Expectation Propagation Detector for Extra-Large Scale Massive MIMO

Abstract

The order-of-magnitude increase in the dimension of antenna arrays, which forms extra-large-scale massive multiple-input-multiple-output (MIMO) systems, enables substantial improvement in spectral efficiency, energy efficiency, and spatial resolution. However, practical challenges, such as excessive computational complexity and excess of baseband data to be transferred and processed, prohibit the use of centralized processing. A promising solution is to distribute baseband data from disjoint subsets of antennas into parallel processing procedures coordinated by a central processing unit. This solution is called subarray-based architecture. In this work, we extend the application of expectation propagation (EP) principle, which effectively balances performance and practical feasibility in conventional centralized MIMO detector design, to fit the subarray-based architecture. Analytical results confirm the convergence of the proposed iterative procedure and that the proposed detector asymptotically approximates Bayesian optimal performance under certain conditions. The proposed subarray-based EP detector is reduced to centralized EP detector when only one subarray exists. In addition, we propose additional strategies for further reducing the complexity and overhead of the information exchange between parallel subarrays and the central processing unit to facilitate the practical implementation of the proposed detector. Simulation results demonstrate that the proposed detector achieves numerical stability within few iterations and outperforms its counterparts.

arXiv comment15 pages, 16 figures, accepted by IEEE Transactions on Wireless Communications for publication

References in corpus (6)

• Massive MIMO for Next Generation Wireless Systems
• Turbo Compressed Sensing with Partial DFT Sensing Matrix
• Bayesian Optimal Data Detector for Hybrid mmWave MIMO-OFDM Systems with Low-Resolution ADCs
• Efficient DSP and Circuit Architectures for Massive MIMO: State-of-the-Art and Future Directions
• Bayesian Optimal Data Detector for mmWave OFDM System with Low-Resolution ADC
• Generalized Turbo Signal Recovery for Nonlinear Measurements and Orthogonal Sensing Matrices

Cited by in corpus (4)

• Statistical Tools and Methodologies for Ultrareliable Low-Latency Communications -- A Tutorial
• Accelerated Randomized Methods for Receiver Design in Extra-Large Scale MIMO Arrays
• A Bayesian Receiver with Improved Complexity-Reliability Trade-off in Massive MIMO Systems
• Improving Spectral Efficiency via Pilot Assignment and Subarray Selection Under Realistic XL-MIMO Channels