Hybrid Precoding for Multiuser Millimeter Wave Massive MIMO Systems : A Deep Learning Approach

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In multi-user millimeter wave (mmWave) multiple-input-multiple-output (MIMO) systems, hybrid precoding is a crucial task to lower the complexity and cost while achieving a sufficient sum-rate. Previous works on hybrid precoding were usually based on optimization or greedy approaches. These methods either provide higher complexity or have sub-optimum performance. Moreover, the performance of these methods mostly relies on the quality of the channel data. In this work, we propose a deep learning (DL) framework to improve the performance and provide less computation time as compared to conventional techniques. In fact, we design a convolutional neural network for MIMO (CNN-MIMO) that accepts as input an imperfect channel matrix and gives the analog precoder and combiners at the output. The procedure includes two main stages. First, we develop an exhaustive search algorithm to select the analog precoder and combiners from a predefined codebook maximizing the achievable sum-rate. Then, the selected precoder and combiners are used as output labels in the training stage of CNN-MIMO where the input-output pairs are obtained. We evaluate the performance of the proposed method through numerous and extensive simulations and show that the proposed DL framework outperforms conventional techniques. Overall, CNN-MIMO provides a robust hybrid precoding scheme in the presence of imperfections regarding the channel matrix. On top of this, the proposed approach exhibits less computation time with comparison to the optimization and codebook based approaches.Peer reviewe

RSSI-Based Hybrid Beamforming Design with Deep Learning

Hybrid beamforming is a promising technology for 5G millimetre-wave communications. However, its implementation is challenging in practical multiple-input multiple-output (MIMO) systems because non-convex optimization problems have to be solved, introducing additional latency and energy consumption. In addition, the channel-state information (CSI) must be either estimated from pilot signals or fed back through dedicated channels, introducing a large signaling overhead. In this paper, a hybrid precoder is designed based only on received signal strength indicator (RSSI) feedback from each user. A deep learning method is proposed to perform the associated optimization with reasonable complexity. Results demonstrate that the obtained sum-rates are very close to the ones obtained with full-CSI optimal but complex solutions. Finally, the proposed solution allows to greatly increase the spectral efficiency of the system when compared to existing techniques, as minimal CSI feedback is required.Comment: Published in IEEE-ICC202

Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming

Hybrid beamforming is a promising technique to reduce the complexity and cost of massive multiple-input multiple-output (MIMO) systems while providing high data rate. However, the hybrid precoder design is a challenging task requiring channel state information (CSI) feedback and solving a complex optimization problem. This paper proposes a novel RSSI-based unsupervised deep learning method to design the hybrid beamforming in massive MIMO systems. Furthermore, we propose i) a method to design the synchronization signal (SS) in initial access (IA); and ii) a method to design the codebook for the analog precoder. We also evaluate the system performance through a realistic channel model in various scenarios. We show that the proposed method not only greatly increases the spectral efficiency especially in frequency-division duplex (FDD) communication by using partial CSI feedback, but also has near-optimal sum-rate and outperforms other state-of-the-art full-CSI solutions.Comment: Submitted to IEEE Transactions on Wireless Communication

Beamforming in MISO Systems: Empirical Results and EVM-based Analysis

We present an analytical, simulation, and experimental-based study of beamforming Multiple Input Single Output (MISO) systems. We analyze the performance of beamforming MISO systems taking into account implementation complexity and effects of imperfect channel estimate, delayed feedback, real Radio Frequency (RF) hardware, and imperfect timing synchronization. Our results show that efficient implementation of codebook-based beamforming MISO systems with good performance is feasible in the presence of channel and implementation-induced imperfections. As part of our study we develop a framework for Average Error Vector Magnitude Squared (AEVMS)-based analysis of beamforming MISO systems which facilitates comparison of analytical, simulation, and experimental results on the same scale. In addition, AEVMS allows fair comparison of experimental results obtained from different wireless testbeds. We derive novel expressions for the AEVMS of beamforming MISO systems and show how the AEVMS relates to important system characteristics like the diversity gain, coding gain, and error floor.Comment: Submitted to IEEE Transactions on Wireless Communications, November 200

