The Application Of Low Quantization Bits On Signal Detection In Heterogeneous Network

Massive MIMO technology is one of the hotspots in 5G research. On the basis of traditional MIMO, the base station will be equipped with a large number of antennas, in order to improve the capacity and spectral efficiency of the wireless communication system. However, hundreds or thousands of antennas bring great power comsumption, huge amounts of data, and high costs. These issues must be considered when deploying large scale atennas. It can effectively alleviate these problems by using the ADCs with low quantization bits at base station side.Heterogeneous network is an important form of furture communication, because it can satisfy the service qualitys of different users in the system. And the concept of heterogeneous networks has been introduced formally into LTE-R10. When the HetNet meets Massive MIMO, it can meet the communication demands of indoor users or hotspot users efficiently, providing higher quality of service. In this paper, it adopts a simplified heterogeneous network model based on Massive MIMO.The paper firstly provides a brief overview on Massive MIMO system and heterogeneous network model, and notes the importance of low-bit quantization in Massive MIMO. In single-input single-output system using 1-bit quantization, channel capacity is analyzed. Then extended to multi-user Massive MIMO system, using LS channel estimation scheme and MRC receiver, the system capacity is analyzed in this scenario. This paper makes a comparison of low quantization bits at different SNR and number of antennas on the achievable rate, and the result indicates that it can get a high user rate when the base station large scale antenna array.In order to obtain better detector performance, the paper studies receivers with low quantization bits. First, it compares the MRC and ZF receiver. For low quantization bits, Bayesian detector is an effective signal detection scheme, which will obtain good performance by introducing GAMP algorithm to computing the posterior probabilities. But, the complexity of GAMP detector is too high, it’s difficult to implement on hardware side. Another signal detection algorithm is derived from the maximum likelihood method, and this method is an approximate maximum likelihood detection by convex optimization. The iteration number to converge will be reduced with the increase of the number of base station antennas.In the heterogeneous network, using Massive MIMO technology and low quantization bits at the macro base stations, to carry out the joint estimation of the useful signal and the interference, we propose a joint iterative detection based on GAMP detection. By comparing the detection performance with different quantization bits and that in the unquantized-case, it illustrates the effectiveness of this iterative detection scheme. Besides, Precoding is an effective interference suppression scheme, and it can reduce the interference to the users, which do not belong to the base station. Finally, the paper compares the effects of different quantization bits on precoding performance in heterogeneous networks.