policy, most computation tasks are accomplished off-line. Hence, little real-time calculation is required which fits the requirement of practical applications. Moreover, the signaling procedure and computation process are designed carefully to reduce the time delay between spectrum sensing and data transmission, which is crucial for enhancing the accuracy of traffic prediction and improving the performance of interference mitigation. The benefits of spectrum sensing and cooperative relay techniques are demonstrated by our numerical experiments.Comment: 5 pages, 3 figures, to appear in IEEE International Conference on Communications (ICC 2011