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，当考虑多输入多输出（MIMO）架构时，复杂度通常是平方的。几个长回波的快速出现与高数据速率相结合有利于正交频分复用OFDM的使用。基于OFDM的通信由于所使用的非常低复杂度的频率均衡方案而特别有吸引力。然而，新的策略，如信道缩短和稀疏递归最小二乘，SPARLS，均衡正在出现。这些使得单载波技术通过仅选择用于信道均衡的有效路径而在计算上更具竞争力。所有这些问题都直接影响功耗，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。在此上下文中，长期平均指的是用于移动的能量效率的多个连续连接或呼叫，或者等效地指的是用于在基站处的数百个同时用户检测的短期功耗。

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.