Abstract
In this paper, we investigate the downlink transmission for full-dimension multiple-input multiple-output (FD-MIMO) systems over correlated Rician fading channels. With only statistical channel state information at the BS, the beamforming vectors of each user are decoupled from each other through the maximization of average signal-to-leakage-plus-noise ratio (SLNR) lower bound, and the optimal beamforming vector which involves the line-of-sight component and correlation matrix of both directions is derived. To reduce the time of acquiring the beamforming vector for each user, a deep learning (DL)-based algorithm is proposed. A sub-optimal analytical beamforming algorithm is also proposed for comparison. Based on the proposed beamforming method, the ergodic rate of each user is analyzed and simple closed-form approximations are derived, which are very useful in evaluating the system performance and user scheduling. Moreover, two user scheduling algorithms referred to as most dissimilar and minimum interference-to-signal factor algorithm are proposed, which greatly reduce the complexity compared to the sum rate based greedy scheduling algorithm. Simulation results validate the performance of the proposed beamforming and scheduling algorithms, and verify the accuracy of the derived ergodic rate approximations.
Highlights
Massive multiple-input multiple-output (MIMO) [1], [2] has been recognized as one of the key techniques in future wireless communication systems to meet the high spectral and energy efficiency demand [3]
The instantaneous channel state information (CSI) can be acquired at the base stations (BSs) through uplink training for time division duplexing (TDD) systems [2], the downlink training and the corresponding CSI feedback overhead are unacceptably high for frequency division duplexing (FDD) systems with large numbers of antenna
To reduce the amount of CSI required at BS and achieve relatively high ergodic sum rate, we investigate its beamforming and user scheduling algorithm exploiting only statistical CSI
Summary
Massive multiple-input multiple-output (MIMO) [1], [2] has been recognized as one of the key techniques in future wireless communication systems to meet the high spectral and energy efficiency demand [3]. X. Li et al.: Joint Scheduling and Deep Learning-Based Beamforming for FD-MIMO Systems joint spatial division and multiplexing transmission algorithm exploiting both statistical and reduced-dimensional effective CSI was proposed in [9]. In [25], with instantaneous CSI, the random beamforming method was applied in conventional multiuser MIMO systems to achieve multiuser multiplexing gain Exploiting both statistical and part of instantaneous CSI, [26] proposed a location-assisted two-layer precoding scheme for massive FD-MIMO air-to-ground transmission system. Note that applying the traditional data-driven DL method, the neural network takes the statistical CSI of all scheduled users as input and outputs their beamforming vectors It requires a lot of training time in addition to a huge data set to train neural network [34]. The complex number field is represented by C, and E{·} denotes the expectation
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