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Model-Driven Deep Learning for Massive Space-Domain Index Modulation MIMO Detection
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DOI：10.23919/JCC.fa.2023-0157.202310.png)
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摘要
En 中文
In this paper, a powerful model-driven deep learning framework is exploited to overcome the challenge of multi-domain signal detection in space -domain index modulation (SDIM) based multiple in-put multiple output (MIMO) systems. Specifically, we use orthogonal approximate message passing (OAMP) technique to develop OAMPNet, which is a novel signal recovery mechanism in the field of compressed sensing that effectively uses the sparse property from the training SDIM samples. For OAMPNet, the prior probability of the transmit signal has a significant impact on the obtainable performance. For this rea-son, in our design, we first derive the prior probability of transmitting signals on each antenna for SDIM-MIMO systems, which is different from the conventional massive MIMO systems. Then, for massive MIMO scenarios, we propose two novel algorithms to avoid pre-storing all active antenna combinations, thus considerably improving the memory efficiency and reducing the related overhead. Our simulation results show that the proposed framework outperforms the conventional optimization-driven based detection algorithms and has strong robustness under different antenna scales.
Keyword:
deep learning
generalized spatial mod-ulation
index modulation
massive MIMO
message