Browsing by Author "Studer, Christoph"
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Item Decentralized Baseband Processing for Massive MU-MIMO Systems(IEEE, 2017) Li, Kaipeng; Sharan, Rishi; Chen, Yujun; Goldstein, Tom; Cavallaro, Joseph R.; Studer, ChristophAchieving 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.Item High-Throughput Data Detection for Massive MU-MIMO-OFDM Using Coordinate Descent(IEEE, 2016) Wu, Michael; Dick, Chris; Cavallaro, Joseph R.; Studer, ChristophData detection in massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems is among the most critical tasks due to the excessively high implementation complexity. In this paper, we propose a novel, equalization-based soft-output data-detection algorithm and corresponding reference FPGA designs for wideband massive MU-MIMO systems that use orthogonal frequency-division multiplexing (OFDM). Our data-detection algorithm performs approximate minimum mean-square error (MMSE) or box-constrained equalization using coordinate descent. We deploy a variety of algorithm-level optimizations that enable near-optimal error-rate performance at low implementation complexity, even for systems with hundreds of base-station (BS) antennas and thousands of subcarriers. We design a parallel VLSI architecture that uses pipeline interleaving and can be parametrized at design time to support various antenna configurations. We develop reference FPGA designs for massive MU-MIMO-OFDM systems and provide an extensive comparison to existing designs in terms of implementation complexity, throughput, and error-rate performance. For a 128 BS antenna, 8-user massive MU-MIMO-OFDM system, our FPGA design outperforms the next-best implementation by more than 2.6× in terms of throughput per FPGA look-up tables.Item Implementation Trade-Offs For Linear Detection In Large-Scale MIMO Systems(IEEE, 2013-06) Yin, Bei; Wu, Michael; Studer, Christoph; Cavallaro, Joseph R.; Dick, ChrisIn this paper, we analyze the VLSI implementation tradeoffs for linear data detection in the uplink of large-scale multiple-input multiple-output (MIMO) wireless systems. Specifically, we analyze the error incurred by using the suboptimal, low-complexity matrix inverse proposed in Wu et al., 2013, ISCAS, and compare its performance and complexity to an exact matrix inversion algorithm. We propose a Cholesky-based reference architecture for exact matrix inversion and show corresponding implementation results on an Virtex-7 FPGA. Using this reference design, we perform a performance/complexity trade-off comparison with an FPGA implementation for the proposed approximate matrix inversion, which reveals that the inversion circuit of choice is determined by the antenna configuration (base-station antennas vs. number of users) of large-scale MIMO systems.Item Implicit vs. Explicit Approximate Matrix Inversion for Wideband Massive MU-MIMO Data Detection(Springer, 2017) Wu, Michael; Yin, Bei; Li, Kaipeng; Dick, Chris; Cavallaro, Joseph R.; Studer, ChristophMassive multi-user (MU) MIMO wireless technology promises improved spectral efficiency compared to that of traditional cellular systems. While data-detection algorithms that rely on linear equalization achieve near-optimal error-rate performance for massive MU-MIMO systems, they require the solution to large linear systems at high throughput and low latency, which results in excessively high receiver complexity. In this paper, we investigate a variety of exact and approximate equalization schemes that solve the system of linear equations either explicitly (requiring the computation of a matrix inverse) or implicitly (by directly computing the solution vector). We analyze the associated performance/complexity trade-offs, and we show that for small base-station (BS)-to-user-antenna ratios, exact and implicit data detection using the Cholesky decomposition achieves near-optimal performance at low complexity. In contrast, implicit data detection using approximate equalization methods results in the best trade-off for large BS-to-user-antenna ratios. By combining the advantages of exact, approximate, implicit, and explicit matrix inversion, we develop a new frequency-adaptive e qualizer (FADE), which outperforms existing data-detection methods in terms of performance and complexity for wideband massive MU-MIMO systems.Item Low Complexity Detection and Precoding for Massive MIMO Systems: Algorithm, Architecture, and Application(2014-12-03) Yin, Bei; Cavallaro, Joseph R.; Aazhang, Behnaam; Hicks, Illya V.; Studer, ChristophMassive (or large-scale) MIMO is an emerging technology to improve the spectral efficiency of existing (small-scale) MIMO wireless communication systems. The main idea is to equip the base station (BS) with hundreds of antennas that serves a small number of users (in the orders of tens) simultaneously in the same