Channel Estimation in Beyond-5G Massive MIMO Datasets

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Deep Learning at the Edge for Channel Estimation in Beyond-5G Massive MIMO


Abstract

Massive multiple input and multiple output (mMIMO) is a critical component in upcoming 5G wireless deployment as an enabler for high data rate communications. mMIMO is effective when each corresponding antenna pair of the respective transmitter-receiver arrays experiences an inde- pendent channel. While increasing number of antenna elements increases the achievable data rate, at the same time computing the channel state information (CSI) becomes prohibitive expensive. In this paper, we propose to use deep learning via a Multi- Layer Perceptron architecture that exceeds the performance of traditional CSI processing methods like Least Square (LS) and Linear Minimum Mean Square Error (LMMSE) estimation, thus leading to beyond fifth-generation (B5G) networking paradigm wherein machine learning fully drives networking optimization. By computing the CSI of all pairwise channels simultaneously via our deep learning approach, our method scales with large antenna arrays as opposed to traditional estimation methods. The key insight here is to design the learning architecture such that it is implementable on massively parallel architectures, such as GPU or FPGA. We validate our approach by simulating a 32-element array base station, and a user equipment with a 4- element array operating on millimeter wave (mmWave) frequency band. Results reveal an improvement up to 5 and 2 orders of magnitude in BER with respect to fastest LS estimation and optimal LMMSE respectively, substantially improving the end- to-end system performance, and higher spatial diversity for lower SNR regions, achieving up to 4 dB gain in received power signal compared to performance obtained through LMMSE estimation.

Fig 1:  Classical and proposed channel sounding architectures for B5G mMIMO.
Dataset Description 

We use Communication Toolbox and Phased Array Toolbox within MATLAB to set up an mMIMO transmitter/receiver scenario. Specifically, we simulate a downlink end-to-end transmission from a BS equipped with NT = 32 URA antennas and a UE with NR = 4 ULA antennas, resulting in a 32x4 MIMO channel. Devices operate on a carrier frequency of 28 GHz, using 100 MHz bandwidth and FFT size of 256, resulting in 234 usable sub-carriers. We use a geometric scattering channel model without a line of sight (LoS) path with 100 scatterers that, for every transmission, are randomly placed on a spherical surface around the UE, which has a radius of 10 percent of the distance between UE and BS, while the position of UE and BS are assumed to be fixed, with a distance of 500 m. We generate Channel Sounding preamble frames, as explained earlier, having length L = NT OFDM symbols, and simulate transmission through a multi-path scattering channel model with Ns = 100 scatterers, as well as adding thermal noise. For training, we simulate 9000 complete transmissions, that is, including both Channel Sounding and Data Transfer phases, which are divided in 85 percent and 15 percent ratios for training and validation. In order to generate enough variation in channel realizations, we uniformly sample random seeds in the range U[1, 107], used to generate unique channel states.

Download our datasets 

Please use the below links to download the datasets:

  • Training Dataset #1: The total training set for a 32x4 mMIMO system consisting of 9,000 received Channel Sounding frames, for a total of 1,152,000 LTF preambles (noiseless);

  • Testing Dataset #2: A set of 500 Channel Sounding transmissions for each SNR level considered, ranging from SNR -23 dB to 10 dB.

These datasets were used for the paper "Deep Learning at the Edge for Channel Estimation in Beyond-5G Massive MIMO", accepted at IEEE Wireless Communication Magazine. Any use of this dataset, which results in an academic publication or other publication that includes a bibliography, should contain a citation to our paper. Here is the reference for the work:

@article{ belgiovine2021, author={Belgiovine, Mauro and Sankhe, Kunal and Bocanegra, Carlos and Roy, Debashri and Chowdhury, Kaushik R.}, journal={IEEE Wireless Communications}, title={Deep Learning at the Edge for Channel Estimation in Beyond-5G Massive MIMO}, year={2021}, volume={28}, number={2}, pages={19-25}, doi={10.1109/MWC.001.2000322} }

Source code 

The source code is published on Github.

Performance evaluation

The following plots show the performance of the proposed model compared to Least Square (LS) and Linear Minimum Mean Square Error (LMMSE) for different SNR levels on end-to-end metrics Bit Error Rate (BER) and signal power boost obtained through beamforming. Normalized Mean Square Error (NMSE) is computed with respect to the perfect channel estimation and demonstrates the superior quality of the proposed channel estimator in lower SNR scenarios.

Fig 2:  Performance of proposed method vs. LS and LMMSE OFDM-MIMO channel estimation for (from left to right) Bit Error Rate (BER), beamforming power gain and Normalized Mean Squared Error (NMSE).