Hybrid precoding and combining design for millimeter-wave multi-user MIMO based on SVD

In this paper, we focus on the millimeter-wave multi-user multiple-input-multiple-output (mmWave MU-MIMO) systems and propose a low-complexity hybrid precoding and combining design, which is applicable to both fully-connected structures and sub-connected structures. Based on the channel knowledge of each user, the analog combiner for each user is independently designed based on the singular value decomposition (SVD), while the analog precoder is obtained by the conjugate transposition to maximize the effective channel gain. Then, with the resulting effective analog channel, low-dimensional baseband precoders can be efficiently applied. The proposed scheme requires no optimization techniques or any complicated iterative algorithms, while the numerical results show that it can approach the performance of fully digital schemes and even achieve a better performance in some scenarios. It is also observed that sub-connected structures can achieve a much higher power efficiency compared to fully-connected structures and are therefore promising for the future green communication systems

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

SIC-MMSE method based wireless precoding technique for millimetre-wave MIMO system

A communication method is proposed using Minimum Mean Square Error (MMSE) precoding and Successive Interference Cancellation (SIC) technique for millimetre-wave multiple-input multiple-output (mm-Wave MIMO) based wireless communication system. Background: The mm-Wave MIMO technology for wireless communication system is the base potential technology for its high data transfer rate followed by data instruction and low power consumption compared to Long-Term Evolution (LTE). The mm-Wave system is already available in indoor hotspot and Wi-Fi backhaul for its high bandwidth availability and potential lead to rate of numerous Gbps/user. But, in mobile wireless communication system this technique is lagging because the channel faces relative orthogonal coordination and multiple node detection problem while rapid movement of nodes (transmitter and receiver) occur. Methods/Improvement: To improve the conventional mm-wave MIMO nodal detection and coordination performance, the system processes data using symbolized error vector technique for linearization. Then the MMSE precoding detection technique improves the link strength by constantly fitting the channel coefficients based on number of independent service antennas (M), Signal to Noise Ratio (SNR), Channel Matrix (CM) and mean square errors (MSE). To maintain sequentially encoded user data connectivity and to overcome data loss, SIC method is used in combination with MMSE. Improvements: MATLAB was used to validate proposed system performance. Simulation analysis shown that, with the increase number of antennas use, the spectral efficiency also increased and higher then millimetre-wave MIMO or Single MMSE system. This research observed that, hybrid controller or combined control method have the better efficiency then single method, where SIC-MMSE based hybrid controller is a good example

Hybrid Precoder and Combiner Design with Low Resolution Phase Shifters in mmWave MIMO Systems

Millimeter wave (mmWave) communications have been considered as a key technology for next generation cellular systems and Wi-Fi networks because of its advances in providing orders-of-magnitude wider bandwidth than current wireless networks. Economical and energy efficient analog/digial hybrid precoding and combining transceivers have been often proposed for mmWave massive multiple-input multiple-output (MIMO) systems to overcome the severe propagation loss of mmWave channels. One major shortcoming of existing solutions lies in the assumption of infinite or high-resolution phase shifters (PSs) to realize the analog beamformers. However, low-resolution PSs are typically adopted in practice to reduce the hardware cost and power consumption. Motivated by this fact, in this paper, we investigate the practical design of hybrid precoders and combiners with low-resolution PSs in mmWave MIMO systems. In particular, we propose an iterative algorithm which successively designs the low-resolution analog precoder and combiner pair for each data stream, aiming at conditionally maximizing the spectral efficiency. Then, the digital precoder and combiner are computed based on the obtained effective baseband channel to further enhance the spectral efficiency. In an effort to achieve an even more hardware-efficient large antenna array, we also investigate the design of hybrid beamformers with one-bit resolution (binary) PSs, and present a novel binary analog precoder and combiner optimization algorithm with quadratic complexity in the number of antennas. The proposed low-resolution hybrid beamforming design is further extended to multiuser MIMO communication systems. Simulation results demonstrate the performance advantages of the proposed algorithms compared to existing low-resolution hybrid beamforming designs, particularly for the one-bit resolution PS scenario

Analog-Digital Beamforming in the MU-MISO Downlink by use of Tunable Antenna Loads

We investigate the performance of multi-user multiple-input-single-output (MU-MISO) downlink in the presence of the mutual coupling effect at the transmitter. Contrary to traditional approaches that aim at eliminating this effect, in this paper we propose a joint analog-digital (AD) beamforming scheme that exploits this effect to further improve the system performance. A jointly optimal AD beamformer is firstly obtained by iteratively maximizing the minimum received signal-to-interference-plus-noise ratio (SINR) in the digital domain, followed by an optimization on the load impedance of each antenna element in the analog domain. We further introduce a decoupled low-complexity approach, with which existing closed-form beamforming schemes can also be efficiently applied. For the consideration of hardware imperfections in practice, we study the case where the analog load values are quantized, and propose a sequential search scheme based on greedy algorithm to efficiently obtain the desired quantized load values. Moreover, we also investigate the imperfect channel state information (CSI) scenarios, where we prove the optimality for closed-form beamformers, and further propose the robust schemes for two typical CSI error models. Simulation results show that with the proposed schemes the mutual coupling effect can be exploited to further improve the performance for both perfect CSI and imperfect CSI