Abstract

     Traditional MIMO decoding schemes are complex, impractical, and perform poorly for massive multiple-input multiple-output (M-MIMO) systems. Deep learning (DL) has recently emerged to perform many complex operations more efficiently within a shorter time. This paper proposes a learning-based network (DLNet) to design an M-MIMO decoder. The DLNet network architecture is designed by iteratively unfolding the gradient descent algorithm. The proposed DLNet decoder consists of 15 neural networks (NN) layers with some trainable parameters. This work considered uplink Rayleigh and correlated M-MIMO channels, which are perfectly known to the receiver. With the knowledge of the received signals and the M-MIMO channels, the proposed DLNet decoder decodes the messages of all the users. In the M-MIMO perspective, the proposed DLNet has been evaluated for symbol-error-rate (SER) performance, algorithm complexity, and run-time requirement. The simulations show that the proposed DLNet converges faster than other available decoders and performs better than other M-MIMO decoding schemes, by at least 2 dB in SER and at least 11 times faster than the baseline (OAMP-Net) and nine times less complex.

Introduction

     Massive multiple-input multiple-output (M-MIMO) is one of the primary transmission techniques for fifth-generation (5G) wireless communication systems. M-MIMO shows many advantages over classical MIMO schemes and can achieve all of its merits on a grander scale [1]. It mainly uses a large antenna array at a base station (BS) and a few antennas at user equipment (UE) to increase the data rate, link reliability, and coverage and reduce outage in the wireless systems. Additionally, using a sufficiently large number of antennas at BS and UE, the noise and intracell interference can be averaged out [2]. Conventional MIMO decoders are mainly developed using mathematical and information theory concepts that only capture the approximate behavior of the system. This makes it more challenging to perform end-to-end optimization of the communication system in practice and produces sub-optimal performance. These limitations on conventional MIMO decoders motivate researchers to investigate new M-MIMO decoding techniques that give near-optimal solutions, if not optimal, with less complex hardware implementation while faster processing speed. This paper proposes a Massive MIMO decoder using a deep neural network (DNN) that can satisfy the above criteria.

Conclusion

     This paper proposed a DL-based M-MIMO signal decoder DLNet. We have simulated its SER performance, implementation complexity, and decoding time requirement over frequency flat Rayleigh fading and correlated M-MIMO channels. The proposed DLNet is a 15 layers DNN based on the projected gradient descent-based solution of the ML decoder. The proposed DLNet provides near-optimal SER performance without knowing the SNR level and is computationally inexpensive. The training of DLNet was done on the entire channel distribution, which makes it robust and implementable in a system with changing channel values where the receiver perfectly knows the channel state. After doing single-time training, the proposed DLNet decoder can decode signals accurately over various channels. The data obtained from real-world channels and real hardware could be used to train the DLNet, and thus it can be optimized for specific practical M-MIMO environments. Its iterative layering structure enables a flexible complexity-accuracy trade-off required for many modern communication systems.