1,250 research outputs found
Beam Management for Millimeter Wave Beamspace MU-MIMO Systems
Millimeter wave (mmWave) communication has attracted increasing attention as
a promising technology for 5G networks. One of the key architectural features
of mmWave is the use of massive antenna arrays at both the transmitter and the
receiver sides. Therefore, by employing directional beamforming (BF), both
mmWave base stations (MBSs) and mmWave users (MUEs) are capable of supporting
multi-beam simultaneous transmissions. However, most researches have only
considered a single beam, which means that they do not make full potential of
mmWave. In this context, in order to improve the performance of short-range
indoor mmWave networks with multiple reflections, we investigate the challenges
and potential solutions of downlink multi-user multi-beam transmission, which
can be described as a high-dimensional (i.e., beamspace) multi-user
multiple-input multiple-output (MU-MIMO) technique, including multi-user BF
training, simultaneous users' grouping, and multi-user multibeam power
allocation. Furthermore, we present the theoretical and numerical results to
demonstrate that beamspace MU-MIMO compared with single beam transmission can
largely improve the rate performance of mmWave systems.Comment: The sixth IEEE/CIC International Conference on Communications in
China (ICCC2017
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
- …