On the Matrix Inversion Approximation Based on Neumann Series in Massive MIMO Systems

Zero-Forcing (ZF) has been considered as one of the potential practical precoding and detection method for massive MIMO systems. One of the most important advantages of massive MIMO is the capability of supporting a large number of users in the same time-frequency resource, which requires much larger dimensions of matrix inversion for ZF than conventional multi-user MIMO systems. In this case, Neumann Series (NS) has been considered for the Matrix Inversion Approximation (MIA), because of its suitability for massive MIMO systems and its advantages in hardware implementation. The performance-complexity trade-off and the hardware implementation of NS-based MIA in massive MIMO systems have been discussed. In this paper, we analyze the effects of the ratio of the number of massive MIMO antennas to the number of users on the performance of NS-based MIA. In addition, we derive the approximation error estimation formulas for different practical numbers of terms of NS-based MIA. These results could offer useful guidelines for practical massive MIMO systems.Comment: accepted to conference; Proc. IEEE ICC 201

Large-Scale MIMO Detection for 3GPP LTE: Algorithms and FPGA Implementations

Large-scale (or massive) multiple-input multiple-output (MIMO) is expected to be one of the key technologies in next-generation multi-user cellular systems, based on the upcoming 3GPP LTE Release 12 standard, for example. In this work, we propose - to the best of our knowledge - the first VLSI design enabling high-throughput data detection in single-carrier frequency-division multiple access (SC-FDMA)-based large-scale MIMO systems. We propose a new approximate matrix inversion algorithm relying on a Neumann series expansion, which substantially reduces the complexity of linear data detection. We analyze the associated error, and we compare its performance and complexity to those of an exact linear detector. We present corresponding VLSI architectures, which perform exact and approximate soft-output detection for large-scale MIMO systems with various antenna/user configurations. Reference implementation results for a Xilinx Virtex-7 XC7VX980T FPGA show that our designs are able to achieve more than 600 Mb/s for a 128 antenna, 8 user 3GPP LTE-based large-scale MIMO system. We finally provide a performance/complexity trade-off comparison using the presented FPGA designs, which reveals that the detector circuit of choice is determined by the ratio between BS antennas and users, as well as the desired error-rate performance.Comment: To appear in the IEEE Journal of Selected Topics in Signal Processin

Efficient DSP and Circuit Architectures for Massive MIMO: State-of-the-Art and Future Directions

Massive MIMO is a compelling wireless access concept that relies on the use of an excess number of base-station antennas, relative to the number of active terminals. This technology is a main component of 5G New Radio (NR) and addresses all important requirements of future wireless standards: a great capacity increase, the support of many simultaneous users, and improvement in energy efficiency. Massive MIMO requires the simultaneous processing of signals from many antenna chains, and computational operations on large matrices. The complexity of the digital processing has been viewed as a fundamental obstacle to the feasibility of Massive MIMO in the past. Recent advances on system-algorithm-hardware co-design have led to extremely energy-efficient implementations. These exploit opportunities in deeply-scaled silicon technologies and perform partly distributed processing to cope with the bottlenecks encountered in the interconnection of many signals. For example, prototype ASIC implementations have demonstrated zero-forcing precoding in real time at a 55 mW power consumption (20 MHz bandwidth, 128 antennas, multiplexing of 8 terminals). Coarse and even error-prone digital processing in the antenna paths permits a reduction of consumption with a factor of 2 to 5. This article summarizes the fundamental technical contributions to efficient digital signal processing for Massive MIMO. The opportunities and constraints on operating on low-complexity RF and analog hardware chains are clarified. It illustrates how terminals can benefit from improved energy efficiency. The status of technology and real-life prototypes discussed. Open challenges and directions for future research are suggested.Comment: submitted to IEEE transactions on signal processin

Fast matrix inversion updates for massive MIMO detection and precoding

In this letter, methods and corresponding complexities for fast matrix inversion updates in the context of massive multiple-input multiple-output (MIMO) are studied. In particular, we propose an on-the-fly method to recompute the zero forcing (ZF) filter when a user is added or removed from the system. Additionally, we evaluate the recalculation of the inverse matrix after a new channel estimation is obtained for a given user. Results are evaluated numerically in terms of bit error rate (BER) using the Neumann series approximation as the initial inverse matrix. It is concluded that, with fewer operations, the performance after an update remains close to the initial one.info:eu-repo/semantics/acceptedVersio

A Low Complexity Space-Time Block Codes Detection for Cell-Free Massive MIMO Systems

The new generation of telecommunication systems must provide acceptable data rates and spectral efficiency for new applications. Recently massive MIMO has been introduced as a key technique for the new generation of telecommunication systems. Cell-free massive MIMO system is not segmented into cells. Each BS antennas are distributed throughout the environment and each user is served by all BSs, simultaneously. In this paper, the performance of the multiuser cell-free massive MIMO-system exploying space-time block codes in the uplink, and with linear decoders is studied. An Inverse matrix approximation using Neumann series is proposed to reduce the computational and hardware complexity of the decoding in the receiver. For this purpose, each user has two antennas, and also for improving the diversity gain performance, space-time block codes are used in the uplink. Then, Neumann series is used to approximate the inverse matrix in ZF and MMSE decoders, and its performance is evaluated in terms of BER and spectral efficiency. In addition, we derive lower bound for throughput of ZF decoder. The simulation results show that performance of the system , in terms of BER and spectral efficiency, is better than the single-antenna users at the same system. Also, the BER performance in a given system with the proposed method will be close to the exact method.Comment: 5 pages, 4 figures, Accepted for ICEE202