In very-large multiple-input multiple-output (MIMO) systems, the base station (BS) is equipped with very large number of antennas as compared to previously considered systems. There are various advantages of increasing the number of antennas, and some schemes require handling large matrices for joint processing (pre-coding) at the BS. The dirty paper coding (DPC) is an optimal pre-coding scheme and has a very high complexity. However, with increasing number of BS antennas, linear pre-coding performance tends to that of the optimal DPC. Although linear pre-coding is less complex than DPC, there is a need to compute pseudo inverses of large matrices. In this paper we present a low complexity approximation of down-link Zero Forcing (ZF) linear pre-coding for very-large multi-user MIMO systems. Approximation using a Neumann series expansion is opted for inversion of matrices over traditional exact computations, by making use of special properties of the matrices, thereby reducing the cost of hardware. With this approximation of linear pre-coding, we can significantly reduce the computational complexity for large enough systems, i.e., where we have enough BS antenna elements. For the investigated case of 8 users, we obtain 90% of the full ZF sum rate, with lower computational complexity, when the number of BS antennas per user is about 20 or more.

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Approximative matrix inverse computations for very-large MIMO and applications to linear pre-coding systems

In very-large multiple-input multiple-output (MIMO) systems, the BS (base station) is equipped with very large number of antennas as compared to previously considered systems. There are various advantages of increasing the number of antennas, and some schemes would require handling large matrices for joint processing (pre-coding) at the base station. The dirty paper coding (DPC) is an optimal pre-coding scheme and has a very high complexity. However with increasing number of BS antennas linear pre-coding performance tends to that of the optimal DPC. Although linear pre-coding is less complex than DPC, there is a need to compute pseudo inverses of large matrices. In this paper we present a low complexity approximation of down-link Zero Forcing linear pre-coding for very-large multi-user MIMO systems. Approximation using a Neumann series expansion is opted for inversion of matrices over traditional exact computations, by making use of special properties of the matrices, thereby reducing the cost of hardware. With this approximation of linear pre-coding, we can significantly reduce the computational complexity for large enough systems, i.e., where we have enough BS antenna elements. For the investigated case of 8 users, we obtain 90% of the full ZF sum rate, with lower computational complexity, when the number of BS antennas per user is about 20 or more. (Less)

/pdf/approximative-matrix-inverse-computations-for-very-large-11jc7svqyb.pdf

Approximative Matrix Inverse Computations for Very-large MIMO and Applications to Linear Pre-coding Systems

As a branch of sphere decoding, the K -best method has played an important role in detection in large-scale multiple-input-multiple-output (MIMO) systems. However, as the numbers of users and antennas grow, the preprocessing complexity increases significantly, which is one of the major issues with the K -best method. To address this problem, this paper proposes a preprocessing algorithm combining Cholesky sorted QR decomposition and partial iterative lattice reduction (CHOSLAR) for K -best detection in a 64-quadrature amplitude modulation (QAM) 16 $\times$ 16 MIMO system. First, Cholesky decomposition is conducted to perform sorted QR decomposition. Compared with conventional sorted QR decomposition, this method reduces the number of multiplications by 25.1% and increases parallelism. Then, a constant-throughput partial iterative lattice reduction method is adopted to achieve near-optimal detection accuracy. This method further increases parallelism, reduces the number of matrix swaps by 45.5%, and reduces the number of multiplications by 67.3%. Finally, a sorting-reduced K -best strategy is used for vector estimation, thereby, reducing the number of comparators by 84.7%. This method suffers an accuracy loss of only approximately 1.44 dB compared with maximum likelihood detection. Based on CHOSLAR, this paper proposes a fully pipelined very-large-scale-integration architecture. A series of different systolic arrays and parallel processing units achieves an optimal tradeoff among throughput, area consumption, and power consumption. This architectural layout is obtained via TSMC 65-nm 1P9M CMOS technology, and throughput metrics of 1.40 Gbps/W (throughput/power) and 0.62 Mbps/kG (throughput/area) are achieved, demonstrating that the proposed system is much more efficient than state-of-the-art designs.

