Computationally efficient wireless reception algorithms under a long term low energy constraint

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Multi-user wireless communication with active mobility is computationally challenging. Additionally, the complexity typically squares when the Multiple Input Multiple Output, MIMO, architectures are taken into account. A rapid appearance of several long echoes combined with high data rates favor the use of Orthogonal Frequency Division Multiplexing, OFDM. OFDM-based communications are particularly appealing because of the very low complexity frequency equalizing scheme which is used. However, new strategies such as channel shortening and sparse Recursive Least-Squares, SPARLS, equalization are emerging. These render single carrier techniques more computationally competitive by selecting only significant paths for channel equalization. All these issues have a direct impact on power consumption, thereby on battery life. This project aims at reducing the long term average energy consumption related to signal processing. In this context, long term averaging refers either to a multiplicity of successive connections, or calls, for mobile energy efficiency, or equivalently to short-term power consumption for hundreds of simultaneous users detection at a base station. The objective of the proposed project is to obtain and evaluate new algorithms for signal reception by realistically benchmarking them through average complexity as opposed to peak computational load. The methodology consists in leveraging the latest communications techniques by implementing them from this new perspective. The project will involve 4 graduate students working on leading edge technology, requiring of a mixture of algorithmic and microelectronic skills which have always been eagerly desired by ETS' industrial partners.