Abstract
In this paper, we propose two deep learning (DL) based receiver schemes in uplink multiple-input multiple-output (MIMO) systems. In the first scheme, we design a pilot-assisted MIMO receiver using a data-driven full connected neural network. This data-driven receiver can recover transmitted signal directly in an end-to-end manner without explicitly estimating channel. In the second scheme, we adopt a model-driven network which combines communication knowledge with DL. The model-driven scheme divides the MIMO receiver into channel estimation subnet and signal detection subnet, and each subnet is composed of a traditional solution as initialization and a DL network to further improve the accurate. The simulation results show that both of the two schemes achieve better bit error ratio (BER) performance than traditional methods. In particular, the data-driven scheme can achieve optimal BER performance in low-dimensional MIMO systems, while the model-driven scheme can be trained with fewer trainable parameters and outperforms the data-driven scheme in high-dimension MIMO systems.
Highlights
High data-rate demands are becoming more and more challenging with the rapid development of mobile devices
Different from the existing deep learning (DL)-based signal detector that is only adapted to the system with the fixed channel [32]–[34] or the known channel state information (CSI) [35]–[37], we propose two schemes considering both channel estimation and signal detection, which can be applied to time-varying multiple-input multipleoutput (MIMO) channels
PROPOSED DATE-DRIVEN RECEIVER FOR MIMO SYSTEMS we present the architecture of the FullCon receiver for MIMO systems
Summary
High data-rate demands are becoming more and more challenging with the rapid development of mobile devices. To solve the problem of spectrum resources scarcity and increasing throughput requirements, multiple-input multipleoutput (MIMO) has become one of the key technologies in the future network communication systems [1]–[4]. MIMO allows multiple antennas to send and receive messages simultaneously at transmitting and receiving terminals. It can effectively improve system capacity and spectrum efficiency without changing the system bandwidth and signal transmission power [5], [6]. The associate editor coordinating the review of this manuscript and approving it for publication was Ning Zhang. A. BACKGROUND OF SIGNAL DETECTION AND CHANNEL ESTIMATION
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