Algorithm and Architecture of a Low-Complexity and High-Parallelism Preprocessing-Based K -Best Detector for Large-Scale MIMO Systems

The aim of the thesis is to develop algorithms and architectures to meet the high data rate, low complexity requirements of the future mobile communication systems. Algorithms, architectures and implementations for detection, channel estimation and interference mitigation in the multipleinput multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) receivers are presented. The performance-complexity trade-offs in different receiver algorithms are studied and the results can be utilized in receiver design as well as in system design. Implementation of detectors for spatial multiplexing systems is considered first. The linear minimum mean squared error (LMMSE) and the K-best list sphere detector (LSD) are compared to the successive interference cancellation (SIC) detector. The SIC algorithm was found to perform worse than the K-best LSD when the MIMO channels are highly correlated. The performance difference diminishes when the correlation decreases. With feedback to the transmitter, the performance difference is even smaller, but the full rank transmissions still require a more complex detector. A reconfigurable receiver, using a simple or a more complex detector as the channel conditions change, would achieve the best performance while consuming the least amount of power in the receiver. The use of decision directed (DD) channel estimation is also studied. The 3GPP long term evolution (LTE) based pilot structure is used as a benchmark. The performance and complexity of the pilot symbol based least-squares (LS) channel estimator, the minimum mean square error (MMSE) filter and the DD space-alternating generalized expectation-maximization (SAGE) algorithm are studied. DD channel estimation and MMSE filtering improve the performance with high user velocities, where the pilot symbol density is not sufficient. With DD channel estimation, the pilot overhead can be reduced without any performance degradation by transmitting data instead of pilot symbols. Suppression of co-channel interference in the MIMO-OFDM receiver is finally considered. The interference and noise spatial covariance matrix is used in data detection and channel estimation. Interference mitigation is applied for linear and nonlinear detectors. An algorithm to adapt the accuracy of the matrix decomposition and the use of interference suppression is proposed. The adaptive algorithm performs well in all interference scenarios and the power consumption of the receiver can be reduced.

/pdf/equalization-and-channel-estimation-algorithms-and-qqboboir91.pdf

Equalization and channel estimation algorithms and implementations for cellular MIMO-OFDM downlink

This paper proposes a flexible dual-mode soft-output multiple-input multiple-output (MIMO) detector to support open-loop and closed-loop in Chinese enhanced ultra high throughput (EUHT) wireless local area network (LAN) standard. The proposed detector uses minimum mean square error (MMSE) sorted QR decomposition (MMSE-SQRD) to produce channel preprocessing result, which is realized by a modified systolic array architecture with concurrent sorting. Moreover, the adopted square-root MMSE algorithm for closed-loop reuses MMSE-SQRD preprocessing to largely save hardware overhead. In addition, an optimized K-Best detection algorithm is proposed for open-loop, which increases throughput by odd-even parallel sorting and produces high quality soft-output with discarded paths (DPs). A flexible VLSI architecture is designed for the proposed dual-mode detector, which supports $1\times 1\sim 4\times 4$ antennas and BPSK $\sim$ 64-QAM modulation configuration. Implemented in SMIC 65 nm CMOS technology, the detector is capable of running at 550 MHz, which has a maximum throughput of 2.64 Gb/s for K-Best detection and 3.3 Gb/s for linear MMSE detection. The proposed detector is competitive to recent published works and meets the data-rate requirement of the EUHT standard.

Design and Implementation of Flexible Dual-Mode Soft-Output MIMO Detector With Channel Preprocessing

Due to the growing demand of transmission capacity of the wireless communication system, multiple-input multiple-output orthogonal-frequency-division-multiplexing (MIMO-OFDM) communication system requires more and more MIMO antennas and a large number of OFDM subcarriers. Thus, the QR decomposition becomes one of the computational bottlenecks in the QR-based MIMO detection. The proposed Givens-Rotation-based QR decomposition algorithm features efficient parallel processing with sorting function that resolves the trade-off between the detection performance and hardware utiliztion efficiency. According to the algorithm, we designed and implemented a 2×2∼8×8 QR decomposition processor using TSMC 0.18/μm 1P6M CMOS technology. The throughput of the QR decomposition processor achieves 6.8×104 SQRD per second, which outperforms other works in the literature after being normalized by the MIMO dimension.

Cheng-Ming Clien

Papers

A 2×2–8×8 sorted QR decomposition processor for MIMO detection