Conjugate Gradient-based Soft-Output Detection and Precoding in Massive MIMO Systems

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-to-interference-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 soft-output detection and precoding, and we compare it to exact and approximate methods. Our results reveal that the proposed method outperforms existing algorithms for massive MIMO systems with realistic antenna configurations.Comment: to appear at IEEE GLOBECOM 201

A Flexible LDPC/Turbo Decoder Architecture

Low-density parity-check (LDPC) codes and convolutional Turbo codes are two of the most powerful error correcting codes that are widely used in modern communication systems. In a multi-mode baseband receiver, both LDPC and Turbo decoders may be required. However, the different decoding approaches for LDPC and Turbo codes usually lead to different hardware architectures. In this paper we propose a unified message passing algorithm for LDPC and Turbo codes and introduce a flexible soft-input soft-output (SISO) module to handle LDPC/Turbo decoding. We employ the trellis-based maximum a posteriori (MAP) algorithm as a bridge between LDPC and Turbo codes decoding. We view the LDPC code as a concatenation of n super-codes where each super-code has a simpler trellis structure so that the MAP algorithm can be easily applied to it. We propose a flexible functional unit (FFU) for MAP processing of LDPC and Turbo codes with a low hardware overhead (about 15% area and timing overhead). Based on the FFU, we propose an area-efficient flexible SISO decoder architecture to support LDPC/Turbo codes decoding. Multiple such SISO modules can be embedded into a parallel decoder for higher decoding throughput. As a case study, a flexible LDPC/Turbo decoder has been synthesized on a TSMC 90 nm CMOS technology with a core area of 3.2 mm2. The decoder can support IEEE 802.16e LDPC codes, IEEE 802.11n LDPC codes, and 3GPP LTE Turbo codes. Running at 500 MHz clock frequency, the decoder can sustain up to 600 Mbps LDPC decoding or 450 Mbps Turbo decoding.NokiaNokia Siemens Networks (NSN)XilinxTexas InstrumentsNational Science Foundatio

A New MIMO Detector Architecture Based on A Forward-Backward Trellis Algorithm

In this paper, a recursive Forward-Backward (F-B) trellis algorithm is proposed for soft-output MIMO detection. Instead of using the traditional tree topology, we represent the search space of the MIMO signals with a fully connected trellis and a Forward-Backward recursion is applied to compute the a posteriori probability (APP) for each coded data bit. The proposed detector has the following advantages: a) it keeps a fixed throughput and has a regular datapath structure which makes it amenable to VLSI implementation, and b) it attempts to maximize the a posteriori probability by tracing both forward and backward on the trellis and it always ensures that at least one candidate exists for every possible transmitted bit xk ∈ {− 1, +1}. Compared with the soft K-best detector, the proposed detector significantly reduces the complexity because sorting is not required, while still maintaining good performance. A maximum throughput of 533Mbps is achievable at a cost of 576K gates for 4 x 4 16-QAM system.NokiaNational Science Foundatio

On the Achievable Rates of Decentralized Equalization in Massive MU-MIMO Systems

Massive multi-user (MU) multiple-input multiple-output (MIMO) promises significant gains in spectral efficiency compared to traditional, small-scale MIMO technology. Linear equalization algorithms, such as zero forcing (ZF) or minimum mean-square error (MMSE)-based methods, typically rely on centralized processing at the base station (BS), which results in (i) excessively high interconnect and chip input/output data rates, and (ii) high computational complexity. In this paper, we investigate the achievable rates of decentralized equalization that mitigates both of these issues. We consider two distinct BS architectures that partition the antenna array into clusters, each associated with independent radio-frequency chains and signal processing hardware, and the results of each cluster are fused in a feedforward network. For both architectures, we consider ZF, MMSE, and a novel, non-linear equalization algorithm that builds upon approximate message passing (AMP), and we theoretically analyze the achievable rates of these methods. Our results demonstrate that decentralized equalization with our AMP-based methods incurs no or only a negligible loss in terms of achievable rates compared to that of centralized solutions.Comment: Will be presented at the 2017 IEEE International Symposium on Information Theor

LOW-COMPLEXITY AND HIGH-PERFORMANCE SOFT MIMO DETECTION BASED ON DISTRIBUTED M-ALGORITHM THROUGH TRELLIS-DIAGRAM

This paper presents a novel low-complexity multiple-input multipleoutput (MIMO) detection scheme using a distributed M-algorithm (DM) to achieve high performance soft MIMO detection. To reduce the searching complexity, we build a MIMO trellis graph and split the searching operations among different nodes, where each node will apply the M-algorithm. Instead of keeping a global candidate list as the traditional detector does, this algorithm keeps multiple small candidate lists to generate soft information. Since the DM algorithm can achieve good BER performance with a small M, the sorting cost of the DM algorithm is lower than that of the conventional K-best MIMO algorithm. The proposed algorithm is very suitable for high speed parallel processing.NokiaNokia Siemens Networks (NSN)XilinxNational Science Foundatio

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

Scalable Architecture of MIMO Multi-carrier CDMA System on Programmable Logic

In this paper, a scalable architecture of the multicarrier CDMA system using Multiple-Input-Multiple-Output (MIMO) technology is designed in the programmable logic array. The system-level partitioning with different architecture design entries is described. The overall computing architecture for complex signal processing blocks, e.g., channel estimation, frequency domain equalization, demodulation etc is described. The MIMO architecture is easily extended from a SISO system with single antenna. This scalable architecture demonstrates resource utilization efficiency and easy extension to MIMO configurations

UNIFIED DECODER ARCHITECTURE FOR LDPC/TURBO CODES

Low-density parity-check (LDPC) codes on par with convolutional turbo codes (CTC) are two of the most powerful error correction codes known to perform very close to the Shannon limit. However, their different code structures usually lead to different hardware implementations. In this paper, we propose a unified decoder architecture that is capable of decoding both LDPC and turbo codes with a limited hardware overhead. We employ maximum a posteriori (MAP) algorithm as a bridge between LDPC and turbo codes. We represent LDPC codes as parallel concatenated single parity check (PCSPC) codes and propose a group sub-trellis (GST) decoding algorithm for the efficient decoding of PCSPC codes. This algorithm achieves about 2X improvement in the convergence speed and is more numerically robust than the classical ”tanh” algorithm. What is more interesting is that we can generalize a unified trellis decoding algorithm for LDPC and turbo codes based on their trellis structures. We propose a reconfigurable computation kernel for log-MAP decoding of LDPC and turbo codes at a cost of ∼15% hardware overhead. Small lookup tables (LUTs) with 9 entries of 2-bit data are designed to implement the log-MAP algorithm. Fixed point (6:2) simulation results show that there is negligible or nearly no performance loss by using this LUT approximation compared to the ideal case. The proposed architecture results in scalable and flexible datapath units enabling parallel decoding of LDPC/turbo codes.NokiaNational Science Foundatio