Complexity reduced zero-forcing beamforming in massive MIMO systems

Abstract

Abstract-Data gathering is one of the most popular applications in multi-hop wireless sensor networks. Since resources are limited, it is important to efficiently allocate the resource Massive multi-input multi-output (m-MIMO) systems can provide a high degree of freedom in signal transmission, enabling to simultaneously serve a number of users with high transmissin capacity. Conventional zero-forcing beamforming (ZFBF) techniques can transmit multi-user signal while completely canceling out interbeam interference. However, they may have implementation difficulty when applied to m-MIMO systems mainly due to hugh processing complexity. In this paper, we design a complexity reduced ZFBF scheme by means of sequential interference cancellation. We first determine the beam weight according to the use of conventional maximum ratio transmission (MRT) scheme and calculate the corresponding interbeam interference. We calculate so-called an interference cancellation vector by sequentially cancelling out a predetermined number of interference sources in an order of the strongest interference. Finally, we determine the beam weight by adding the interference cancellation vector to the MRT beam weight. The number of interbeam interferences to be cancelled out can be pre-determined taking into consideration of the processing complexity and required performance. As the number of interbeam interferences to be cancelled out increases, the performance of the proposed scheme approaches to that of ZFBF. The numerical and simulation results show that the proposed scheme can achieve about 90% capacity of ZFBF while requiring 2∼7% processing complexity of ZFBF in various operating environments with the use of 32 128 transmit antennas.

Orthogonal Versus Zero-Forced Beamforming in Multibeam Antenna Systems: Review and Challenges for Future Wireless Networks

“…The impact of decreasing the number of zeros (i.e. complexity reduced zero-forcing [140]) on the SINR and the processing burden was also investigated.…”

Trade‐offs between the quality of service, computational cost and cooling complexity in interference‐dominated multi‐user SDMA systems

“…Therefore, two different reduced complexity ZF algorithms are investigated here in use case B for the optimised array. Firstly, only the strongest N c interferers are cancelled for each user as done in [10]. According to [10], the number of FLOPS in this case (#ℱ ZF ) of ZF (for N c > 0) is given by…”

“…Among the listed study areas, signal processing aspects consist of the study on linear, non-linear, narrowband, wideband, switched or adaptive beamforming algorithms. In this field, it is desired to achieve low computational complexity and high processing speed [10]. Some related works include the effect of total antenna number and radio frequency chains on the performance of code book-based or ZF-based beamforming [11][12][13] and combination of adaptive beamforming techniques with an antenna selection procedure [14].…”

Active Multiport Subarrays for 5G Communications

“…(A-ii) The DSP complexity is remarkably less as compared to the fully-digital beamforming. For example, if we assume conjugate-beamforming (CB) or zero-forcing (ZF) pre-coding at the baseband, the number of floating point operations (F CB , F ZF ) is given by [30] F CB = K(14N − 2)…”