Abstract-Large-scale (or massive) multiple-input multipleoutput (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.Index Terms-Approximate matrix inversion, FPGA design, large-scale (or massive) MIMO, linear soft-output detection, minimum mean square error (MMSE), Neumann series, VLSI.

Abstract-Massive multiple-input multiple-output (MIMO) promises improved spectral efficiency, coverage, and range, compared to conventional (small-scale) MIMO wireless systems. Unfortunately, these benefits come at the cost of significantly increased computational complexity, especially for systems with realistic antenna configurations. To reduce the complexity of data detection (in the uplink) and precoding (in the downlink) in massive MIMO systems, we propose to use conjugate gradient (CG) methods. While precoding using CG is rather straightforward, soft-output minimum mean-square error (MMSE) detection requires the computation of the post-equalization signal-tointerference-and-noise-ratio (SINR). To enable CG for soft-output detection, we propose a novel way of computing the SINR directly within the CG algorithm at low complexity. We investigate the performance/complexity trade-offs associated with CG-based softoutput detection and precoding, and we compare it to existing exact and approximate methods. Our results reveal that the proposed algorithm is able to outperform existing methods for massive MIMO systems with realistic antenna configurations.

Abstract-The high processing complexity of data detection in the large-scale multiple-input multiple-output (MIMO) uplink necessitates high-throughput VLSI implementations. In this paper, we propose-to the best of our knowledge-first matrix inversion implementation suitable for data detection in systems having hundreds of antennas at the base station (BS). The underlying idea is to carry out an approximate matrix inversion using a small number of Neumann-series terms, which allows one to achieve near-optimal performance at low complexity. We propose a novel VLSI architecture to efficiently compute the approximate inverse using a systolic array and show reference FPGA implementation results for various system configurations. For a system where 128 BS antennas receive data from 8 single-antenna users, a single instance of our design processes 1.9 M matrices/s on a Xilinx Virtex-7 FPGA, while using only 3.9% of the available slices and 3.6% of the available DSP48 units. I. INTRODUCTIONMultiple-input multiple-output (MIMO) combined with spatial multiplexing [1] is the key technology in most modern wireless communication standards, such as 3GPP LTE or IEEE 802.11n. MIMO technology offers improved link reliability and higher data rates compared to single-antenna systems by simultaneously transmitting multiple data streams in the same frequency band. However, conventional point-to-point and multi-user (MU) MIMO wireless systems already start to approach the theoretical throughput limits. Consequently, novel transmission technologies become necessary to meet the ever-growing demand for higher data rates without further increasing the communication bandwidth [2], [3].Large-scale MIMO (or massive MIMO) is an emerging technology, which uses antenna arrays having orders of magnitude more elements at the base station (BS) compared to conventional (small-scale) MIMO systems, while simultaneously serving a small number of users in the same frequency band [2]. This technology promises further improvements in spectral efficiency and link reliability over conventional (small-scale) MIMO systems [3], [4]. In addition, large-scale MIMO has the potential to reduce the operational power consumption at the BS [2], [5].Unfortunately, the benefits of large-scale MIMO come at the cost of significantly increased computational complexity in the BS compared to small-scale MIMO systems. Specifically, data detection in the large-scale MIMO uplink is among the most critical tasks, as the presence of hundreds of antennas at the BS requires novel detection algorithms that scale favorably to high-dimensional problems. Since optimal methods, such as maximum-likelihood (ML) detection or sphere

Abstract-Achieving high spectral efficiency in realistic massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems requires computationally-complex algorithms for data detection in the uplink (users transmit to base-station) and beamforming in the downlink (base-station transmits to users). Most existing algorithms are designed to be executed on centralized computing hardware at the base-station (BS), which results in prohibitive complexity for systems with hundreds or thousands of antennas and generates raw baseband data rates that exceed the limits of current interconnect technology and chip I/O interfaces. This paper proposes a novel decentralized baseband processing architecture that alleviates these bottlenecks by partitioning the BS antenna array into clusters, each associated with independent radio-frequency chains, analog and digital modulation circuitry, and computing hardware. For this architecture, we develop novel decentralized data detection and beamforming algorithms that only access local channel-state information and require low communication bandwidth among the clusters. We study the associated trade-offs between error-rate performance, computational complexity, and interconnect bandwidth, and we demonstrate the scalability of our solutions for massive MU-MIMO systems with thousands of BS antennas using reference implementations on a graphic processing unit (GPU) cluster.

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