Generalizing projected gradient descent algorithm for massive MIMO detection based on deep-learning
The projected gradient descent(PGD)-based detector,which consists of two basic operations,pro-jection and gradient descent(GD),was studied to achieve the performance improvement for massive multiple input multiple output(MIMO)detection.In a PGD-based detector for massive MIMO system,since the pro-jection and GD step have different loss functions,necessary compromise has to be made to balance them dur-ing iterations.For this reason,the generalized PGD(GPGD)method was proposed with flexible choices of projection and GD.Different from traditional way of performing projection and GD alternatively,GPGD im-plements projection after every multiple GD steps offers significant advantages.Meanwhile,the step-size of GD was also investigated for convergence efficiency.After that,by unfolding the proposed GPGD method with deep neural networks,the self-corrected auto-detector was established to achieve better decoding perform-ance and efficiency.The simulation results show that the GPGD method achieves an apparent system gain and has a significant superiority.
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