Quantized Massive MU-MIMO-OFDM Uplink (1509.07928v2)

Abstract: Coarse quantization at the base station (BS) of a massive multi-user (MU) multiple-input multiple-output (MIMO) wireless system promises significant power and cost savings. Coarse quantization also enables significant reductions of the raw analog-to-digital converter (ADC) data that must be transferred from a spatially-separated antenna array to the baseband processing unit. The theoretical limits as well as practical transceiver algorithms for such quantized MU-MIMO systems operating over frequency-flat, narrowband channels have been studied extensively. However, the practically relevant scenario where such communication systems operate over frequency-selective, wideband channels is less well understood. This paper investigates the uplink performance of a quantized massive MU-MIMO system that deploys orthogonal frequency-division multiplexing (OFDM) for wideband communication. We propose new algorithms for quantized maximum a-posteriori (MAP) channel estimation and data detection, and we study the associated performance/quantization trade-offs. Our results demonstrate that coarse quantization (e.g., four to six bits, depending on the ratio between the number of BS antennas and the number of users) in massive MU-MIMO-OFDM systems entails virtually no performance loss compared to the infinite-precision case at no additional cost in terms of baseband processing complexity.

• The paper introduces a novel quantization model that reduces ADC power and cost while maintaining performance in massive MU-MIMO-OFDM uplinks.
• It presents efficient MAP-based channel estimation and MMSE data detection algorithms that address nonlinear quantization effects via convex optimization.
• Simulations confirm that using low-resolution ADCs (4-6 bits) incurs minimal performance loss, validating the effectiveness of mismatched quantization models for practical deployments.

Overview of Quantized Massive MU-MIMO-OFDM Uplink

The paper "Quantized Massive MU-MIMO-OFDM Uplink" addresses the design and implementation challenges associated with utilizing coarse quantization in the context of massive multi-user (MU) multiple-input multiple-output (MIMO) systems employing orthogonal frequency-division multiplexing (OFDM). The research presents novel algorithms for channel estimation and data detection under the scenario of wideband, frequency-selective channels, which have been less explored compared to the extensively studied frequency-flat channels.

Key Contributions

1. Quantization Model: The paper introduces a quantization model that coarsely quantizes both the in-phase and quadrature components of received signals at the base station (BS). This approach promises significant reductions in power consumption, hardware costs, and the data rate of raw analog-to-digital converter (ADC) outputs, which typically pose a bottleneck in large antenna arrays.
2. Channel Estimation and Data Detection: The paper proposes computationally efficient algorithms for maximum a-posteriori (MAP) channel estimation and minimum mean square error (MMSE) data detection tailored for the quantized system. These algorithms leverage convex optimization techniques which can be solved efficiently even with the nonlinearities introduced by the quantizer.
3. Performance Evaluation: Through simulation, it is shown that relatively low bit-depth quantization (e.g., 4 to 6 bits) incurs minimal performance loss in terms of bit error rates compared to the ideal infinite-precision scenario, thus supporting the feasibility of using coarse quantization in practical systems.
4. Mismatched Quantization Models: To trade-off between complexity and performance, mismatched quantization models are proposed. These models approximate the quantization errors with Gaussian noise, resulting in simpler yet still effective detection algorithms.

Numerical Results and System Implications

The numerical results indicate that for a BS with more antennas than users, specifically a ratio of 8:1 or more, the performance loss due to coarse quantization is negligible. The paper prescribes a quantization resolution of around four bits per sample and demonstrates that such settings yield an almost equivalent performance to that of an infinite-precision ADC setup. Notably, when scaled to larger BS arrays, using mismatched models does not lead to a substantial performance degradation while significantly simplifying the receiver architecture.

Future Directions and Speculations

The findings have profound implications for the design of next-generation cellular networks, particularly for high-density urban environments where the deployment of numerous BS antennas is feasible. Coarse quantization could substantially reduce the ADC cost and power consumption, facilitating the realization of cost-effective and energy-efficient massive MIMO systems. Future research could further explore the challenges of synchronization and noise variance estimation in the presence of coarse quantization, potentially improving the robustness and applicability of these systems.

Moreover, applying these quantization techniques to other wireless communication standards like SC-FDMA used in LTE uplinks could expand the utility of the proposed algorithms. It is also of interest to explore integration strategies for filtering mechanisms prevalent in typical digital receivers to ascertain the relevancy and compatibility of the quantized approach.

In summary, while the paper builds on established principles of MIMO and OFDM, it innovatively adapts them to capitalize on the benefits offered by coarse quantization, offering a compelling argument for its implementation in massive MIMO scenarios.