Quantization and Noise Impact Over Feedback Reduction of MIMO Systems Using Compressive Sensing

Em se tratando de comunicaÃÃes mÃveis, a troca de informaÃÃes sobre os estados do canal entre as antenas receptoras e transmissoras à uma importante ferramenta para a melhoria do desempenho do sistema. Assim, nesse trabalho foram analisados sistemas MIMO multiplexados espacialmente, Spatially Multiplexed MIMO (SM-MIMO), com informaÃÃes do estado do canal no transmissor, Channel State Information (CSI), limitadas e duas tÃcnicas de detecÃÃo linear do sinal e prÃ-equalizaÃÃo do sinal Zero Forcing (ZF) e Minimum Mean Square Error (MMSE). Para essa limitaÃÃo dois esquemas foram considerados: Quantization Codebook (QC) e Compressive Sensing (CS). Compressive Sensing à usado para gerar um CSI comprimido a ser enviado pelas antenas receptoras por um canal de feedback a fim de reduzir a quantidade de informaÃÃo enviada pelas mesmas. Portanto, nesse trabalho, o desempenho das duas tÃcnicas foram comparadas por simulaÃÃes computacionais das curvas da taxa de erro de bit, Bit Error Rate (BER), de acordo com a variaÃÃo da relaÃÃo sinal ruÃdo, Signal to Noise Ratio (SNR), considerando as duas abordagens QC e CS. AlÃm disso, a presenÃa do erro de quantizaÃÃo e do ruÃdo, no canal de feedback, tambÃm foi avaliada para o esquema de CS.Concerning to mobile communications, the information exchange over the channel states between receiving antennas and transmiting antennas is an important tool to enhance the system performance. Thus, in this work, spatially multiplexed MIMO (SM-MIMO) systems with limited Channel State Information (CSI) were analyzed considering two techniques of linear signal detection and pre-equalization Zero Forcing (ZF) and Minimum Mean Square Error (MMSE). Due to this limitation two schemes were considered: Quantization Codebook (QC) e Compressive Sensing (CS). Compressive Sensing is used to generate a reduced CSI feedback to the transmitter in order to reduce feedback load into the system. Therefore, in this work, the performance of the techniques were compared by computational simulations of Bit Error Rate (BER) curves according to the variation of the Signal to Noise Ratio (SNR) for the two considered approaches QC and CS. Furthermore, the presence of quantization error and noise, in the feedback link, were also evaluated for the CS scheme