Channel Estimation Error, Oscillator Stability And Wireless Power Transfer In Wireless Communication With Distributed Reception Networks

This dissertation considers three related problems in distributed transmission and reception networks. Generally speaking, these types of networks have a transmit cluster with one or more transmit nodes and a receive cluster with one or more receive nodes. Nodes within a given cluster can communicate with each other using a wired or wireless local area network (LAN/WLAN). The overarching goal in this setting is typically to increase the efficiency of communication between the transmit and receive clusters through techniques such as distributed transmit beamforming, distributed reception, or other distributed versions of multi-input multi-output (MIMO) communication. More recently, the problem of wireless power transfer has also been considered in this setting. The first problem considered by this dissertation relates to distributed reception in a setting with a single transmit node and multiple receive nodes. Since exchanging lightly quantized versions of in-phase and quadrature samples results in high throughput requirements on the receive LAN/WLAN, previous work has considered an approach where nodes exchange hard decisions, along with channel magnitudes, to facilitate combining similar to an ideal receive beamformer. It has been shown that this approach leads to a small loss in SNR performance, with large reductions in required LAN/WLAN throughput. A shortcoming of this work, however, is that all of the prior work has assumed that each receive node has a perfect estimation of its channel to the transmitter. To address this shortcoming, the first part of this dissertation investigates the effect of channel estimation error on the SNR performance of distributed reception. Analytical expressions for these effects are obtained for two different modulation schemes, M-PSK and M2-QAM. The analysis shows the somewhat surprising result that channel estimation error causes the same amount of performance degradation in ideal beamforming and pseudo-beamforming systems despite the fact that the channel estimation errors manifests themselves quite differently in both systems. The second problem considered in this dissertation is related to oscillator stability and phase noise modeling. In distributed transmission systems with multiple transmitters in the transmit cluster, synchronization requirements are typically very strict, e.g., on the order of one picosecond, to maintain radio frequency phase alignment across transmitters. Therefore, being able to accurately model the behavior of the oscillators and their phase noise responses is of high importance. Previous approaches have typically relied on a two-state model, but this model is often not sufficiently rich to model low-cost oscillators. This dissertation develops a new three-state oscillator model and a method for estimating the parameters of this model from experimental data. Experimental results show that the proposed model provides up to 3 dB improvement in mean squared error (MSE) performance with respect to a two-state model. The last part of this work is dedicated to the problem of wireless power transfer in a setting with multiple nodes in the transmit cluster and multiple nodes in the receive cluster. The problem is to align the phases of the transmitters to achieve a certain power distribution across the nodes in the receive cluster. To find optimum transmit phases, we consider a iterative approach, similar to the prior work on one-bit feedback for distributed beamforming, in which each receive node sends a one-bit feedback to the transmit cluster indicating if the received power in that time slot for that node is increased. The transmitters then update their phases based on the feedback. What makes this problem particularly interesting is that, unlike the prior work on one-bit feedback for distributed beamforming, this is a multi-objective optimization problem where not every receive node can receive maximum power from the transmit array. Three different phase update decision rules, each based on the one-bit feedback signals, are analyzed. The effect of array sparsity is also investigated in this setting

State-of-the-art assessment of 5G mmWave communications

Deliverable D2.1 del proyecto 5GWirelessMain objective of the European 5Gwireless project, which is part of the H2020 Marie Slodowska- Curie ITN (Innovative Training Networks) program resides in the training and involvement of young researchers in the elaboration of future mobile communication networks, focusing on innovative wireless technologies, heterogeneous network architectures, new topologies (including ultra-dense deployments), and appropriate tools. The present Document D2.1 is the first deliverable of Work- Package 2 (WP2) that is specifically devoted to the modeling of the millimeter-wave (mmWave) propagation channels, and development of appropriate mmWave beamforming and signal processing techniques. Deliver D2.1 gives a state-of-the-art on the mmWave channel measurement, characterization and modeling; existing antenna array technologies, channel estimation and precoding algorithms; proposed deployment and networking techniques; some performance studies; as well as a review on the evaluation and analysis toolsPostprint (published version