Optimal Data Detection in Large MIMO

Abstract

Large multiple-input multiple-output (MIMO) appears in massive multi-user MIMO and randomly-spread code-division multiple access (CDMA)-based wireless systems. In order to cope with the excessively high complexity of optimal data detection in such systems, a variety of efficient yet sub-optimal algorithms have been proposed in the past. In this paper, we propose a data detection algorithm that is computationally efficient and optimal in a sense that it is able to achieve the same error-rate performance as the individually optimal (IO) data detector under certain assumptions on the MIMO system matrix and constellation alphabet. Our algorithm, which we refer to as LAMA (short for large MIMO AMP), builds on complex-valued Bayesian approximate message passing (AMP), which enables an exact analytical characterization of the performance and complexity in the large-system limit via the state-evolution framework. We derive optimality conditions for LAMA and investigate performance/complexity trade-offs. As a byproduct of our analysis, we recover classical results of IO data detection for randomly-spread CDMA. We furthermore provide practical ways for LAMA to approach the theoretical performance limits in realistic, finite-dimensional systems at low computational complexity.

References in corpus (13)

• Massive MIMO for Next Generation Wireless Systems
• Message Passing Algorithms for Compressed Sensing
• Expectation-Maximization Gaussian-Mixture Approximate Message Passing
• Large-Scale MIMO Detection for 3GPP LTE: Algorithms and FPGA Implementations
• Compressive Imaging using Approximate Message Passing and a Markov-Tree Prior
• Message-Passing Estimation from Quantized Samples
• A Message-Passing Receiver for BICM-OFDM over Unknown Clustered-Sparse Channels
• A Factor Graph Approach to Joint OFDM Channel Estimation and Decoding in Impulsive Noise Environments
• Symbol Error Rate Performance of Box-relaxation Decoders in Massive MIMO
• Generalized Approximate Message Passing for Estimation with Random Linear Mixing
• Massive MIMO: How many antennas do we need?
• Does ${\ell }_p$-minimization outperform ${\ell }_1$-minimization?
• Fixed Points of Generalized Approximate Message Passing with Arbitrary Matrices

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• A Bayesian Receiver with Improved Complexity-Reliability Trade-off in Massive MIMO Systems
• Mismatched Data Detection in Massive MU-MIMO
• Capacity Optimality of AMP in Coded Systems
• Optimal Data Detection and Signal Estimation in Systems with Input Noise
• A Linear Bayesian Learning Receiver Scheme for Massive MIMO Systems