EVM as generic QoS trigger for heterogeneous wieless overlay network

Fourth Generation (4G) Wireless System will integrate heterogeneous wireless overlay systems i.e. interworking of WLAN/ GSM/ CDMA/ WiMAX/ LTE/ etc with guaranteed Quality of Service (QoS) and Experience (QoE).QoS(E) vary from network to network and is application sensitive. User needs an optimal mobility solution while roaming in Overlaid wireless environment i.e. user could seamlessly transfer his session/ call to a best available network bearing guaranteed Quality of Experience. And If this Seamless transfer of session is executed between two networks having different access standards then it is called Vertical Handover (VHO). Contemporary VHO decision algorithms are based on generic QoS metrics viz. SNR, bandwidth, jitter, BER and delay. In this paper, Error Vector Magnitude (EVM) is proposed to be a generic QoS trigger for VHO execution. EVM is defined as the deviation of inphase/ quadrature (I/Q) values from ideal signal states and thus provides a measure of signal quality. In 4G Interoperable environment, OFDM is the leading Modulation scheme (more prone to multi-path fading). EVM (modulation error) properly characterises the wireless link/ channel for accurate VHO decision. EVM depends on the inherent transmission impairments viz. frequency offset, phase noise, non-linear-impairment, skewness etc. for a given wireless link. Paper provides an insight to the analytical aspect of EVM & measures EVM (%) for key management subframes like association/re-association/disassociation/ probe request/response frames. EVM relation is explored for different possible NAV-Network Allocation Vectors (frame duration). Finally EVM is compared with SNR, BER and investigation concludes EVM as a promising QoS trigger for OFDM based emerging wireless standards.Comment: 12 pages, 7 figures, IJWMN 2010 august issue vol. 2, no.

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