Une méthode de détermination d’un ensemble optimal de décision : Application aux communications efficaces énergétiquement

International audience. Abstract-The problem of designing a finite decision set for energy-efficient MIMO communications imposed by a finite-feedback-rate channel is revistied in this paper. An algorithm which combines the approach of Invasive Weed Optimization (IWO) and differential evolution is applied. We provide a numerical analysis which illustrates the benefits of our point of view. In particular, given a performance loss level, the feedback rate can by reduced by 2 when the transmit decision set has been designed properly instead of feedbacking quantized channel state information as current wireless systems do. The impact on energy-efficiency is seen to be even more significant.Cet article aborde pour la première fois le problème de la détermination de l’ensemble optimal de décision (àl’émission) pour les communications MIMO efficaces énergétiquement en présence d’une voie de retour à débit fini. Pour résoudre ce problème, nous concevons un algorithme évolutionnaire exploitant l’approche "Invasive Weed Optimization". L’intérêt de notre approche par rapport au paradigme classique (prise de décision utilisant une version quantifiée du canal fournie par la voie de retour) est très bien illustré par les résultats numériques typiques fournis

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