Trainable Projected Gradient Detector for Sparsely Spread Code Division Multiple Access
This work addresses efficient signal detection for future wireless communications, offering a scalable solution with incremental improvements in computational efficiency.
The paper tackles the problem of multiuser detection in sparsely spread code division multiple access (SCDMA) systems by proposing a trainable projected gradient detector, which achieves nearly the same performance as a conventional belief propagation detector but with lower computational cost and fewer parameters, enabling use in massive systems.
Sparsely spread code division multiple access (SCDMA) is a promising non-orthogonal multiple access technique for future wireless communications. In this paper, we propose a novel trainable multiuser detector called sparse trainable projected gradient (STPG) detector, which is based on the notion of deep unfolding. In the STPG detector, trainable parameters are embedded to a projected gradient descent algorithm, which can be trained by standard deep learning techniques such as back propagation and stochastic gradient descent. Advantages of the detector are its low computational cost and small number of trainable parameters, which enables us to treat massive SCDMA systems. In particular, its computational cost is smaller than a conventional belief propagation (BP) detector while the STPG detector exhibits nearly same detection performance with a BP detector. We also propose a scalable joint learning of signature sequences and the STPG detector for signature design. Numerical results show that the joint learning improves multiuser detection performance particular in the low SNR regime.