Deep Learning Approach to Channel Sensing and Hybrid Precoding for TDD Massive MIMO Systems

This paper proposes a deep learning approach to channel sensing and downlink hybrid analog and digital beamforming for massive multiple-input multiple-output systems with a limited number of radio-frequency chains operating in the time-division duplex mode at millimeter frequency. The conventional downlink precoding design hinges on the two-step process of first estimating the high-dimensional channel based on the uplink pilots received through the channel sensing matrices, then designing the precoding matrices based on the estimated channel. This two-step process is, however, not necessarily optimal, especially when the pilot length is short. This paper shows that by designing the analog sensing and the downlink precoding matrices directly from the received pilots without the intermediate channel estimation step, the overall system performance can be significantly improved. Specifically, we propose a channel sensing and hybrid precoding methodology that divides the pilot phase into an analog and a digital training phase. A deep neural network is utilized in the first phase to design the uplink channel sensing and the downlink analog beamformer. Subsequently, we fix the analog beamformers and design the digital precoder based on the equivalent low-dimensional channel. A key feature of the proposed deep learning architecture is that it decomposes into parallel independent single-user DNNs so that the overall design is generalizable to systems with an arbitrary number of users. Numerical comparisons reveal that the proposed methodology requires significantly less training overhead than the channel recovery based counterparts, and can approach the performance of systems with full channel state information with relatively few pilots.Comment: 6 Pages, 4 figures, to appear in IEEE GLOBECOM 2020 Open Workshop on Machine Learning in Communications (OpenMLC

Hybrid Beamforming and One-Bit Precoding for Large-Scale Antenna Arrays

Employing large antenna arrays is a promising candidate for the next generation of wireless systems. However, the conventional fully digital beamforming methods which require one high resolution radio frequency (RF) chain per antenna element is not viable for large antenna arrays due to the high cost and high power consumption of RF chain components. To address this hardware limitation challenge, this thesis considers two architectures: (1) Hybrid beamforming architecture in which the overall beamformer consists of a low-dimensional digital beamformer followed by an analog beamformer; (2) One-bit beamforming architecture in which one RF chain is dedicated for each antenna element but with only 1-bit resolution per complex dimension. There are three parts to this thesis. The first part considers hybrid beamforming design for narrowband flat-fading channels. It is shown that the hybrid beamforming architecture can realize any fully digital beamformer exactly if the number of RF chains is twice the total number of data streams. For cases with fewer number of RF chains, heuristic designs are proposed for both the transmission scenario of a single-user multiple-input multiple-output (SU-MIMO) system and a downlink multi-user multiple-input single-output (MU-MISO) system. For each scenario, the proposed design is numerically shown to achieve a performance close to the performance of the fully digital beamforming baseline. The second part studies the hybrid beamforming design for broadband millimeter wave (mmWave) systems with orthogonal frequency division multiplexing (OFDM) modulation where it is desirable to design common analog beamformer for the entire band. First, for a SU-MIMO system, it is shown that hybrid beamforming with a small number of RF chains can asymptotically approach the performance of fully digital beamforming for a sufficiently large number of transceiver antennas. For systems with a practical number of antennas, heuristic designs are then proposed to maximize the overall spectral efficiency for both SU-MIMO and MU-MISO scenarios. It is numerically shown that the proposed algorithms with practical number of RF chains can already approach the performance of fully digital beamforming baselines. In the final part of this thesis, the 1-bit symbol-level precoding architecture for a downlink massive MIMO system with quadrature amplitude modulation (QAM) signalling is studied. First, a constellation range design as well as a non-linear one-bit precoding design is proposed in order to minimize the average symbol error rate (SER) for the single-user scenario. Those designs are further generalized for the multi-user scenario. Finally, the performance of the proposed scheme is analytically studied and it is shown that for large-scale antenna arrays there is a constant 2dB gap between the proposed design and the conventional zero-forcing (ZF) scheme with per-symbol power constraint. The simulation results verify that the proposed design can achieve a promising performance for large antenna arrays with low resolution RF chains.Ph.D