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

具有主动移动性的多用户无线通信在计算上具有挑战性。此外，当考虑到多输入多输出（MIMO）架构时，复杂性通常是平方的。几个长回波的快速出现与高数据速率相结合，有利于使用正交频分复用（OFDM）。基于ofdm的通信由于使用了非常低复杂度的频率均衡方案而特别具有吸引力。然而，新的策略如信道缩短和稀疏递归最小二乘，SPARLS，均衡化正在出现。这些使得单载波技术通过只选择重要路径进行信道均衡，在计算上更具竞争力。所有这些问题都直接影响到功耗，从而影响电池寿命。该项目旨在降低与信号处理相关的长期平均能耗。在这种情况下，长期平均指的是移动能源效率的连续连接或呼叫的多重性，或者相当于在基站中同时检测数百个用户的短期功耗。

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.