365 research outputs found
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
Joint Hybrid Precoder and Combiner Design for mmWave Spatial Multiplexing Transmission
Millimeter-wave (mmWave) communications have been considered as a key
technology for future 5G wireless networks because of the orders-of-magnitude
wider bandwidth than current cellular bands. In this paper, we consider the
problem of codebook-based joint analog-digital hybrid precoder and combiner
design for spatial multiplexing transmission in a mmWave multiple-input
multiple-output (MIMO) system. We propose to jointly select analog precoder and
combiner pair for each data stream successively aiming at maximizing the
channel gain while suppressing the interference between different data streams.
After all analog precoder/combiner pairs have been determined, we can obtain
the effective baseband channel. Then, the digital precoder and combiner are
computed based on the obtained effective baseband channel to further mitigate
the interference and maximize the sum-rate. Simulation results demonstrate that
our proposed algorithm exhibits prominent advantages in combating interference
between different data streams and offer satisfactory performance improvement
compared to the existing codebook-based hybrid beamforming schemes
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
Hybrid MIMO Architectures for Millimeter Wave Communications: Phase Shifters or Switches?
Hybrid analog/digital MIMO architectures were recently proposed as an
alternative for fully-digitalprecoding in millimeter wave (mmWave) wireless
communication systems. This is motivated by the possible reduction in the
number of RF chains and analog-to-digital converters. In these architectures,
the analog processing network is usually based on variable phase shifters. In
this paper, we propose hybrid architectures based on switching networks to
reduce the complexity and the power consumption of the structures based on
phase shifters. We define a power consumption model and use it to evaluate the
energy efficiency of both structures. To estimate the complete MIMO channel, we
propose an open loop compressive channel estimation technique which is
independent of the hardware used in the analog processing stage. We analyze the
performance of the new estimation algorithm for hybrid architectures based on
phase shifters and switches. Using the estimated, we develop two algorithms for
the design of the hybrid combiner based on switches and analyze the achieved
spectral efficiency. Finally, we study the trade-offs between power
consumption, hardware complexity, and spectral efficiency for hybrid
architectures based on phase shifting networks and switching networks.
Numerical results show that architectures based on switches obtain equal or
better channel estimation performance to that obtained using phase shifters,
while reducing hardware complexity and power consumption. For equal power
consumption, all the hybrid architectures provide similar spectral
efficiencies.Comment: Submitted to IEEE Acces
Achievable Rates of Multi-User Millimeter Wave Systems with Hybrid Precoding
Millimeter wave (mmWave) systems will likely employ large antenna arrays at
both the transmitters and receivers. A natural application of antenna arrays is
simultaneous transmission to multiple users, which requires multi-user
precoding at the transmitter. Hardware constraints, however, make it difficult
to apply conventional lower frequency MIMO precoding techniques at mmWave. This
paper proposes and analyzes a low complexity hybrid analog/digital beamforming
algorithm for downlink multi-user mmWave systems. Hybrid precoding involves a
combination of analog and digital processing that is motivated by the
requirement to reduce the power consumption of the complete radio frequency and
mixed signal hardware. The proposed algorithm configures hybrid precoders at
the transmitter and analog combiners at multiple receivers with a small
training and feedback overhead. For this algorithm, we derive a lower bound on
the achievable rate for the case of single-path channels, show its asymptotic
optimality at large numbers of antennas, and make useful insights for more
general cases. Simulation results show that the proposed algorithm offers
higher sum rates compared with analog-only beamforming, and approaches the
performance of the unconstrained digital precoding solutions.Comment: to be presented in IEEE ICC 2015 - Workshop on 5G & Beyond - Enabling
Technologies and Application
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