More Like this

1. Feedforward Architectures for Decentralized Precoding in Massive MU-MIMO Systems

   https://doi.org/10.1109/ACSSC.2018.8645086  

   Li, Kaipeng ; Jeon, Charles ; Cavallaro, Joseph R. ; Studer, Christoph ( October 2018 , 2018 IEEE 52nd Asilomar Conference on Signals, Systems, and Computers)

   Massive multi-user multiple-input multiple-output (MU-MIMO) enables significant gains in spectral efficiency and link reliability compared to conventional, small-scale MIMO technology. In addition, linear precoding using zero forcing or Wiener filter (WF) precoding is sufficient to achieve excellent error rate performance in the massive MU-MIMO downlink. However, these methods typically require centralized processing at the base-station (BS), which causes (i) excessively high interconnect and chip input/output data rates, and (ii) high implementation complexity. We propose two feed-forward architectures and corresponding decentralized WF precoders that parallelize precoding across multiple computing fabrics, effectively mitigating the limitations of centralized approaches. To demonstrate the efficacy of our decentralized precoders, we provide implementation results on a multi-GPU system, which show that our solutions achieve throughputs in the Gbit/s regime while achieving (near-)optimal error-rate performance in the massive MU-MIMO downlink. 

   more » « less
2. Decentralized Coordinate-Descent Data Detection and Precoding for Massive MU-MIMO

   https://doi.org/10.1109/ISCAS.2019.8702740  

   Li, Kaipeng ; Castaneda, Oscar ; Jeon, Charles ; Cavallaro, Joseph R. ; Studer, Christoph ( May 2019 , 2019 IEEE International Symposium on Circuits and Systems (ISCAS))

   Massive multiuser (MU) multiple-input multiple-output (MIMO) promises significant improvements in spectral efficiency compared to small-scale MIMO. Typical massive MU-MIMO base-station (BS) designs rely on centralized linear data detectors and precoders which entail excessively high complexity, interconnect data rates, and chip input/output (I/O) bandwidth when executed on a single computing fabric. To resolve these complexity and bandwidth bottlenecks, we propose new decentralized algorithms for data detection and precoding that use coordinate descent. Our methods parallelize computations across multiple computing fabrics, while minimizing interconnect and I/O bandwidth. The proposed decentralized algorithms achieve near-optimal error-rate performance and multi-Gbps throughput at sub-1 ms latency when implemented on a multi-GPU cluster with half-precision floating-point arithmetic. 

   more » « less
3. Design Trade-offs for Decentralized Baseband Processing in Massive MU-MIMO Systems

   https://doi.org/10.1109/IEEECONF44664.2019.9048727  

   Li, Kaipeng ; McNaney, James ; Tarver, Chance ; Castaneda, Oscar ; Jeon, Charles ; Cavallaro, Joseph R. ; Studer, Christoph ( November 2019 , 2019 IEEE 53rd Asilomar Conference on Signals, Systems and Computers)

   null (Ed.)

   Massive multi-user (MU) multiple-input multiple-output (MIMO) provides high spectral efficiency by means of spatial multiplexing and fine-grained beamforming. However, conventional base-station (BS) architectures for systems with hundreds of antennas that rely on centralized baseband processing inevitably suffer from (i) excessive interconnect data rates between radio-frequency circuitry and processing fabrics, and (ii) prohibitive complexity at the centralized baseband processor. Recently, decentralized baseband processing (DBP) architectures and algorithms have been proposed, which mitigate the interconnect bandwidth and complexity bottlenecks. This paper systematically explores the design trade-offs between error-rate performance, computational complexity, and data transfer latency of DBP architectures under different system configurations and channel conditions. Considering architecture, algorithm, and numerical precision aspects, we provide practical guidelines to select the DBP architecture and algorithm that are able to realize the full benefits of massive MU-MIMO in the uplink and downlink. 

   more » « less
4. Hardware-Aware Beamspace Precoding for All-Digital mmWave Massive MU-MIMO

   https://doi.org/10.1109/LCOMM.2021.3105233  

   Gonultas, Emre ; Taner, Sueda ; Gallyas-Sanhueza, Alexandra ; Mirfarshbafan, Seyed Hadi ; Studer, Christoph ( August 2021 , IEEE Communications Letters)

   null (Ed.)

   Massive multi-user multiple-input multiple-output (MU-MIMO) wireless systems operating at millimeter-wave (mmWave) frequencies enable simultaneous wideband data transmission to a large number of users. In order to reduce the complexity of MU precoding in all-digital basestation architectures that equip each antenna element with a pair of data converters, we propose a two-stage precoding architecture which first generates a sparse precoding matrix in the beamspace domain, followed by an inverse fast Fourier transform that converts the result to the antenna domain. The sparse precoding matrix requires a small amount of multipliers and enables regular hardware architectures, which allows the design of hardware-efficient all-digital precoders. Simulation results demonstrate that our methods approach the error-rate performance of conventional Wiener filter precoding with more than 2x lower complexity. 

   more » « less
5. Finite-Alphabet Wiener Filter Precoding for mmWave Massive MU-MIMO Systems

   https://doi.org/10.1109/IEEECONF44664.2019.9048799  

   Castaneda, Oscar ; Jacobsson, Sven ; Durisi, Giuseppe ; Goldstein, Tom ; Studer, Christoph ( November 2019 , Asilomar Conference on Signals, Systems, and Computers)

   Power consumption of multiuser (MU) precoding is a major concern in all-digital massive MU multiple-input multiple-output (MIMO) basestations with hundreds of antenna elements operating at millimeter-wave (mmWave) frequencies. We propose to replace part of the linear Wiener filter (WF) precoding matrix by a Finite-Alphabet WF Precoding (FAWP) matrix, which enables the use of low-precision hardware that consumes low power and area. To minimize the performance loss of our approach, we present methods that efficiently compute mean-square error (MSE)-optimal FAWP matrices. Our results show that FAWP matrices are able to approach infinite-precision error-rate and error vector magnitude performance with only 3-bit precoding weights, even when operating under realistic mmWave propagation conditions. Hence, FAWP is a promising approach to substantially reduce power consumption and silicon area in all-digital mmWave massive MU-MIMO systems. 

   more » « less