frequency band. In such a system, the data detection and precoding are among the most challenging tasks in terms of computational complexity and performance. Although theoretical results show that simple detection and precoding algorithms are able to achieve optimal error rate performance when the number of BS antennas approaches infinity, the systems with realistic antenna configurations have to resort to computationally expensive algorithms to achieve near-optimal performance. In this research, we show that by utilizing the special property of massive MIMO systems, approximate linear detection and precoding can deliver near-optimal error rate performance with low complexity. We first propose approximate methods relying on Neumann series. This approach requires lower computational complexity than that of an exact inversion while delivering near-optimal results when there is a large ratio between BS and user antennas. We then develop a novel reconfigurable VLSI architecture to perform both the necessary Gram matrix computation and Neumann series based matrix inversion. The Neumann series approach, however, suffers from a considerable error-rate performance loss if the ratio of BS to user antennas is not large enough. To improve the performance, we investigate the conjugate gradient (CG) method (without explicitly computing matrix inversion) and conjugate gradient least square (CGLS) method (without explicitly computing Gram matrix and matrix inversion). Although CG and CGLS for precoding are rather straightforward, the necessary signal-to-interference-and-noise-ratio (SINR) for soft-output detection is not computed by CG and CGLS. To solve this problem, we propose an exact and an approximate method to compute the SINR within CG and CGLS algorithm with low complexity. We show that compared to exact and Neumann series based linear methods, CG based detection and precoding method is suitable for systems with small to medium number of users, while CGLS is suitable for systems with large number of users. A novel reconfigurable VLSI architecture is then proposed to support the both CG and CGLS.Item On the achievable rates of decentralized equalization in massive MU-MIMO systems(IEEE, 2017) Jeon, Charles; Li, Kaipeng; Cavallaro, Joseph R.; Studer, ChristophMassive 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 feed forward 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.Item Sparse Signal Separation in Redundant Dictionaries(IEEE, 2012) Aubel, Céline; Studer, Christoph; Pope, Graeme; Bölcskei, HelmutWe formulate a unified framework for the separation of signals that are sparse in “morphologically” different redundant dictionaries. This formulation incorporates the socalled “analysis” and “synthesis” approaches as special cases and contains novel hybrid setups. We find corresponding coherencebased recovery guarantees for an 1-norm based separation algorithm. Our results recover those reported in Studer and Baraniuk, ACHA, submitted, for the synthesis setting, provide new recovery guarantees for the analysis setting, and form a basis for comparing performance in the analysis and synthesis settings. As an aside our findings complement the D-RIP recovery results reported in Candès et al., ACHA, 2011, for the “analysis” signal recovery problem minimize x Ψ x 1 subject to y − A x 2 ≤ ε by delivering corresponding coherence-based recovery results.Item Video Compressive Sensing for Spatial Multiplexing Cameras Using Motion-Flow Models(SIAM, 2015) Sankaranarayanan, Aswin C.; Xu, Lina; Studer, Christoph; Li, Yun; Kelly, Kevin F.; Baraniuk, Richard G.Spatial multiplexing cameras (SMCs) acquire a (typically static) scene through a series of coded projections using a spatial light modulator (e.g., a digital micromirror device) and a few optical sensors. This approach finds use in imaging applications where full-frame sensors are either too expensive (e.g., for short-wave infrared wavelengths) or unavailable. Existing SMC systems reconstruct static scenes using techniques from compressive sensing (CS). For videos, however, existing acquisition and recovery methods deliver poor quality. In this paper, we propose the CS multiscale video (CS-MUVI) sensing and recovery framework for high-quality video acquisition and recovery using SMCs. Our framework features novel sensing matrices that enable the efficient computation of a low-resolution video preview, while enabling high-resolution video recovery using convex optimization. To further improve the quality of the reconstructed videos, we extract optical-flow estimates from the low-resolution previews and impose them as constraints in the recovery procedure. We demonstrate the efficacy of our CS-MUVI framework for a host of synthetic and real measured SMC video data, and we show that high-quality videos can be recovered at roughly $60\times$ compression.