Efficient Joint Channel Estimation, Detection, and Decoding for MIMO URLLC Systems

Concetti Chiave

The authors propose two novel trainable joint channel estimation, detection, and decoding (JCDD) receivers for MIMO ultra-reliable and low-latency communication (URLLC) systems encoded by short low-density parity-check (LDPC) codes. The receivers integrate the channel estimation, data detection, and LDPC decoding into a unified framework to mitigate error propagation and processing delay issues in traditional turbo receivers.

Sintesi

The paper focuses on the receiver design for MIMO URLLC systems, which face challenges due to the use of short channel codes and few pilot symbols. The authors develop two JCDD problem formulations based on the maximum a posteriori (MAP) criterion for Gaussian MIMO channels and sparse mmWave MIMO channels, respectively. These formulations integrate the pilots, bit-to-symbol mapping, LDPC code constraints, and channel statistical information.
The authors then solve the challenging non-convex JCDD problems using alternating direction method of multipliers (ADMM) algorithms, where closed-form solutions are achieved in each ADMM iteration. Furthermore, two JCDD neural networks, called JCDDNet-G and JCDDNet-S, are built by unfolding the derived ADMM algorithms and introducing trainable parameters.
Simulation results show that the proposed trainable JCDD receivers can outperform traditional turbo receivers with affordable computational complexities. The key highlights include:

Formulation of MAP-based JCDD problems for Gaussian MIMO and sparse mmWave MIMO channels.
Derivation of ADMM-based algorithms to solve the JCDD problems efficiently.
Development of model-driven JCDD neural networks (JCDDNet-G and JCDDNet-S) using deep unfolding.
Incorporation of relaxed and accelerated ADMM structures and multi-stage multi-layer training strategies to improve performance.
Demonstration of the proposed JCDD receivers outperforming traditional turbo receivers for MIMO URLLC systems.

Statistiche

The number of transmit antennas is Nt.
The number of receive antennas is Nr.
The total number of time slots in a transmission block is T, with TP pilot slots and TD data slots.
The number of information bits is K, and the LDPC codeword length is N.
The number of parity-check nodes in the LDPC code is M.
The number of bits mapped to each QAM symbol is Q.
The number of channel clusters in the mmWave MIMO channel is Ncl.

Citazioni

"The receiver design for multi-input multi-output (MIMO) ultra-reliable and low-latency communication (URLLC) systems can be a tough task due to the use of short channel codes and few pilot symbols."
"To address the issues, we advocate to perform joint channel estimation, detection, and decoding (JCDD) for MIMO URLLC systems encoded by short low-density parity-check (LDPC) codes."
"It is interesting to find via simulations that the proposed trainable JCDD receivers can outperform the turbo receivers with affordable computational complexities."

Approfondimenti chiave tratti da

Trainable Joint Channel Estimation, Detection and Decoding for MIMO URLLC Systems

by Yi Sun,Hong ... alle arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07721.pdf

Domande più approfondite

How can the proposed JCDD receivers be extended to support multi-user MIMO URLLC scenarios

To extend the proposed JCDD receivers to support multi-user MIMO URLLC scenarios, we can modify the network architecture to accommodate multiple users. This can be achieved by incorporating additional layers or branches in the neural network to handle the signals from different users. Each user's data can be processed separately through the network, and the final outputs can be combined or decoded accordingly. By adjusting the input and output dimensions of the network and considering the interference between users, the JCDD framework can be adapted to effectively handle multi-user scenarios in MIMO URLLC systems.

What are the potential limitations or drawbacks of the deep unfolding approach used in the JCDD neural network designs

While the deep unfolding approach used in the JCDD neural network designs offers benefits such as leveraging expert knowledge and reducing the training burden, there are potential limitations and drawbacks to consider. One limitation is the increased complexity of the network due to the unfolding of iterative algorithms, which can lead to longer training times and higher computational requirements. Additionally, the performance of the network may heavily rely on the initialization of the parameters and the choice of hyperparameters, making it more challenging to optimize and fine-tune the model effectively. Furthermore, the deep unfolding approach may suffer from overfitting if not properly regularized, leading to reduced generalization capabilities on unseen data.

Can the JCDD framework be applied to other communication systems beyond MIMO URLLC, such as massive MIMO or non-orthogonal multiple access (NOMA) systems

The JCDD framework can be applied to various communication systems beyond MIMO URLLC, including massive MIMO and non-orthogonal multiple access (NOMA) systems. In the case of massive MIMO, the JCDD receivers can be adapted to handle a large number of antennas at the transmitter and receiver, exploiting spatial diversity and multiplexing gains. By adjusting the network architecture and parameters to accommodate the increased dimensions of massive MIMO systems, the JCDD framework can effectively estimate channels, detect signals, and decode data in such setups. Similarly, for NOMA systems, the JCDD framework can be tailored to address the unique challenges of non-orthogonal multiple access schemes, such as interference management and user separation. By incorporating the specific characteristics of NOMA into the network design, the JCDD approach can enhance the performance of communication systems utilizing NOMA techniques.