In this paper, we consider multiple-input-multiple-output detection using deep neural
networks. We introduce two different deep architectures: a standard fully connected multi …

In this paper, we consider the use of deep neural networks in the context of Multiple-Input-
Multiple-Output (MIMO) detection. We give a brief introduction to deep learning and propose …

In this paper, we propose a deep learning based sphere decoding (SD) scheme to reduce
the detection complexity for the multiple-input multiple-output (MIMO) communication …

In this paper, we propose a deep learning based breadth-first sphere decoding (SD) scheme
to reduce the detection complexity for multiple-input multiple-output (MIMO) communication …

Signal detection plays an essential role in massive Multiple-Input Multiple-Output (MIMO)
systems. However, existing detection methods have not yet made a good tradeoff between …

Not only to reduce the candidates but also to maintain detection performance in multiple-
input multiple-output (MIMO) detection, an adaptive overlapped cluster (AOC) scheme to …

In this paper, we investigate the performance of a large-scale multiple-input multiple-output
(LS-MIMO) receiver, which deploys a deep neural network and a low-density parity-check …

Sphere detector (SD) is an effective signal detection approach for the wireless multiple-input
multiple-output (MIMO) system since it can achieve near-optimal performance while …

Considering the shortcomings of large storage space requirements and high complexity in
multiple-symbol differential detection algorithm in current Multiple Input Multiple Output …

The complexity of Breadth First Signal Decoder (BSIDE) is an efficient algorithm to obtain
optimum Maximum Likelihood (ML) performance with lower complexity. In this paper, a …